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| fundamentals of clinical data science: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| fundamentals of clinical data science: Fundamentals of Clinical Data Science Andre Dekker, Michel Dumontier, Pieter Kubben, 2020-10-09 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book's promise is no math, no codeand will explain the topics in a style that is optimized for a healthcare audience. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors. |
| fundamentals of clinical data science: Fundamentals of Clinical Data Science , 2019 |
| fundamentals of clinical data science: Fundamentals of Clinical Trials Lawrence M. Friedman, Curt Furberg, David L. DeMets, 1998 This classic reference, now updated with the newest applications and results, addresses the fundamentals of such trials based on sound scientific methodology, statistical principles, and years of accumulated experience by the three authors. |
| fundamentals of clinical data science: R for Health Data Science Ewen Harrison, Riinu Pius, 2020-12-31 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. |
| fundamentals of clinical data science: Foundations of Clinical Research Leslie Gross Portney, Mary P. Watkins, 2015 Draw upon the foundations necessary for finding and interpreting research evidence across all healthcare professions. Revised to reflect the most current changes in the field of clinical research in rehabilitation and medicine, you'll find a growing emphasis on evidence-based practice (EBP) as well as new vocabulary that is being integrated into research and practice across disciplines. |
| fundamentals of clinical data science: Introduction to Biomedical Data Science Robert Hoyt, Robert Muenchen, 2019-11-24 Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises. |
| fundamentals of clinical data science: Fundamentals of Medical Imaging Paul Suetens, 2009-08-06 Fundamentals of Medical Imaging, second edition, is an invaluable technical introduction to each imaging modality, explaining the mathematical and physical principles and giving a clear understanding of how images are obtained and interpreted. Individual chapters cover each imaging modality – radiography, CT, MRI, nuclear medicine and ultrasound – reviewing the physics of the signal and its interaction with tissue, the image formation or reconstruction process, a discussion of image quality and equipment, clinical applications and biological effects and safety issues. Subsequent chapters review image analysis and visualization for diagnosis, treatment and surgery. New to this edition: • Appendix of questions and answers • New chapter on 3D image visualization • Advanced mathematical formulae in separate text boxes • Ancillary website containing 3D animations: www.cambridge.org/suetens • Full colour illustrations throughout Engineers, clinicians, mathematicians and physicists will find this an invaluable aid in understanding the physical principles of imaging and their clinical applications. |
| fundamentals of clinical data science: Principles and Practice of Clinical Research John I. Gallin, Frederick P Ognibene, 2011-04-28 The second edition of this innovative work again provides a unique perspective on the clinical discovery process by providing input from experts within the NIH on the principles and practice of clinical research. Molecular medicine, genomics, and proteomics have opened vast opportunities for translation of basic science observations to the bedside through clinical research. As an introductory reference it gives clinical investigators in all fields an awareness of the tools required to ensure research protocols are well designed and comply with the rigorous regulatory requirements necessary to maximize the safety of research subjects. Complete with sections on the history of clinical research and ethics, copious figures and charts, and sample documents it serves as an excellent companion text for any course on clinical research and as a must-have reference for seasoned researchers.*Incorporates new chapters on Managing Conflicts of Interest in Human Subjects Research, Clinical Research from the Patient's Perspective, The Clinical Researcher and the Media, Data Management in Clinical Research, Evaluation of a Protocol Budget, Clinical Research from the Industry Perspective, and Genetics in Clinical Research *Addresses the vast opportunities for translation of basic science observations to the bedside through clinical research*Delves into data management and addresses how to collect data and use it for discovery*Contains valuable, up-to-date information on how to obtain funding from the federal government |
| fundamentals of clinical data science: Analytics in Healthcare Christo El Morr, Hossam Ali-Hassan, 2019-01-21 This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field. |
