Modern diagnostic methods (next-generation sequencing) are one of the current hopes with regard to a personalised medicine. By applying detailed genetic analysis, it is possible to not only improve the prediction of potential risks  but also the precision of therapy by targeting it to a specific genetic variant. However, there is no international standard for creating, structuring and/or transferring the results of a genetic test report. This type of test report often contains large amounts of complex information, and a standardised and consistent structure would offer potential benefits to all. These include reduced expenditure of time (due to the elimination of information-conversion steps), improved safety  and improved clinical information gathering (by the respective linkage to scientific data and literature). Especially in regard to secondary use, a standardised (electronic) format would improve the suitability of these data in retrospective studies and basic research. In this study, we analysed the format and content of 96 genetic testing reports (germline and somatic) from Germany, Switzerland and Austria. Based on these results, we summarised and discussed potentially critical data that were demonstrated to be reported inconsistently, and propose a baseline structure for reporting that would also ease future electronic conversion. Commonly referred to in the context of “personalised medicine” or “precision medicine”, modern diagnostic technologies offer a powerful means for scientists and clinicians to choose an appropriate therapy, predict disease development or estimate hereditary risks. Whilst the overall concept of personalising treatment  is not new, it gained new momentum when the human genome was sequenced for the first time by the Human Genome Project (HGP) consortium . CompariIt took 20 international institutions in the HGP about 13 years (1990–2003) to sequence a single human genome at a cost of ~$2.7 billion, but since then time requirement as well as costs have dropped significantly due to enormous technical advances. As of now, even an individual genome can be analysed within a couple of days for less than $5000 using next-generation sequencing (NGS) , the cosEvaluation. Assessment of the test result quality requires that they are presented in a standardised format , and that details of the materials and methods used are provided with their respective sensitivities, specificities and limitations.Interpretation. Conclusions drawn from the results, e.g., disease risk estimation, are based on published literature, databases and predictions made in silico using computational algorithms. Interpretation of genetic testing results can be challenging [llenging , especiaExchange. Especially in regard to electronic reporting of patient tests, the results must always be communicated to the referring physician in a secure format. As this information is also transmitted frequently across national (country) boundaries, especially in rare disease when an external expert opinion may be required, it is important that a standardised format (template) is used to ensure that the correct information is communicated to the end user.However, having access to large amounts of detailed data also requires appropriate methodologies for handling and using them. Specifically, this can be considered as three distinct components:While recommendations and guidelines on reporting do exist, these do in general focus on the clinical unambiguity of the results that are reported back to the referring clinician –8 ratherThe presented work reports the results from this study, and discusses further aspects potentially relevant to secondary use and future electronic reporting, indicating a need for standardised structure and content in genetic testing reports.BRCA gene testing in ovarian cancer, which used tumour tissue, and subsequently was categorised as belonging to the area of molecular pathology, albeit focussing on a germline variant.Ninety-six genetic test reports were provided for analysis by the EMQN. The reports represented the output from 63 distinct laboratories from three different German-speaking countries , participating in eight different EQA schemes Table . For assAll of the reports analyses were anonymised by the EMQN and identified only by a unique code. This code was identical for each laboratory, independent of the EQA scheme, and therefore enables tracking of the laboratory between each EQA scheme. All the study team members consented to a non-disclosure agreement.Using outputs from the EQA schemes facilitated the direct comparison of reports submitted for a distinct clinical indication. In addition, since the original data  were available (provided by the EMQN to accompany the samples), it was possible to retrospectively assess the presence or absence of key pieces of information against a scoring matrix of key requirements.Indication and patient data .Materials and methods.Results.Interpretation and expert opinion.Details/additional data (if applicable).For each report, page count, as well as the structural order, of the following sections was assessed:In order to identify “key elements”, all reports then were analysed in detail and broken down into their basic components , resulting in a list of information that in an ideal case, should be included in any paper-form report. This list then was reviewed with clinical experts and expanded to include additional elements that are currently not included in genetic testing reports by default, but which would be beneficial with regard to electronic processing. For example, most reports currently do include the date the analysis was requested and the date the report was written, but do not always or rarely state when the sample was received, processed or when the analysis was finished, respectively. While usually not of much importance, these data may become relevant in the circumstances of a suspicious result or other deviations from expectations, and could be easily integrated automatically into an electronic form without burdening the user with additional work. In addition, this list was analysed for compliance with the ISO 15189  [Finally, all reports then were analysed for each single key reporting element, resulting in a statistical evaluation regarding its presence/absence within each report.In general, a strong variation already in the sheer amount of text in between the respective reports was observable, as the length of the reports varied from minimalistic  to extensive .Of course, as represented in Table Regarding the order, as depicted in Table Following the analysis of the reports provided, a set of key elements were determined and grouped Table , with 1–It should be noted that not all of the key elements are considered mandatory in every report. For example, data on the patient’s family, hereditary risk and genetic testing/counselling of family members may be irrelevant without a germline-associated background. However, it would be beneficial to include these data into the report for the sake of completeness in a secondary use setting .All reports were analysed for these key elements, and the respective level of detail was assessed . The respective results are presented in Tables Whilst all the reports evaluated contained the basic essential information needed by the requesting clinician , the analysis also showed that the general reporting structure and content order varies considerably. This might be only an inconvenience when handling an individual case, where missing data might be more easily demanded, but may cause problems when evaluating multiple reports from different laboratories. Especially in larger studies, this could make the analysis significantly more time consuming, and increase the risk of data being missed or misinterpreted.Therefore, especially in regard with secondary use in retrospective studies, we suggest that a more formalised structural requirement for reports could be inspired by the scientific IMRaD standard mentioned above. Although laboratories should be allowed to have a certain degree of flexibility, especially with regard to the order of the specific elements, we suggest that reports should be subdivided/grouped