The genes in the gene set are not necessarily related to the cancer and will make the model unreliable. Therefore, in our study, we thoroughly investigated the performance of different machine learning methods on stratifying breast cancer patients with a single type of genomic features. Then, we proposed a strategy, which can take into account the degree of correlation between genes and cancer patients, to identify the features from mRNAs and microRNAs, and evaluated the performance of the models with the new combined features of the multiscale genomic features. The results showed that, compared with the models constructed with a single type of features, the models with the multiscale genomic features generated by our proposed method achieved better performance on stratifying the ER status of breast cancer patients. Moreover, we found that the identified multiscale genomic features were closely related to the cancer by gene set enrichment analysis, indicating that our proposed strategy can well reflect the biological relevance of the genes to breast cancer. In conclusion, modelling with multiscale genomic features closely related to the cancer not only can guarantee the prediction performance of the models but also can effectively provide candidate genes for interpreting the mechanisms of cancer.In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g.,  Compared with the microarray technology, next-generation sequencing technology including DNA sequencing , 2 and RGenerally, the whole modeling procedure mainly includes the gene selection and the parameter estimation of prediction models. The clinical cancer samples were firstly divided into two groups according to the phenotypic status. By comparing the phenotypic differences between the two groups of samples, a set of genes can be identified by a statistical method with an arbitrary cut-off. In previous studies, only the single type of genomic features, including mRNA expression level , microRNTherefore, to explore the benefits of integrating multiscale genomic features for clinical prediction, we thoroughly investigated the performance of models constructed with a single type of genomic features, and as a comparison, we constructed models with multiscale features to predict the same clinical endpoints. Considering that the performance of the models is mainly related to the predictability of clinical endpoints , we evalt-test, 24,585 mRNAs and 722 microRNAs for each sample, in total, were kept as features by using a cut-off of p < 0.05. In addition, to validate the performance of the proposed strategy, we also applied it to stratifying the patients in KIRC and THCA data sets, which included 588 and 567 patients, respectively. The patients with tumor stages III and IV were categorized into the high-risk group, and the rest of the patients were categorized into the low-risk group. We used the same modeling procedure that had been used in the BRCA data set to stratify the patients.The mRNA and microRNA sequencing data of breast invasive carcinoma (BRCA) samples as well as the clinical information were downloaded from the NCI's Genomic Data Commons (GDC) , which iFold Change (FC), OneR [ReliefF [InfoGain [GainRatio [FC, the log 2-tranformed fold change for each mRNA or microRNA between the samples in the ER positive and ER negative groups was calculated and used for ranking the features. When extracting the top n features, we separately took the top n/2 features from upregulated and downregulated directions. OneR separately used each feature to classify the samples and calculated the error rate and then assigned a weight to the feature according to the error rate. As for ReliefF, it randomly selected a sample from the training set as well as its k nearest neighbors of the same and different classes. By calculating the differences of features between the two classes of samples, each feature was assigned a weight. The method updated the weights continuously through multiple resampling. Both InfoGain and GainRatio were derived from information theory and assigned the weights to the features according to their information entropies [GainRatio can be considered as a normalization of InfoGain, but the information entropies of the features calculated by GainRatio were totally different from those calculated by InfoGain. In the end, we ranked the features by their weights and selected the top n features for modeling.Five commonly used feature selection methods, namely, C), OneR , ReliefF[ReliefF , InfoGaiInfoGain , and GaiainRatio , were inntropies . GainRatAdaBoostM1, Dagging, LogitBoost, RandomCommittee, RandomForest, RotationForest, MultiClassClassifier, RandomSubSpace, Decorate, and MultiBoostAB) and ten single classifiers , were involved in our study. For each modeling method, we evaluated the performance of the models, which were separately constructed by using the expression levels of the top n mRNAs and microRNAs identified by the five feature selection methods. For the purpose of comparison, all the models were constructed by directly using the script interface of WEKA (version 3.8.4) [To investigate the model performance with a single type of genomic features, 20 modeling methods, including ten ensemble learning methods (n 3.8.4)  with defhttp://github.com/slundberg/shap [M is the number of features, S is a subset of the features, f is the model, Si}all/{ represents all the possible subsets that exclude feature i, and fx is the conditional expectation function. With the Shapley values, a model can be represented as a linear combination of the Shapely values:ϕ0(f) = fx(∅). The implementation of the Shapley value is complicated since there would be too many terms for evaluating, and usually only a few approximate ways are used [The Shapley value was firstly proposed by Shapely based on game theory , which werg/shap , and thiare used .Sx,y could be generated by the explainer, where x and y are the index of samples and features. The Shapley values could be different according to the samples, but our target is to find out the feature which could influence the samples as much as possible. Therefore, a 10-fold crossvalidation strategy was used to select the features: (1) The data sets were divided into 10 folds, and every fold could be the test data set and the others as the training data sets. (2) For every fold, the training set was used to generate the Shapley values Sx,yk, then the importance of features was acquired by computing the summation of the absolute values:In this work, to generate the Shapley values, the data set was firstly trained by a random forest classifier from the SKlearn package. Then, the tree explainer was generated from the trained model. The Shapley values Ssubyf, we filtered Ssubyf by keeping the top 300 maximum values . Then, the overall feature importance could be generated by computing the summation:Ssubyk, and all the features which had Fy larger than 5 were picked out for subsequent modeling. In this study, the selected feature was ranked in the top 300 among those which were more than 6 folds.Since there will be lots of values in every In this study, the mRNA sequencing and microRNA sequencing data of 1025 BRCA samples were used to investigate predictive models with single type and multiple types of genomic features. The BRCA samples were stratified into the ER positive and ER negative groups according their ER status. Using the expression levels of mRNAs and microRNAs, we separately applied 20 machine learning algorithms with five feature selection methods to construct models for the stratification of BRCA samples. As a comparison, the combined features including expression levels of mRNAs and microRNAs were also used to build the models. For the purpose of obtaining the interpretable features, we proposed the SHAP method to identify the genomic features which were closely related to the cancer. The selected mRNAs and microRNAs were analyzed by gene set enrichment analysis. The entire workflow is depicted in n mRNAs and microRNAs  were separately selected by five feature selection methods and used as features for 20 modeling algorithms. To evaluate the model performance, the top MultiClassClassifier and LibLINEAR with mRNAs as features were greater than 0.700  by using the expression levels of mRNAs as features.The mean MCCs across different machine learning methods and different feature selection methods are shown in an 0.700 . For thean 0.700 . It indiAs for the feature selection methods, there was no significant difference in the mean MCC values between five feature selection methods when using mRNAs as features to construct the ensemble predictive models , which pTo demonstrate the importance of selecting interpretable genomic features for modeling, we applied SHAP to generate the features from mRNAs and microRNAs, as well as from multiscale genomic features, which are an integration of mRNAs and microRNAs in this study. As a comparison, we separately constructed the models with all the features of mRNAs, microRNAs, and the multiscale genomic features. The random forest algorithm, which performed best in the previous step, was applied for modeling. The prediction results for the independent test set are shown in If all the mRNAs were used as features, the model performance (mean MCC = 0.791) was slightly worse than the best model in the previous step (mean MCC = 0.813). It indicated that, for the same modeling method, the feature selection process can help remove the useless variables and improve the prediction performance of the model to some degree. Then, we proposed SHAP to rank the features and took the top 300 features for modeling. For the single type of genomic features, we finally identified 289 mRNAs and 291 microRNAs from the total features of 24,585 mRNAs and 722 microRNAs, respectively. From the multiscale feature set, we identified 290 genomic features including 283 mRNAs and 7 microRNAs. After the feature selection procedure, the performance of the model constructed with mRNAs as features was improved (median MCC = 0.798). Similarly, the median MCC achieved by the model with multiscale genomic features was 0.825, which was higher than that achieved by the model before feature selection (median MCC = 0.798). Interestingly, after feature selection, the prediction performance of the model with mRNA as a feature is close to that of the model with multiscale genomic features. We inspected the selected features in the list of multiscale genomic features and found that only seven out of 290 features were microRNAs and the rest were mRNAs. It indicated that the contribution of multiscale genomic features was dominated by the expression levels of mRNAs, which might be more related to the phenotypic difference of the samples. As for the microRNAs, the performance of the model was the worst when it was used for predicting the ER status, regardless of whether feature selection was conducted or not. Our proposed strategy and the same modeling procedure were also applied to the KIRC and THCA data sets. The prediction results for KIRC and THCA are shown in Supplementary Figures https://david.ncifcrf.gov/) [Pathways in cancer (p = 0.026) and MicroRNAs in cancer (p = 0.036) were directly associated with cancers. The top ten enriched Gene Ontology (GO) terms that related to the biological process and molecular functions of the genes are listed in Tables GO:0042592~homeostatic process and GO:0048871~multicellular organismal homeostasis) and the response to drugs (GO:0042493~response to drug). In addition, the gene set was also related to the regulation of hormones (GO:0010817~regulation of