| fundamentals of clinical data science: Clinical and Translational Science David Robertson, Gordon H. Williams, 2016-11-25 Clinical and Translational Science: Principles of Human Research, Second Edition, is the most authoritative and timely resource for the broad range of investigators taking on the challenge of clinical and translational science, a field that is devoted to investigating human health and disease, interventions, and outcomes for the purposes of developing new treatment approaches, devices, and modalities to improve health. This updated second edition has been prepared with an international perspective, beginning with fundamental principles, experimental design, epidemiology, traditional and new biostatistical approaches, and investigative tools. It presents complete instruction and guidance from fundamental principles, approaches, and infrastructure, especially for human genetics and genomics, human pharmacology, research in special populations, the societal context of human research, and the future of human research. The book moves on to discuss legal, social, and ethical issues, and concludes with a discussion of future prospects, providing readers with a comprehensive view of this rapidly developing area of science. Introduces novel physiological and therapeutic strategies for engaging the fastest growing scientific field in both the private sector and academic medicine Brings insights from international leaders into the discipline of clinical and translational science Addresses drug discovery, drug repurposing and development, innovative and improved approaches to go/no-go decisions in drug development, and traditional and innovative clinical trial designs |
| fundamentals of clinical data science: Designing Clinical Research Stephen B. Hulley, Steven R. Cummings, Warren S. Browner, Deborah G. Grady, Thomas B. Newman, 2011-11-30 Designing Clinical Research sets the standard for providing a practical guide to planning, tabulating, formulating, and implementing clinical research, with an easy-to-read, uncomplicated presentation. This edition incorporates current research methodology—including molecular and genetic clinical research—and offers an updated syllabus for conducting a clinical research workshop. Emphasis is on common sense as the main ingredient of good science. The book explains how to choose well-focused research questions and details the steps through all the elements of study design, data collection, quality assurance, and basic grant-writing. All chapters have been thoroughly revised, updated, and made more user-friendly. |
| fundamentals of clinical data science: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| fundamentals of clinical data science: Introduction to Statistical Methods for Clinical Trials Thomas D. Cook, David L DeMets, 2007-11-19 Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial. After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals. Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials. |
| fundamentals of clinical data science: Fundamentals of Clinical Medicine Humayun J. Chaudhry, 2004 This new edition is fully updated to address and include: significant changes in hospital environments, including managed care delivery methodologies, how Medicare and Medicaid work, and the role of nurse practitioners, physician assistants, and other ancillary hospital personnel patient screening recommendations from agencies such as the U.S. Preventive Services Task Force and the American Cancer Society convenient lists of common drug reactions and commonly used alternative and complementary medicine treatment modalities expanded, commonly used medical abbreviations and acronyms. Students benefit from a convenient handbook that offers a crucial link between basic sciences years and the hospital experience, and are aided in achieving mastery of proper physical diagnosis and medical work-up for accurate diagnosis. Includes concise disease pathophysiology reviews. |
| fundamentals of clinical data science: Data Analytics in Biomedical Engineering and Healthcare Kun Chang Lee, Sanjiban Sekhar Roy, Pijush Samui, Vijay Kumar, 2020-10-18 Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. - Examines the development and application of data analytics applications in biomedical data - Presents innovative classification and regression models for predicting various diseases - Discusses genome structure prediction using predictive modeling - Shows readers how to develop clinical decision support systems - Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks |
| fundamentals of clinical data science: The Fundamentals of Clinical Research P. Michael Dubinsky, Karen A. Henry, 2022-01-26 This book focuses on the practical application of good clinical practice (GCP) fundamentals and provides insight into roles and responsibilities included in planning, executing, and analyzing clinical trials. The authors describe the design of quality into clinical trial planning and the application of regulatory, scientific, administrative, business, and ethical considerations. Describes the design of quality into the clinical trial planning Has end-of-chapter questions and answers to check learning and comprehension Includes charts that visually summarize the content and allow readers to cross-reference details in relevant chapters Offers a companion website containing supplemental training resources |
