Computer technology has brought about incredible changes in medicine and healthcare, greatly improving the efficiency and accuracy of medical treatment. Since December 2019, in the face of the global effects of COVID-19, the significance of computer technology, and big data in particular, together with the collaborative network and unmanned technology, has been recognized by healthcare staff everywhere. Modern medical science cannot evolve without the involvement of computer science.This book presents the proceedings of the 2021 Workshop on Computer Methods in Medicine & Health Care (CMMHC 2021), the autumn edition of the TDI conferences, held as a virtual, online event on 24 - 26 September 2021. Researchers from renowned universities, laboratories and hospitals in China, Italy and Japan contributed to the workshop, and findings from both basic and clinical medicine are included in the 14 papers collected here. Big data technology appeared in 20% of all papers as the most popular topic, with one paper covering big data optimization and two describing its application. The book shares practical experiences and enlightening ideas from computer-based medicine and will be of interest to researchers in and practitioners of modern medicine everywhere.

Despite a strong commitment to delivering quality health care, persistent problems involving medical errors and ineffective treatment continue to plague the industry. Many of these problems are the consequence of poor information and technology (IT) capabilities, and most importantly, the lack cognitive IT support. Clinicians spend a great deal of time sifting through large amounts of raw data, when, ideally, IT systems would place raw data into context with current medical knowledge to provide clinicians with computer models that depict the health status of the patient. Computational Technology for Effective Health Care advocates re-balancing the portfolio of investments in health care IT to place a greater emphasis on providing cognitive support for health care providers, patients, and family caregivers; observing proven principles for success in designing and implementing IT; and accelerating research related to health care in the computer and social sciences and in health/biomedical informatics. Health care professionals, patient safety advocates, as well as IT specialists and engineers, will find this book a useful tool in preparation for crossing the health care IT chasm.

Computer technology has brought about incredible changes in medicine and healthcare, greatly improving the efficiency and accuracy of medical treatment. Since December 2019, in the face of the global effects of COVID-19, the significance of computer technology, and big data in particular, together with the collaborative network and unmanned technology, has been recognized by healthcare staff everywhere. Modern medical science cannot evolve without the involvement of computer science. This book presents the proceedings of the 2021 Workshop on Computer Methods in Medicine & Health Care (CMMHC 2021), the autumn edition of the TDI conferences, held as a virtual, online event on 24 – 26 September 2021. Researchers from renowned universities, laboratories and hospitals in China, Italy and Japan contributed to the workshop, and findings from both basic and clinical medicine are included in the 14 papers collected here. Big data technology appeared in 20% of all papers as the most popular topic, with one paper covering big data optimization and two describing its application. The book shares practical experiences and enlightening ideas from computer-based medicine and will be of interest to researchers in and practitioners of modern medicine everywhere.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

The last decade has seen great progress in modern healthcare; progress which could not have been achieved without the developments in computer science and technology. Image processing, disease modeling, biosensors and bioprinting are just some of the innovations which have contributed to improving the accuracy and efficiency of diagnosis and the more effective treatment of patients. This book presents the accepted papers from CMMHC2022, the Workshop on Computer Methods in Medicine & Health Care, hosted from Hainan, China and held as an online event from 22 to 25 September 2022. CMMHC is aimed at fostering high-quality research by bringing together scholars, doctors, engineers, and radiologists to discuss emerging ideas, approaches, theories, frameworks, and practices in preventive healthcare technology. The organizers received 40 submissions for the 2022 workshop. These were subjected to a thorough peer-review process, with each paper being reviewed by at least 2 members of the Technical Committee. From the original submissions, 15 were selected for presentation and publication, resulting in a final acceptance rate of less than 40%. The main focus of the papers is hospital informatisation, medical imaging and health management, which continue to be major research hotspots. The book offers an overview of recent research and developments in the field of computer methods in medicine and healthcare, and will provide a useful reference to the direction of future developments for those researchers and practitioners facing the challenges and demands of the era of big data.

The practice of modern medicine and biomedical research requires sophisticated information technologies with which to manage patient information, plan diagnostic procedures, interpret laboratory results, and carry out investigations. Biomedical Informatics provides both a conceptual framework and a practical inspiration for this swiftly emerging scientific discipline at the intersection of computer science, decision science, information science, cognitive science, and biomedicine. Now revised and in its third edition, this text meets the growing demand by practitioners, researchers, and students for a comprehensive introduction to key topics in the field. Authored by leaders in medical informatics and extensively tested in their courses, the chapters in this volume constitute an effective textbook for students of medical informatics and its areas of application. The book is also a useful reference work for individual readers needing to understand the role that computers can play in the provision of clinical services and the pursuit of biological questions. The volume is organized so as first to explain basic concepts and then to illustrate them with specific systems and technologies.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research. - Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications - Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. - Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.

In today’s modernized world, the field of healthcare has seen significant practical innovations with the implementation of computational intelligence approaches and soft computing methods. These two concepts present various solutions to complex scientific problems and imperfect data issues. This has made both very popular in the medical profession. There are still various areas to be studied and improved by these two schemes as healthcare practices continue to develop. Computational Intelligence and Soft Computing Applications in Healthcare Management Science is an essential reference source that discusses the implementation of soft computing techniques and computational methods in the various components of healthcare, telemedicine, and public health. Featuring research on topics such as analytical modeling, neural networks, and fuzzy logic, this book is ideally designed for software engineers, information scientists, medical professionals, researchers, developers, educators, academicians, and students.

Provides a collection of medical IT research in topics such as clinical knowledge management, medical informatics, mobile health and service delivery, and gene expression.