hormone levels). As for the molecular functions of the genes, GO terms related to protein binding and enzyme activity were significantly enriched. Our results suggested that the mRNAs identified by our strategy are closely associated with the mechanism of cancers.To elucidate the biological relevance of the selected genomic features to the cancers, the gene set enrichment analysis was conducted in the subsequent analysis. The KEGG pathways enriched by using DAVID (rf.gov/)  with 289r p = 0.06 were dihttps://genecodis.genyo.es/) [MicroRNAs in cancer was significantly enriched, indicating that the microRNAs identified by our strategy were mainly associated with cancer. The top 10 enriched GO terms of biological processes indicated that the 291 microRNAs were significantly associated with gene silencing (GO:0035195~gene silencing by miRNA) and inhibition of translation (GO:0035278~miRNA mediated inhibition of translation). Most of the top 10 GO terms referred to the negative regulation of genes. The enriched GO terms of molecular functions reflected that the functions of 291 microRNAs were correlated with DNA and RNA binding.For the enrichment analysis of 291 microRNAs, it was conducted by using GeneCodis 4.0 (nyo.es/) , which iLZTS1 and multiple targets in the Hippo pathway [STAT6 [IGF-1 [RKIP [In addition, only seven microRNAs were involved in the final decided multiscale genomic feature set. Four of them, namely, miR-135b, miR-190b, miR-224, and miR-588, had been reported to be associated with various cancers. miR-135b is closely related to cervical cancer, lung cancer, and prostate cancer. It is commonly upregulated in cervical cancer cell lines, and the downregulation of miR-135b can inhibit the growth of cervical cancer cells . It can  pathway  and inhiy [STAT6 . miR-1906 [IGF-1 . Both mi-1 [RKIP . miR-588-1 [RKIP .For the studies in tumorigenesis, the purpose of modeling is not only to accurately predict the prognosis of cancer patients but also to identify genes with significant biological relevance to cancers. Considering that most of the existing modeling processes involve steps of handcrafted gene selection, it is possible to neglect cancer-related genes or to include false positive genes in the selected gene set, which will interfere with the subsequent biological analysis. Therefore, in this study, we thoroughly conducted a comparative study on the model performance with different genomic features and different modeling methods and proposed a strategy to identify the disease-related genes. All the models were tested by using the mRNA and microRNA sequencing data of BRCA samples. According to the ER status of the patients, the samples were categorized into the ER positive and ER negative groups, which were used as the clinical endpoint for prediction.The model performance mainly depended on which type of genomic features was used for modeling. The models with the expression levels of mRNAs as features showed better performance than those with the expression levels of microRNAs Figures  and 3, iWhen applying our strategy to the genomic feature selection, 289 mRNAs and 291 microRNAs were selected from the total features of mRNAs and microRNAs, respectively. The results showed that, compared with the performance of the models constructed with all mRNAs as features (mean MCCs = 0.791), the model performance was improved when modeling with the features identified by our proposed strategy (mean MCCs = 0.800). As for the models with multiscale genomic features, the performance of the models also had been improved to some extent . It indiIn order to illustrate the effectiveness of multiscale genomic features and our proposed strategy for feature selection, we used the ER status of BCRA patients and the tumor stages of KIRC and THCA as the prediction endpoints to evaluate the performance of various models. It is worth noting that the predictive models were data dependent. For different prediction endpoints, the performance of the models will change a lot. The deep optimization of the models would be also conducive to improving the prediction performance of the models, although in this study, the use of a different number of features and default parameters had no significant impact on model performance. As the number of mRNAs is larger than that of miRNAs, if we apply feature selection on the multiple genomic inputs with a simple combination, the top-ranked ones are more likely to be dominated by mRNAs, which can be easily found in our selected feature lists. For leveraging multiscale genomic data, this is a challenging problem that is worth further discussion. Furthermore, we only used the FPKM normalized gene expression values as features in the current study to construct the predictive models, and using different transcript quantification methods applied to represent the gene expression levels, such as RSEM , might rIn conclusion, compared with a single type of genomic features, multiscale genomic features can provide more information of cancer for constructing the predictive models and help to improve the prediction performance of the models. The integration of multiple genomic features will lead to a sharp increase in the number of features. Effective screening of genomic features closely related to cancer is important for the prediction performance of the models. Our proposed strategy can well identify the genomic features of breast cancer and effectively improve the stratification of breast cancer patients having different estrogen receptor status.


Healthcare improvement science (HIS) is the generation of knowledge to cultivate change towards improving health systems performance. Our purpose was to evaluate the experience of European nursing students after an intensive one-week summer program conducted in 2019 at the University of Alicante in Spain. The educational intervention combined theoretical and practical HIS contents, with students from different countries, educational programs, and health systems. The intervention was evaluated under a qualitative approach through the open discussion group technique based on the method of participatory action research (PAR), with a total of 25 students who reflected about their experiences and perceptions during the intervention. The responses were used to improve the program’s contents, its didactics, and organization. Nursing empowerment, professional recognition, and healthcare research were some of the seven main categories identified through the systematic content analysis method triangulated by three experienced researchers. According to the students’ replies, values like compassion, respect, or empathy were identified as key elements of care. Promoting international students’ networking emerged as the key to creating a positive provision for change and the generation of improvement initiatives. Building a HIS culture may potentially provide future healthcare professionals with critical thinking skills and the resources needed to improve their future work settings. Over the period 1999–2010 the Bologna Reform in the European Union highlighted the importance of value-centered education across Europe in the field of health studies. In line with this, patient safety should be of upmost importance for healthcare professionals, while fundamental values like compassion, integrity, or human dignity, among others, are key to delivering the highest level of quality of care. However, those values are still not widely included in the training process of healthcare professionals in Europe and are not observed as part of improvement initiatives in the educational and healthcare fields ,4,5,6.From 2013 to 2015 the Improvement Science Training for European Healthcare Workers (ISTEW) project funded by the European Commission evidenced the gap in the provision of accredited health improvement science (HIS) education across Europe and outlined the need to improve quality of care services and related education. The most representative ISTEW outcomes were (a) the European HIS consensus definition, known as the Bled definition, (b) four HIS training modules, and (c) the Healthcare Improvement Science Evaluation Framework (HISEF) ,8,9. TheAs stated before, based on the previous gap analysis of nursing studies in Europe conducted during the ISTEW Project there is a lack of specific training for nurses focused on the following items: development of improvement-based and critical thinking, quality improvement measurements, systems thinking, and safety practices . TherefoA practical and theoretical educational intervention regarding HIS was conducted consecutively from 2016 to 2019, focusing on the four main HIS modules developed by the ISTEW project: (a) the development of improvement-based and critical thinking, (b) quality improvement measurements, (c) systems thinking, and (d) safety practices. For our research purpose we concentrated on the qualitative data collected in the 2019 course. The educational intervention was evaluated under the scope of participatory action research (PAR), which was selected as the qualitative method. Within PAR, the subject becomes the protagonist and participates in the change itself. Citing Cassell and Symon , PAR enaAlthough we used the HISEF as the evaluation tool of the HIS learning, which included open-ended and closed questions together with Likert scales, we considered this insufficient for our qualitative goal. For that reason, further qualitative research to capture students’ personal perspectives and experiences was needed. In order to fill this gap, a plenary discussion and brainstorming session was conducted at the end of the intervention in 2019, providing an approach to the participants’ perceptions and experiences. The session had four main topics: (a) take-home ideas, (b) values learnt, (c) previous HIS experience, and (d) initiatives that students would implement to improve their local settings and also the recently visited ones during the Summer Program.The educational intervention for healthcare future professionals and its evaluation was conducted in July 2019 at the University of Alicante. Since the ISTEW project ended, this course has been the only implementation initiative with regard to the specific educational modules created in the project. It consisted of a one-week 50-h program divided into theory and practice. Students had the chance to visit Spanish public and private hospitals as well as primary health care centers, observing, detecting, and discussing similarities and differences with regard to their healthcare contexts and contrasting such practical experience with the knowledge achieved in the theoretical sessions. The purpose of this intervention was to develop their theoretical and practical knowledge about HIS contents and values, promoting critical thinking, developing improvement-based thinking and behavior, creating awareness, and consequently generating a HIS culture. During the course, students created their own projects designing HIS interventions in practice by using scientific HIS evidence and sources  and presented their ideas in a dynamic environment where all students could make their input and interact to one another. For our research purpose we conducted the discussion session in the main classroom used for the course at the University of Alicante on the last day once the program had been fully completed.Twenty-five nursing students from other European Higher Education Institutions such as the University of The West of Scotland in the United Kingdom, the Waterford Institute of Technology in Ireland, the Laurea University of Applied Sciences in