This book constitutes refereed proceedings of the 8th China Conference on China Health Information Processing Conference 2022 held in Hangzhou, China from August 26–28, 2022. The 14 full papers presented in this volume were carefully reviewed and selected from a total of 35 submissions. The papers in the volume are organised according to the following topical headings: healthcare natural language processing;healthcare data mining and applications

This book provides an illustrative overview of some of the key methodological and technical innovations that form the cutting edge of current research in behavioral medicine. It is divided into three sections. Part I consists of six chapters describing the impact on behavioral medicine research of novel developments in diverse areas such as molecular genetics, neuroendocrine assessment, laboratory radionuclide measurement of cardiac function, and the development of electronic event monitors for measuring compliance with medication regimens. In addition, new applications of long-available assessment techniques in clinical neuropsychology to behavioral issues in cardiovascular disease are reviewed. Part II includes four chapters which review methods and programs of research dealing with aspects of the ambulatory monitoring of moods and behavioral activities in conjunction with a variety of physiological processes and/or disease states. Finally, Part III provides two chapters which focus on novel theoretical and/or conceptual approaches--instead of the typical methodological innovations--that have guided recent research in behavioral oncology and in cardiovascular disease and the clustering syndrome of cardiovascular risk factors that relate to insulin metabolism.

This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers.

Human Information Processing: An Introduction to Psychology, Second Edition, was written to reflect recent developments, as well as anticipate new directions, in this flourishing field. The ideas of human information processing are relevant to all human activities, most especially those of human interactions. The book discusses all the traditional areas and then goes beyond: consciousness, states of awareness, multiple levels of processing (and of awareness), interpersonal communication, emotion, and stress. The book begins with an introduction to some of the more interesting phenomena of perception and poses some of the puzzles faced by those who would attempt to unravel the structures. Separate chapters cover the systems of most interest for human communication: the visual system and the auditory system; the structure of the nervous system; and the systems of memory: sensory information storage, short-term memory, and long-term memory. Subsequent chapters deal with the different aspects of memory, including show how memory is used in thought, in language, and in decision making. Also examined are the neurological basis of memory and the representation of knowledge within memory.

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.

This book constitutes the papers presented at the Evaluation Track of the 8th China Conference on Health Information Processing, CHIP 2022, held in Hangzhou, China during October 21–23, 2022. The 20 full papers included in this book were carefully reviewed and selected from 20 submissions. They were organized in topical sections as follows: text mining for gene-disease association semantic; medical causal entity and relation extraction; medical decision tree extraction from unstructured text; OCR of electronic medical document; clinical diagnostic coding.

This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models. Recent years have seen a remarkable expansion of knowledge about the anatomical organization of the part of the brain known as the basal ganglia, the signal processing that occurs in these structures, and the many relations both to molecular mechanisms and to cognitive functions. This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models. Organized in four parts - fundamentals, motor functions and working memories, reward mechanisms, and cognitive and memory operations - the chapters present a unique admixture of theory, cognitive psychology, anatomy, and both cellular- and systems- level physiology written by experts in each of these areas. The editors have provided commentaries as a helpful guide to each part. Many new discoveries about the biology of the basal ganglia are summarized, and their impact on the computational role of the forebrain in the planning and control of complex motor behaviors discussed. The various findings point toward an unexpected role for the basal ganglia in the contextual analysis of the environment and in the adaptive use of this information for the planning and execution of intelligent behaviors. Parallels are explored between these findings and new connectionist approaches to difficult control problems in robotics and engineering. Contributors James L. Adams, P. Apicella, Michael Arbib, Dana H. Ballard, Andrew G. Barto, J. Brian Burns, Christopher I. Connolly, Peter F. Dominey, Richard P. Dum, John Gabrieli, M. Garcia-Munoz, Patricia S. Goldman-Rakic, Ann M. Graybiel, P. M. Groves, Mary M. Hayhoe, J. R. Hollerman, George Houghton, James C. Houk, Stephen Jackson, Minoru Kimura, A. B. Kirillov, Rolf Kotter, J. C. Linder, T. Ljungberg, M. S. Manley, M. E. Martone, J. Mirenowicz, C. D. Myre, Jeff Pelz, Nathalie Picard, R. Romo, S. F. Sawyer, E Scarnat, Wolfram Schultz, Peter L. Strick, Charles J. Wilson, Jeff Wickens, Donald J. Woodward, S. J. Young

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.

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.