Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology. It is usually explained that big data refers to the data integration which is difficult to deal with the existing database management tools, which has both massive characteristics and complexity characteristics. Big data are frequently characterized as the five Vs—volume, velocity, variety, value, and veracity.Medical data have the characteristics of disease diversity, heterogeneity of treatment and outcome, and the complexity of collecting, processing, and interpreting data.Medical big data have several unique characteristics that differ from big data in other disciplines: medical big data are often difficult to obtain2Today's society produces massive amounts of data all the time. Database technology is a software science that researches, manages, and applies databases. The data in the database are processed and analyzed by studying the basic theory and implementation methods of the structure, storage, design, management and application of the database. The main medical public databases are described in Table 2.1To reduce the cancer burden of the population, the National Cancer Institutes established a monitoring, epidemiological, and final results database (SEER) for cancer patients in 1973.The SEER database has a large sample size, high quality, and strong statistical power, which can provide tumor‐related researchers with high clinical reference value data. Researchers can obtain partial data through the application of the account number. There are three ways to obtain data from the SEER database: the first way is obtained by SEER*Stat software, this method is the simplest and widely used; the second method is download the compressed file from the SEER official website, extract the binary data after decompression, and then use software such as R is converted to data in the normal format and this method requires the user to have certain software knowledge; the last way is used by applying to the management personnel for DVD discs and using SEER*Stat without high‐speed internet support. Radiation therapy and chemotherapy variables in public databases have been removed since the November 2016 data submission. These variables can be obtained after signing an additional data usage agreement. The protocol describes the integrity of the chemoradiotherapy treatment variables and the potential bias in the use of chemoradiotherapy data.The SEER database is one of the most representative tumor databases in North America, and some of the data are free to the public.2.2Severe medicine is a discipline that studies the characteristics and regularity of any injury or disease that leads to the development of the body in the direction of death, and treats severe diseases. The focus of this discipline is on the monitoring of critically ill patients, the implementation of organs for organ dysfunction or debilitating organs. Support, so that patients can win the time to remove the cause under the condition of ensuring oxygen delivery and maintaining organ function. As we all know, intensive care unit (ICU) is in a very special important position in the hospital, and undertakes the treatment of patients with serious diseases.https://mimic.physionet.org/about/releasenotes/).https://mimic.physionet.org/mimicdata/carevue/) and the IMD Soft Meta Vision ICU System (https://mimic.physionet.org/mimicdata/metavision/). From 2001 to 2008, the Philips Care Vue clinical information system was used to track patients for a minimum of 4 years; from 2008 to 2012, the IMD Soft Meta Vision ICU system was used to track patients for a minimum of 90 days.In order to promote the work of intensive medical research, the MIMIC  database jointly issued by the Massachusetts Institute of Technology's Computational Physiology Laboratory, Beth Israel Dikang Medical Center, and Philips Medical supported by the National Institutes of Health. The clinical diagnosis and treatment information of more than 40 000 real patients living in the ICU of the Beacon Israel Dikang Medical Center from 2001 to 2012 was collected.https://github.com/MIT-lcp/mimiccode), there is an open‐source code package for analyzing patient characteristics that can be downloaded and used free of charge by researchers around the world. When bugs or improvements are found, you can modify it yourself, and then you can pull request, when the platform merge, you can successfully share your modified code package to the world, other users can also use it for free. The MIMIC database has great support for the research in the fields of critical medicine, evidence‐based medicine, clinical big data mining, and medical monitoring equipment data analysis, and has achieved fruitful results.The MIMIC database involves coding work during its use, which is a challenge for clinicians. On the GitHub platform . The project is a cohort of international collaborations conducted by the University of North Carolina at Chapel Hill Population Center in conjunction with the Center for Nutrition and Health of the Chinese Center for Disease Control and Prevention.The CHNS project, the China Resident Health and Nutrition Survey, is an open public platform . Paid access, the fee standard is 330 dollars. Researchers should really apply the CHNS database information, and a detailed reading to fully understand the CHNS project research documents is a necessary prerequisite. The CHNS database website provides a clear and detailed study description document. The questionnaires, database descriptions, ID variable names, etc. of the calendar year are there in the CHNS database description file.CHNS, a longitudinal cohort study of international cooperation, began in 1989 and has conducted 10 surveys until the year of 2015. The research covers data on the health and nutritional status of Chinese residents at the individual level, family level, and community level. Since then, Chinese national health, nutrition, medical, economic, social and other research has provided more comprehensive data support.2.4As an important measure of the level of international economic and social development, population ageing not only means an increase in the number of elderly people but also poses severe challenges to the economy and society.The Health and Retirement Research (HRS), supported by the National Institute for Ageing (NIAU01AG009740) and social security management, is a longitudinal research group survey conducted by the University of Michigan since 1992 and has established a representative large sample database.The HRS database has a large sample size, high quality, and is complex. In order to make the data easier to study, the HRS data are classified into public data and sensitive/restricted data. Anyone can create an account on the HRS data download site to get public data while restricting data and sensitive health data requires a separate application. The HRS database can be accessed in seven areas, including biennial data products, vertical data, nonelected year studies, sensitive health data , researcher contributions, RAND's contribution data, and cognitive, economic projects. Each subdataset file can be read by three different statements: SAS, SPSS, or Stata.The HRS database is a database of resources related to ageing in the United States regarding changes in health and economic environment. Most of the public data in this database are freely available through user registration. Its multidisciplinary data focuses on surveys of income and wealth, health, awareness, and use of health services, work and retirement, and contact with the family. Since 2006, data collection has expanded to include biomarkers and genetics as well as greater depth of psychology and social background. This mixed‐economy, health and psychological information database provides the unprecedented potential for researchers' work.2.5With the advent of the era of big data, data reusability and data sharing policies are attracting global attention. The infrastructure and related regulations for data management and data sharing have been rapidly developing in the past decade. Since 2003, the National Institutes of Health has required all large funds to fund research projects to disclose their data. PLOS One, the world's largest open‐access journal, requires authors to submit their data to a public database platform while the article is published. The BMJ Publishing Group recommends that authors store data in the Dryad database while submitting the manuscript.http://dryad2.lib.ncsu.edu/pages/organization). The Dryad database helps researchers realize that data can be archived for long periods of time and open for free reuse. As of February 2018, there were more than 600 journals working with the Dryad database, with more than 60 000 data files and 2.3 million downloads (http://dryad2.lib.ncsu.edu/).The Dryad database was funded by the National Science Foundation and was established in September 2008 as a nonprofit membership organization. The Dryad database stores research data in the fields of medicine, biology, and ecology. It is open to the world and can be downloaded free of charge and reused. Dryad was born out of the initiative of leading journals and scientific groups in the fields of biology and ecology, and they encourage researchers who submit journals to submit data to a professional database to store data and share data  that can be used for data references. Dryad will perform a necessary check on each submitted file. For example, whether a file can be opened, whether there is a virus, whether there is a copyright restriction, and whether sensitive data are displayed. Dryad also checks the integrity and correctness of the metadata. For example, information about related publications delayed dates for data publication, index keywords, etc. Once the article is published online, the data package will be published publicly, unless the data provider chooses to post the data. Because the title, abstract, author, etc. of the article often changes during the publishing process, Dryad will confirm and update this information based on accepted or published articles.Compared to other public database platforms, the Dryad database is more efficient in data sharing by working with many mainstream journals. By assigning DOIs to metadata, data can be referenced, increasing the scientific data utilization rate while increasing the academic reputation of researchers and publishers. Dryad has a detailed management policy for data maintenance and data disaster recovery, so data can be stored for a long time. The use of data “zero thresholds” and friendly interface also make the Dryad database more and more popular among researchers.2.6http://www.ukbiobank.ac.uk), is the world's largest database of biomedical samples and officially opened all data to global researchers on 30 April 2017. Between 2006 and 2010, UK Biobank recruited 500 000 volunteers aged 40‐69 from across the United Kingdom to obtain baseline data, including family history, drug history, and health status.UK