Finland, and the University of Alicante itself participated. All of them agreed to be part of the plenary discussion and participated in the cyclic process of reflection–action–reflection based on PAR principles in which the researchers evaluated continuously each intervention, interacting constantly with them .Students’ experiences through the course were collected from the discussion session conducted. Notes were taken manually by one researcher. Another experienced researcher moderated the session in which students and educators participated, and the other researcher was the observer. The full transcribed notes are in the About the first topic, researchers explored the main idea that students referred as having learnt. After the analysis of this topic, eight categories came up corresponding to the most-repeated ideas .Continuing with topic 1, in In the second topic the most significant value learnt for each student was highlighted. After the analysis of the answers, classification was performed with regard to the five most repeated values for the students. In order, the most repeated value was Teamwork, followed by Respect, Passion, and Humanization of Care/Compassion, with Communication being the least repeated.Thirdly, the question “What would you improve in this context and in your context” was asked. This section is about the exchange of improvement, which reflects the different improvements and/or changes that students think can be made both in Spain, where the course took place, and in their country of origin. For the response analysis, the thematic units extracted from the first question have been reused, defined, and finally a selection of the most repeated answers has been presented in Finally, in the This study aimed to evaluate the European nursing students’ experiences and perceptions after an educational intervention on healthcare improvement science (HIS). This qualitative study and others have demonstrated how relevant healthcare improvement science is at all professional and educational stages for the nursing profession ,8,10. DeThis research contributes to an understanding of how healthcare improvement science education provides nursing students with the confidence to make changes in their future work settings, delivering safe, effective, person-centered, efficient, equitable, and timely care . To assuThe methodology used is effective in capturing student transformation, experiences, and perceptions during the course. The new section during the 2019 course and presented in the tables was perfectly combined with the HISEF to deeply understand students’ perspectives and experiences. In relation to the main categories and topics identified, a tendency can be observed. In accordance with the results obtained in the literature reviewed conducted by Lillo et al. , keywordThe content analysis method selected had a potential risk regarding the researchers’ implication when analyzing the data and drawing conclusions. To prevent this, three researchers participated in the analysis process through an analysis triangulation. On the other hand, quantitative data obtained from the HISEF should be prospectively compared with the qualitative information collected, improving both evaluation methods in order to capture the students’ experiences as accurately as possible. Moreover, students from other countries and from other health professions should be included towards to provide more evidence. However, despite the limitations, this paper is a starting point that provides useful information about nursing students’ interactions within a global HIS perspective.In relation to the qualitative technique used, the type of open discussion group ran the risk of leaving out feedback from those participants who were less self-confident in expressing their opinion in public. In order to avoid this, all participants were asked one-by-one in a safe and open atmosphere, encouraging them to express their opinions and facilitating the discussion among all members. Finally, further evaluation rounds would be needed in future educational interventions in order to see if the last HISEF version after the 2019 course better captured quantitative data and whether the results were coherent with the qualitative data collected through the open discussions. Further course editions are planned as soon as face-to-face education and travel between countries without restrictions are possible.The new summer course evaluation process was conducted successfully, and the students’ experiences and perceptions were well captured, as detailed previously. Students improved their critical thinking and knowledge in HIS and professional values and learned about the ways things are done in other cultural contexts. The educators also had the chance to improve the didactics, contents, and organization of the course. The PAR method is useful for students to reflect about course contents and ideas for improvement. An increase in students’ motivation, inspiration, and willingness for a transformation based on improvement emerged. Nevertheless, with the current available data long-term consequences in healthcare systems cannot be demonstrated at this early stage. A longer follow-up phase for students is needed.


On the other hand, the COVID-19 pandemic put a wide range of psychological pressure on health care workers. Problems such as depression, anxiety, insomnia, and distress have been reported in many cases2. Mental health problems related to health care professionals need proper and comprehensive management during the COVID-19 pandemic3.The high transmission power and lethality of COVID-19 disease have led to special attention to the disease and efforts to control it4. Exposure to physical and mental trauma, high work responsibilities, enduring the loss of patients and colleagues, and the risk of infection are examples of these difficult conditions. For this reason, in addition to facilitating certain conditions for them, as resources needed by society, psychological support for these people is also very important5. Identifying their mental health problems and addressing these potential problems is the first step regarding an effective intervention. However, there seems to be a serious limitation in the expression of these problems by health care staff6.Following the COVID-19 pandemic, demand for health care staff has increased dramatically. Given the need of society for their effective and permanent presence, it is very important to pay attention to their expected needs7. We will face a group of personnel who, despite the risk of mental health problems or having some degree of these problems, do not try to improve their conditions. This can pose many challenges for both these individuals and the patients under their care and the related health systems.“In general, in any biological disaster, fear, uncertainty, and stigmatization themes are common and may act as a barrier to physical and mental health interventions”8. In another study followed by the SARS epidemic, both groups of staff, with a history of SARS and no history of SARS, shared a common concern about infecting their families9. Although the group that had a history of SARS, thought more about discrimination related to having SARS and other health issues. They saw themselves as more vulnerable to social and occupational discrimination. They also showed a greater prevalence of bone pain, lethargy, and physical weakness, and in addition to attributing some of them to medication side effects, some of these conditions could be attributed to the psychological effects on patients' concerns. As such, there appear to be many barriers to mental health care for staff. Different conditions may also lead to referrals to other medical specialties. In this regard, providing training and creating appropriate awareness of this group of personnel can play an effective role in addressing these issues.In a relatively similar experiment, in the context of Sever Acute Respiratory Syndrome (SARS) epidemics, most staff members were very concerned on becoming infected, although they generally considered this risk to be part of their job situation. About 20% of patients with SARS at that time were health care workers. On the other hand, about half of them experienced social stigmatization and even some of them were, in a way, rejected by the family11. Having confidence in health policy makers can provide the conditions for the delivery of mental health problems without worries and provide the conditions for the improvement of the current situation. Lack of trust can also lead to many concerns, such as feelings of worry about job stability and a lack of proper support. The combination of these issues can increase concerns about the expression of mental problems by staff.Another important point in this regard is the need for trustworthy behavior and avoidance of denying facts. This can be seen in the sudden spread of COVID-19 in some countries. As we have seen in the Italian experience and historically, we have seen similar conditions during the H1N1 flu pandemic10. They may deny the matter or consider it insignificant. The use of methods such as self-blame and avoidance can be used by this group of personnel, which can be very worrying. In addition, many normal adaptive coping strategies, such as social communication, exercise, and leisure, have been severely restricted during the COVID-19 pandemic4.In addition, a very important point is that health care workers sometimes use maladaptive coping strategies9. Social support was directly related to self-efficacy and was negatively related to stress and anxiety among staff12. In staff who lack proper social support, the risk of low self-efficacy is higher. These people are prone to mental health disorders. There are also concerns that they will not raise issues related to mental health13. Having the right social support and even using social campaigns at a higher level can be effective in this regard.Self-efficacy is the other important point in this regard. Defects in self-efficacy which seen among a group of medical workers can be related to the fear of getting sick, and in some of them, it has been associated with post-traumatic stress disorderHealth care providers, especially in the field of COVID-19 pandemics, are at risk for mental health disorders and failure to follow up to manage possible disorders. In order to strive to achieve the right mental health and psychological well-being in health care personnel, especially in the context of chronic stress, it is important to pay attention to creating the right conditions at the individual and organizational levels.14.At the individual level, the use of appropriate coping methods such as problem-solving , emotion-based coping (to reduce isolation and increase support), meaning-based coping (for unresolved issues and permanent distress) accompanied with Organizational resilience such as material reserves, back up plans, succession plans and proper management can be effective15. This also seems to be helpful to express freely the mental health problems of health care personnel. Proper social support also plays an important role in this regard.Creating a sense of trust, by meeting the needs of staff for proper personal protection and job stability, is one of the most important ways to express and pursue treatment for possible mental health disorders. The elimination of stigmatization requires public awareness at the community level with the help of social communication tools and efforts to address it at the individual levelThe authors declare that there is no conflict of interest.


