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The widespread usage of electronic health records (EHRs) for clinical research has produced multiple electronic phenotyping approaches. Methods for electronic phenotyping range from those needing extensive specialized medical expert supervision to those based on semi-supervised learning techniques. We present Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE), an R- package phenotyping framework that combines noisy labeling and anchor learning. APHRODITE makes these cutting-edge phenotyping approaches available for use with the Observational Health Data Sciences and Informatics (OHDSI) data model for standardized and scalable deployment. APHRODITE uses EHR data available in the OHDSI Common Data Model to build classification models for electronic phenotyping. We demonstrate the utility of APHRODITE by comparing its performance versus traditional rule-based phenotyping approaches. Finally, the resulting phenotype models and model construction workflows built with APHRODITE can be shared between multiple OHDSI sites. Such sharing allows their application on large and diverse patient populations.

The QSR Nvivo 2.0 is one of the latest versions of qualitative data analysis software package. Taking on board our experience as QSR Nvivo 2.0 users we describe here the software's most important tools. The QSR Nvivo 2.0 was used to facilitate the qualitative analysis of data gathered in a Health and Education research. Considering the shortage of published materials about the issue, which suggests a lack of knowledge about the program in our context, our aim is to show how the QSR Nvivo 2.0 can assist qualitative data analysis.

The 2019 Next Generation Public Health meeting provided several useful recommendations on how big data and artificial intelligence (AI) could enhance public health.1The Lancet Public HealthNext generation public health: towards precision and fairness.Lancet Public Health. 2019; 4: e209Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar To realise the full benefits of these developments, I propose two further recommendations. First, research studies should be done that will enable us to better understand the strengths, limitations, and applications of these new tools and data. Second, we need to train individuals who can bridge a skills gap that will enable the public health science community to fully engage with these developments. Much of the historical research and investment into the use of big data and AI has focused on applications in genomics and personalised medicine. Today's public health challenges require evidence informed by complex systems models of the upstream drivers of health, such as the environment, education, and employment.2Rutter H Savona N Glonti K et al.The need for a complex systems model of evidence for public health.Lancet. 2017; 390: 2602-2604Summary Full Text Full Text PDF PubMed Scopus (487) Google Scholar Multi-sectoral data about upstream health determinants can be linked to personal data (eg, from health-care records, mobile phones, and wearable devices) to increase the accuracy of complex systems models, our understanding of such systems, and opportunities for intervention. These data can also be used to inform the development and delivery of complex public health trials, natural experiments, and system change evaluations that have the potential to be undertaken more rapidly and efficiently than in the past. However, such data, tools, and methods must be evaluated and compared to more traditional study designs when applied to the public health data science tasks of description, prediction, causal inference,3Hernán MA Hsu J Healy B A second chance to get causal inference right: a classification of data science tasks.Chance. 2019; 32: 42-49Crossref Google Scholar and public health trials. These comparison studies should include cross-study and cross-workflow analyses with triangulation of results.4Lawlor DA Tilling K Davey Smith G Triangulation in aetiological epidemiology.Int J Epidemiol. 2016; 45: 1866-1886PubMed Google Scholar This practice will enable a better understanding of the relative strengths and weaknesses of different methods and facilitate decisions on when to use—and when to avoid—different study designs. Research, including modelling studies, should be done to help navigate the inherent tensions, and possible synergies, between interventions (including digital interventions) targeted at high-risk groups, and the application of universal approaches to tackle the health risks associated with the largest burdens of disease (including smoking, alcohol, diet, and physical activity). To move forward with this work, public health training curriculums must evolve. Newly trained public health data scientists should be taught about how big data and AI can—and cannot—be applied to public health problems, and should be part of a transdisciplinary community that can collaborate within and beyond academia to influence the upstream drivers of health. Crucially, this training should highlight issues of health equity, which is at increased risk with the advent of algorithm-induced inequalities. For example, socially excluded populations are much less likely to be included in datasets used to train AI algorithms, resulting in these algorithms excluding and further marginalising these individuals. Additionally, the traditional skills of leadership, advocacy, and policy development will remain crucial if public health data science is to result in tangible improvements in the health of the public. RWA is honorary consultant in public health data science at Public Health England and undertakes public health data science teaching and research. Next generation public health: towards precision and fairnessDevelopments in big data and artificial intelligence are transforming medicine, but public health has somehow been lagging behind in embracing their potential. Public health policy and practice are being outpaced by rapid technological advancements in digital health, which are already being applied in the real world. Given that the choices made over the next decades will determine the success and fairness of our digital health future, now is a critical time to reflect on this digital revolution and what it means for public health. Full-Text PDF Open AccessThe need for a complex systems model of evidence for public healthDespite major investment in both research and policy, many pressing contemporary public health challenges remain. To date, the evidence underpinning responses to these challenges has largely been generated by tools and methods that were developed to answer questions about the effectiveness of clinical interventions, and as such are grounded in linear models of cause and effect. Identification, implementation, and evaluation of effective responses to major public health challenges require a wider set of approaches1,2 and a focus on complex systems. Full-Text PDF