| fundamentals of clinical data science: Fundamentals of Clinical Hematology Marcella Liffick Stevens, 1997 This reference presents the fundamentals of hematology, including erythrocytes, leukocytes, thrombocytes and coagulation, and briefly discusses disease states in a concise, yet comprehensive manner! This reader-friendly text features outlines, objectives, study questions, bibliographies, Do It Now application exercises, special emphasis statements, and Fast Facts summaries. Demonstrates the integration, collaboration, balance, and wholeness of quality clinical laboratory practices by introducing related areas and their procedures. |
| fundamentals of clinical data science: Healthcare Analytics for Quality and Performance Improvement Trevor L. Strome, 2013-10-02 Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement. |
| fundamentals of clinical data science: Fundamentals of Hand Therapy Cynthia Cooper, 2013-11-06 Perfect for hand therapy specialists, hand therapy students, and any other professional who encounters clients with upper extremity issues, Fundamentals of Hand Therapy, 2nd Edition contains everything you need to make sound therapy decisions. Coverage includes hand anatomy, the evaluation process, and diagnosis-specific information. Expert tips, treatment guidelines, and case studies round out this comprehensive text designed to help you think critically about each client's individual needs. Overall, a very clear readable style is adopted throughout, with theory supported by various anecdotal case studies. Excellent use is made of illustrations, and many chapters contain the helpful addition of 'clinical pearls' or 'tips from the field', which are an attempt to make transparent the links between theory and practice. In conclusion, this is an excellent core text for reference purposes. Reviewed by: British Journal of Occupational Therapy Date: Aug 2014 Clinical Pearls and Precautions highlight relevant information learned by the experienced author and contributors that you can apply to clinical practice. Case examples included in the diagnoses chapters in Part Three demonstrate the use of clinical reasoning and a humanistic approach in treating the client. Diagnosis-specific information in the final section of the book is well-organized to give you quick access to the information you need. Special features sections such as Questions to Discuss with the Physician, What to Say to Clients, Tips from the Field, and more help readers find their own clinical voices. Online sample exercises give you a pool to pull from during professional practice. NEW! Chapters on yoga and pilates provide guidance into new ways to treat upper extremity problems. NEW! Chapter on wound care gives you a thorough foundation on how wounds impact therapeutic outcomes. NEW! Chapter on orthotics has been added to cover basic splinting patterns. NEW! Online resources help assess your understanding and retention of the material. |
| fundamentals of clinical data science: Healthcare Analytics Made Simple Vikas (Vik) Kumar, 2018-07-31 Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare. |
| fundamentals of clinical data science: Critical Thinking in Clinical Research Felipe Fregni, Ben M. W. Illigens, 2018 Critical Thinking in Clinical Research explains the fundamentals of clinical research in a case-based approach. The core concept is to combine a clear and concise transfer of information and knowledge with an engagement of the reader to develop a mastery of learning and critical thinking skills. The book addresses the main concepts of clinical research, basics of biostatistics, advanced topics in applied biostatistics, and practical aspects of clinical research, with emphasis on clinical relevance across all medical specialties. |
| fundamentals of clinical data science: Fundamentals of Evidence Based Medicine Kameshwar Prasad, 2013-08-16 This is a basic book on evidence-based medicine (EBM). It starts with an introduction to the topic. It outlines the relationship between EBM and research and quality of care. Then It goes on to cover the most commonly used modules of EBM, i.e. therapy, diagnosis, prognosis and meta-analysis. Each module starts with an introduction to fundamental concepts, and description of the related research process, and then follows the critical appraisal of related type of research artcle. At the end, it covers the different systems of grading of level of evidence and strength of recommendations. The book also has three examples of critical appraisal on diagnosis, therapy, and meta-analysis. |