The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)–funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed.This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020–funded projects.Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed.Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work.Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations. The morbidity and mortality associated with cancer are rapidly increasing globally because of population growth and aging, reflecting the changes in the prevalence and distribution of major risk factors of cancer . These greal-world context is at a less advanced stage. The clinical assessment of technology poses many challenges and is influenced by numerous variables. As a result, there is an emphasis on the early evaluation of technology by including key stakeholders throughout the design and development phases (sometimes referred to as a cocreation process).Pioneering research is now being conducted in the field of cancer care technology, resulting in the development of novel solutions to a diverse spectrum of problems in this area. However, the process of evaluating these innovations and their operation within a Evidently, as researchers involved in the creation of novel technologies for cancer care, we must consider and share both the innovative concepts being developed as well as how they are being assessed and accepted in real-world or clinical settings.To this end, an international workshop was convened to consider the current status of technological and data-driven innovations in cancer care, to identify key challenges and opportunities, and to formulate recommendations aimed at accelerating the rate of progress in the data-driven management of cancer. The two workshop instances led to a series of publications. This paper discusses key topics arising from the workshops and subsequent discussions among their participants.An international expert consensus-building workshop named Tech4Cancer  was heldThe workshop was supported by four European Union (EU) projects, namely BOUNCE, CATCH ITN, DESIREE, and MyPal, funded by Horizon 2020 (H2020). The BOUNCE project ,6 considMyPal  aims to The primary deliverable from this workshop was a set of articles that summarize key issues already published by IEEE. During the paper presentation, several topics were identified through discussion among the participants. After the second instance of the workshop, participants were invited to participate in a focus group discussing status, challenges, opportunities, and recommendations in the area of technological and data-driven cancer management; the outcome is reported in this study.The writing group met by teleconference, and participants were asked to propose topics according to their interest and expertise and to select topics that they could actively contribute to. Several topics depicting challenges, opportunities, and recommendations were selected. Subsequently, leaders for each topic were identified and a structure was proposed. A formal consensus process was not used; however, the structured and open discussions did not reveal any fundamental disagreements about the nature of the topics, although the discussions supported the refinement and specificity of topics. Once all contributions were collected, a homogenization and integration process led to a final draft, where all participants commented and discussed, leading to the final submitted version.engagement by the patient [A fundamental requirement for the effectiveness of any eHealth intervention, including interventions addressed to patients with cancer and survivors, is a certain level of  patient , who is  patient . The lac patient . The iss patient .activation of the patient in the management of their own health [adherence, which refers to whether the intervention is used as intended by its developers [Formulating a concrete definition of patient engagement in the context of eHealth is a challenge by itself , as thern health . Certainn health . Consequvelopers ,19. Thisuniversal or even widely applicable assessment instruments difficult. Instead, most research efforts propose solutions for assessing user engagement for very specific purposes, modalities, and contexts pertaining to eHealth [The situation is not significantly better when it comes to the assessment of patient engagement. The lack of consensus in the conceptualization of user engagement makes the design of appropriate  eHealth ,20,21. F eHealth ; this wo eHealth .The scientific literature sheds some light on the techniques that have been employed to achieve, maintain, and improve patient engagement. According to a psychology study , patientBehavioral techniques, such as motivational interviewing, goal setting, and planning, which are related to patient actions when managing their health condition.Cognitive techniques, such as question-asking tasks and psychoeducation sessions, which are related to patient thoughts and received information concerning their health condition.Emotional techniques, such as positive psychology exercises and expressive writing tasks, which are related to experienced patient feelings and emotions when adjusting to their new health condition.Most interventions developed for older adults employ patient engagement techniques from behavioral and/or cognitive categories, but not all categories . The latHowever, the main barrier in building a critical mass of literature on patient engagement with eHealth systems is the fact that very few studies address or report the topic. For instance, a systematic review on published trials discovered that only 23 of 2777 reviewed trials reported any patient engagement activities . The litIn contrast to the other challenges discussed in this work, the solution to patient engagement may not be rooted in technology itself but rather related to the way technology is designed. To this end, there is a growing body of research demonstrating the value of cocreation and participatory design in the development of novel digital health services, including services for cancer care. Participatory design is one of the pillars of the revolutionary predictive, personalized, preventive, and participatory (P4) cancer medicine ,31, and The main barrier is a lack of research culture for considering the involvement of the end user  in the design process. Despite this barrier, participatory design is expected to become the norm in eHealth technology development in the upcoming years.Methods section) is an example of a research effort that has committed to a participatory design approach, implemented primarily via a series of focus group discussions on eHealth interventions to be developed with the participation of all the involved stakeholders  [The MyPal project  .The use of the participatory approach as early as possible in the design of innovative technological solutions for patients with cancer presents a good opportunity to improve patient engagement . The metParticipatory design should start as early as possible in the development lifecycle of an eHealth system or service, and it should rely on established methodological tools.Representative samples of the intended patient populations need to be selected for participation in the co-design and cocreation activities. This is especially important for heterogeneous patient populations.Participatory design findings should be fused with other sources of knowledge .These recommendations can complement pre-existing published efforts to deliver more generic guidelines for developing engaging eHealth technologies. For example, the work presented in Karekla et al  specifieData are fuel for any machine learning (ML) project . It is wML, especially deep learning, can effectively learn with big data. However, it cannot effectively learn with small data because of various issues, for example, overfitting, noise, outliers, and sampling bias, which can render the learned model effectively useless. Effective learning with small data is a challenge.The annotation problem can be addressed via the use of annotation tools or services in many ways, such as (1) providing annotation tools so that annotation can be performed more effectively and easily, existing tools include Lionbridge artificial intelligence (AI)  and CompAnother research question is regarding the procedure to add the value of ML results under the constraint of the available knowledge. Moreover, as knowledge and the latest clinical evidence, such as clinical practice guidelines (CPGs), are usually in paper-based formats and written in natural language, another research question arises regarding the procedure to automatically represent knowledge in a structured and computerized manner.To solve the annotation problem scientifically, a desirable approach is to design a new ML algorithm that requires minimal feedback from human experts. This will only be possible if domain-specific constraints can be imposed on the learning process. This will reduce the model space as well as the variance in learning. Thus, one research question is on the procedure to reduce model space by domain-specific constraints.learning with small data problem is to use domain knowledge in the learning process or knowledge-based learning. ML requires data as well as knowledge (common sense and domain-specific knowledge) implicitly or explicitly. When there are a lot of data, ML requires a small amount of knowledge; when there is not much data, ML requires a large amount of knowledge to reduce the model search space. Model-based ML can be seen as an example of knowledge-based learning, where knowledge can be specified by experts in the form of variables and their dependencies. This approach has been successfully demonstrated in various case studies. One weakness, however, is that formalizing knowledge is not a straightforward task, and it requires capturing experience from clinicians through inverse engineering and making clinical statements as explicit as possible for a computerized system. A desirable approach is a knowledge-based learning algorithm that (1) can be easily extended by data-driven findings and (2) uses standardized terminologies to provide interoperability and eases the updating and maintenance of the latest evidence. For example, within the DESIREE project, a digital breast cancer patient was formalized as a knowledge model ontology. The ontology employed standardized terminologies to identify univocally all identified clinical terms and procedures, which included the knowledge reported in standardized guidelines such as the National Comprehensive Cancer Network (NCCN) [An interesting approach to solve the k (NCCN)  or the Ek (NCCN)  guidelinThe multitude of successful instantiations of digital interventions has opened exciting new directions for acquiring, delivering, and sharing data, and has already proven the potential to leverage cost-effective, patient-centered cancer care applications -44. EmbeGuided by the current state of the art, we identified a set of opportunities for digital interventions related to patient-centered cancer care. We have considered the most cited systematic reviews in the last 3 years  and older systematic reviews . These opportunities tackle the inherent challenges that we identified in practice. The first challenge is data collection and integration. Here, the core opportunity relates to exploring and exploiting the aggregation of heterogeneous and distributed data, including personal, professional, and health-related information . Such syExtracting Patient Portraits section).When considering clinical data heterogeneity and the specificity of patient data, data projection is another challenge that is present in any technological intervention. We delineate a good opportunity to build individualized clinical recommendations based on the data interpretation projected in actions upon the patient. This initiative will be based on the patient’s risk profile and evidence-based guidelines . ML techniques  can fuse heterogeneous data in a common representation , as demonstrated by the recent deep learning systems used on multi-omics data sets to drive precision oncology care .In terms of data interpretation and translation, we recommend the use of tools to extract and represent the medical substrate by synthesizing only relevant aspects in a declarative way. ML techniques  can handle very large and sparse data  to capture the sequential character of the data and are suitable for modeling context dependencies in inputs . Such syTo address the challenges in the area, we recommend the development of clinical projections  from individualized patient recommendations to therapy plans that embed temporal, procedural, and reasoning processes. ML techniques, such as temporal hierarchical task networks, can dynamically generate personalized therapy plans for oncology patients , followiFinally, tackling data augmentation, we recommend incorporating the lived experiences of the patients . ML techpatient portraits that can be employed in practice to determine optimal treatment strategies for subgroups of patients with similar cancer care needs and preferences. Building a patient portrait is hence an endeavor that follows a bottom-up approach that includes patient profiling based on patient phenotype algorithms [Individual biomedical and nonbiomedical patient characteristics should guide any provided chronic care—digital or not. These insights are used to develop and validate gorithms -64; integorithms ; personagorithms ; and indgorithms . To builThe series of opportunities we identified target the selection of relevant heterogeneous and multimodal data correlated with the diagnosis. To extract the opportunities and frame our recommendations, we systematically analyzed a series of studies ranging from genomic data and phenotypes to demographics and psychological data. This methodology allowed us to capture the most relevant dimensions for extracting a patient profile. More precisely, the focus was on exploiting the correlations among multiple data sources to build a digital patient portrait consistent along all dimensions.Such an initiative requires powerful data mining and ML algorithms, which can provide an efficient and compressed representation of a patient's digital profile, subsequently guiding therapeutic schemes.One challenge identified relates to data relevance. Here, there is an opportunity to identify relevant genetic, phenotypical, physiological, lifestyle, and medical data correlated with the diagnosis. Exploiting such an opportunity can improve a patient's profile and the overall effect of the intervention, especially in progression-free survival . Such stGiven the data deluge describing each patient, exploring and exploiting data correlations is another challenge we identified in the context of extracting a patient’s profile. This challenge provides a clear opportunity to exploit the correlations among the multiple identified modalities toward building an individual or personalized patient digital portrait that consistently captures all dimensions of the patient’s disease evolution.Such an exploration unveils another challenge, namely multimodal data fusion , and theportrayal.The last challenge we identified as being crucial in any effort to extract a patient’s profile is the possibility of embedding individualized data  in patient cohorts . InitialOn the basis of the identified opportunities and related challenges, we established a set of recommendations supporting a digital intervention design that (1) exploits available data, (2) extracts underlying correlations, and (3) integrates the multitude of representations in a structured object guiding therapeutic interventions in cancer care.When addressing the challenge of identifying data relevance, we recommend selecting the data or feature subset that best characterizes the statistical property of a certain variable  subject to the constraint that these data or features are as mutually dissimilar to each other as possible but as marginally similar to a certain class of patients as possible. For this task, ML tools such as minimum redundancy feature selection  ) can be To address data correlations, we recommend the use of ML models, such as long short-term memory (LSTM)  networksThe next challenge identified is the fusion of the available multimodal data. Our recommendation is to use a data-driven feature learning class of approaches. Typically, they are based on deep networks that can directly learn the hidden characteristics of the data from different sources. As such, we recommend, for instance, the use of deep neural networks to extract features from genomic and clinical data , convoluFinally, the last identified challenge is the possibility of embedding individualization data in the patient profile. We recommend performing individual cognitive interviews and focus groups with patients to learn about their relevant needs, experiences, fears, aspirations, and expectations.From the ML point of view, a solution for developing personalized patient embeddings that is capable of processing such data is a combination of well-proven autoencoder methods with extensions to some of the metrics to account for data sparsity and multimodality . Such stA patient portrait that can capture complex relationships in physiological signals, nonbiomedical data, and personal data embedding is key to accurately predict the stages of interventions for different patients and is necessary for successful personalized therapy.Cancer is remarkably heterogeneous across individuals. This heterogeneity makes treatment difficult for caregivers because they cannot accurately predict how the disease will progress to guide treatment decisions. Therefore, tools that help to predict the individualized trajectory of cancer can help improve the quality of health care ,82. GiveA significant need relates to making disease predictions by leveraging baseline information and additional time-dependent clinical markers as they are collected. Such an approach is the focal challenge of personalized medicine: integrative analysis of heterogeneous data from an individual’s medical history to improve cancer care. We identified several key challenges and associated opportunities linked to this.The first challenge relates to the fact that markers in clinical data are irregularly and sparsely sampled. Here, we identified a valuable opportunity for handling data and choosing specific latent variable models to summarize and extract information from the irregularly sampled and sparse data. This should simultaneously ensure sidestepping the issue of jointly modeling the data-generating processes . Such syAnother challenge identified relates to the learning of a disease trajectory and is linked to the inherent computational complexity. Imposed by the clinical setup, we identified an opportunity to predict the entire disease progression trajectory from the observed patient records without many training labels on the ground-truth stages that a patient acquired, in mechanistic models of disease progression. A joint approach is prone to alleviate the inherent variability in prediction.This challenge opens the stage for the next challenging point, namely continuous adaptation and updates in the face of disease progression heterogeneity. In handling such a challenge, there is a clear opportunity for continuous-time adaptation and updates to new observations and new data (markers). This provides novel computational methods for predicting, for instance, disease phenotype from molecular and genetic profiling .Recommendations section.Finally, another challenge we identified refers to the observed versus latent data artifacts. Addressing such a challenge demands tools for capturing latent factors in disease expression and not only observed features as a crucial aspect for patient-tailored cancer therapies. We further elaborate this in the following In this section, we match the challenges to practical recommendations in designing digital interventions when predicting disease trajectories for patients with cancer.To handle irregularly and sparsely sampled markers in clinical data, we recommend, from the ML perspective, the use of discriminative models that condition on marker histories instead of jointly modeling them. Such an approach will not be sensitive to miss-specified dependencies across marker types and inherent irregularities and sparsity. For example, functional data analysis  can be eresolutions of the data [However, the task of predicting the disease trajectory comes with its inherent computational complexity. To address this challenge, we believe that an ideal candidate would be a machine model that grows linearly in the number of marker types included in the model. This makes such a task applicable to cancer prognosis, where many different markers are recorded over time. Generative models can account for disease trajectory shapes using components at the population, subpopulation, and individual levels, which simultaneously allows for heterogeneity across and within individuals and enables statistical strength to be shared across observations at different the data . MoreoveIndependent of the prediction model, the challenge is to continuously adapt and update in the face of progression heterogeneity. From the ML point of view, we recommend the use of a model capable of being applied dynamically in continuous time and updated as soon as any new data are available . Such approaches can model the transition of disease stages or states, which implies that the progression is continuous, and the transition probability to the future state relies only on the current state and the time span. Instantiation of such causality-based ML was used to infer the underlying somatic staging of tumors from next-generation sequencing data .normal for each physiological value or is more robust to sparseness in the physiological data.Moving away from the modeling decision, the last challenge lies in the observed versus latent data artifacts. We believe that a powerful tool is an ML model that accounts for latent factors and covariates influencing disease expression, as standard regression models rely on observed features alone to explain variability . For exaOver the past decades, early diagnosis, new drugs, and more personalized treatment have led to impressive increases in survival rates of patients with cancer. However, the most mitigating side effects of commonly used therapies are a severe problem in oncology, leading to dose reduction, treatment delay, or discontinuation ,92.With the increasing number of cancer survivors, more attention is being paid to persistent sequelae of tumor treatment and supportive measures -95 used The first challenge we identified is the identification of therapeutic sequelae. This challenge offers the opportunity to develop interventions capable of assessing what deficits  a specific patient has as a consequence of cancer therapy.The next challenge arises when the intervention needs to quantify the magnitude of therapy sequelae. Here, we identify a clear opportunity to measure the level of deficit or dysfunction induced by the therapy. This is crucial in (1) designing the follow-up therapy scheme, (2) choosing a rehabilitation strategy, and (3) determining the therapy trajectory and dosage.The last challenge we identified is the adaptive parametrization of rehabilitation. This challenge brings a valuable opportunity to take steps toward personalized treatment, namely, to parameterize the rehabilitation scheme according to the specific deficit type and level to drive rehabilitation.To cope with patient sensory, motor, and cognitive deficit variability, it is necessary to perform a precise assessment of the 3 different dimensions. We believe that the 3 main challenges we identified as high-potential opportunities for digital interventions are also good candidates for ML algorithms. This technology can learn underlying correlations in patient data and generalize for robust prediction . The mapWhen tackling the identification of therapy sequelae, we recommend exploiting and mining large sets of structured and unstructured data describing a patient to identify correlations among various data types and how they map to a certain type of dysfunction. We propose the use of semisupervised techniques  that wiMoreover, to address the magnitude of the therapy sequelae, we recommend the use of deep learning models, especially convolutional neural networks. This is because such networks are capable of learning relevant feature representation from unstructured data, such as pathological images or medical records. This, subsequently, allows deep learning methods to achieve good results in tasks such as regression, detection, and segmentation, which underlie the magnitude estimation.Finally, addressing the challenge of adaptive parametrization of rehabilitation, we recommend using methods that combine learning capabilities for regressing arbitrary nonlinear functions  and adaptation through guided searches in parameter spaces .Patient data needed for the provision of the best treatment are often scattered across different systems rather than stored in a single location. Technologies that facilitate care coordination through interoperability are improving, but a seamless flow of information from one care setting to another still requires more progress. Without having a full picture, it is difficult to provide the best care in an era where cancer is considered to be a chronic disease, and there is an increased demand for consistent follow-up in terms of monitoring and early management of symptoms that indicate that cancer might have returned.Interoperability is a primary consideration to achieve communication among applications, medical devices, and health care providers , althougThe currently popular use of EHRs has alleviated some of the barriers in using data from medical records for research, although fully interoperable electronic medical record systems are not yet a reality. Several efforts to develop and apply standards in the collection, extraction, and integration of data by standardization bodies, governments, the research community, and industry are in progress  with theStandards, such as those developed in the United States and Canada, to guide EHR vendors and public health central cancer registries in the implementation of standardized electronic reporting -115 can It is a fact that some data resolution may be lost during the process of mapping EHR fields to a formally described abstraction layer, which may be alleviated by the use of knowledge models such as ontologies as a knowledge background mapper; however, these common interfaces support queries across EHRs or the extraction of patient data in the same format to allow the merging of patient sets between numerous institutions.Health information technology (IT) brings clinical data and patient information together and guides oncologists