Biobank  and X‐ray technology to brain, heart, and bone of more than 100 000 volunteers.UK Biobank's application process has high requirements for researcher's and research institutions' research background, research purposes and research motivation, including the need to provide evidence of recently published academic results to ensure that research is conducted in good faith.The most significant advantage of UK Biobank is that all volunteers recruited are registered with the UK National Health Service (NHS) and agree to link their medical records. This allows UK Biobank to track the health and health of all volunteers in detail through national medical data.The disadvantage of UK Biobank is that the sample provider must fill out a detailed basic situation questionnaire, including its name, gender, NHS number, disease information, etc., and there are inevitable privacy leaks.We believe that UK Biobank will provide more comprehensive research data and biological sample coverage in the future, providing global researchers with more efficient and convenient resource registration, application and use services, as well as more secure information security.2.7BioLINCC was established in 2008 by the National Heart, Lung, and Blood Institute (NHLBI). The Institute provides global leadership in the prevention and treatment of heart, lung, and blood diseases and supports basic, transformational, and clinical research in these areas. By establishing BioLINCC, NHLBI provides medical researchers with access to scientific data and access to biological samples, maximizing the utilization of research resources for NHLBI development and maintenance. These resources are the NHLBI biological sample library managed by the Blood Disease Resources Department since 1975 and the NHLBI database managed by the Cardiovascular Science Research Center since 2000.https://biolincc.nhlbi.nih.gov/) was established in October 2009. The site provides clinical and epidemiological research data and biological samples from more than 110 research institutes collected by NHLBI. BioLINCC is actively engaged in data sharing and is loved by many medical science and technology workers. Each year, more than 100 research project leaders apply to BioLINCC for their clinical data. A study from the affiliated hospital of Yale University School of Medicine in 2015 showed that more than 90% of users are satisfied with the data shared by BioLINCC and are suitable for conducting their clinical research using this data. Half of the users have used data to complete their research, and 67% of them have published articles with more than 1000 articles.The BioLINCC public website , a dynamic analysis of gene expression profiling data, is a newly developed web server for cancer and normal gene expression profiling and interactive analysis, filling the gaps in cancer genomics big data information and helping clinical research people use public data resources more efficiently.http://gepia.cancer-pku.cn/index.html) was developed by Professor Zhang Zemin from Peking University. The RNA‐Seq data set used by GEPIA is based on the UCSC Xena project (http://xena.ucsc.edu). The project was calculated by standard pipelines and analyzed RNA sequencing expression data from 9736 tumors and 8587 normal samples from the TCGA and GTEx projects. TCGA produced 9736 tumor samples in 33 cancer types, and this project only provided 726 standard samples. The imbalance between tumors and standard data can lead to inefficiencies in various identification analyses, so GEPIA also integrates data from GTEx. The GTEx project produced RNA sequencing data for 8 000 standard samples. At the same time, the UCSC Xena project recalculated the TCGA and GTEx raw RNA‐Seq data using standard pipelines, which made the two datasets compatible. Therefore, TCGA and GTEx data can be integrated for very comprehensive expression analysis. The expression data of TCGA and GTEx are recalculated under the same pipeline and can be directly compared. GEPIA uses MySQL to create databases. The topic analysis process is done by R and PerL. Web‐based interactive display with php provides key interactive analysis of GPIIA, including tumor/normal differential expression profiling, section mapping, based on tumor type or pathological staging. Analysis modules such as analysis, patient survival analysis, similar gene detection, correlation analysis, and dimensionality reduction analysis, as well as rapid customization are used.GEPIA  database is a multiomics approach to determining molecular changes that drive the development and progression of childhood cancer. The TARGET database targets childhood tumors, and major disease items include acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), kidney tumors (KT), model systems (MDLS), neuroblastoma (NBL), and osteosarcoma (OS). TARGET detects the genome, transcriptome, and epigenetics of specific childhood cancers through sequencing and chip technology. A multiomics approach is used to generate a comprehensive molecular alteration map for each type of cancer . By calculating and validating biological functions to determine which changes disrupt the functional pathways of genes, promote cancer growth, progression, and survival, thereby, identifying candidate therapeutic targets and prognostic markers from cancer‐related changes. The TARGET program originated from two pilot projects: ALL and NBL.To date, TARGET consists of five projects, ALL, AML, KT, NBL, and OS. It is managed by NCI's Office of Cancer Genomics and Cancer Therapy Evaluation Program. ALL is one of the major types of childhood leukemia. The ALL project clarifies the comprehensive molecular characteristics to identify genetic changes in the initiation and progression of childhood cancer that are difficult to treat. AML is a cancer derived from immature white blood cells in the bone marrow or myeloblasts. About 25% of children with leukemia have AML. Through comprehensive genome‐wide identification, researchers can develop more targeted treatments based on genetic and epigenetic changes found to improve the prognosis of children with AML. KT is a kidney tumor. Children's KTs account for about 7% of childhood cancer. The vast majority are nephroblastoma. Through genome‐wide research, it is hoped that key molecules of these tumors will be discovered so that better treatments can be developed to improve the prognosis of patients. NBL is a neuroblastoma. Neuroblastoma (NB) tumors are the most common extracranial solid tumors in children, and have become one of the most important diseases that affect children's physical and mental health. The NBL project clarifies comprehensive molecular characteristics to identify genetic changes in the initiation and progression of cancer in high‐risk or difficult‐to‐treat children. OS is osteosarcoma. TARGET generates genomic data for selected pediatric cancers and provides access to discover therapeutic targets for childhood cancer and translate these findings into clinical applications.The TARGET large database targets children's tumors, although it contains fewer types of diseases, but it is more targeted. To a certain extent, the database can help researchers conduct more in‐depth disease research and lead to more precise treatment options.2.11Severe medicine is an inevitable trend and a prominent symbol of the development and progress of modern medicine. It is an era product of the development of medical science to a fairly high level. There are many difficult problems involved in critical medicine, including the application and management of noninvasive ventilation, the rational use of antibiotics, the implementation of nutritional assessment and nutritional support, the indications for analgesia and sedatives, and the scope of application of the ICU risk assessment model.The eICU Collaborative Research Database (eICU‐CRD) is a large public database created by the Philips Group in collaboration with the Massachusetts Institute of Technology (MIT) Laboratory for Computational Physiology (LCP).https://github.com/mit-lcp/eicu-code).To obtain access to the eICU Collaborative Research Database, you must first apply for registration.With the advent of health information networks, humans need to develop cost‐effective systems to reduce the time and effort spent recording health care data. Patients in the ICU are closely monitored throughout the hospital stay to detect changes in the condition. The patient's condition changes require the medical worker to modify the treatment plan in time. The eICU collaborative research database solves the problem that it is difficult for medical workers to have a lot of time and energy to collect a large amount of complete information, and it is free to open to medical workers all over the world.2.12The GEO database is an international public function gene expression repository created by NCBI. The data have powerful inclusion and storage capabilities that allow users or researchers to submit, save, and retrieve many different types of data. GEO provides a simple submission process and format whose data source relies on data submission from researchers. GEO data submission follows the MIAME principles. The GEO database architecture not only provides researchers with a wealth of disease‐related gene expression profiles, but also provides tools for querying and downloading experiments and gene expression data, allowing users to query and download interesting research and gene expression profiles. The GEO database contains the raw data and the data set or map generated from the original data. GEO's raw data are placed in three different entity databases: platform, sample, and series.The search results of the GEO dataset include name, description, species, platform, submitter contact, series, publication time, numeric type, and the number of samples. The search results of the GEO expression map show the expression level of a gene for all samples in the form of pictures. The experimental conditions in the search results facilitate us to observe the difference in expression levels of a gene under different conditions. Each dataset outlines its research data report and purpose, showing the number of platforms, samples, and series associated with it, from which researchers can select the research content of interest to download the data.GEO also offers GEO2R online analysis tools. GEO2R is an interactive web tool that uses GEO2R to screen differentially expressed genes, allowing users to compare two or more groups of GEO series to identify genes that are differentially expressed under different experimental conditions, and the results are shown to be significant (sorted gene tables). GEO2R uses the GEOquery and limmaR packages from the Bioconductor project to perform a comparison of the original processed data tables provided by the submitters. Unlike GEO's other dataset analysis tools, GEO2R does not rely on collated datasets, but instead queries the original series of matrix data files.Developed and maintained by NCBI, GEO is