Secondary Analysis of Electronic Health Records


Book Description

This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.




Machine Learning for Health Informatics


Book Description

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.







Expert Clouds and Applications


Book Description

The book features original papers from International Conference on Expert Clouds and Applications (ICOECA 2023), organized by RV Institute of Technology and Management, Bangalore, India, during February 9–10, 2023. It covers new research insights on artificial intelligence, big data, cloud computing, sustainability, and knowledge-based expert systems. The book discusses innovative research from all aspects including theoretical, practical, and experimental domains that pertain to the expert systems, sustainable clouds, and artificial intelligence technologies. The thrust of the book is to showcase different research chapters dealing with the design, development, implementation, testing and analysis of intelligent systems, and expert clouds, and also to provide empirical and practical guidelines for the development of such systems.







HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies


Book Description

This book constitutes late breaking papers from the 22nd International Conference on Human-Computer Interaction, HCII 2020, which was held in July 2020. The conference was planned to take place in Copenhagen, Denmark, but had to change to a virtual conference mode due to the COVID-19 pandemic. From a total of 6326 submissions, a total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings before the conference took place. In addition, a total of 333 papers and 144 posters are included in the volumes of the proceedings published after the conference as “Late Breaking Work” (papers and posters). These contributions address the latest research and development efforts in the field and highlight the human aspects of design and use of computing systems. The 54 late breaking papers presented in this volume were organized in two topical sections named: User Experience Design and Evaluation Methods and Tools; Design Case Studies; User Experience Case Studies.




Data Visualization Tools for Business Applications


Book Description

In today’s data-driven business landscape, the ability to extract insights and communicate complex information effectively is paramount. Data visualization has emerged as a powerful tool for businesses to make informed decisions, uncover patterns, and present findings in a compelling manner. From executives seeking strategic insights to analysts delving into operational data, the demand for intuitive and informative visualizations spans across all levels of an organization. Data Visualization Tools for Business Applications comprehensively equips professionals with the knowledge and skills necessary to leverage data visualization tools effectively. Through a blend of theory and hands-on case studies, this book explores a wide range of data visualization tools, techniques, and methodologies. Covering topics such as business analytics, cyber security, and financial reporting, this book is an essential resource for business executives and leaders, marketing professionals, data scientists, entrepreneurs, academicians, educators, students, decision-makers and stakeholders, and more.




New Perspectives in Medical Records


Book Description

This book provides innovative practical suggestions regarding the production and management of medical records that are designed to address the inconsistencies and errors that have been highlighted especially in relation to national eHealth programs. Challenges and lessons that have emerged from the use of clinical information and the design of medical records are discussed, and principles underpinning the implementation of health IT are critically examined. New trends in the use of clinical data are explored in depth, with analysis of issues relating to integration and sharing of patient information, data visualization, big data analytics, and the requirements of modern electronic health records. The spirit pervading the book is one of co-production, in which the needs of practitioners are taken into account from the outset. Readers will learn the basic concepts of how clinical information emanating from the doctor–patient relationship can be effectively integrated with genetic and environmental data and analyzed by complex algorithms with the goal of improving medical decision making and patient care. The book, written by European experts and researchers, will be of interest to all stakeholders in the field, including doctors, technicians, and policy makers.




Data-Driven Approach for Bio-medical and Healthcare


Book Description

The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.




Registries for Evaluating Patient Outcomes


Book Description

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.