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 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.

"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.

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.

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.

The aim of medical research has always been to gain scientific knowledge which will serve to improve the diagnosis, therapy and prevention of diseases. It is also becoming increasingly important to take account of the changing circumstances of medical care. Factors such as the ageing of society and the recent pandemic have not only led to greater use of medical care, but have also put the human resources and infrastructural basis of the health system under great pressure. Such developments call for science-based solutions which can better adapt medical action to the needs of patients to ensure that medicine remains affordable and accessible for all. This book is the 6th volume of the German Medical Data Science series in Studies in Health Technology and Informatics and presents the proceedings of the joint conference of the 67th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology (GMDS) and the 14th Annual Meeting of the Technology and Methods Platform for Networked Medical Research (TMF). The conference was entitled Medicine in Transition - More Precise, More Integrative, More Sustainable. It was due to be held from 21-25 August 2022 in Kiel, Germany, but was changed to an online event on the same dates due to an increasing surge in cases of coronavirus. The pandemic has not only disrupted the planning of many events, it has also impressively demonstrated the importance of technical and methodological aspects of digitization. The 13 papers included here address the challenges of and opportunities for the digitization so vital for the functionality of the modern healthcare system, and the book will be of interest to all those involved in the planning and delivery of healthcare.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.