in making evidence-based care decisions that lead to improved outcomes. The potential benefits of interoperable interconnected tools and health systems are particularly important for oncology , as provData must be able to provide a complete look at the patient’s medical history so that physicians can see what cancer medicines and treatments did or did not work. Clinicians also need to be able to avoid recommending the same procedure twice, prescribing a medicine the patient already tried, or missing results from a diagnostic test.The need for consolidation and standardization efforts to create interoperable solutions  and the As already proposed by , enablinAs mentioned in , standarThe open use and sharing of big data, without compromising patients’ rights to privacy and confidentiality, should be promoted.equipoise), requiring an individualized and personalized health care process involving the clinician and the patient in a close interaction for the best decision making [Current clinical care is gradually moving toward patient-clinician shared decision making, as patient involvement can provide insights into best health states or outcomes in each case, apart from establishing a partnership that will help clinicians understand their patients’ preferences . In a con making . Neverthpreference-sensitive decisions  of high levels of uncertainty about the best clinical procedure to follow [It is necessary to overcome many barriers to ensure success in the inclusion of patient preferences during the decision-making process and to track their impact in the care services provided . First, o follow .As a complex disease, cancer involves many clinical specialties, with the need of creating a clear taxonomy  for patients’ preferences that will serve as a standardization over all involved disciplines . This kind of approach would help in the harmonization of the different points of view on the measurement of patients’ preferences by labeling and extracting this information in a processable and understandable way ,127. FinAs presented in the previous sections, the evidence used in medical practice is based on clinical guidelines, which are often used as the evidence basis for the clinical DSS (CDSS) . These gCDSSs provide the opportunity not only to quickly access the latest available evidence but also to incorporate new sources of information that can support the decision-making process. At the same time, the new IT era enables the acquisition of PROMs using questionnaires or even more sophisticated wearables that can measure activity, sleep, or other vital signs that can be translated to patient outcomes. These automatic ubiquitous technologies increase the knowledge required by patients. Processing these data and deducing the desired results will be very helpful. For example, tracking the daily activities of a user provides estimates of how active a patient is, which can be correlated with their depression and/or fatigue levels . Other techniques could also aid the acquisition of patient outcome information, such as the Ecological Momentary Assessment tests (short questionnaires that are sent to the patient frequently to obtain updates on their status). Thus, the collected data could be used to assess how good the treatment given was for each patient, not in the scope of a randomized control trial but in the real-world environment, without having a specific patient population, but the whole population.In this context, in the DESIREE project focusing on primary breast cancer, new ways of including PROMs to assess guideline recommendations were explored. The aim of including PROMs does not conflict with the quality of evidence and strength of recommendation measurements but provides other quality attributes that contribute to the decision-making process, considering the patient status reported by the patients themselves.There are some specifications that should be considered for this quality assessment, which could be named questionnaire based–patient-reported outcome measures, and they include (1) consideration of standard questionnaires for each medical case, such as the International Consortium for Health Outcomes Measurement questionnaires , (2) defWhen implementing CPGs, several characteristics must be considered to ensure both good health care quality levels and clinicians’ satisfaction. They must assure the validity and reliability of their clinical content, along with their clinical applicability in real clinical settings, and must be clear when defining the procedures to be followed in the current clinical performance procedures within a health care system ,136. NevActual trends move toward highly interactive computerized systems focusing on intuitively presenting complex clinical cases, where clinicians may access and check computerized clinical data and take away insights from all of this information in a more natural and intuitive way, alleviating the ambiguities of the guidelines through a data-driven approach guided by previous practice . The digThe proposed directions for realizing these needs include the promotion of standardized clinical terminology that facilitates the understanding and univocal interpretation of the clinical data to be analyzed and the clinical knowledge formalized in the CIGs ; to be Clinical guidelines are tedious to develop, and it is even more difficult to interpret and put them into a computer-interpretable way. This usually requires the close collaboration of knowledge engineers and medical experts. A closely related and important issue is the guideline development process or how CPG development working groups are composed. Usually, these teams comprise quality auditors or managers who are guided by their opinions, interests, and experience and intend to formalize evidence, seeking appropriateness of the provided recommendations but ignore the iterative and causal reasoning of clinicians . DependiIn this context, it is crucial to have tools supporting the easy updating of CIGs used for CDSS by clinicians themselves or knowledge engineers who do not necessarily have to understand the technology and/or the programming language used. Thus, interfaces that are easy to use and easy to understand are required for this purpose. This limitation was highlighted in the DESIREE project; however, this is an issue not only for cancer care but also for other clinical specialties.Therefore, we propose an authoring tool for CPG formalization  that shoThe change in both hardware and software over the past decade has been remarkable. Equally noteworthy has been the ever-increasing internet speed and the accompanying growth in the demand for connectivity. This growth and development have increased our ability to take on challenging projects to improve early diagnosis and improve the quality of cancer care. However, most discussions relating to health data and associated analytical tools often emphasize data privacy and security at the expense of other topics. These talks often overlook the dynamic nature of both health data and the software used in the analysis of those data. In addition, the popularity of internet-based apps and the use of such tools by patients for self-diagnosis necessitate a call to action. There is a need to examine the reliability and trustworthiness of health-related computational tools used in diagnosis and DSS by health care providers. Here, we will focus on the reliance and reliability of computational tools with an emphasis on cancer care and diagnosis.Reliance and reliability of information in health care are incredibly important. These attributes are particular when it comes to genetic information associated with cancer, which is highly critical in the development of optimal clinical intervention strategies. With an increasing number of people falling victims to medical misinformation and propaganda on the internet , advocacseal of approval from prominent institutions) or the use of the message-digest algorithm 5 (MD5) hash to ensure data integrity work to build and maintain that trust. Health IT requires a method to assure the credibility of the results generated by various computational tools available on the web. Unfortunately, none of the previously listed solutions are applicable to health software because of the dynamic nature of data as well as the software .There is no turning back from this path of dependence on the tools and information available via the internet. People have a reasonable expectation of establishing trust and validation of these tools. Initiatives such as the Secure Socket Layer certification system, implemented for encrypting sensitive information sent across the internet or the use of digital badge to indicate or attest to adhere to an acceptable standard and/or individual skill competency  and area under the curve (AUC) correlate well with confidence in the algorithms used in computer-aided design (CAD) systems. If the CAD system provides a recommendation with moderate or low confidence, then a radiologist may deem the recommendation to be useless, even if the classifier being used has a high overall value for ACC and/or AUC. However, if the confidence is much higher, the clinician may deem the recommendation more useful in supporting their decision making. Therefore, building CAD systems in which clinicians have confidence is essential if those CAD systems are to be adopted and are to play a role in fully exploiting the range of digital information available for assisting diagnosis.trust by clinicians in the outputs that the CAD systems generate [Recent studies discuss the failure of CAD systems in terms of a lack of generate -151. Whegenerate .Exploratory studies for breast mass classification  and clasTo fully exploit the range of digital information available for assisting diagnosis, it is important to identify and implement specific actions to increase the trust of physicians in cancer CAD systems and overcome the barriers to adoption of such systems. Most cancer CAD systems have used ACC and AUC as the main performance evaluation metrics; however, confidence measures must also be considered, as the traditionally used metrics are inadequate for informing clinicians in terms of the confidence that they might place on the recommendations provided. Besides, research in the design of classifiers that are incorporated into CAD systems is essential if future CAD systems are to be trusted by clinicians and adopted as a valued and reliable, and indeed routine, element of the cancer diagnosis process.locked algorithms, that is, algorithms whose functions do not change, rather than adaptive ones, that is, whose behavior can change over time based on new data [locked before marketing [Data and image analysis algorithms usually require regulatory approval by the Food and Drug Administration (FDA) in the United States and conformity assessment leading to a Conformité Européenne mark in the EU , under tnew data . Adaptivarketing .blackbox nature, and model degradation, among others, have to be included in the validation process to better ensure safety and build public trust [AI- or ML-based software modifications , and (4) implementing mechanisms that support transparency and real-world performance monitoring.Regulations that aim to validate AI in a safe and transparent way must consider doing so without compromising the potential of AI. Moreover, characteristics of AI that may be seen as risks such as biases in data, its ic trust . The FDAtrustworthy AI: human agency and oversight, including fundamental rights; technical robustness and safety; privacy and data governance; transparency, diversity, nondiscrimination, and fairness [In the EU, there are no regulatory guidelines that specifically cover AI in health care. Nevertheless, draft ethics guidelines have been published by the High-Level Expert Group on AI in April 2019, proposing 7 key requirements that AI systems should meet to be realized as fairness ,163; socThe frameworks enumerated above are, however, still being piloted or discussed. To properly define a regulatory framework for users, stakeholders and use cases (data flows) should be identified and defined. New regulatory frameworks should be built to provide guidance for the validation or qualification of AI tools within different scenarios and pathways , taking into consideration the adaptive nature of AI-based tools. The framework should consolidate input from scientific experts and health authorities and should take into consideration published relevant guidelines, for example, from the High-Level Expert Group on AI, the Medical Device Coordination Group and relevant implementing EU legislation, and international regulations .This paper presents the key topics that were discussed as part of two international workshops on the current status of technological and data-driven innovations in cancer care. Key challenges and opportunities have been identified, and several recommendations have been made to facilitate the acceleration of progress in the data-driven management of cancer. The workshops presented the work that was being conducted in four Horizon 2020 EU–funded projects, namely BOUNCE, CATCH ITN, DESIREE, and MyPal. These projects provide a rich landscape of the challenges and opportunities of the current state of the art of new technologies in cancer care. The authors have presented these issues and discussed recommendations that can be used for further research as well as practical implementation of such tools in cancer.