accordingly:Indication: data on patient, sample and clinical referral indication.Materials/methods: methodological approaches, devices and panels used , as well as databases consulted.Results: description of variant(s) detected (or excluded) using HGVS nomenclature.Assessment: biological and clinical interpretation of results including estimated pathogenicity, disease risk assessment and (if applicable) recommendations.Details: detailed results, further information on disease/variant, external references, etc.Using this adapted “IMRAD” grouping  would facilitate better data sharing, especially if adhering to established electronic data standards, as it would allow the user to filter/fetch the information needed selectively. Note that, while most of the 63 laboratories in this study were already compliant to this structuring, reports from six laboratories still demonstrated structures that did not conform to it and subsequently made analysis more difficult.BRCA genes). In addition, only 34 of the 56 laboratories (60.7%) fully restated the clinical referral details on the patient’s family history as they were provided by the EMQN, although this value varied in between trials. As this classification would only cover for a normal karyotype, known aberrations  should be clearly indicated in the indication section. Also, note that laboratories in general do not perform a confirmation of the biological sex as long as it is not part of the requested testing or part of internal quality control.Gender and referral information: most prominent and potentially critical is the lack of the patient’s gender in 11 of the 63 reports (17.5 %). Even though the patient is referred to partially as “Ms/Mrs” or “Mr” this does not necessarily mean that the patient is biologically female or male. This information is often essential for subsequent risk assessment  scheme, the female patient’s family history consisted of four pieces of information: the patient was an only child, with no children herself, there was no history of disease within the family and the paternal branch of the family tree was mostly males. Among the 37 participating laboratories in this EQA, only 11 (29.7%) included all this information, 14 (37.8%) partially, while the remaining 12 (32.4%) did not include any of these details. Furthermore, in the same EQA, only 9 laboratories fully, plus 9 laboratories partially (each 24.3%) stated that previous tests (including results) had been undertaken, whilst 19 laboratories (51.4%) omitted this information.Incomplete/missing data: reports often did not reflect all parts of the clinical referral information originally provided with the request. For example, in hereditary breast cancer  demonstrated the importance of context-specific presentation and phrasing of results. Of the eight laboratories participating, 2 (25.0%) stated “no mutation found”, 3 (37.5%) “no pathogenic mutation found” and 3 (37.5%) “mutation c.736T>G found–likely benign/assumed neutral”. Postulating that due to future findings a variant originally considered as of uncertain significance or even benign might indeed become clinically relevant [Results and assessment: one of the EQA’s  laboratories did not state anything on the informed consent this can be seen as an example for an information that is taken for granted (as it is required by law in the countries involved in this study) and therefore omitted. However, also for legal protection, the laboratories should always state that they verified whether the patient’s informed consent was given.Sample handling after testing: only 2 of the 63 laboratories (3.2%) clearly stated that the sample material was either stored or discarded following analysis. Again, this is likely because, in general, samples are retained after testing and therefore this is taken as granted if not stated otherwise. However, as current development tends towards patients managing or at least having access to all their (electronic) health files, they might assume that the sample is simply used up during testing or discarded afterwards, rather than stored. Therefore, this statement is important not only in case of a potentially required re-testing, but also with regard to patient empowerment.Among the pieces of key information analysed within the study, as demonstrated in the results, many were not given consistently in the reports. While these may be of no consequence with regard to the individual medical case, those data might be crucial for use in clinical studies, help in troubleshooting, ensure legal protection, etc. This includes but is not limited to the following examples:Thus, if laboratories consider potential secondary use in their reports, as well as making their reports easily convertible into an electronic format, the elements listed in this study may offer an insight on key requirements for inclusion in a report. In brief, the overall information should be broken down into single pieces of data, which then can be coded and retrieved unambiguously by application of established terminologies and standards. However, care must be taken not to omit aspects that are often considered as obvious or taken as granted, for this might result in confusion, especially among non-experts (such as the patients themselves).Our study clearly demonstrates that the clinical reports analysed, while of course conveying the necessary information relevant to the requesting clinician, and therefore meeting the needs of clinical decision-making, often lack details and structuring that would make the report useable as a stand-alone source for secondary use or direct electronic conversion, respectively. However, the landscape of genetic testing is changing rapidly and new targeted therapies are quickly coming to market. To facilitate this drug companies are dependent on running informative clinical trials and effective stratification of patient cohorts is essential in this activity. Therefore, we argue that the informativeness of clinical reports could be improved through better standardisation of structure and content, which would be beneficial for clinical studies, and electronic communication of results.A uniform structure would reduce the risk of misinterpretation and loss of information at a national and global scale. Here, internationally accepted standards and terminologies are key elements in order to ensure technical interoperability and clinical unambiguity. In cases where there are multiple applicable and established terminologies to choose from , laboratories should be free to select their own discretion as long as it is clearly stated.www.snomed.org) and Logical Observation Identifiers Names and Codes  in an electronic genetic testing report could fulfil these requirements. Appliance of these terminologies is expected to not only already cover all the elements elaborated in this study but also future aspects, since both are under continuous development.The authors suggest that the use of HL7’s formats , in combination with the international terminologies SNOMED Clinical Terms , or on the contrary, the laboratories took even more care over their EQA reports than routine clinical referrals. In addition, legislative regulations differ in between Germany, Austria and Switzerland, which might affect those aspects summarised in point 6 “Other/Formal/Legal/Recommendations”.Finally, the laboratory setting with respect to test type may affect the presence of certain information on a report. For example, specific national regulations may exclude laboratories from adding therapeutic recommendations to clinical test reports.Based on the findings presented in this study, and HL7’s specifications for genetic testing reports , it is pHowever, it has to be kept in mind that national laws, especially with regard to data protection, must to be taken into consideration. Data that are collected routinely in one country might not be allowed to be disclosed, stored or even asked for in another country. An example would be “Ethnicity”, which is considered a common information in the USA, but highly problematic in Germany. In these cases, an entry should be provided and marked as “unavailable” or similar to indicate that it has been omitted intentionally.


