Fundamentals Of Clinical Data Science

Author : Pieter Kubben,Michel Dumontier,Andre Dekker
Publisher : Springer
Page : 219 pages
File Size : 51,7 Mb
Release : 2018-12-21
Category : Medical
ISBN : 9783319997131

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Fundamentals of Clinical Data Science by Pieter Kubben,Michel Dumontier,Andre Dekker Pdf

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.

Author : Lawrence M. Friedman,Curt Furberg,David L. DeMets
Publisher : Springer Science & Business Media
Page : 384 pages
File Size : 43,6 Mb
Release : 1998
Category : Clinical trials
ISBN : 0387985867

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Fundamentals of Clinical Trials by Lawrence M. Friedman,Curt Furberg,David L. DeMets Pdf

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.

Author : Ewen Harrison,Riinu Pius
Publisher : CRC Press
Page : 354 pages
File Size : 55,7 Mb
Release : 2020-12-31
Category : Medical
ISBN : 9781000226164

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R for Health Data Science by Ewen Harrison,Riinu Pius Pdf

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.

Author : S. Fernandez
Publisher : Createspace Independent Publishing Platform
Page : 0 pages
File Size : 42,6 Mb
Release : 2015-08-08
Category : Clinical trials
ISBN : 1516986571

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The Fundamentals of Clinical Data Management by S. Fernandez Pdf

"The Fundamentals of Clinical Data Management" is a manual for Sponsors, CROs, Investigators, Clinical Trial Monitors and Managers and Clinical Research Professionals to learn the basic concepts of Clinical Data Management. This book will focus on the topic which includes: Clinical Information Flow, Roles and Responsibilities of CDM Personnel, Guidelines Associated with CDM, Data Management Plan, CRF Designing, Data Collection, Cleaning and Data Validation, Study setup and Database Designing, Laboratory Data and Adverse Event Data Management, Report Creation and Data Closure, Data Archiving, Privacy and Security etc.

Author : Andre Dekker,Michel Dumontier,Pieter Kubben
Publisher : Unknown
Page : 218 pages
File Size : 41,8 Mb
Release : 2020-10-09
Category : Computers
ISBN : 101327590X

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Fundamentals of Clinical Data Science by Andre Dekker,Michel Dumontier,Pieter Kubben Pdf

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. 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.

Author : Avrim Blum,John Hopcroft,Ravindran Kannan
Publisher : Cambridge University Press
Page : 433 pages
File Size : 43,6 Mb
Release : 2020-01-23
Category : Computers
ISBN : 9781108485067

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Foundations of Data Science by Avrim Blum,John Hopcroft,Ravindran Kannan Pdf

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Author : Anonim
Publisher : Unknown
Page : 219 pages
File Size : 55,9 Mb
Release : 2019
Category : Electronic
ISBN : 3319997149

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Fundamentals of Clinical Data Science by Anonim Pdf

Author : Leslie Gross Portney,Mary P. Watkins
Publisher : Unknown
Page : 0 pages
File Size : 46,9 Mb
Release : 2015
Category : Biomedical Research
ISBN : 0803646577

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Foundations of Clinical Research by Leslie Gross Portney,Mary P. Watkins Pdf

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.

Author : Stephen P. Glasser
Publisher : Springer
Page : 461 pages
File Size : 43,7 Mb
Release : 2014-06-02
Category : Medical
ISBN : 9783319054704

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Essentials of Clinical Research by Stephen P. Glasser Pdf

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.

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 42,6 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Author : John I. Gallin,Frederick P Ognibene
Publisher : Elsevier
Page : 448 pages
File Size : 42,9 Mb
Release : 2011-04-28
Category : Medical
ISBN : 0080489567

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Principles and Practice of Clinical Research by John I. Gallin,Frederick P Ognibene Pdf

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

Author : Thomas D. Cook,David L DeMets
Publisher : CRC Press
Page : 465 pages
File Size : 47,9 Mb
Release : 2007-11-19
Category : Mathematics
ISBN : 9781584880271

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Introduction to Statistical Methods for Clinical Trials by Thomas D. Cook,David L DeMets Pdf

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.

Author : Christo El Morr,Hossam Ali-Hassan
Publisher : Springer
Page : 105 pages
File Size : 43,6 Mb
Release : 2019-01-21
Category : Medical
ISBN : 9783030045067

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Analytics in Healthcare by Christo El Morr,Hossam Ali-Hassan Pdf

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.

Author : David Robertson,Gordon H. Williams
Publisher : Academic Press
Page : 812 pages
File Size : 48,7 Mb
Release : 2016-11-25
Category : Science
ISBN : 9780128021118

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Clinical and Translational Science by David Robertson,Gordon H. Williams Pdf

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

Author : Bikesh Kumar Singh,G.R. Sinha
Publisher : CRC Press
Page : 248 pages
File Size : 53,6 Mb
Release : 2022-02-17
Category : Computers
ISBN : 9781000540406

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Machine Learning in Healthcare by Bikesh Kumar Singh,G.R. Sinha Pdf

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.