The value of our health and medical research investment is at risk unless we foster the discipline of biostatistics Every year, Australia's National Health and Medical Research Council (NHMRC) spends around $800 million on medical and public health research,1 much of which depends critically on the correct analysis and interpretation of data. We argue here that the value of our health research investment, in terms of improved health and lives saved, is at risk unless serious attention is paid to fostering the core scientific discipline of biostatistics. This risk is heightened by the expansion of research possibilities offered by the era of big data, which is rapidly enhancing the availability and scale of new information, necessitating ever deeper understanding of statistical issues and computational tools. Concerns surrounding the inadequate foundations of biostatistics in Australia were raised in a statement emanating from the International Society for Clinical Biostatistics conference held in Melbourne in August 2018 (in conjunction with the Australian Statistical Conference), the largest gathering of research biostatisticians that has ever occurred in Australia.2 Statistical reasoning provides the theoretical basis for extracting knowledge from data in the presence of variability and uncertainty. It is a critical element of most empirical research in public health and clinical medicine, with the best studies incorporating biostatistical input on aspects from study design to data analysis and reporting. Biostatistical methods underpin key public health research disciplines, such as epidemiology and health services research, a role that reflects the core nature of the discipline of biostatistics. Similarly, bioinformatics and computational biology are important new areas in data-intensive biomedical research that are underpinned by statistical concepts and methods, along with components heavily informed by other core disciplines such as computer science and mathematics. The critical role of biostatistics was affirmed in a recent review of the scale of waste and inefficiency in health research, which observed that, “These issues [of poor study design, conduct and analysis] are often related to misuse of statistical methods, which is accentuated by inadequate training in methods,”3 echoing similar observations made over two decades earlier.4 Importantly, biostatistics, as a subdiscipline of statistics (arguably, the original “data science”5), is an established scientific discipline of its own and is not simply a toolkit of techniques that need to be used correctly. Sound biostatistical work requires not only an understanding of mathematics, probability and sources of bias, which underpin statistical theory and methods, but also (and increasingly) extensive technical skills, including computing. In-depth training is needed to develop these skills along with the understanding required to conceptualise problems and navigate the tricky waters between real-world health questions and complex techniques. As noted in a recent review, such training would be very difficult to achieve for most clinicians.6 Superficial understanding of statistics can easily lead to unscientific practice (recently characterised as “cargo-cult statistics”7) and may be seen as responsible in large part for the current “crisis of reproducibility” in research.8 A prominent example is the evolution of beliefs concerning the risk of cardiovascular disease associated with postmenopausal oestrogen therapy. Influential observational studies in the late 1990s claimed to demonstrate evidence of reduced risk of heart attacks, a conclusion that was contradicted by a major randomised trial.9 Careful re-analysis of the observational data, guided by contemporary statistical thinking about confounding and time-dependent changes in risk, produced results that were similar to the randomised trial.10 The emerging era of big data heightens the need for biostatistical expertise, with more decision makers and researchers aiming to extract value from complex messy data, and increasing use of packaged software by individuals with insufficient understanding of the underlying methods. Big data require both an advanced understanding of fundamental statistical concepts and methods, including recent developments in causal reasoning,11 as well as enhanced capacity in computational tools such as dimensionality reduction, distributed processing, machine learning and natural language processing. More data do not necessarily mean better data, and more analytics does not necessarily mean better science, as the quality and reproducibility of research findings will remain highly dependent on the design of the data collection, an understanding of associated limitations and resulting biases, as well as appropriate analytical methods.12, 13 Successful establishment of biostatistics as a core discipline within academic health and medical research requires recognition of biostatistics as an academic discipline, central to the intellectual infrastructure of the broader research enterprise. This implies the need for structures that support a range of levels of biostatistical work, from non-specialists such as clinicians, to masters level biostatistics graduates and doctoral students, through to postdoctoral researchers and research leaders in biostatistical methodology. The need for academic activity across this range is similar in other areas of science, but is widely overlooked for biostatistics because of the tendency to regard the field as simply a toolkit of techniques rather than an evolving research discipline of its own. Biostatistical research develops and evaluates rigorous methods for drawing conclusions from new study designs and new data types, an extensive process that involves mathematical derivations and conceptualisations, simulation studies, detailed case studies, and translation