one of the well‐known comprehensive databases for storing and querying chip data. There are various chip technology platforms. The Gene Expression Omnibus (GEO) was created in 2000 and the last modified date is 26 July 2016. The researchers explored the potential biological value through the deep mining and analysis of the gene expression data information provided by the gene chip, and applied it to the research of gene analysis, gene expression and regulation, disease diagnosis, and drug screening. The mining and analysis of gene expression profile data help to understand the function of genes and the interactions between genes, and to analyze the genetic characteristics and functions of genes. GEO adapts to the development trend of chip database, reduces the cost of chip detection, shortens the data reading time, efficiently and rationally utilizes resources, and integrates data of more researchers.2.13People have always been concerned about the dangers of diseases that endanger human health. Accurately grasping the burden of various diseases around the world is of great significance for understanding the degree of damage and development of diseases, improving the efficiency of health services, and promoting the health and social and economic development of residents.The GBD is a comprehensive health loss study. The GBD database contains all GBD disease, risk, etiology, injury, natural injury, and sequela syndrome. Indicators that measure the GBD include: deaths, loss of life (YLLs), lifespan disability (YLDs), life‐limiting disability (DALYs), prevalence, morbidity, life expectancy, probability of death, and healthy life expectancy (HALE), maternal mortality (MMR), and total exposure (SEV). The extracted data indicators (units) include: quantity, ratio, percentage, year, and probability of death. The year of extractable data is: the annual results of all measures from 1990 to 2017, and all GBD age groups; Gender: male, female, or a combination of both. The research areas are divided into: GBD super regions, regions, countries, and selected subnational units, World Health Organization regions, World Bank income levels, etc. The data can be downloaded free of charge to help a wide range of clinical researchers.Although the GBD database can query and download data, including many search parameters can cause problems: Query sometimes causes the file to ignore certain results specified in the query: specific age groups, years, etc.; query all locations at the same time and many or all of the reasons, age groups, years, etc. will appear incomplete data. This tool is not available for Internet Explorer 10 and earlier.3With the advent of the information age, data mining is increasingly being used in clinical practice. With information technology, medical records and follow‐up data can be stored and extracted more efficiently. At the same time, look for potential relationships or laws from medical data to gain effective knowledge of diagnosis and treatment of patients; increase the accuracy of disease prediction, detect disease at an early stage, and improve cure rate. Different from the traditional research methods, data mining is to mine information and discover knowledge without explicit assumptions, that is, without prior research and design, the information obtained should have three characteristics: previously unknown, effective, and practical. The emergence of data mining technology is not to replace the traditional statistical analysis technology, but the extension and extension of statistical analysis methodology. Data mining methods can be divided into two categories: descriptive and predictive. Descriptive patterns characterize the general nature of data including association analysis and clusters analysis. Predictive patterns are summarized on current data including classification and regression.3.13.1.1Association analysis, also known as association mining, is the search for frequent patterns, associations, correlations, or causal structures that exist between project collections or collections of objects in transaction data, relational data, or other information carriers. In other words, correlation analysis is the discovery of the connection between data from large amounts of data. Shopping basket analysis is a classic example of correlation analysis. It mainly analyzes the customer's buying habits by discovering different products in the customer's shopping basket. Knowing which items are often purchased by customers at the same time can help retailers develop marketing plans. This phenomenon is the correlation between goods in the store. The association analysis consists of two steps: first, list all the high‐frequency items in the set; then, generate frequent association rules based on the high‐frequency items. The second step is the generation of association rules. According to the high frequency item group obtained in the first step, if the rule satisfies the minimum confidence, the rule is an association rule. Machine learning methods for association analysis include: Apriori algorithm, FP tree frequency set algorithm, and Upgrade Lift.The Apriori algorithm is based on the a priori principle and reflects the relationship between the subset and the superset: that is, all nonempty subsets of frequent itemsets must be frequent, and all supersets of infrequent sets must be infrequent. If item set I does not satisfy the minimum support thresholds, then I is not frequent. Frequent mode refers to the fact that the various items that appear in each shopping record actually reflect the nature of a combination. The combination of these items is unordered in the record, and this disordered combination is called “pattern.” Some of these modes have low frequency and some have high frequency. It is generally considered that the higher frequency is usually more instructive. This high frequency mode is called “frequent mode.” Therefore, the nature of the Apriori algorithm is mainly used to search for candidates when searching for frequent itemsets. Apriori algorithm can better avoid blind search and improve the efficiency of frequent item set search.The FP tree is constructed by reading in transactions one by one and mapping the transactions to a path in the FP tree. Since different transactions may have several identical items, their paths may partially overlap. The more the paths overlap each other, the better the compression effect obtained by using the FP tree structure; if the FP tree is small enough to be stored in the memory, the frequent itemsets can be extracted directly from the structure in the memory without having to repeatedly scan and store the data on the hard disk. The main idea of the FP tree frequency set algorithm is to compress the frequency set in the database into a frequent pattern tree after the first pass scan, while still retaining the associated information, and then separately mining the condition bases.Regardless of the Apriori algorithm or the FP tree frequency set algorithm, in some cases, even if the two indicators of support and confidence are relatively high, the rules generated may still be useless. Lift gives a new indicator of the quality of the evaluation rules. Lift indicates the intensity of a given random occurrence of the predecessor and the back part, which provides an improved message to increase the probability of occurrence of the next piece of the given front piece.3.1.2The classification algorithm must know the information of each category in advance, and all the data to be classified have corresponding categories. When the above conditions are not met, we need to try cluster analysis. Cluster analysis is to study how to classify similar things into one category. Clustering divides similar objects into different groups or more subsets by static classification, so that member objects in the same subset have similar properties. There are several clustering methods: partition‐based algorithm, hierarchical clustering algorithm, density‐based algorithm, and grid‐based algorithm.N objects are roughly divided into K classes, and then the unreasonable classification is modified according to some optimal principle. The advantages of the K‐means algorithm are that it is simple, fast, easy to understand, and has low time complexity. However, the K‐means are poorly processed for high‐dimensional data and do not recognize nonspherical clusters.The K‐means method is the most commonly used and most basic clustering algorithm in cluster analysis. It is based on the prototype and partitioned distance technique. According to the given parameter K, the The hierarchical clustering algorithm hierarchically decomposes the data set. It is divided into agglomerative hierarchical clustering and top‐down divisive hierarchical clustering. Commonly used hierarchical clustering methods include BIRCH, CURE, ROCK, Chameleon, and other algorithms. This type of algorithm initially treats each point as a cluster. Clusters and clusters are combined according to closeness, and proximity can be defined differently according to the different meanings of “close.” The above combination process ends when further combinations result in undesired results under multiple causes.In order to find clusters of arbitrary shape, the cluster can be regarded as a dense region separated by sparse regions in the data space, which is the core idea based on the density algorithm. Common methods include DBSCAN, OPTICS, and DENCLUE methods. DBSCAN is the most representative. It is a density‐based clustering based on high‐density connected regions, which connects core objects and their neighborhood‐difficult regions as clusters. Mainly used to deal with noise. The density of object O can be measured by the number of objects close to O. The core idea of the algorithm is to find all core points, boundary points, and noise points. DBSCAN does not need to input the number of clusters to be divided and can handle clusters of various shapes. However, the time complexity of the algorithm is high, so high‐dimensional data cannot be processed.Based on the partitioning and hierarchical clustering methods, the nonconvex shape clusters can not database. The algorithm that can effectively find the arbitrarily shaped clusters is based on the density algorithm, but the density‐based algorithm generally has a high time complexity. From 1996 to 2000, the research data mining scholars have proposed a large number of grid‐based clustering algorithms. The grid method can effectively reduce the computational complexity of the algorithm and is also sensitive to density parameters. The grid‐based clustering method uses a multiresolution grid data structure. The advantage of this method is that the processing speed is extremely fast and depends only on the number of elements in each dimension in the quantization space. Common methods include STING, CLIQUE, and WaveCluster. STING, based on grid multiresolution, space is divided into square units, corresponding to different resolutions; CLIQUE combined with the idea