Deep inside the temporal lobe of the brain, the hippocampus has a central role in our ability to remember, imagine and dream. Related research article Spanò G, Pizzamiglio G, McCormick C, Clark IA, De Felice S, Miller TD, Edgin JO, Rosenthal CR, Maguire EA. 2020. Dreaming with hippocampal damage. eLife9:e56211. doi: 10.7554/eLife.56211Our most vivid dreams are a remarkable replication of reality, combining disparate objects, actions and perceptions into a richly detailed hallucinatory experience. How does our brain accomplish this? It has long been suspected that the hippocampus contributes to dreaming, in part due to its close association with memory: according to one estimate, about half of all dreams contain at least one element originating from a specific experience while the subject was awake . AlthougBut are the dreams of patients with damage to hippocampus truly ‘normal’? Or alternatively, might such damage, while not preventing dreams, alter the form in which they are expressed? Indeed, there is reason to think that the hippocampus supports crucial aspects of dream construction beyond the simple insertion of memories. Recent work in the cognitive neurosciences has established that the hippocampus, in addition to being involved in the formation of memories, is also part of a brain system that is involved in using memory to construct novel imagined scenarios and simulate possible future events . As a reNow, in eLife, Eleanor Maguire of University College London (UCL) and colleagues – including Goffredina Spanò as first author – report that the dreams of four amnesia patients lacking a hippocampal memory system do not have the richness of detail found in most dreams . BesidesThese observations partially echo the reports of Clara Torda from more than a half century ago, who characterized the dreams of amnesia patients as ‘shorter’, ‘simpler’, ‘repetitious’ and ‘stereotyped’ . But TorAs with many studies of rare neurological patients, the latest work must be interpreted with caution due to the small sample size. For example, patient dreams were not significantly shorter than control dreams, leading to an apparently selective deficit in specific types of details reported , rather than a general deficit in the length of the dream. On average, however, the control dreams contained more than twice the number of informative words as the patient dreams, and the lack of a statistical difference between the two groups may be a mere artifact of the low sample size.Nonetheless, these observations and a handful of similar studies are helping us to understand how the hippocampus contributes to the dreaming process. The work of Spanò et al. – who are based at UCL, the Royal Free Hospital in London, University Hospital Bonn and the universities of Arizona and Oxford – suggests hippocampal damage disrupts dreaming in ways that mirror how it also disrupts imagination. This suggests that, rather than being an entirely distinct phenomenon, dreaming is a part of a continuum of spontaneous, constructive thought and imagery continuously generated across the sleep and waking states.


Covid-19 represents a systemic event—a state of emergency—that disrupts the routines of societies from the level of individuals to institutions, nations, and global interaction. Revealing the vulnerability of the intensively interconnected world suggests a juxtaposition with another systemic crisis: the climate emergency. Drawing on some key literature on the different aspects of “events”—as heightened political semiosis (Wagner-Pacifi), as (possible) transformation of social and symbolic structures (Sewell), and as moments where new horizons are opened (Arendt)—this essay suggests three intersecting themes where reactions to Covid-19 help to sharpen the crucial questions of future journalism: the role of “knowledge” and expertise, the power of national framing, and the challenge of covering the new imperatives and possibilities of everyday life. Events appear against the stable background of the usual and the expected, sometimes dramatically. Events take shape and meaning against the taken-for-granted backdrop. Literary genre of realism exemplifies this dynamic well: detailed descriptions of the milieu, landscape, and people’s everyday habits set the stage for something worth telling ? A useful way to pose this question is to look at the overlapping challenges revealed by immediate reactions to the pandemic and imperatives of adjusting to the unfolding climate crisis. Drawing from some key literature on “events,” I will raise three concerns for future journalism: the role of expertise, the weight of the nation state, and the challenge of reporting changing everyday life. They point not only to the problematic legacy of journalism but also to new opportunities that these overlapping systemic states of “emergency” suggest.political semiosis, an unpredictable whirl of meanings, where three aspects of public speech and action intertwine. The performative power of speech creates and changes realities, causing tangible effects . Demonstrative aspects of words and gestures signal and shape relationships in time, in space and between people .” Speech acts also function as representation, they craft interpretations for circulation   “objective is to finish developing and then manufacture and distribute a proven coronavirus vaccine as fast as possible. We’d love to see if we can do it prior to the end of the year.”The Guardian, May 14, 2020)Standing just behind him, Anthony Fauci, an infectious diseases expert wearing a face mask, cast his glance down and reached to adjust his tie. Trump did not wear a face mask. . At the same time, the scene is symptomatic of a broken public discourse, indeed highlighting the need to think beyond the paradigm of a polyphony vague plurality of “knowledges.” Without a claim for an epistemic dimension for public discourse—a capacity to come to terms with the world, and to know what should be done—both journalism and public expertise will wither.This is a snapshot of what Thinking about systemic crises raises questions about structures. In Logics of History, The Covid-19 pandemic highlights existing structural fissures in the political order. Most obviously, the virus has entered a context where the twentieth-century commonly held distinction between left and right is being replaced by the polarity that features “nationalists-conservatives” and “cosmopolitan-liberals”—a polarity moving into place long before the coronavirus began moving through the neighborhoods of Wuhan.Journalism routinely leans toward nationalism, particularly in moments of crises, and the coverage of the pandemic has centered much on the actions