The diagnosis of cancer and its treatment have an incomparable impact on a patient’s life. In the early postoperative stages after the surgical treatment of oral squamous cell carcinoma (OSCC), functions and well-being are limited, which leads to a fundamental decline of the quality of life (QoL). To date, no studies have been performed that focus on the development of special aspects during the time of the in-patient stay of OSCC patients. With the results of this cross-sectional study, we are able to identify those patients who tend to require special support. This cross-sectional study determined the postoperative QoL with a questionnaire (QU) that was handed out twice to OSCC patients after surgery during their inpatient stay. The questions were based on the European Organisation for Research and Treatment of Cancer (EORTC)’s Quality of Life Questionnaire (QLQ)-C30 and QLQ-H&N35. In our study, we found that for postoperative OSCC patients, eating, swallowing and speech were influenced the most. After decannulation, tracheotomy showed no impact on functions. Social contact was impaired at both timepoints. Especially female patients consider themselves to be more impaired on the scale of social contact. QoL should be checked with a standardized QU as an established tool during hospitalization in every oncology department. Only this procedure can pinpoint those patients who have struggles with their surgical outcome and need more assistance. The diagnosis of cancer has an incomparable impact on a patient’s life, their quality of life, and also raises a wide variety of issues . The sixIn order to measure QoL, the European Organisation for Research and Treatment of Cancer (EORTC) developed a Quality of Life Questionnaire Core-30 (EORTC QLQ-C30) in conjunction with module H&N-35; this has become a highly tested measure with a large body of evidence ensuring reliability and validity ,11,12,13All included patients suffered from OSCC and were treated surgically during the year 2019 at the Department of Oral and Maxillofacial Surgery, Klinikum rechts der Isar, Technische Universitaet Muenchen, Germany. The Ethics commission of the Technische Universitaet Muenchen gave full approval of the study. The reference number is 275/19 S. This cross-sectional study was conducted according to the declaration of Helsinki. Consent for publication was obtained by every patient by a signed written form. The datasets during and/or analyzed during the current study are available from the corresponding author on reasonable request. QoL was evaluated by using two questionnaires (QU) see  based onp-values smaller than 0.05.In addition to the two QU, the following data were collected: basic patient data such as age, gender, tumor location, TNM  stage, type of microvascular flap reconstruction and comorbidities. The data were listed and processed in Microsoft Excel  for descriptive statistics. Within Microsoft Excel, the Wilcoxon signed rank test was used for the analysis of score differences at both timepoints. Moreover, patients were divided into groups for each TNM classification, if tracheotomy or neck dissection was performed, and gender. The Mann–Whitney U Test, also performed in Microsoft Excel, was used to analyze the score between the two groups. Data were considered statistically significant for Of the 26 patients included in this study, 14 (54%) were female and 12 (46%) were male. The mean age was 67 years (range 31–85 years). During the first survey, 17 (65%) patients had a nasogastral feeding tube and one patient had a percutaneous endoscopic gastrostomy (PEG) tube. Three patients had a PEG at the time of the second QU and left hospital with a PEG-tube  and the second QU 10 days  after surgery. For the first QU it was not possible to interview the patients at a specific day after surgery because of prolonged postoperative intensive care, postoperative delirium and remaining cannulated after tracheotomy for a longer period of time. We set it on that day, when the cannula was removed, and in cases of surgery without tracheotomy, we set day 2 after surgery for the day for QU I. The second QU depended on the day patients were discharged from hospital and therefore differs regarding which point in time. This implies that the median time of stay in hospital is 11 days because, in 23 cases, patients answered the second QU on the last day before leaving the hospital. The overall highest impairment involved speech problems, with a median in score of 25, followed by role functioning. Furthermore, almost no differences were found between the first and second QU with regard to these scales. The median of speech problems did not change and, although the median of role functioning jumped from 25 to 50, no difference was seen in the interquartile range (IQR). Furthermore, the score of insomnia decreased to a median of 38 and that of fatigue to a median of 50. However, additional questioning revealed that sleep was strongly influenced by the in-patient stay. The patients stated, while answering the QUs, that their sleep was interrupted by the patient sharing their room and by nurses entering the room at night in order to carry out checks. Social contact problems were impaired as reflected by the score of 75, but no measurable differences were apparent between the two QUs.p-value of <0.001.Nevertheless, two scales improved significantly during the in-patient stay. On the one hand, problems with swallowing improved to a median score of 56 at the time of the second QU. This also means that the patients were still experiencing problems with regard to swallowing when they left hospital. On the other hand, physical functioning improved to a median score of 100 at QU II from a score of 67 at QU I. The Wilcoxon signed rank test showed a difference concerning the aspects of swallowing and physical functioning between both QUs with a Importantly, we need to mention once again that 17 patients (65%) had a nasogastral feeding tube see  when theIn With respect to gender, In this cross-sectional study, QoL was assessed by using two QUs based on the EORTC QLQ-C30 and EORTC QLQ H&N-35 at two different timepoints after surgery for OSCC patients. The aim of this study was to evaluate QoL early after surgery and by the end of hospitalization. As EORTC QLQ C30 and H&N-35 QU were developed to evaluate QoL in all head and neck cancers without specification as to the localization , this stSurgery for OSCC affects many structures that are necessary for adequate articulation . Our patRole functioning was also impaired greatly in both QU. In addition, tumor stage 3 and 4 shows minor differences in median score and IQR. As the measurement of the role functioning of patients in hospital is complicated, the created QU included only one question concerning this scale, in contrast to the EORTC C30 questionnaire containing two questions in this regard. Therefore, role functioning was of less concern in this study. The patients also suffered from insomnia and fatigue. However, as stated above, the sleep of patients was strongly influenced by the in-patient stay. Therefore, no conclusion could be drawn as to whether their insomnia and fatigue were related to their disease and postoperative condition or to their stay in hospital. Nevertheless, gender-specific differences were identifiable. Although these findings are not significant , furtherA significant improvement was apparent concerning swallowing problems between QU I and QU II. Although surgery for OSCC obviously affects the swallowing function, this improvement might be explained by postoperative swelling, which declines during the in-patient stay. QU I was given mostly on the sixth day after treatment to the patients, the reduction in swelling might strongly influence swallowing in a positive way and lead to a much higher score in QU II. However, patients were provided with a nasogastral feeding tube intraoperatively in order to provide nutrition and to prevent any disturbances in healing. During the second survey, 17 patients still had a nasogastral feeding tube. Linked to this, it is important to evaluate the nutrition situation of the patient, since malnutrition is affecting the prognosis and also the QoL, as seen before in other studies and other morbidities . TherefoWith regard to physical functioning, this scale was not as much affected by the surgery as was swallowing. Nevertheless, the Wilcoxon test showed a significant improvement in scores between QU I and QU II. This improvement is probably explained on the basis of the reacquired mobility at the time of QU II and, thus, the patient having the ability to wash and dress themselves and eat without help. Moreover, a difference can also be seen in the score with regard to tumor stage. Again, advanced tumor stages showed a poorer result in QU I. Nevertheless, during the QU II, this score also returns almost to baseline in this group.The scale for dry mouth also showed poorer values and did not change between QU I and QU II. Indeed, the scale for dry mouth also receives poor scores in long-term QoL, since radiation therapy is playing a much more significant role than surgery . MoreoveThe social contact item shows no differences between QU I and II, although social contact is impaired at both timepoints. Older and male patients tend to rate their appearance more positively ,32. ThisOverall, these findings help to understand the feelings and impairments of patients after surgery of OSCC and therefore build a foundation for increasing postoperative support. For example, a possible starting point for improvement could be to assist patients regarding sleeping problems. As stated above, patients reported that their sleep was mainly interrupted by hospital staff, which could be avoided in certain situations. On the other hand, surveillance by hospital staff is very important to ensure safe conditions. Further research in the future could provide even more starting points for improvement.1. This would also be important to compare the situation and problems of the inpatient stay to the situation of the patients during the follow-up period.Some shortcomings exist with regard to this study. The postoperative QoL may be influenced by some other issues that we could not fully observe with the QU due to a small sample size. However, in addition, the sample size of this study is representative enough to illustrate the effects of the treatment of handing out the QU and the development of QoL scores. Furthermore, we gained the experience that the QU is inappropriate for patients that will leave the hospital with a cannula. We also see this fact