of the grid and density clustering, subspace clustering for high‐dimensional data; WaveCluster using wavelet analysis. The boundaries of the cluster become clearer.3.23.2.1y = w'x + e, and e is a normal distribution with an error obeying a mean of 0. Regression analysis can be divided into a linear regression analysis and multiple linear regression analysis according to the number of independent variables. A linear regression analysis contains only one independent variable and one dependent variable, and a straight line can approximate the relationship between the two. If the regression analysis includes two or more independent variables, and the linear relationship between the dependent variable and the independent variable is called multiple linear regression analysis. In practice, a phenomenon is often associated with multiple factors. When performing regression analysis, you need two or more independent variables. This regression is called multiple regression. It is more effective and more realistic to predict or estimate the dependent variable by the optimal combination of multiple independent variables than to predict or estimate with only one independent variable. Therefore, multiple linear regression is more practical than one‐dimensional linear regression. Multiple linear regression analysis consists of three steps: the first step is using the collected data to establish a regression equation; second, performing a hypothesis test on the regression equation obtained by the analysis; third, when the regression equation has significant significance, it is necessary to perform the partial regression coefficient for each independent variable. Hypothesis testing, after eliminating the variables with no significant partial regression coefficients, re‐establish the multiple regression equations that do not contain the variables and repeat the above process. Its basic principle is to apply the least‐squares method to the regression of the linear regression model.Traditional regression is a statistical analysis method that uses ordinary linear regression to determine the quantitative relationship between two or more variables. It is widely used. Its expression is Most of the statistical models of traditional methods have specific requirements on the data, and the model itself has a mathematical form that can be clearly expressed. The pros and cons of the model are mostly judged according to the test obtained from the assumption of the distribution of the data. However, in the actual work process, it is difficult to make any assumptions about the distribution of data in the real world. At the same time, it is difficult to describe with a limited mathematical formula. The machine learning method has no assumptions about the data, and the results are also cross‐over. The method of verification judges that the prediction model based on the algorithm or program is quite effective and the result of cross‐validation is easily understood and accepted by the majority of practical workers. The machine learning methods for regression models are decision tree, adaptive boosting, bagging, random forests, support vector machines, nearest neighbor algorithm, and artificial neural network.3.2.2Classification is a supervised learning process. The goal is to “tag” the data to extract valuable data. The more accurate the categories are, the more valuable the results will be. Usually, the following methods are used: logistic regression, probit regression, classical discriminant analysis: there are not many classification levels of independent variables, and there are two levels of dependent variables; discriminant analysis: there are not many classification levels of independent variables, and there are more than two dependent variables level; machine learning method: there are many levels of independent variable classification. Building a classification model can help us better understand the data. However, there are limitations. The information of each category must be known in advance, and all the data to be classified have corresponding categories. When the dependent variable is a categorical variable and the independent variable contains multiple categorical variables or the categorical variable has a high level, the classical statistic is not applicable, and the machine learning method is more practical for processing complex data, and the accuracy is better.427 The potential for big data is still to be discovered. Although it is not easy to generate new findings and conclusions in massive amounts of data, as long as effective investments are made on the right systems, key breakthroughs in technology and workforce are available, and future big data analysis, visualization, and artificial intelligence can be foreseen. The convenience and change in medical care and life are worth looking forward to. The potential for big data is still to be discovered. However, medical big data mining still faces enormous challenges, mainly in the following: medical knowledge concept is complex, medical knowledge reasoning key technology has not broken through; medical information sources are wide, data modality is high, the latitude is high, the type is unbalanced, and structure is complicated. The hospital's electronic medical record system is poor in openness and scalability; the out‐of‐hospital process is poorly regulated. Although it is not easy to generate new findings and conclusions in massive data, we can foresee the future medical and life convenience of big data analysis as long as we make productive investments in appropriate systems and achieve key breakthroughs in technology and workforce.The use of new cutting‐edge disciplines to generate big data and analyze big data is a trend that has evolved between traditional medicine and precision medicine. The development of big data will help the global application of precision medicine and the emergence of new health management models.5This article first briefly introduces the database and data mining methods commonly used in the era of big data. With the advent of the information age, data mining is increasingly being used in clinical practice. With information technology, medical records and follow‐up data can be stored and extracted more efficiently. At the same time, look for potential relationships or patterns from medical data to gain useful knowledge of the diagnosis and treatment of patients. At the same time, it can also increase the predictive accuracy of the disease, find the disease at an early stage, and improve the cure rate. The database we introduced is only a small part, and there are many databases worthy of research attention, such as Catalogue of Somatic Mutations in Cancer (COSMIC), The Human Gene Mutation Database (HGMD), Oncomine, cBioPortal for Cancer Genomics , Sequence Read Archive(SRA), WHO Mortality Database, Orphanet, Database of Genomic Variants (DGV), Online Mendelian Inheritance in Man (OMIM), etc. With the deepening of theoretical research and further practical exploration, medical data mining will play an influential role in the diagnosis and treatment of diseases, medical research and teaching, and hospital management.The authors declare no conflict of interest.


Research has long observed the absence of gender in child care policy, media, and elections. However, the COVID-19 pandemic has invoked critical questions about child care and its importance to states’ economic recoveries around the world. In this research note, we analyze news coverage of child care in major Canadian daily newspapers to explore whether and how news narratives regarding child care are shifting in light of the COVID-19 pandemic. In particular, are we seeing a focus on women and gender in child care coverage amid the compounding pressures that women face in the current social and economic climate? The results of our analysis suggest that the pandemic has not shifted the conversation on child care and that current coverage principally reflects long-standing trends in child care framing. We find that gender remains systematically written out of coverage of child care, occluded by a larger focus on health-, economic-, and accessibility-related concerns about child care services. Gender and women do not figure prominently in discussions of child careThe COVID-19 pandemic, however, has created conditions favorable to taking women and gender more seriously in the child care discussion. Globally, schools and child care facilities have been closed or their operations have been significantly reduced for months, starting in mid-March 2020 in Canada. Unsurprisingly, data suggest that women are handling a disproportionate share of the additional work of child care and homeschooling,There has been a genuine acknowledgment of the pandemic's disproportionate effects on women, so the question we tackle in this article is whether women and gender have been central to news coverage of child care, which is an essential service for women's resumption of their regular work activities. In this research note, we examine print news coverage in Canada from March 1 to May 31, 2020, to address the question. Overall, we find similar patterns to pre-COVID-19 studies, with very little focus on women or gender  closure of licensed facilities.Gendered concerns: Coverage related to gendered aspects of child care, including the disproportionate burden of care that women have incurred throughout the pandemic.Using computer-assisted content analysis techniques , we identified four key dimensions of child care in the news sample:Next, we describe news coverage of child care over the period, specifically analyzing the frequency of the four dimensions of care in the sample, including any shifts in framing associated with the journalist's sex or the date of coverage.p < .001), as several provinces announced their plans for “reopening” the economy. These articles generally discussed the economic fallout from the pandemic and the measures governments have taken to support families and child care businesses to ensure that they are able to reopen in the ensuing phases of the recovery.Economic concerns represent the next most prominent theme in coverage of child care during the pandemic. The importance of child care to Canada's economic recovery was raised in 72% of articles in the sample, particularly from the final weeks of April onward . Although there is relative parity in stories written by male and female journalists,As Around the world, child care policy and practice have critical gender-related dimensions. Accessible, affordable child care is a necessary ingredient for women's equality, especially in the context of economic and social recovery from COVID-19. Our analyses suggest that the media information environment has not engaged sufficiently with these realities and may be contributing to the neglect of women and gender in this policy area, the normalization of gender-related care imbalances, and the removal of these challenges from the public eye and public accountability.