taken by national governments to protect national populations. The “nation” shows its dual political power here. On one hand, the nation is depicted as a positive frame of interpretation that can evoke a sense of solidarity and moral responsibility. On the other hand, this frame can also cover up crucial differences and moral discrepancies between nations and among diverse populations within nations. We heard the words “protecting,” “right to life,” and “vulnerable groups” mostly in reference to “our” citizens.A stronger naturalization of the national grammar of news spells potential problems for transnational experts and other social actors to promote better climate communication. It can easily empower the latest arguments against rapid climate action, arguing that climate change might indeed be real, but it makes little sense for “us” to change our ways—because (for instance) of China’s raising emissions and increasing overpopulation. As such narrow-minded and short-sighted political realism gains strength, the position of journalism addressing the climate crisis weakens.inside the nationalistic frames from which they report virus contagion numbers and death tolls—or the emission footprints, drought, flood damage, and species die-offs of the climate change era. At the same time, we need perspective that looks at our nations from the outside. These tasks demand well-cultivated transnational professional, expert, and advocacy networks that support the authority of journalism. Covering the pandemic is a key moment for re-articulating what “nations” and “states” mean in the era of systemic, global crises. In this sense, Covid-19 coverage plows the soil for future climate coverage.This is not to recommend more of hollow praise cosmopolitanism and the blessings of globalization. Instead, journalists should be pushed to do work that looks things intervene with developing human interpretations. In this line of reasoning, it was Galileo’s telescope (rather than the man himself) that pushed the emerging modern world around a corner, giving “an earth-bound creature” the chance to see itself from the perspective of the vast universe. Although the Copernican revolution was already developing as “speculation and imagination,” the telescope turned it into a sense-related, material observation (ervation , p. 260.You do not have to be much of a philosopher to see the analogue to the virus. The virus drills another wormhole into the modern bulwark between social and natural order and shows how a material factor becomes an actor in our social and political networks. For the media-Covid19-climate intersection, however, the point is not only to fuel a higher awareness of a global post-human condition.can be redefined in a systemic crisis. This is the point that Perhaps for journalism, the key lesson is more mundane: the acute, material power of the pandemic to change our everyday life. It has forced in a glimpse of a new perspective: how routines of individuals, institutions, and nations everyday life that a low-carbon society will demand?Concern about public expertise and worry about legacy of narrow nationalism are important—but they are also drawn from the usual repertoire of critical media scholarship. For better and worse, they point to journalism’s structural alliances that play a powerful role as journalism adjusts to the post-pandemic context. The idea of pandemic as a “dress rehearsal” raises a somewhat new question.


With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments. Fast pace, continuously flowing data and a very large data size is categorized as a data stream. Storage incapability of accumulating an entire stream in a system and mining fast flowing data poses limitations on the processing capabilities of conventional data mining algorithms, making stream mining a challenging task. Moreover, from such characteristics of streaming data arise the possibility of concept drift, a situation where overtime change in data distribution occurs. Transitions in target data distribution can be caused by factors like unorthodox system behavior, data deviation, and changes in environmental conditions. A failure to conform to the Independent and Identically Distributed (IID) principal of probability theory is a big challenge for stream mining, raising the issue of concept drift. When a rapid change occurs in data distribution, it is called abrupt, and when the variation occurs over time, slowly and seamlessly it is called gradual. A variation of gradual concept drift—incremental concept drift—is generated from multiple sources of data streams. In this case, an abrupt or gradual drift occurs repeatedly and forms a pattern, which is called recurring. The Sliding Window Model is probably the most effectively and widely used technique to handle these challenges. Identifying various types of drifts and modifying itself accordingly is imperative for a concept drift detector.Class Imbalance, a known problem in data mining, arises when the total number of occurrences of one instance is in the majority compared to the other class or classes, causing overfitting problems in the case of a positive class and decreasing the results immensely in case of a negative class. Unlike concept drift, this problem is not limited to streaming data, but found in static data as well. Class imbalance can be found in many domains, such as fraud detection, anomaly detection, medical diagnosis, oil spillage detection, and facial recognition in security, among others. Studies have shown that the problems of concept drift and class imbalance affect the mining capacity and results a great deal. But most of the efforts made to resolve these problems are one dimensional. It is important to handle concept drift and class imbalance simultaneously. The coexistence of concept drift and class imbalance can be found in several real-world examples, such as smart homes, patient activity monitoring devices, IoMT for smart healthcare etc. The Internet-of-Medical-Things (IoMT) is a collection of smart medical devices and applications, as shown in As reported by New England College, IoMT includes wearable devices, sensor-enabled hospital beds, infusion pumps, medication-tracking systems, medical supply and equipment inventory tracking, etc. There are smart watches that monitor heart rates and track movement. Similarly, there are contact lenses to read glucose levels, biometric stamps that report the wearer’s vitals, and necklaces that analyze chewing and swallowing—and alert the wearer when they’ve had too many carbohydrates. All these devices continuously generate data in the form of a high-speed stream, which needs to be analyzed in real-time. Therefore, in