| fundamentals of clinical data science: Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics 8 E; South Asia Edition;e-Book Nader Rifai, 2019-07-16 Get the foundational knowledge you need to successfully work in a real-world, clinical lab with Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics, 8th Edition. From highly respected clinical chemistry expert Nader Rifai, this condensed, easier-to-understand version of the acclaimed Tietz Textbook of Clinical Chemistry and Molecular Diagnostics uses a laboratory perspective to guide you through selecting and performing diagnostic lab tests and accurately evaluating the results. Coverage includes laboratory principles, analytical techniques, instrumentation, analytes, pathophysiology, and more. This eighth edition features new clinical cases from The Coakley Collection, new questions from The Deacon's Challenge of Biochemical Calculations Collection, plus new content throughout the text to ensure you stay ahead of all the latest techniques, instrumentation, and technologies. Condensed version of the clinical chemistry bible offers the same authoritative and well-presented content in a much more focused and streamlined manner. Coverage of analytical techniques and instrumentation includes optical techniques, electrochemistry, electrophoresis, chromatography, mass spectrometry, enzymology, immunochemical techniques, microchips, automation, and point of care testing. Updated chapters on molecular diagnostics cover the principles of molecular biology, nucleic acid techniques and applications, and genomes and nucleic acid alterations, reflecting the changes in this rapidly evolving field. Learning objectives, key words, and review questions are included in each chapter to support learning. More than 500 illustrations plus easy-to-read tables help readers better understand and remember key concepts |
| fundamentals of clinical data science: Clinical Trials Tom Brody, 2016-02-19 Clinical Trials, Second Edition, offers those engaged in clinical trial design a valuable and practical guide. This book takes an integrated approach to incorporate biomedical science, laboratory data of human study, endpoint specification, legal and regulatory aspects and much more with the fundamentals of clinical trial design. It provides an overview of the design options along with the specific details of trial design and offers guidance on how to make appropriate choices. Full of numerous examples and now containing actual decisions from FDA reviewers to better inform trial design, the 2nd edition of Clinical Trials is a must-have resource for early and mid-career researchers and clinicians who design and conduct clinical trials. - Contains new and fully revised material on key topics such as biostatistics, biomarkers, orphan drugs, biosimilars, drug regulations in Europe, drug safety, regulatory approval and more - Extensively covers the study schema and related features of study design - Incorporates laboratory data from studies on human patients to provide a concrete tool for understanding the concepts in the design and conduct of clinical trials - Includes decisions made by FDA reviewers when granting approval of a drug as real world learning examples for readers |
| fundamentals of clinical data science: The Comprehensive Guide To Clinical Research Chris Sauber, Dan Sfera, 2019-04-21 Condensing the most important topics in all of clinical research in an easy to understand presentation. The 20 percent of what you need to know in order to be 80 percent proficient!The authors who have operated various levels of businesses in the clinical research industry since 2005 believe that more practical information pertaining to clinical research needs to be accessible to individuals who are new to the industry or are curious about entering the rewarding world of clinical trials.This book reads in an easy to understand style and is based on proven methods the authors have developed to train their own employees and students of their various clinical research academies throughout the years. Picking this up and absorbing the information will allow anyone to gain much better insight into the complicated dynamics of clinical research. This practical roadmap is all you will need to get started on your clinical trial journey!In this book you will learn about:Regulations and the history as well as evolution of GCP.Clinical Research Site OperationsMonitoring Dynamics and Typical Monitoring VistsCRO ActivitiesSponsor Level DynamicsIndustry VendorsCommon Career Opportunities and Employment Roadmaps |