The adoption of digital technologies and sensors at farm level can give several benefits in terms of animal welfare, environmental sustainability, and farmer’s quality of life; but the evaluation of possible productive returns is still the most critical aspect. At the same time, it is very difficult to plan a controlled contemporary study with a dairy farm that involves adopting or not adopting the different technologies today available, and measure without any bias their productive performance at herd level. This paper used the results of a planned survey in dairy farms in Lombardy  and tries to quantify if there is a gap in productivity between farms with different adoption degree of digital technologies in herd management. Through a statistical two-stage treatment regression technique, the study was able to find a positive relationship between technology adoption and herd productivity.This paper aimed at verifying if and to what extent the use of information technologies for dairy farming positively affects productivity of farmed herd. To do this we estimated the effects of precision farming on a productivity index at herd level, utilizing individual farms data of about 500 livestock farms. Farms are specialized in bovine milk production and are localized in Lombardy, that is one of the most important areas of Italian dairy farming. Using a two-stage treatment regression model, to solve the selection bias due to both observed and un-observed individual heterogeneity in the technology adoption, the study found a positive relationship between adopter status and the proxy of herd productivity. The main future challenge faced by the agriculture industry is how to increase production in order to respond to the growth in food demand, while preserving the natural resources and environment. Particularly, for protein products of animal origin, it will be increasingly important to introduce innovations that contribute to the improvement of productivity of such productive systems in a sustainable manner. The productivity of dairy livestock has grown a lot in recent years at the European level. The average yield in the past was mainly driven by improvements in the individual animal (genetics) and production process  and reflected significant changes in the structure of the sector (farms that remain on the market are those who are the biggest in size terms and the most efficient). Currently, improving farm-level efficiency appears to be the main way to further increase the sector productivity. Generally, digital and geospatial technologies to monitor, assess, and manage soil, climatic and genetic resources, allow to balance the economic, environmental, and social dimensions of sustainable food production . Also, tIn general, precision farming tools help farmers to improve input allocation decisions, by which lower production costs and/or increase outputs . SpecifiThe majority of specific literature showed that the information gathered in this way is quite useful in assisting farmers in making input allocation and herd management decisions, that are better than it would be with conventional management practices . HoweverPrevention of clinical disease due to a better peripartum management of the cow may prevent most of the clinical diseases in the first 21 d of lactation, which may cause milk, fat, and protein losses  that havWhether PLF technologies can improve or not the productive level of dairy farms, after their introduction, is not a consolidated result because this could be affected by other several changes related, for example, to the training of farm personnel as well as to other management issues typical of a transition period. This might affect a simple evaluation carried comparing the same farms just before and just after the technology adoption. The aim of this paper is to introduce an alternative approach with a two stages regression as a reasonable way to by-pass the problem from a time series evaluation of the changes from PLF adoption.As we said before, the study wanted to estimate the differentials of productivity between those farms adopting of PLF technology and those who are non-adopters. To do this, we predicted an average productivity index at the herd level, with a dummy variable representing PLF adoption, trying to keep fixed the other factors that could influence the productivity measure used. The herd productivity index was the 305-d average milk yield per cow performance for each herd.Overall, PLF adoption helps to better manage the herd, and, more specifically, the several types of sensors used by sample farms could be grouped in: (a) Sensors to detect the estrum for an efficacy reproduction; (b) sensors for milk production and quality controls; and (c) sensors for timely and effective livestock disease surveillance. These different management factors, as proved in the specific literature, have a direct effect on the chosen yield productivity measure. For example, health problems affect milk production in various ways for each kind of disease , even atOn the other hand, herd productivity could depend on many other factors, among which three main categories can be distinguished: Natural resource endowments , technologies , techniques of herd management . In order to isolate these effects on our productivity variable and to measure the differences due to the management efficiency of herd, net of other influencing components, we tried to keep the other non-observed factors fixed. In other words, we tried to restrict the analysis to a “no-interference” setting. To do this we adopted different empirical strategies.First of all, we carried out interviews in an area that is highly homogeneous from a production point of view, specifically as regards milking technologies, the main techniques used to manage the herd  and the natural capital  used by livestock farms .Secondly, to reduce estimations bias due to the self-selection problem, we used a linear regression with endogenous treatment effects and other covariates to control for farm productive scale. Data were collected through a survey conducted during the year 2016 submitted to a sample of 490 dairy livestock farms located in the Cremona administrative province. In this area, the milk livestock sector is of economic importance and a massive presence of large farms typify the productive structure of sector, which averagely have about 200 cattle farmed. Specifically, the interviewed farms are located in three homogeneous areas from a production point of view: The Cremasco area, the Casalasco area, and the Cremonese area. The sample represented the 82% of the dairy farms in the province of Cremona  at the tThe empirical approach used was the estimation of an “adoption gain”, that could derive from the use of several sensor systems to support herd management, calculated as differences in the herd productivity index. Methodologically, using a two-stage treatment regression model, we estimated the average treatment effect on treated (ATET), that measures the difference in mean outcomes between units (farms) assigned to the treatment  and units (farms) assigned to the control (non-adopters).A two-stage regression technique allowed us to estimate an average treatment effect and other parameters of a linear regression model augmented with endogenous binary-treatment variable. This technique reduces the probable bias of estimations due to the self-selection problem. The bias could derive from the presence of some farms’ characteristics that induce farmers self-selection into the choice of technology adoption and are also correlated with outcomes (Y). In general, in a non-randomized setting it is essential to identify the treatment effect, considering the different pre-treatment conditions between treated and untreated . Such spjy and an equation for the endogenous treatment jt,jx are the covariates used to model the outcome, jw are the covariates used to model treatment assignment, and the error terms jε and ju are bivariate normal with mean zero and covariance matrixMore formally, the endogenous treatment-regression model is composed of an equation for the outcome jx and jw are unrelated to the error terms; in other words, they are exogenous. The covariates Technically, by running the official STATA (version 16.1) command called etregress, with option twosteps, whose estimation is based on the  Heckman The first stage procedure performed a probit model (treatment equation) to estimate the likelihood that farm adopts sensor technologies, using as Y variable a dichotomous taking on value 1 if at least one sensor system is present in the farm and 0 otherwise. For this equation, as independent variables we used the localization of farm, the farm size and the age of farmer, to model the characteristics that influence the choice of adoption. We emphasize that our choice of using these regressors is widely supported by most of literatures, which have explored on the adoption process of such innovations . ParticuLarger farms are also hypothetically more likely to adopt a new technology and specifically farm size was identified as the most important aspect influencing the adoption of precision farming technologies . HoweverIn the second stage the STATA command regresses the main equation, evaluating the adoption effect on a proxy of farm’s productivity. As already said, the productivity index (Y) is the tons of milk averagely produced by one cow in a year, taken in their natural logarithm. For this equation our interest variable is the dichotomous one, representing PLF adoption, whose coefficient was estimated by the treatment equation. Moreover, to take also account the scale effect on productivity level, we have included two variables as covariates, the herd size and its square, aiming to capture heterogeneity in the scale effect.Regarding the age of farmers, as we can better see with the Finally, by simply comparing the 305-d average milk yield per cow, we can see that it is higher for adopters than for non-adopters of about 0.5 tons per cow, evidencing the differences we are interested in and that we will estimate with the regression model. More in detail, The results of the two stage regressions are reported in the exp (β) − 1}) Then, bringing the inverse of log of coefficient we can calculate a sort of semi-elasticity of 12%, i.e., an ATET of being sensor’ adopter of 1.2 tons of milk per head. The coefficients of both variables, which represent the size of herd, are significant but the first is positive while the second (its square) has negative sign. The interpretation is that, as we expected, the productive scale has a positive effect on the productivity, but at the decreasing rate. In other words, we can see that the scale effect has a non-linear trend, first increasing and then decreasing following a convex curve. To illustrate, results of the main regression (column 1) show that, after having  solved the bias due to omitted variables and parity of dimensional conditions of herds, there is a positive effect from using any kind of sensor on farm productivity index. The coefficient of sensors informs us that the intercept increases Y by 0.11  informs us that without the instrumented correction the ATET measure would have been underestimated. Finally, Sigma is the standard error of the residual in the productivity equation.This empirical exercise wants to explore the existence on an “adoption effect”, by the estimate of the differences in herd productivity, using individual farms data of 490 livestock farms. Farms are specialized on bovine milk production and are located in one of the most representative areas of Italian dairy farming . In this regard, we emphasize that farms of this area were highly homogeneous from a production point of view, in terms of genetical capital (breeds) utilized by farms, organization of stalls and management of the herds. The homogeneous production techniques used by farms allow us to make comparisons between those farms who shared the same technological frontier, other than the use of ICT. Moreover, using a two-stage treatment regression technique, we try to solve the selection bias due to unobserved individual heterogeneity and auto-selection of PLF adopters. The main results showed positive relationship between adopter status and the productivity index.The positive effect of PLF tools on a proxy of farm technical management may be explained by several indirect factors. As stated above, our analysis was conducted regardless the category of tool adopted by the farmers, evidencing a generalized positive effect on milk production. The reasons for this result can be found in the literature about the direct or, more frequently, indirect effect of the main kinds of adopted tools in our survey. Simple examples follow. The data from each milking is one of the most useful to monitor the risk of mastitis . This reMost of the literature evidenced that the improvement in PLF tools efficiency needs to pass through an adequate adoption of new management strategies at herd level. At the beginning, for example, a lot of expectation about robotic milking performance focused essentially on the possible increase in milk production, due to the related increase in milking frequency. Only several years later, the attention was shift on the opportunity to adopt a new herd management to optimize the time use (occupation rate) of each robotic milking stall , mainly In one of the few studies attempting to estimate the possible advantages from PLF adoption,  reportedThere are few studies about a possible substitution of capital for labor with the adoption of new technologies in the PLF adoption scenario. One interesting paper was published by Steeneveld et al. (2012)  about roIn the case of milk yield recording devices, a better individual monitoring of the cow could allow a more targeted individual management according to the animal productive ability. One example of this may be the possibility to adequate day by day the amount of supplementary concentrate supplied to the cow to better fit her requirement according to its productivity .Among the systems studied by the North European researchers , the intFor tools like estrus detection systems, the economic evaluation of the investment must consider the performance in terms of sensitivity and specificity. As reported by Rutten et al. 2014) [ [4] for Our results confirmed that PLF would help farms to improve their productivity through a better management of herd, suggesting that efforts to increase the adoption of these technologies and the dissemination of adopters’ best practices could contribute to the increase in sector productivity, by improving efficiency in herd management. On the other hand, if there are such efficiency improvements, one wonders why not all farms adopt these technologies? Actually, we should consider some other factors. The technology adoption generally increases capital costs, primarily because of higher maintenance costs and depreciation, and it is not so clear when and at which level of investment corresponds a net gain for farm in the adoption of PLF technologies. Moreover, the change from conventional production system to PLF system requires a new management approach and a corresponding change in labor tasks, that could limit the adoption even in the presence of high net gains. In this case, policy interventions addressed at training farmers to make them more confident with such innovative technologies are welcomed. However, more empirical researches are needed to better understand the effects of PLF technologies adoption on the productivity of heterogenous farms and signaling the possible frictions that undermine the potential positives effects at farm level.


Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient  intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460). Undernutrition, including protein-energy and micronutrient deficiency, remains at an unacceptably high level in many low- and middle-income countries (LMIC) . ChildhoEfforts have been made in high-income countries to minimize reporting bias and improve the accuracy of dietary recall and records. These include shifting from pencil-and-paper–based 24HR to computer- or web-based applications such as Intake24 , myfood2Advancement in smartphone and wearable technologies provides an opportunity for collaboration between nutritionists and engineers to develop low-cost, convenient, quicker, and more accurate methods of dietary intake assessment. Mobile phone–based methods, where respondents are asked to upload images of foods and meals eaten, can be used as a stand-alone (image-based) method or to assist traditional self-reported dietary intake methods (image-assisted). Images are intended to aid in the estimation of portion sizes and in helping respondents remember the foods eaten but not reported . FurtherThe development of methods that require minimal or no user input, the so-called “passive” methods, could help break through the barriers around misreporting bias and errors associated with self-reported dietary intake methods. Such passive methods include wearable cameras and sensors that automatically capture images of food consumption and produce the nutritional analysis of the intake. Herein, we describe the protocol of a study designed to develop and validate an objective, passive, image- and sensor-based dietary assessment method for estimating food and nutrient intake in households in Ghana and Uganda.This project is designed to validate an objective, passive, image- and sensor-based dietary assessment method for the estimation of dietary intake of adults and children in households in LMIC. The study uses wearable and fixed camera devices around households to capture images of food preparation and cooking, and employs automated [artificial intelligence (AI) and deep learning] and manual  techniques to recognize foods on the images, and estimate portion size and nutrient content. This passive, image-based method will be validated against an established method of assessment of true intake—supervised weighed food record in this case—to provide a measure of its accuracy. In addition to validation, the accuracy of the new method will be compared with interviewer-administered 24-h dietary recalls, a widely used dietary intake assessment method, in field studies in Ghana and Uganda.The field studies for this project will be conducted in Ghana and Uganda. In each country both rural and urban/periurban communities have been selected for inclusion to facilitate the validation of the passive dietary assessment method across diverse geographical locations and cultural and community practices relating to food acquisition, preparation, consumption, and eating. In the 2 countries, households comprising a child/children aged <5 y and/or an adolescent child or children (aged 10–19 y) will be invited to participate. These age groups are of interest because of their association with marked nutritional requirement and prevalence of undernutrition. Thus, the validation of these dietary assessment tools in populations at risk of undernutrition will support future diet- and nutrition-related research in the area. In each community, local field staff and community leaders will help to identify households for recruitment and participation. Informed consent will be obtained from adults in each household, and assent from minors aged 13–17 y, before enrolment into the study. For the purpose of informed consent, trained local field staff with knowledge of the local language and culture will explain the full details of the study to the participant, covering all aspects of the study as detailed in the participant information sheet. The participant information sheet will be translated into local languages and read to illiterate participants. All participants will be given ≤48 h to decide whether they would like to participate. In addition, candidate participants will be given the opportunity to speak to an in-country study investigator or external nutrition and health personnel about the study. If a recruitee agrees to participate, written consent will be obtained through either a signature or fingerprint. Participating households will be compensated as per local ethics committee recommendation.Foodcam—a stereoscopic camera mounted in kitchens or food preparation areas to capture images of a cooking process. The key features of Foodcam include: i) two 5-MP (megapixel) cameras with electronically switchable visible/infrared filers, enabling imaging in visible and infrared spectra; ii) an infrared structured light projector, for use in 3-dimensional image reconstruction of food items; and iii) a passive infrared motion detector, for motion sensing and auto-switch of the device. Foodcam is powered by a built-in rechargeable battery, which when fully charged can continuously capture images for ≤14 h. It has robust internal data storage capacity, allowing for ≤1 wk of continuous data collection and storage.Automatic Ingestion Monitor, version 2 (AIM-2)—an improved version of a previously described model  of video, and the battery, when fully charged, is adequate for capturing video events of an entire day of food intake.eButton—a circular chest-pin–like device, worn by attaching it to clothing using a needle-clip or worn using a lanyard or necklace (necklace . eButtoneHAT—a camera device attached to the brim of a hat. An electronic component (including camera and SD card) similar to those of eButton is embedded underneath the front tip of the brim of a hat. This special design enables imaging of breastfeeding episodes, to provide an estimation of the frequency and duration of breastfeeding in 1 d. A mother or caregiver wears the hat during breastfeeding, and the camera, which is angled toward the chest region, takes time-lapse images every 3–5 s of the feeding. eHAT could also be used to capture images of other infant feeding practices, such as bottle-feeding or spoon-feeding of young toddlers in a situation where the infant being fed is positioned on the chest region of the mother or caregiver.To capture the feeding/eating practices across the different age groups, individual preferences, and the specific food-related activities of people in LMIC, different camera- and sensor-based devices will be integrated into a comprehensive passive dietary assessment system. This system comprises camera- and sensor-based devices for passive capture of images of food preparation and intake, a data repository for image storage, and custom software for food recognition, volume estimation, and nutritional analysis. To achieve these objectives the following camera and sensor devices , and assent of teenagers (aged 13–17 y). Consented households will be visited twice and, on each visit, a meal of Ghanaian and Kenyan origin will be cooked. At the start and during cooking, all ingredients for making the meals will be weighed and recorded, and Foodcam will be mounted in the kitchen overlooking the cooking area to take time-lapse images of the food preparation. At the end of cooking, foods served to the children will be weighed and recorded before eating, and the children will wear a camera device during eating to take images of their food intake. Children will be asked to eat until full and any leftover food will be weighed and recorded for completion of weighed food records. For families who eat using shared plates, Foodcam will be used in the dining area to record their food intake. Food images captured by the devices will be downloaded and stored in cloud storage. Finally, a device assessment form will be given to the children to provide an acceptability rating (ranging from 1 to 5) on how comfortable the devices were on their body, their ease of use, how much they interfered with their eating, and the children's willingness to use similar devices in the future. Mothers will be allowed to complete the questionnaire for younger children who are unable to provide coherent answers, and to guide older children in answering the questions, without the interference of study staff. Portion sizes and nutritional analyses will be estimated from the food images and validated against weighed food records.The Imperial College Research Ethics Committee has provided ethics approval for these studies: approval reference numbers are 18IC4780 and 18IC4795 for the study at the CRF and at households of participants, respectively.The purpose of this study is to evaluate the feasibility, acceptability, and performance of passive assessment devices in capturing food acquisition, preparation, and intake of households in Ghana and Uganda. In addition, this study aims to collect information on household structure, food, and eating behavior through an in-depth household interview, to inform the configuration and deployment of the camera devices appropriate for the food environment and eating culture of households in Ghana and Uganda.Twenty-eight households (14 in each country) will be recruited and enrolled into the study. Prior to the recruitment of households, comprehensive community engagement meetings will be held with the community liaison office of the host institution, area government nutrition and health offices, community elders, and members of the communities to discuss the study and the use of camera devices to capture images of household food acquisition, preparation, and intake. Concerns raised by the communities will be addressed before the recruitment of households or start of the study.1) has ≥1 parent (mother or father); 2) has a child or children aged <5 y and/or adolescent child/children (aged 10–19 y); 3) members of the household eat the same prepared meal ; and 4) willingness to use camera devices to record food preparation and intake. Enrollment of households will be through the household head or the primary food preparer, other adults in the households, and assent of teenagers.Community elders and local field staff will help in identifying households for recruitment. Households will be enrolled when they meet these inclusion criteria: Trained local field staff conversant with the local dialect will visit consenting households to conduct an in-depth key informant interview with the primary food preparer. The interview will elicit an understanding of the culture around the household's choice of ingredients, common recipes, food preparation, eating practice, eating environment, and structure of the household to inform the choice or configuration of the passive dietary assessment devices to use in the household. The interviewer will ask the primary food preparer how many people live in the household, what is the household's primary food source , what are the common household