the medical domain, not only is timely analysis important, but also the detection of concept drift also becomes a mandatory task. For example, the medical sensors measuring for general healthcare or rehabilitation may switch their roles for ICU emergency operations when required. However, the occurrence of abnormal patient activity is a typical example of class imbalance. For example, in a typical patient monitoring unit such as an ICU, the medical sensors keep transmitting normal activity data, and very gradually an abnormal activity is been sensed and transmitted by the wearable devices/sensors. This shows the true ratio of imbalance data, which need to be analyzed through cutting edge techniques. There are certain challenges when we try to solve concept drift and class imbalance together. First, as we know, it is recommended that the class imbalance removal techniques are applied before passing data to the classifier, so that the classifier does not have to perform any extra computations to cater for class imbalance. Conflict arises when we also have to consider concept drift in the data. An active concept drift approach looks for drift in the incoming data. So, there is a need to configure these steps so that they do not interfere with each other’s processes and results. Reactive Drift Detection Method (RDDM)—which is based on the Drift Detection Method (DDM), a well-known drift detector—claims to be an improved version of DDM, as it detects most drift more rapidly than DDM, and delivers a higher accuracy than DDM in most situations. One of the limitations of RDDM is that it takes into account the concept drift of a data stream alone, and does not consider the most commonly occurring problem of class imbalance, not just present in the streaming data, but in the conventional/static data as well. These problems not only co-exist in real life datasets, but also have a negative influence on each other.This work proposes the Enhanced Reactive Drift Detection Method (ERDDM), a consolidated solution for the problems of class imbalance and concept drift in data streams. ERDDM is an intensified and improved version of RDDM, which tackles the conundrum of treating concept drift along with class imbalance problems. ERDDM is an automated solution that provides an ensemble method to remove class imbalance from a sliding window of live data streams and gets improved results from the balanced data.In addition, the Synthetic Minority Over-Sampling Technique (SMOTE) approach was adopted to deal with class imbalance, rather than utilizing a cost-function based approach, due to its computational complexity. We claim that ERDDM has enhanced the existing drift detector algorithm to perform better in terms of low prediction errors, high rate of detected drifts, and less processing time.In this paper, we propose a new concept drift detection method: ERDDM. Our new method efficiently detects the abrupt and gradual concept drifts in large data streams. Moreover, ERDDM has been tested against RDDM, EDDM, and STEPD, using a number of base learners, such as Naïve Bayes, Adaptive Random Forest (ARF), Kappa Updated Ensemble (KUE), Accuracy Weighted Ensemble (AWE), Leverage Bagging, and OzaBagAdwin. Experimental results show that ERDDM is significantly superior to the other three methods in terms of accuracy for detecting imbalanced data streams.The rest of this article is organized as follows: Researchers have tried to handle concept drift through different approaches over time, which includes sliding window vs. batch processing, fixed length window vs. variable length window, fixed stream speed vs. variable stream speed, labeled vs. unlabeled or partially labeled data points, ignoring the processing results of a window vs. keeping a history of the drift ratio of each window, using a drift alert mechanism vs. maintaining drift logs to be used later, and so on. The whole concept drift detection process has evolved over time by making such choices and enhancing the process incrementally. Researchers have proposed many concept drift detection methods ,14,15,16Some prior work ,2,3,4 doWork done on gradual concept drift detection includes solutions proposed by Pears et al. , KithulgSome researchers ,14,15,16Another method, k-Nearest Neighbor-based space-Partitioning Schema NNPS , caters The proposed method, Automatically Adjusting Size of Ensemble Algorithm with Temporal Changes (ASE-TC), works by comparing the results of the weakest classifier in the ensemble for a given data chunk. The KLD value is computed for that classifier, and if it is greater than the user-defined threshold, then that classifier is used in the ensemble for next iteration. KLD works well within a controlled environment where the expected drift type is known and the KLD value is also predefined. Another ensemble-based technique known as Drift Detection Ensemble DDE , is backMost of the work ,21,22,23Chaoliang et al.  have donAnother study  on classAccording to , in the A strong point of the proposed framework is the implementation of two-dimensional sampling and archiving policies, i.e., uniform and biased. First, the uniform sampling is applied, and then, on the basis of uniform sampling results, biased sampling is applied. This is done before the data is passed onto the GP classifier. Later, the archiving policy is implemented, which decides the type of archiving on basis of GP classifier’s results.In an earlier study on the combined solution of concept drift and class imbalance, Ditzler et al.  designedIn , Mirza eIn a similar study done by Ren et al. , authorsAnother recent study by Wang et al.  gives anIn a recent study, a Kappa Updated Ensemble (KUE) classification algorithm was proposed by Cano and Krawczyk  for drifThis section provides technical insights to ERDDM, our proposed drift detection method that provides the concerted solution for abrupt and gradual concept drifts, as well as class imbalance in the data stream. This includes the implementation detail of ERDDM in MOA, and also describes the deficiencies highlighted in RDDM, which our proposed method aims to resolve.The main idea behind ERDDM is to increase the performance by decreasing the error rate in the drift detection process in RDDM. The RDDM model was based on two basic assumptions, i.e., first, when there are large numbers of data points belonging to same concept, the mean error rate is not affected until after a large number of prediction errors are reported. This causes a delay in drift detection and ultimately results in decrease of classification accuracy. Second, both DDM and RDDM use two levels of notification to pass on the information to the classifier of a drift in the