| fundamentals of clinical data science: Introduction to Clinical Engineering Samantha Jacques, Barbara Christe, 2020-08-06 Introduction to Clinical Engineering focuses on the application of engineering practice within the healthcare delivery system, often defined as clinical engineering. Readers will explore the fundamental concepts integral to the support of healthcare technology to advance medical care. The primary mission of clinical engineers is the utilization of medical devices, software, and systems to deliver safe and effective patient care throughout technology's lifecycle. This unique and interdisciplinary workforce is part of the healthcare team and serves as the intersection between engineering and medicine. This book is aimed at practitioners, managers, students, and educators to serve as a resource that offers a broad perspective of the applications of engineering principles, regulatory compliance, lifecycle planning, systems thinking, risk analysis, and resource management in healthcare. This book is an invaluable tool for healthcare technology management (HTM) professionals and can serve as a guide for students to explore the profession in depth. - Offers readers an in-depth look into the support and implementation of existing medical technology used for patient care in a clinical setting - Provides insights into the clinical engineering profession, focusing on engineering principles as applied to the US healthcare system - Explores healthcare technology, hospital and systems safety, information technology and interoperability with medical devices, clinical facilities management, as well as human resource management |
| fundamentals of clinical data science: Clinical Precision Medicine Judy S. Crabtree, 2019-11-15 Clinical Precision Medicine: A Primer offers clinicians, researchers and students a practical, up-to-date resource on precision medicine, its evolving technologies, and pathways towards clinical implementation. Early chapters address the fundamentals of molecular biology and gene regulation as they relate to precision medicine, as well as the foundations of heredity and epigenetics. Oncology, an early adopter of precision approaches, is considered with its relationship to genetic variation in drug metabolism, along with tumor immunology and the impact of DNA variation in clinical care. Contributions by Stephanie Kramer, a Clinical Genetic Counselor, also provide current information on prenatal diagnostics and adult genetics that highlight the critical role of genetic counselors in the era of precision medicine. - Includes applied discussions of chromosomes and chromosomal abnormalities, molecular genetics, epigenetic regulation, heredity, clinical genetics, pharmacogenomics and immunogenomics - Features chapter contributions from leaders in the field - Consolidates fundamental concepts and current practices of precision medicine in one convenient resource |
| fundamentals of clinical data science: Essentials of Clinical Research Stephen P. Glasser, 2014-06-02 In its extensively revised and updated Second Edition, this book provides a solid foundation for readers interested in clinical research. Discussion encompasses genetic, pharmacoepidemiologic and implementation research. All chapters have been updated with new information and many new tables have been added to elucidate key points. The book now offers discussion on how to handle missing data when analyzing results, and coverage of Adaptive Designs and Effectiveness Designs and new sections on Comparative Effectiveness Research and Pragmatic Trials. Chapter 6 includes new material on Phase 0 Trials, expanded coverage of Futility Trials, a discussion of Medical Device approval, Off Label Drug use and the role of the FDA in regulating advertising. Additional new information includes the role of pill color and shape in association with the placebo effect and an examination of issues surrounding minority recruitment. The final chapter offers a new section on manuscript preparation along with a discussion of various guidelines being adopted by journals: CONSORT, STROBE, PRISMA, MOOSE and others; and coverage of Conflicts of Interest, Authorship, Coercive Citation, and Disclosures in Industry-Related Associations. Building on the strengths of its predecessor in its comprehensive approach and authoritative advice, the new edition offers more of what has made this book a popular, trusted resource for students and working researchers alike. |
| fundamentals of clinical data science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| fundamentals of clinical data science: The Health Care Data Guide Lloyd P. Provost, Sandra K. Murray, 2011-12-06 The Health Care Data Guide is designed to help students and professionals build a skill set specific to using data for improvement of health care processes and systems. Even experienced data users will find valuable resources among the tools and cases that enrich The Health Care Data Guide. Practical and step-by-step, this book spotlights statistical process control (SPC) and develops a philosophy, a strategy, and a set of methods for ongoing improvement to yield better outcomes. Provost and Murray reveal how to put SPC into practice for a wide range of applications including evaluating current process performance, searching for ideas for and determining evidence of improvement, and tracking and documenting sustainability of improvement. A comprehensive overview of graphical methods in SPC includes Shewhart charts, run charts, frequency plots, Pareto analysis, and scatter diagrams. Other topics include stratification and rational sub-grouping of data and methods to help predict performance of processes. Illustrative examples and case studies encourage users to evaluate their knowledge and skills interactively and provide opportunity to develop additional skills and confidence in displaying and interpreting data. Companion Web site: www.josseybass.com/go/provost |