meals, household's choice of ingredients, common recipes, distribution of food among household members , eating practice , eating environment , and cooking space . All the in-depth key informant interviews will be audio-recorded in the language of choice of the participants and the transcripts transcribed in English for analysis. During the interview process, care will be taken that the activity is not too intrusive, and that the dignity and privacy of the household are maintained.After completion of data collection on the composition, structure, and eating habits of the household, a household device deployment plan will be implemented based on the response obtained for each household. This plan will include the devices to use and their placement around the households. For example, a plan of mounting Foodcam in an open cooking area for households that do not have a built kitchen, use of devices to monitor shared plated eating for practicing households, and use of eHAT to capture images of breastfeeding episodes in households that have a breastfeeding infant. The proposed deployment plan will be discussed with members of the households before any testing. Concerns raised by household members, especially those around what is acceptable or not, will be considered.When an agreement on the deployment of the devices within the households has been reached, testing of the feasibility and acceptability of the devices will commence. This testing will occur within a week, including a weekend day, in the following order:Day 1 (household training on the use of the devices). On this day, field staff will train household members how to wear the camera devices on their body. Each household member will be assigned only 1 device. The primary food preparer or representative will be trained how to mount the Foodcam in the kitchen or cooking area. If the household uses shared plates to eat, the members will be trained how to mount the device in the dining area of the household. The demonstration will include training on how to switch the devices on and off, how to record using the devices, and how to download the images captured by the devices. During the training session, staff will address any questions or concerns that the participants have raised. After the training, a convenient day will be agreed with the household on the next day of testing the device. Depending on the size of the household, this training could take 1–2 h.Day 2 (first day of recording using the devices). On this day, Foodcam will be used to record all the food preparation/cooking in the household. Each member of the household will be given a camera device to record his/her food intake and food-related activities for the whole day. Local field staff will visit the households in the morning to supervise mounting of the Foodcam in the kitchen area and help household members with wearing their individual devices. The field staff will stay at the household for some time (between 30 min and 1 h) to ensure all the devices are properly mounted/worn and answer any questions or concerns answered. Field staff will take extra care to ensure their presence does not cause a great deal of inconvenience to household members and does not interfere greatly with their routine. Staff will conduct spot checks around the time of consumption of the household's main meals  to ensure all the devices are worn correctly. Finally, staff will return to the households in the evening, after the final household meal is eaten, to gather all the devices, thank the participants for their cooperation, arrange a convenient day for the next day of testing, and return to the project office.At the office, all the images will be downloaded, reviewed, and organized by meal. Any problems or challenges encountered during the recording will be discussed with the local study coordinator, site principal investigator, and the technology team to ensure the problems are rectified prior to the next day of testing the devices.Day 3 (assessment of battery life and user-friendliness of the devices). On this day, field staff will return to the household to test the battery life of the devices. This is important to establish whether the battery life in the field testing matches that of the manufacturer's expectation. The staff will also monitor the use of the devices by the participants. This involves assessing the ability of the participants to switch the devices on and off, recharge the devices using a portable charger, and connect to a laptop to download the images captured from the previous day of testing. This is to ensure families have full confidence in the devices and hopefully minimize any potential doubts about their use in the households. The staff will also address any problems encountered during data collection on the previous day and make sure household members are ready for using the device on the next day.Day 4 (second day of recording using the device). On this day, field staff will return to the household in the morning to provide a clean set of devices to use for that day and supervise their mounting and installation. The same procedure described above in Day 2 of testing will be followed. On this day, however, household members (except breastfed infants) will be assigned an individual wearable device different from the 1 they used previously, to ensure everyone has a feel of the different devices to help in the evaluation of the device's acceptability.Finally, a field staff member different from those interacting with the households in the 4-d period will visit the households. S/he will present household members with a brief questionnaire to determine the acceptability of the devices among the study participants. The questionnaire will ask the household members to rate (from 1 to 5) the specific characteristic of each device such as ease of use, convenience on the body, how much it interferes with their eating and daily routine, how much they would like to use the device in the future, and their preferred choice of device. This questionnaire should take 10–15 min to complete.Reliably records image/sensor data in the field with no or minimal missing data.Provides information to enable accurate identification of food, portion size estimation, and conversion to nutrients—i.e., sufficient image/data quality for a nutritionist to estimate nutritional intake.Usability—i.e., the device is user friendly and can be used by a nonengineer.Battery life—sufficient duration for the data collection session.Minimal recharge time—does not require any recharge during use or at most, only once.Comfortable and convenient to wear—no irritation to the wearer and does not disrupt their daily activities.Installation—easy to install/setup by household members in the field.Suitable for most households—i.e., device is rated at least average or above for acceptability  from household members.The following criteria will be used to assess the feasibility, performance, and reliability of the passive dietary assessment devices: This phase of the study is crucial because it will inform whether the devices are of good acceptability and performance rating before proceeding to the validation study. In the event that the acceptability and performance of the devices are poor, we will work to improve the devices and/or the factors responsible for the poor performance to ensure the devices and conditions for testing are good enough to conduct a validation study.The purpose of this study is to test the validity of passive dietary assessment method relative to a weighed food record, in estimating an individual's dietary intake. The weighed food record is used as the gold standard method in this study. A tablet-based, interviewer-administered multiple-pass 24-h dietary recall will also be performed for comparison. Portion size picture aids will be used in the 24-h dietary recall.Local field staff and community leaders will help in identifying 88 households (44 in each country) for enrollment in this study. The criteria for inclusion are the same as those described for Study 2. Foodcam will be mounted in the household's kitchen or cooking area to capture images of food preparation. Household members will be given a camera device to record their whole-day (waking hours) food intake, and the captured images will be used in the estimation of their dietary intake. However, only 2 randomly selected index cases (an adult and a child) in each household will have their corresponding dietary intake assessed using weighed food records and 24-h dietary recall. Dietary intake data from the devices and the 24-h dietary recall will be compared with weighed food records for accuracy evaluation. Prior to using the devices in the households, a questionnaire (similar to that used in Study 2) will be used to understand the structure, composition, and eating habits of the households to help implement a device deployment plan, especially for those who were not recruited in Study 2. To help recognize and effectively manage the data collection and logistical challenges that might arise in this study, this validation study will be conducted in 2 phases:Phase 1: Pilot testing of the validation protocol in a sample population. This phase is aimed at testing the study protocol in a sample of the households to help identify and address challenges that might arise during the data collection process. Study teams in the 2 countries will recruit 8 households each  for this purpose. The camera devices will be set up to record food preparation and intake in the households for a day. On the same day, the meals of the 2 index cases in each household will be weighed before and after eating. On the next day, a staff member not involved in the data collection on the previous day will visit the households to complete the multiple-pass 24-h dietary recalls—a method comprising multiple and sequential layers of questioning to increase accuracy in the reported food intake. The primary caregiver will complete the recall data for smaller children .Data from the devices, weighed food record, and 24-h recall will be uploaded onto the cloud-based study database. These data will help determine the fidelity of the study protocol, robustness of the image capture and data transfer, identify challenges, and help inform any potential amendments to the protocol before the validation study in the target population.Phase 2: Validation in the target population. In this phase, the passive dietary assessment devices will be validated in 88 households in the 2 countries. The devices will be set up to record food preparation and intake in the recruited households for 3 nonconsecutive days, preferably 2 weekdays and a weekend day. On each day, study staff will visit the households in the morning to mount the Foodcam in the kitchen/cooking area and help members of the household wear their devices. The devices will record an individual's food intake for the whole day during waking hours. At the end of each day, staff will return to