data. The first level is the warning, i.e., when a potential concept drift is identified, then a warning is generated, and the preceding instances are monitored for the continuation of the drift. In case the drift continues, after a specified threshold the alert is generated, and the classifier is notified of the drift. At this point, however, the type of drift is not determined, because it could take few windows to clarify whether the drift is abrupt or gradual. DDM had a weakness to stay at warning level for longer period of time. To handle these problems, a few design decisions were made by RDDM, such as re-calculating the DDM parameters with a smaller number of instances within a window to determine the existence of drift and generate an alert in time. So, what RDDM does is that it reduces the amount of data points in a concept on every iteration, so a large concept is divided into small parts, which makes it easy to process for drift detection. Another decision made by RDDM is to avoid re-calculating the DDM parameters once the warning level is generated. The reason behind this is that recalculating during the warning level will make the algorithm work slowly, and there is also a chance of over-calculation in the case of a long concept drift. As a workaround, a threshold of instances is set by RDDM before the warning level is converted into alert level. Until this threshold is not achieved, the re-calculation of the DDM parameters is not done. There are two possibilities in this scenario: first, if the detector issues a warning and a certain amount of data points are processed and the warning is still on, it means that the drift, although not yet confirmed, most probably has already started, so in that case, the warning level is increased to notify the classifier to adjust itself. Second, if the drift occurs during the warning level, then it means that most probably the starting point of the drift was the same instance when the warning was generated. So RDDM starts to re-calculate the parameters.RDDM added three new parameters to the list of DDM parameters in order to implement the new logic. A total of six parameters are used by RDDM, which are n: minimum number of instances before monitoring changes; αw: warning level; αd: drift level, max: maximum size of concept, min: minimum size of stable concept and warnLimit: warning limit of instance. The default values of these parameters set by RDDM are n = 129, αw = 1.773, αd = 2.258, max = 40000, min = 7000 and warnLimit = 1400, respectively. We have kept these six parameters and their default values in our proposed algorithm, because some experimentation showed that these default values work well with our changes too. Also, comparing ERDDM with RDDM will be more relevant if both are tested on the same parameters.where The minimum value of The core change introduced in the RDDM by this study is the replacement of the concept drift detection formula used by RDDM with the Page Hinkley (PH) Test. The Page Hinkley Test is widely used as the method to detect change in Gaussian Signals. It has a variety of implementations, and researchers have used PH for concept drift detection as well. We believe that the error rate in drift detection can be reduced by applying the PH test at the warning level. The formula for the PH test is as follows:This test basically monitors the difference between When this difference is greater than the threshold (λ) then it is declared that there is a change in distribution, and the algorithm generates a warning to notify the classifier about the drift.m_t is calculated using the aforementioned PH formula. Then the result of m_t is appended in an array. This is required to maintain the history of the m_t values within the window, so that M_t can be calculated by picking the minimum value from the m_t array. Lines 23 and 27 have the main conditions to check the threshold values of M_t and m_t. On basis of these conditions the drift, and warning levels are set on line 24–26 and 28–33.Algorithm 1 shows the pseudo code of ERDDM. The code is implemented in Java language and runs on the Massive Online Analysis (MOA) tool. The algorithm starts with initializing the variables, e.g., rddmDrift and ddmDrift are set to false, a new byte array of storedPredictions is initialized, numInstConcept and numWarning is set to 0, and m_t_array is initialized, which will hold the PH-related m_t values. Our main focus is on the part where change is calculated and compared with the threshold. We have introduced the Page Hinkley Test for the drift detection steps, and these steps can be found from line 17–19 in the algorithm. First, Algorithm 1. Enhanced Reactive Drift Detection MethodInput: stream, n, αw, αd, max, min, warnLimitstoredPredictions ← NEW byte [min]reset m_n,m_p,m_s,m_pmin,m_smin,m_psminrddmDrift ← ddmDrift ← falsenumInstConcept ← numWarnings ← 0m_t_array ← Output:1. foreach instance in stream do2.   pred prediction (instance)3.  If rddmDrift Then4.   reset m_n,m_p,m_s,m_pmin,m_smin,m_psmin5.   Calculate DDM statistics using the element of storedPrediction6.   rddmDrift ← ddmDrift ← false7.   numInstConcept ← numWarning ← 08.  End If9.  Inserts pred into array storedPredictions forgetting oldest value if it is already full 10. m_p ← m_p + (pred – m_p)/m_n11. m_s ← sqrt (m p × (1 – m_p)/m_n)12. m_n ← m_n + 113. numInstConcept ← numInstConcept + 114. warningLevel ← false 15. If numInstConcept _ n Then16.   x_avg ← (1/m_n) x m_p17.  m_t ← (m_s - x_avg) + n18.  m_t_array.add(m_t)19.  M_t = min (m_t_array)20.  If M_t – m_t < m_psmin Then21.   m_pmin ← m_p; m_smin ← m_s22.   m_psmin m_p + m_s End If23. If M_t – m_t > m_pmin + αd × m_smin Then24.  rddmDrift ← ddmDrift ← true25.  If numWarnings ← 0 Then26.   storedPredictions ← pred 27.   End If28. else If M_t – m_t > m_pmin +αw x s_min Then29.  If numWarning >= warnLimit Then30.   rddmDrift ← ddmDrift ← true31.   storedPredictions ← pred32.  else33.   warningLevel ← true34.   numWarnings ← numWarnings + 1 35.  End If36. else numWarnings ← 0 37. End If38. If numInstConcept >= max and not warningLevel Then39.  