of the newly developed methods for use by other researchers. As an example of the key role of new statistical methods, the development of marginal structural models was critical in the wave of research into antiretrovirals for the treatment of human immunodeficiency virus infection, by enabling the appropriate handling of time-dependent confounding in treatment decisions based on CD4 cell count levels that are themselves affected by treatment.14 Experience in methodological research is also an essential component in the training of future biostatistical leaders. As for any academic discipline, in order to support the continued development of extensive training pathways for biostatisticians, we need clearly identified departmental structures within our institutions. These should provide hubs of sufficient critical mass to enable transfer of expertise and knowledge within and between the multiple levels of activity, from non-specialists to research leaders. These hubs need to be embedded within schools of public health, medicine and health sciences, and their partner institutes, and should be led by biostatisticians who are active in methodological research. The fundamental importance of biostatistics to health and medical research has been recognised in other countries. In the United States, many major universities have departments of biostatistics that were established in the 1970s through funding of biostatistical research training programs by the National Institutes of Health, with a call for a renewed effort to expand biostatistical training programs in 2006.15 In a similar vein, the Medical Research Council in the United Kingdom has long funded a national centre in biostatistical methodology — the Medical Research Council's Biostatistics Unit — and, since 2009, a number of methodology hubs whose core research agenda is statistical methodology (www.methodologyhubs.mrc.ac.uk). There are also dedicated streams of funding for methodological research. In continental Europe, the Integrated Design and Analysis of small population group trials (IDeAl) consortium received €3 million over 2013–2019 from the European Union's Framework for Research and Innovation funding program to develop new design and analysis methodologies.16 Long term investment in biostatistical research in these nations means that they are much better placed in terms of methodological infrastructure underpinning their medical research. For example, modern trialists are moving towards adaptive trials and, in particular, platform trials, yet researchers developing such trials in Australia are reliant on biostatistical expertise from overseas. In contrast to Europe and the US, there has never been systematic investment in the development of biostatistics in Australia, either in universities or via national funding schemes. None of the major universities has a department of biostatistics; instead, there are many small groups (or even just individuals), often only loosely connected with each other or within departments or schools that are dominated by disciplines other than medicine and public health. For example, all of the Group of Eight universities have structures that link statistics with mathematics or business, which inhibits the linkage between biostatistical and medical research that is critical for achieving excellence in the planning, conduct and analyses of medical research studies. This landscape is just beginning to change at the University of Melbourne and Monash University, with recent initiatives for the recruitment of research biostatisticians at a range of levels. Among the medical research institutes, the Clinical Epidemiology and Biostatistics Unit at Murdoch Children's Research Institute provides an example of a successful biostatistics core, with academic leadership underpinned by a methodological research program and a “hub and spokes” model whereby staff hold joint positions with our group and the research groups they support. With regards to funding, we are aware of only one example in Australia of direct funding of a group of biostatisticians with a critical mass and a research base in biostatistics: the Victorian Centre for Biostatistics (ViCBiostat), which was established in 2012 under an NHMRC Centre of Research Excellence grant. However, funding of this centre ceased in 2017. The only other possible avenue for funding of biostatistical research in the current climate is short term project and investigator grants, but this is not a sustainable avenue to ensure an ongoing critical mass, particularly given that the downstream impact of methodological research will always tend to make it less competitive than substantively focused medical research. An ongoing commitment in the form of dedicated investment in methodological research is a key requirement for developing and maintaining an essential biostatistics infrastructure. There is unfortunately no quick solution to the problems outlined, but we suggest some steps that we believe are needed to strengthen and develop the biostatistics discipline in Australia: Without investment in biostatistics at these multiple levels, the entire Australian medical research enterprise is at considerable risk of “drowning in data but starving for knowledge”.17 This work was partially supported by an Australian NHMRC Career Development Fellowship (1127984) awarded to Katherine Lee. Research at the Murdoch Children's Research Institute is supported by the Victorian Government's Operational Infrastructure Support Program. The funding sources had no role in this publication. We thank the delegates of the Joint International Society for Clinical Biostatistics and Australian Statistical conference 2018 who attended the meeting to discuss this issue, and members of the Victorian Centre for Biostatistics who provided advice on this manuscript. No relevant disclosures. Not commissioned; externally peer reviewed.