the households to collect all the devices. In addition, the same staff will weigh and record the food intakes, before and after eating, of the index cases for the weighed food record measurement. After each day, a different staff member will visit the households to complete the 24-h dietary recall for the index cases.The dietary data collected from the households will be taken to the project office, where the images will be downloaded, reviewed, organized by meal, and uploaded onto the study database. Custom software (described below) will be used to identify the foods on the images, and estimate the portion size eaten and its nutritional content. Weighed food record and 24HR data will be analyzed by independent staff using nutritional analysis software (Dietplan) to estimate the nutritional content. The dietary intake from the images and 24HR will be compared with the weighed food record for accuracy evaluation.The ethics committees  of University of Georgia, USA, and Noguchi Memorial Institute for Medical Research, Ghana, have approved Studies 2 and 3 for Ghana . Studies 2 and 3 in Uganda have been approved by Mbale Regional Referral Hospital Research Ethics Committee (MRRH-REC) and the Uganda National Council for Science and Technology (UNCST) .This study aims to develop an objective, passive method for accurate estimation of an individual's food and nutrient intake. To facilitate this, the study will explore different image analysis methods to help develop reliable, accurate, and cost-effective backend software capable of automatic food recognition, estimation of portion size, and nutritional content of foods on images captured by the devices in the households  method, a start-of-the-art deep learning technique used in image recognition , will beThis study will use the images of foods captured by the devices to quantify the amount of each food an individual consumed in a day. This will be achieved using the following methods.1) learning the method of estimation; 2) practicing the estimation on a standard practice deck of images with known portion sizes of food; and 3) testing on a selection of images not included in the practice deck. Each assessor will be retested every 3–6 mo to track consistency and accuracy, and additional practice will be conducted if needed before retesting.This method relies on a trained eye to estimate visually the quantity of food on images. To do this accurately, the assessor should be familiar with the local foods as well as having knowledge of the use of aids for portion size estimation. In this study, dedicated local staff in Ghana and Uganda will be trained to perform the visual portion size estimation. Training will consist of: Food images for each eating occasion will be viewed using custom software, the AIM software , 27 and V represents the estimated volume, k is the shape parameter, l is the level parameter, d is the diameter/width of the serveware, and h is the height of the serveware. The shape parameter (k) can be obtained using 2 methods. In the first method (the precise method), the serveware  is filled with water to its brim to measure and record (in grams) the net weight of water the serveware contains. The parameter k is then obtained by dividing the net weight of water [which has the same value in grams as the volume in milliliters ] in the serveware by the calculated volume  of a hypothetical cylinder having the same diameter and height as the serveware. In the second, which is the approximate method, k is selected empirically from a range between 0.5 and 0.8, depending the shape of the serveware. The level parameter (l), which represents the fill level of bowl or cup, is visually estimated from the captured image. In this method, l must be chosen under the condition 0 < l ≤ 1, where 0 and 1, respectively, correspond to “completely empty” and “completely full.”This method estimates food volume using a mathematical model/equation. This model relies on the measurements (height and diameter/width) of serveware  and cookware  used in the household, as a reference scale. To obtain the reference scale, field staff in the 2 countries will arrange to visit the households to measure and record the height and width of the serveware household members use to eat. Image captured by a wearable device at the beginning and end of an eating episode will be used to estimate the volume of food consumed using the formula: In this method, a combination of ellipse extraction algorithm, deep learning, and AI will be used to estimate food volume in different serveware such as bowls, cups, basins, and plates.Generally, in imaging, the circular edge of serveware such as bowls and cups appears elliptical on images because the lens of a wearable camera is usually at a certain angle (∼40°) to the top edge of the serveware. In this study, we will use a custom-made ellipse extraction algorithm to carefully extract the elliptical outline of serveware on the images, overcoming a number of challenges; these include partial obscuring of the elliptical outline by food, and multiple ellipses due to circular food patterns or printed decorations on the serveware. When the desired elliptical outline of the serveware is extracted, the eccentricity of the ellipse, the measured diameter of the container, and several parameters of the camera  are utilized to construct a coordinate transformation from the image pixel coordinates to the real-world coordinates. This transformation is a key step in enabling food volume estimation in a metric unit  , 29.For foods served on plates, we have previously explored the use of fitting a computer-generated net cover (wireframes) of preselected shapes  to the food in an image to aid in the estimation of food volume . AlthougNutritional content of foods captured on the images will be assessed using the AIM software as the interface . The AIMShared plate eating is a reality in many LMIC, and yet there are currently no methods available to accurately estimate the food and nutrient intake of individuals in that setting. This contributes to a large nutritional data gap. In this study, in addition to using Foodcam, we will employ a vision-based approach, which integrates face recognition, hand detection, and food recognition, using a 360° camera . A 360° Continuous, passive capture of images during the day provides a window into the daily activities of an individual. These activities could include food sourcing, food storage, preparation, cooking, eating, and feeding other household members. This study will not be using devices with GPS  functions due to concerns of land ownership/grabbing in some communities. However, the sequence of images captured by the devices during the course of the study is enough to provide information about where individuals in the households are obtaining their daily foods, such as buying from local markets, obtaining from household farm/garden, using food stocks in the household storage facilities, and so forth. The AIM software can collate all the images captured in a day and annotate them for the various food-related activities. In addition, an AI technology  that seqt tests will be used to compare the overall bias between method pairs. The passive and 24-h recall methods will be considered accurate when their estimates of nutrient intake are within ±10% of the intake value measured using weighed food records. The analyses will be conducted among the index cases and then stratified by child and adult, rural and periurban/urban, respectively. To explore factors related to the accuracy of the passive and 24-h recall methods compared with weighed food records, absolute or relative deviation, as appropriate, from the weighed food method (gold standard) will be used as an outcome and be analyzed using multiple linear regression (MLR). Candidate predictors in this MLR analysis include demography, education, urban/rural, child or adult.The data analysis plan will be based on comparison of dietary intake estimated using weighed food records, 24-h dietary recall, and the passive dietary assessment method. This will be achieved using the concordance correlation coefficient (CCC) of Lin et al.  to assesCurrently, we do not have an analysis plan for validation of the data on breastfeeding frequency and duration captured by the eHAT device because there are no comparable analytical methods. However, the use of the device in this study will help us to understand its acceptability and challenges, and help to inform future studies.This article describes the protocol of a study designed to assess whether passive dietary intake assessment devices could be used to provide accurate and objective estimates of individual-level dietary intake data in populations at risk of malnutrition. These wearable sensor- and camera-based devices take time-lapse images of food intake passively, and custom software is used to analyze the captured images for dietary intake estimation, thus providing an objective measure. The article describes the proposed devices, their testing procedures, and the use of custom software for nutritional analysis. The study is made possible by the collaboration of nutritionists and engineers and takes advantages of technological advances that make the collection and analysis of data by wearable cameras and associated software possible.We are validating a comprehensive dietary assessment tool that requires no self-report to assess food preparation and intake in households in Africa. This tool will be validated across different age groups and demographics to take into account the different dietary habits and challenges in accurately assessing dietary intake in different populations. In addition, this study will develop an innovative method of assessing an individual's dietary intake during shared plate eating. Shared plate eating is common in many communities in LMIC but very few methods are available to accurately assess dietary intake of individuals involved in the practice.Despite the well-intended purpose of using the wearable cameras for dietary data capture, we are aware of the ethical implications of using such devices in households. We have thus taken steps to ensure that the privacy and dignity of participants are maintained at all times during the study. These steps include the inclusion of an image recognition algorithm that distinguishes between food and nonfood images, and the subsequent deletion of all nonfood images in our database. The Foodcam and AIM devices require a special software, only available to study staff, to start the devices and retrieve stored data for added security; this is, however, not available to Ear-worn, eButton, and eHAT. In addition, study participants will be given the opportunity to review the images after collection and can delete any image they feel uncomfortable about sharing.In conclusion, our study intends to provide low-cost, objective, fast, and accurate methods of collecting and analyzing dietary intake data in populations in LMIC.