rddmDrift ← true 40. End If41. End ForThe process starts with an input data stream. A sliding window model is used for accepting and reading the stream. The window size is set to 1000 data points, based on stream speed and available memory on the test machine. Data points of the current window are passed onto resampling ensemble. This module is responsible for handling class imbalance. Class imbalance is removed by using the SMOTE technique, which synthetically generates the data points of the minority class. After resampling, the data is passed onto the drift detector. The reason resampling is done before drift detection is that resampling changes the demographic of the data, so there is a high chance that if the drift detection is done first, then the nature of the drift will be changed after resampling, which will affect the results. As discussed in literature review, there are two approaches to detect drift, i.e., active and passive. Our proposed model followed the active approach, which means the drift is detected in the incoming data, because the computational cost and complexity of a changing model is infeasible for passive approach. Drift detector is designed to handle two types of drifts, i.e., abrupt and gradual. A history of drift is maintained in order to detect gradual and recurring drifts, since such kinds of drifts are spanned across multiple windows. An archiving policy is devised for drift history maintenance, because only a limited space can be consumed. Such a policy can be based on removing old drift history after a certain time, or by calculating the weightage of drift information and only keeping those which have weightage above threshold level. Finally, the processed data points are forwarded to the classifier, which makes adjustments in case of drift, and then the classification results are yielded.All experiments have been performed on a Core i7 Apple MacBook Pro with 16GB RAM and a 500GB storage disk. The experiments were performed using the Massive Online Analysis software developed by University of Waikato, New Zealand. An ensemble of class imbalance removal filters and techniques was used to remove class imbalance, and then an enhanced version of the drift detection algorithm was used for the detection of different types of concept drifts. The following subsections explain the dataset details and the steps performed in the experimental study.For experimental study, synthetic benchmark datasets are used, which are very common in concept drift research. There is a total of 42 synthetic datasets used in this experimental study. These datasets are chosen because of their relevance to the drift detection process. It is important to mention here that, for the entire synthetically-generated data streams, different drift sequences were used. For example, for all 50K datasets, we used an interval of 1000 instances to induce drift. Similarly, for 100K datasets, an interval of 5000 instances was used. The detail of these datasets is as follows:The Agarwal Generator is a widely used dataset. It contains the hypothetical data of people applying for a loan. It is a two-class dataset, with class A representing the approval of loan and class B representing rejection of loan. The attributes of this dataset are Salary, Age, Commission, Education Level, Value of the House, ZIP Code, etc.The LED generator dataset is based on the problem of displaying a specific digit on a seven-segment LED display. It includes 105 noises, which gives an equal amount of probability to each digit. The dataset comprises 24 attributes with both numeric and nominal attributes.The RandomTree generator is another synthetic dataset generator which creates the dataset consisting of randomly generated tree branches. The dataset comprises five nominal and four numeric attributes.A mixed generator is a concept drift dataset generator, which makes use of the combination of two numeric and two Boolean attributes to induce drift in data. The formula used to create values of these attributes is A comparison of balanced datasets that are used in the experimental study is given in A similar comparison of imbalanced datasets is given in A comparison of performance of four algorithms: ERDDM, RDDM, EDDM, and STEPD, is analyzed to check the impact of drift detection on the classification model.This empirical study of balanced datasets is performed on four synthetic datasets with six different data sizes each, i.e., Agarwal, LED, Mixed and RandomTree datasets with 50,000, 100,000, 500,000, 1,000,000, 2,000,000 and 3,000,000, respectively.The experiment on imbalanced data is performed on three synthetic datasets with three sizes each, i.e., Agarwal, Mixed and RandomTree datasets with 50,000, 100,000, and 150,000 sizes.Prediction error is the probability an event will happen. It is a calculated value of probability of each possible outcome to occur after the classification is performed. If the prediction error is zero or close to zero, then it can be deduced that the outcome is the expected result. The more it deviates from zero, the result becomes incorrect. Analysis of the time taken to detect drift by four algorithms is performed. This is important because the efficiency of an algorithm, especially a stream-mining algorithm, is highly dependent on how much time it takes to complete its operations. High speed data streams do not give enough time for models to perform operations, so if an algorithm is giving very high accuracy but taking more time than available, then the efficiency of that algorithm becomes useless. Drift detection delay is a critical measure in concept drift detection. It is the number of instances used by detectors to realize that the concept drift has started in the data stream. Ideally, it should be within a window, and not more than a few data points, otherwise the model accuracy starts to drop. Most of the researchers used this measure to show the efficiency of their algorithm. Another empirical study performed was the comparison of the total number of drifts detected by each algorithm. This experiment is important because of two aspects. First, if the detector is detecting more drifts than are actually present in the data, then it means that the detector’s setting is making it sensitive and it needs tweaking. Similarly, if a detector detects fewer drifts than the actual number, then it means the detector is unable to detect drifts of various natures. The experimental study is performed on the synthetic datasets with the following objectives to test certain aspects of the proposed algorithm:A comparative analysis of three benchmark algorithms, i.e., RDDM, EDDM, and STEPD, is performed with proposed ERDDM algorithm.The experiments utilize state-of-the-art classifiers, such as AWE, ARF, OzaBagADWIN, and Leveraging Bagging, to analyze the results.The results are evaluated on the basis of two measures: accuracy and mean evaluation time. In this section, experiments have been performed on two commonly used real-world datasets, namely electricity and intrusion ietection. The electricity dataset has been taken from the MOA data stream repository. This dataset has been described by M. Harries and analyzed by Gama, and it was collected from the Australian New South Wales Electricity Market. The intrusion