BACKGROUND: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

The enormous amounts of data that are generated in the healthcare process and stored in electronic health record (EHR) systems are an underutilized resource that, with the use of data science applications, can be exploited to improve healthcare. To foster the development and use of data science applications in healthcare, there is a fundamental need for access to EHR data, which is typically not readily available to researchers and developers. A relatively rare exception is the large EHR database, the Stockholm EPR Corpus, comprising data from more than two million patients, that has been been made available to a limited group of researchers at Stockholm University. Here, we describe a number of data science applications that have been developed using this database, demonstrating the potential reuse of EHR data to support healthcare and public health activities, as well as facilitate medical research. However, in order to realize the full potential of this resource, it needs to be made available to a larger community of researchers, as well as to industry actors. To that end, we envision the provision of an infrastructure around this database called HEALTH BANK - the Swedish Health Record Research Bank. It will function both as a workbench for the development of data science applications and as a data exploration tool, allowing epidemiologists, pharmacologists and other medical researchers to generate and evaluate hypotheses. Aggregated data will be fed into a pipeline for open e-access, while non-aggregated data will be provided to researchers within an ethical permission framework. We believe that HEALTH BANK has the potential to promote a growing industry around the development of data science applications that will ultimately increase the efficiency and effectiveness of healthcare. Copyright © 2015 held by the authors. Copyright © 2015 for the individual papers by the papers' authors.

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Background UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational "Big Data". Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depend upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope. Issues In the UK, the separate worlds of health data science (bioinformatics, "Big Data") and effective healthcare system design and implementation (clinical informatics, "Digital Health") have operated as 'two cultures'. Much NHS and social care data is of unusably poor quality. Substantial research funding is wasted on 'data cleansing' or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry. Recommendation The UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline's evidence base and mitigate the absence of regulation.Independent evaluation of digital health interventions should be the norm, not the exception. Conclusions Policy makers and research funders need to acknowledge the existing gap between the 'two cultures' and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

The routine operation of modern healthcare systems produces a wealth of data in electronic health records, administrative databases, clinical registries, and other clinical systems. It is widely acknowledged that there is great potential for utilising these routine data for health research to derive new knowledge about health, disease, and treatments. However, the reuse of routine healthcare data for research is not beyond debate. In this paper, we discuss three issues that have stirred considerable controversy among health data scientists. First, we discuss van der Lei's 1st Law of Medical Informatics, which states that data shall be used only for the purpose for which they were collected. Then, we discuss to which extent routine data sources and innovations in analytical methods alleviate the need to conduct randomised clinical trials. Finally, we address questions of governance, privacy, and trust when routine health data are made available for research. While we don't think that there is a definite "right answer" for any of these issues, we argue that data scientists should be aware of the arguments for different viewpoints, respect their validity, and contribute constructively to the debate. The three controversies discussed in this paper relate to core challenges for research with health data and define an essential research agenda for the health data science community.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.

This accessible book is essential reading for those looking for a short and simple guide to basic data analysis. Written for the complete beginner, the book is the ideal companion when undertaking quantitative data analysis for the first time using SPSS. The book uses a simple example of quantitative data analysis that would be typical to the health field to take you through the process of data analysis step by step. The example used is a doctor who conducts a questionnaire survey of 30 patients to assess a specific service. The data from these questionnaires is given to you for analysis, and the book leads you through the process required to analyse this data. Handy screenshots illustrate each step of the process so you can try out the analysis for yourself, and apply it to your own research with ease. Topics covered include : Questionnaires and how to analyse them Coding the data for SPSS, setting up an SPSS database and entering the data Descriptive statistics and illustrating the data using graphs Cross-tabulation and the Chi-square statistic Correlation: examining relationships between interval data Examining differences between two sets of scores Reporting the results and presenting the data Quantitative Data Analysis Using SPSS is the ideal text for any students in health and social sciences with little or no experience of quantitative data analysis and statistics.