| fundamentals of clinical data science: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| fundamentals of clinical data science: Whitcup and Nussenblatt's Uveitis Scott M. Whitcup, H. Nida Sen, 2021-03-31 Comprehensive and readable, Whitcup and Nussenblatt's Uveitis, 5th Edition, covers every aspect of this complex subspecialty in a single convenient volume. This clinically relevant guide covers the medical, pharmacological, and surgical treatment of uveitis in a visually rich, full-color format, offering a complete overview for today's clinicians. New and updated case studies, key points boxes, and more than 400 images and graphics highlight major clinical points and provide insight into real situations that you can apply in practice. - Features multiple chapters on diagnostic approach to help you meet the challenge of making accurate diagnoses. - Reflects advancements and new developments on all aspects of uveitis including new medical and surgical treatments. - Covers key topics such as the use of PCR in diagnostic testing, cataract surgery in patients with uveitis, the use of OCT in diagnosis and treatment, new infectious causes of uveitis (including Zika and Ebola), and novel treatments for inflammatory eye disease. - Includes new clinical photographs, illustrations, and OCT images throughout. - Reviews new randomized clinical trials of new uveitis therapy, as well as the approach to patients with HIV and HIV-associated ocular diseases including CMV retinitis and immune recovery uveitis. - Provides expanded information on masquerade syndromes with important tips on diagnosis and management of intraocular lymphoma. |
| fundamentals of clinical data science: Cooper's Fundamentals of Hand Therapy Christine M. Wietlisbach, 2019-11-03 Written for hand therapy specialists and non-specialists, Cooper's Fundamentals of Hand Therapy, 3rd Edition emphasizes treatment fundamentals, and provides tips and guidelines for hand therapy practice. This easy-to-use illustrated text and reference guide helps further develop your clinical reasoning skills by describing what goes into the evaluation process, highlighting the humanistic side of each encounter through case studies, and providing the wisdom the contributing authors have acquired through years of practice. This new edition also features additional chapters on the use of common physical agents and orthoses, plus added content on how to integrate evidence-based findings into daily hand practice. - UPDATED! Chapter covering Orthoses Essential Concepts reflects the latest information in the field. - Case studies with questions and resolutions help you develop strong clinical reasoning skills while presenting the human side of each client encounter. - Special features sections such as Questions to Discuss with the Physician, What to Say to Clients, Tips from the Field, and more help you find your own clinical voice. - Anatomy sections throughout text highlight important anatomical bases of dysfunctions, injuries, or disorders. - Clinical Pearls highlight relevant information from an experienced author and contributors that you can apply to clinical practice in the future. - Evaluation Techniques and Tips help you master appropriate and thorough clinical evaluation of clients. - Diagnosis-specific information in the final section of the book is well-organized to give you quick access to the information you need. - NEW! Chapter covering Physical Agent Modalities helps you understand how to use common hand therapy tools. - NEW! Evidence-Based Practice content outlines how to closely examine evidence and integrate it into daily hand therapy practice. - NEW! Photos and illustrations throughout provide clear examples of tools, techniques, and therapies. |
| fundamentals of clinical data science: Foundations of Anesthesia Hugh C. Hemmings, Philip M. Hopkins, 2006-01-01 Accompanying CD-ROM ... allows you to download figures into PowerPoint for electronic presentations. -- p. [4] of cover. |
| fundamentals of clinical data science: Foundations of Data Science for Engineering Problem Solving Parikshit Narendra Mahalle, Gitanjali Rahul Shinde, Priya Dudhale Pise, Jyoti Yogesh Deshmukh, 2021-08-21 This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses. |
| fundamentals of clinical data science: Fundamentals of Brain Network Analysis Alex Fornito, Andrew Zalesky, Edward Bullmore, 2016-03-04 Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain |
| fundamentals of clinical data science: Principles and Practice of Clinical Trials Steven Piantadosi, Curtis L. Meinert, 2022-07-19 This is a comprehensive major reference work for our SpringerReference program covering clinical trials. Although the core of the Work will focus on the design, analysis, and interpretation of scientific data from clinical trials, a broad spectrum of clinical trial application areas will be covered in detail. This is an important time to develop such a Work, as drug safety and efficacy emphasizes the Clinical Trials process. Because of an immense and growing international disease burden, pharmaceutical and biotechnology companies continue to develop new drugs. Clinical trials have also become extremely globalized in the past 15 years, with over 225,000 international trials ongoing at this point in time. Principles in Practice of Clinical Trials is truly an interdisciplinary that will be divided into the following areas: 1) Clinical Trials Basic Perspectives 2) Regulation and Oversight 3) Basic Trial Designs 4) Advanced Trial Designs 5) Analysis 6) Trial Publication 7) Topics Related Specific Populations and Legal Aspects of Clinical Trials The Work is designed to be comprised of 175 chapters and approximately 2500 pages. The Work will be oriented like many of our SpringerReference Handbooks, presenting detailed and comprehensive expository chapters on broad subjects. The Editors are major figures in the field of clinical trials, and both have written textbooks on the topic. There will also be a slate of 7-8 renowned associate editors that will edit individual sections of the Reference. |
| fundamentals of clinical data science: Understanding Clinical Research Renato D. Lopes, Robert A. Harrington, 2013-05-22 A complete guide to understanding and applying clinical research results Ideal for both researchers and healthcare providers Understanding Clinical Research addresses both the operational challenges of clinical trials and the needs of clinicians to comprehend the nuances of research methods to accurately analyze study results. This timely resource covers all aspects of clinical trials--from study design and statistics to regulatory oversight--and it delivers a detailed yet streamlined overview of must-know research topics. The text features an accessible three-part organization that traces the evolution of clinical research and explains the bedrock principles and unique challenges of clinical experimentation and observational research. Reinforcing this content are real-life case examples--drawn from the authors' broad experience--that put chapter concepts into action and contribute to a working knowledge of integral research techniques. FEATURES: The most definitive guide to promoting excellence in clinical research, designed to empower healthcare providers to assess a study's strengths and weaknesses with confidence and apply this knowledge to optimize patient outcomes In-depth coverage of fundamental research methods and protocols from preeminent authorities provides readers with an instructive primer and a springboard for ongoing clinical research education Clear, comprehensive three-part organization: Section One: Evolution of Clinical Research offers a succinct history of clinical trials, drug regulations, and the role of the FDA while covering the impact of information technology and academic research organizations Section Two: Principles of Clinical Experimentation takes you through the typical phases of clinical trials in the development of medical products, from initial human subject research to postapproval surveillance studies Section Three: Observational Research highlights the underlying principles, pitfalls, and methods for case-control studies, cohort studies, registries, and subgroup analyses within randomized trials |
| fundamentals of clinical data science: Basic & Clinical Biostatistics: Fifth Edition Susan White, 2019-10-22 Learn to evaluate and apply statistics in medicine, medical research, and all health-related fields A Doody's Core Title for 2023! Basic & Clinical Biostatistics provides medical students, researchers, and practitioners with the knowledge needed to develop sound judgment about data applicable to clinical care. This fifth edition has been updated throughout to deliver a comprehensive, timely introduction to biostatistics and epidemiology as applied to medicine, clinical practice, and research. Particular emphasis is on study design and interpretation of results of research. The book features “Presenting Problems” drawn from studies published in the medical literature, end-of-chapter exercises, and a reorganization of content to reflect the way investigators ask research questions. To facilitate learning, each chapter contain a set of key concepts underscoring the important ideas discussed. Features: Key components include a chapter on survey research and expanded discussion of logistic regression, the Cox model, and other multivariate statistical methods Extensive examples illustrate statistical methods and design issues Updated examples using R, an open source statistical software package Expanded coverage of data visualization, including content on visual perception and discussion of tools such as Tableau, Qlik and MS Power BI Sampling and power calculations imbedded with discussion of the statistical model Updated content, examples, and data sets throughout |
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