as possible improvement for the QU in the future. We will design an appropriate, modified questionnaire for patients that will leave the hospital with a cannula. During the trial, we saw that many patients were not able to answer the QU for the issues mentioned above at both times. An early postoperative single-center clinical trial therefore has some sample size limitations. For this reason, we decided to see this study as a pilot study and use these findings to design a multi-center study. Further, the early postoperative setting is a situation that cannot be handled in a standardized format. On the other hand, questionnaires and surveys of this time period are lacking in literature. Therefore, we see our study as essential to obtaining further understanding of patients’ situations, to improving their situation and, hence, to continue the use of the QU. During the conductance of the study, we addressed the issues of the patients, which we gained in the first QU. This improved the situation of the patients. Moreover, as in every trial focusing on QU and QoL, it is unclear if a higher willingness to answer the QU might be influenced by whether patients experience a better or worse QoL. However, on the other hand, the QU was highly approved by the patients and, indeed, essential information is extractable also in a cohort of this size. In the future, our aim is as we did it with the help of this study, to catch the problems of the patients early at the hospital stay and to solve them during the stay. Further, we aim and to improve the reliability of the trial and popularize application of the QU. Moreover, a preoperative QoL assessment and an assessment of the QoL during follow-up appointments are planned. The present study and our conducted study of the tumor after care in OSCC will help us to solve this aim sucessfullyEating, swallowing and speech problems are influenced the most by surgery. However, we saw an improvement in most of our OSCC patients during the in-patient stay. To our knowledge, this is the first trial to evaluate the early postoperative QoL in OSCC patients. QoL should be checked with a standardized QU as an established tool during hospitalization in every oncology department. Only this procedure can pinpoint those patients who have struggles with their surgical outcome and need more assistance. It is a procedure that should start during their stay in hospital.


The first evidence of individual targeting medicine appeared in ancient times thousands of years ago. Various therapeutic approaches have been established since then. However, even nowadays, conventional therapies do not take into consideration individuals' idiosyncrasy and genetic make-up, failing thus to be effective in some cases. Over time, the necessity of a more precise and effective treatment resulted in the development of a scientific field currently known as “personalized medicine.” The numerous technological breakthroughs in this field have acknowledged personalized medicine as the next generation of diagnosis and treatment. Although personalized medicine has attracted a lot of attention the last years, there are still several obstacles hindering its application in clinical practice. These limitations have come to light recently, due to the COVID-19 pandemic. This review describes the “journey” of personalized medicine over time, emphasizing on important milestones achieved through time. Starting from the treatment of malaria, as a first more personalized therapeutic approach, it highlights the need of new diagnostic tools and therapeutic regimens based on individuals' genetic background. Furthermore, it aims at raising global awareness regarding the current limitations and the necessity of a personalized strategy to overpass healthcare problems and hence, the current crisis. Odyssey written by Homer scientific medicine“,” a precursor to the evidence-based medicine.However, despite the ancient vision and recommendations, medical therapy employed a very broad approach that was based on clinical and genetic/genomic data from heterogeneous populations instead of focusing on each patient, even in recent decades , genotyping, and biochips have made personalized medicine a reality justifying the use of the terminology in the last few decades.Indeed, the unique identity of every person's genome provides valuable information regarding disease onset and progression along with the response to different therapeutic regimens  that the world is currently facing, regarding diagnostic tools and therapeutic strategies.This chapter points out some important milestones through the history of pharmacotherapy that we must keep in mind and use as a beacon for achieving targeted medicine for individuals' needs, which, unfortunately, are not yet applied.The first evidence of malaria is found in 2700 BC into ancient Chinese medical records. Even today, malaria is an extremely serious and fatal disease  and prevent relapse. To eliminate the hemolytic anemia and achieve a better therapeutic outcome, WHO has published guidelines for primaquine administration to reduce the risk of the adverse effect in people with G6PD deficiency. For example, for the prevention of malaria in normal adults the therapeutic approach is to administrate 0.25–0.50 mg/kg body weight daily for a duration of 14 days. However, for people with G6PD deficiency, the dosage is differentiated to 0.75 mg/kg body weight once a week for 8 weeks with close monitoring of the patient's therapy  while diseases such as Ebola and Cholera, which were also of a similar magnitude, are being managed properly resulting in a decrease of the infected populationBiochemical health sciences started to evolve around the 1940s and 1950s at the same time as the development of instrumentation and new research methods. The scientific field responsible for (1) the research of different patients' responses to the drugs and (2) the minimization of the adverse effects caused also by variability on the metabolizing enzymes is known as pharmacogenetics , thousands of genetic variants were linked to several complex diseases overcoming the statistical limitations  that emerged in December 2019 in China and managed to spread rapidly in multiple countries at the beginning of February 2020  of the People's Republic of China 7th edition interferon α (IFN-α), lopinavir/ritonavir, chloroquine phosphate, ribavirin, and arbidol are recommended for empirical therapy against COVID-19 virus  in vitro reproduction of the COVID-19 virus  ,,However, despite the supply challenges due to the increasing demand currently, there are also concerns regarding the performance of the different technologies used  for the identification of IgM/IgG antibodies in patients. It is well-known that the presence of IgM antibodies signals the first line of defense when a patient is infected and the presence of IgG antibodies signals immunity for an individual  and infection and vice versa (Ramakrishnan et al., Biomarkers and especially VEGF-A family are important tools for personalized medicine that can assist the diagnosis as well as the selection of patients for specific treatments. Unfortunately, the majority of them are not yet clinically used. Given the disputable efficacy of various chronic disease prevention strategies, new, stringent biomarkers, including the genetic predisposition ones, should be urgently identified and established in the clinical practice.Personalized medicine is an ancient vision of rising challenges for healthcare systems and the research community throughout the years. It aims at confronting every obstacle on the prevention, diagnosis, and treatment of diseases by targeting each patient individually. Advances in both science and technology have already contributed to significant improvements regarding disease management and clinical outcomes prediction (Conti et al., In Nowadays, 15 years later, personalized medicine still struggles to be applicable. There is no need to leave another 15 years to pass until we understand the necessity of this field in medicine. Overcoming the application obstacles immediately will be an important step toward a new more personalized beginning on medicine. However, this cannot be achieved without providing both to the healthcare community and to the public the necessary information and awareness regarding this field.At the beginning of the Twenty first century, the “-omics” era arose making possible the detailed examination of organisms and their molecular phenotypes. The “-omics” research together with the advanced sequencing technologies increased the possibilities of early prognosis and targeted therapy for several medical conditions. Although sequencing technologies have identified numerous diseases, millions of variants have not yet been interpreted (Visvikis-Siest et al., Furthermore, the human genome is basically the foundation of personalized medicine. It is thought that personalized medicine will improve the practice of medicine based on the better understanding of the characteristics of each individual. Thus, personalized medicine is expected to enhance adherence and minimize harm associated with adverse events (Sorich and McKinnon, The outbreak of the COVID-19 infection is the right occasion and challenge for both research and healthcare professionals to change toward a more personalized approach taking into account individuals' needs. Efficient therapeutic regimens should be discovered and tested in a very short period to minimize the consequences of this infection. New diagnostic tools with increased sensitivity and specificity should be applied and might be the next generation of diagnosis. The combination of new and already established biomarkers could be the key to improving diagnostics tools and treatments, as it happened in the case of VEGF and bevacizumab.Consequently, the COVID-19 pandemic acknowledged the limitations that health care is facing, underlining the important issues that medicine is forced to encounter. Thus, the COVID-19 urged the healthcare system to change adapting a new reality of tailored therapy. A lot of advances are expected to be accomplished and a lot of current limitations to be overpassed in the next years. Personalized medicine, having the patient at the center of attention, is going to shape the future in medicine.SV-S: bibliography search, conception, and writing. DT: bibliography search, writing, and preparation of figures. M-SK: bibliography search and writing. SK: bibliography search and writing. MM: bibliography search and writing. All authors: contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.