The technology currently available for quantifying various biometric, behavioral, emotional, cognitive and psychological aspects of daily life has become increasingly diverse, accurate and accessible. Continued improvements are ongoing. These burgeoning technologies can and will profoundly alter the way lifestyle, health, wellness and chronic diseases are managed in the future. For those pursuing the potential of such digital technologies in the creation of compelling and effective connected healthcare experiences, a number of new concepts have surfaced. We have taken these concepts (many of which originate in engineering) and extended them to be incorporated into managing health risk and health conditions via a blended digital health experience. For example, the advent of mobile technology for health has given rise to concepts such as ecological momentary assessment (EMA) and ecological momentary intervention (EMI) that assess the person’s (digital twin) status and delivers interventions as needed when needed. For such concepts to be fully realized the experience design of mHealth program(s) (aka connected care) should and now can actually guide the end user through a series of self-experiments directed by data-driven feedback from a version of their digital twin. As treatment development and testing moves towards the precision of individual differences inherent in every person and every treatment response (or non-response) group data and more recent big data approaches for generating new knowledge offer limited help to end-users (including practitioners) for helping individuals evaluate their own digital twin generated data and change over time under different conditions. This is the renaissance of N-of-1 or individual science. N-of-1 evaluation creates the opportunity to evaluate each individual uniquely. The rigor and logic of N-of-1 designs have been well articulated and expanded upon for over a half-century. For the clinician, this revitalized form of scientific and behavioral interaction evaluation can help validate or reject the impact a given treatment has for a given patient with increased efficiency and accuracy. Further, N-of-1 can incorporate biological (genomic), behavioral, psychological and digital health data such that users themselves can begin to evaluate the relationships of their own treatment response patterns and the contingencies that impact them. Thus, emerges the self-scientist.

PART I: THE SCIENCE AND THEORY OF REAL-TIME DATA CAPTURE: A FOCUS ON ECOLOGICAL MOMENTARY ASSESSMENT (EMA) 1. Historical Roots and Rationale of Ecological Momentary Assessment (EMA) 2. Retrospective and Concurrent Self-Reports: The Rationale for Real-Time Data Capture 3. Designing Protocols for Ecological Momentary Assessment 4. Special Methodological Challenges and Opportunities in Ecological Momentary Assessment 5. The Analysis of Real-Time Momentary Data: A Practical Guide PART II: APPLICATION OF REAL-TIME DATA CAPTURE: EXEMPLARS OF REAL-TIME DATA RESEARCH 6. Real-Time Data Capture and Adolescent Cigarette Smoking: Moods and Smoking 7. Ecological Momentary Assessment of Physical Activity in Hispanics/Latinos Using Pedometers and Diaries 8. Dietary Assessment and Monitoring in Real-Time 9. Real-Time Data Capture: Ecological Momentary Assessment of Behavioral Symptoms Associated with Eating Disorders 10. Ecological Momentary Assessment for Alcohol Consumption 11. Assessing the Impact of Fibromyalgia Syndrome in Real-Time 12. Evaluating Fatigue of Ovarian Cancer Patients Using Ecological Momentary Assessment 13. Personality, Mood States, and Daily Health 14. Ecological Momentary Assessment as a Resource for Social Epidemiology PART III: FUTURE DEVELOPMENTS IN REAL-TIME DATA CAPTURE 15. Momentary Health Interventions: Where are we and where are we going? 16. Technological Innovations Enabling Automatic, Context-Sensitive Ecological Momentary Assessment 17. Statistical Issues in Intensive Longitudinal Data Analysis 18. Thoughts on the Present State of Real-Tmie Data Capture

BACKGROUND: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. METHODS: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students' engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. RESULTS: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. CONCLUSIONS: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.

Data science is a newly-formed and, as yet, loosely-defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term "data science," particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective.

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.