Ensuring patient safety and providing high-quality health services are the dominant challenges faced by healthcare systems around the world today. The sharing of advanced knowledge and best practice in diagnosis, therapy, process optimization and prevention are essential to achieve this goal; this includes enhanced networking socially and technologically as well as the inclusion of public health and social sciences. This book contains the proceedings of the 13th European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), held in Prague, Czech Republic, in April 2013. The EFMI STC 2013 is Europe’s leading forum for presenting the results of current scientific work in health informatics processes, systems and technologies this year. The title of this 13th conference is Data and Knowledge for Medical Decision Support, and the conference addresses this important field, linking traditional and translational medicine with natural sciences and technology with a view to the design, implementation and deployment of intelligent systems which will meet the expectations of developers and users such as health professionals and patients. Within this context, the authors included here address the important issues of knowledge representation and management, appropriate terminologies and ontologies, the development of reasoning engines, and the modeling and simulation of real systems for decision making. The hot topics of "Big Data" and "Analytics" also receive attention.

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.

Ensuring patient safety and providing high-quality health services are the dominant challenges faced by healthcare systems around the world today. The sharing of advanced knowledge and best practice in diagnosis, therapy, process optimization and prevention are essential to achieve this goal; this includes enhanced networking socially and technologically as well as the inclusion of public health and social sciences.This book contains the proceedings of the 13th European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), held in Prague, Czech Republic, in April 2013. The EFMI STC 2013 is Europe's leading forum for presenting the results of current scientific work in health informatics processes, systems and technologies this year. The title of this 13th conference is Data and Knowledge for Medical Decision Support, and the conference addresses this important field, linking traditional and translational medicine with natural sciences and technology with a view to the design, implementation and deployment of intelligent systems which will meet the expectations of developers and users such as health professionals and patients.Within this context, the authors included here address the important issues of knowledge representation and management, appropriate terminologies and ontologies, the development of reasoning engines, and the modeling and simulation of real systems for decision making. The hot topics of "Big Data" and "Analytics" also receive attention.

This book gathers selected papers presented at the 2020 World Conference on Information Systems and Technologies (WorldCIST’20), held in Budva, Montenegro, from April 7 to 10, 2020. WorldCIST provides a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences with and challenges regarding various aspects of modern information systems and technologies. The main topics covered are A) Information and Knowledge Management; B) Organizational Models and Information Systems; C) Software and Systems Modeling; D) Software Systems, Architectures, Applications and Tools; E) Multimedia Systems and Applications; F) Computer Networks, Mobility and Pervasive Systems; G) Intelligent and Decision Support Systems; H) Big Data Analytics and Applications; I) Human–Computer Interaction; J) Ethics, Computers & Security; K) Health Informatics; L) Information Technologies in Education; M) Information Technologies in Radiocommunications; and N) Technologies for Biomedical Applications.

Medical informatics has revolutionized healthcare in recent years, and one of the major challenges now faced by health professionals everywhere is the further improvement of healthcare by making more effective use of the data from biomedical informatics, not least for education and decision support. This book presents the 52 full papers (accepted from 95 initial submissions) delivered at the Special Topic Conference of the European Federation for Medical Informatics (EFMI STC 2018), held in Zagreb, Croatia, on 15 and 16 October 2018. The EFMI STC is one of Europe`s leading conferences for the sharing of current professional and scientific knowledge in health informatics processes, and the topics covered here have been broadly divided into two sections; decision support and education. Offering an overview of current medical informatics research, this book will undoubtedly prove invaluable for the professional development of healthcare practitioners, as well as contributing to knowledge sustainability within the field of medical informatics.

Decision support systems (DSS) are widely touted for their effectiveness in aiding decision making, particularly across a wide and diverse range of industries including healthcare, business, and engineering applications. The concepts, principles, and theories of enhanced decision making are essential points of research as well as the exact methods, tools, and technologies being implemented in these industries. From both a standpoint of DSS interfaces, namely the design and development of these technologies, along with the implementations, including experiences and utilization of these tools, one can get a better sense of how exactly DSS has changed the face of decision making and management in multi-industry applications. Furthermore, the evaluation of the impact of these technologies is essential in moving forward in the future. The Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering explores how decision support systems have been developed and implemented across diverse industries through perspectives on the technology, the utilizations of these tools, and from a decision management standpoint. The chapters will cover not only the interfaces, implementations, and functionality of these tools, but also the overall impacts they have had on the specific industries mentioned. This book also evaluates the effectiveness along with benefits and challenges of using DSS as well as the outlook for the future. This book is ideal for decision makers, IT consultants and specialists, software developers, design professionals, academicians, policymakers, researchers, professionals, and students interested in how DSS is being used in different industries.

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Written by nationally and internationally recognised experts on the design, evaluation and application of such systems, this book examines the impact of practitioner and patient use of computer-based diagnostic tools. It serves simultaneously as a resource book on diagnostic systems for informatics specialists; a textbook for teachers or students in health or medical informatics training programs; and as a comprehensive introduction for clinicians, with or without expertise in the applications of computers in medicine, who are interested in learning about current developments in computer-based diagnostic systems. Designed for a broad range of clinicians in need of decision support.

Medical Decision Making provides clinicians with a powerful framework for helping patients make decisions that increase the likelihood that they will have the outcomes that are most consistent with their preferences. This new edition provides a thorough understanding of the key decision making infrastructure of clinical practice and explains the principles of medical decision making both for individual patients and the wider health care arena. It shows how to make the best clinical decisions based on the available evidence and how to use clinical guidelines and decision support systems in electronic medical records to shape practice guidelines and policies. Medical Decision Making is a valuable resource for all experienced and learning clinicians who wish to fully understand and apply decision modelling, enhance their practice and improve patient outcomes. “There is little doubt that in the future many clinical analyses will be based on the methods described in Medical Decision Making, and the book provides a basis for a critical appraisal of such policies.” - Jerome P. Kassirer M.D., Distinguished Professor, Tufts University School of Medicine, US and Visiting Professor, Stanford Medical School, US

This essential text provides a readable yet sophisticated overview of the basic concepts of information technologies as they apply in healthcare. Spanning areas as diverse as the electronic medical record, searching, protocols, and communications as well as the Internet, Enrico Coiera has succeeded in making this vast and complex area accessible and understandable to the non-specialist, while providing everything that students of medical informatics need to know to accompany their course.