detection dataset has been taken from the UCI KDD Cup repository. The intrusion detector learning task is to build a predictive model capable of distinguishing between “bad” connections, called intrusions or attacks, and “good” normal connections. The experiments performed on the real-word datasets evaluates following aspects of the proposed algorithm:The results, as seen from Every concept drift directly affects the classification results and worsens the accuracy of the classification model, so if we keep a check on the error rate of the classification accuracy, then we can detect if the numbers deviate from the normal, and deduce that there is drift in the concept. Visualization of the performance of four algorithms, i.e., ERDDM, RDDM, EDDM, and STEPD, in terms of prediction error is given in It can be seen that ERDDM has outperformed all other algorithms and has the least prediction error for all datasets of all sizes. We believe the reason for this is the fact that we have incorporated the Page Hinkley Test, which is used specifically for calculating error rates in data. It can also be seen that ERDDM is scalable, which means with the increase in stream size, the error rate of ERDDM decreases. In comparison, the error rate of STEPD increases with the increase of stream size.This test is very important in concept drift detection, because the sooner the drift is detected, the sooner the model can relearn and is able to handle the forthcoming data points. Otherwise, with passing of every data point, the accuracy of classifier decreases, and the results worsen over time. We can see from Time analysis is another measure which we have used to analyze the performance of ERDDM. The amount of time an algorithm takes to process data points in a sliding window is very important, because, in fast flowing data streams, there is not much time to process data. If the algorithm is slow, or responds late, then there is a high chance that it will miss data points for the next window. This becomes a bigger problem in the case of concept drift detect, as, if the missed data points contain the start of a drift, then the drift detector will miss them, and it will result in detection delay and ultimately a degradation in classification accuracy. As can be seen, there is not much difference in the average times of ERDDM and RDDM, which is understandable because ERDDM is actually an enhancement of RDDM, hence, their algorithm complexity and run time are same. However, if we compare these times with the EDDM algorithm, then a clear difference can be seen, as it takes more time to process. However, STEPD shows less time taken to detect concept drift, but if we compare that with the detection delay and error rate, it shows that STEPD might be taking less time, but not doing much in terms of detection accuracy.The experiment results in Drift count analysis is another aspect of evaluating the drift detection methods. It is important because if a drift detector is accurate, then it will identify only true drifts in data and ignore the warnings and false alarms. It is to be established that all fluctuation in data are not drifts, as minor changes in classification are normal, and the classifier is trained to handle them. But a drift is identified when these fluctuations exceed the threshold level. If a drift detector is too sensitive, then it might wrongly detect minor fluctuations as drift. But also, if a drift detector is not smart enough, it will ignore the drifts, and result in a lower prediction accuracy and a higher prediction error. The comparison of drift detection count of four algorithms is given in Two types of experiments are performed on synthetic datasets, i.e., with class imbalance and without class imbalance. A common trend that can be seen in most of the results is that the prediction error tends to increase in the case of imbalanced data. This shows the impact of the class imbalance problem on the classifier results, and also on the drift detection process. This experiment also proves that the prediction error achieved with imbalanced data is close to the prediction error with balanced data. Since we know that even imbalanced data gives better or equal results, the true positive rate and false positive rate in imbalanced data is always the deciding factor in assuming the accuracy of results. Our proposed algorithm has achieved almost the same results with balanced data. These good results can be comparable to other drift detectors as well.It can be seen from the results presented in This article has proposed ERDDM, a new method for detecting concept drifts in data streams generated by sensors and smart devices. ERDDM enhances RDDM by decreasing its error rate and by incorporating the Page Hinkley Test method into the RDDM algorithm. ERDDM possesses the features of RDDM, such as detecting long concepts, keeping history of concepts and updating them over time, periodically recalculating the statistics for drift and warning levels, and dealing with loss of sensitivity in its predecessor. Moreover, ERDDM also incorporates the solution for the class imbalance problem, which is quite common in sensor data of certain domains. Studies show that concept drift and class imbalance often co-exist in data streams, and if both problems are not solved together, then they affect the prediction result to a great deal. Class imbalance is removed using the SMOTE method, which synthetically oversamples the minority class. For evaluation of the proposed method, 48 synthetic datasets are used, which represent patterns of real-world sensor data. ERDDM is compared with its predecessor RDDM, as well as EDDM and STEPD, which are also drift detection methods from prior studies. The synthetic datasets contain both abrupt and gradual drifts, hence, the evaluation is done separately for both types of drifts. The experiment results show that ERDDM outperforms other methods in prediction error, detected drifts, mean evaluation time, and drift detection delay. It also shows scalability with both small and large size of datasets. Possible future directions from here could be to detect drift types other than abrupt and gradual, such as incremental and recurring. However, the challenge is that acquiring such data streams that have these rare types of drifts is very hard. Generalizing an algorithm to handle all types of drift could also be a very complex task. Another future direction could be to handle partially or fully unlabeled data streams in conjunction with a concept drift and a class imbalance solution. This study can also be used for other application areas that generate data streams with both concept drift and class imbalance problems.