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Interest in medical expert systems, neural networks and other artificial intelligence techniques is on the increase as more healthcare providers realise their potential, and engineers and scientists are discovering that medicine and healthcare are very fertile areas for developing new, or applying existing, intelligent algorithms to real problems. Intelligent systems make it possible to capture expert medical knowledge and to discover new knowledge so as to improve in-patient monitoring, data analysis and decision making, and hence the quality of healthcare.This book contains features which include: neural networks and expert systems techniques, as well as medical neural networks and expert systems. It should be of interest to managers, academics, engineers, scientists and medical practitioners involved in the funding, development and use of intelligent medical systems.
A growing, aging population; the rise to epidemic proportions of various chronic diseases; competing, often overlapping medical technologies; and of course, skyrocketing costs compounded by waste and inefficiency - these are just a few of the multifarious challenges currently facing healthcare delivery. An unexpected source of solutions is being imported from the manufacturing sector: lean thinking. Lean Principles for Healthcare presents a conceptual framework, management principles, and practical tools for professionals tasked with designing and implementing modern, streamlined healthcare systems or overhauling faulty ones. Focusing on core components such as knowledge management, e-health, patient-centeredness, and collaborative care, chapters illustrate lean concepts in action across specialties (as diverse as nursing, urology, and emergency care) and around the globe. Extended case examples show health systems responding to consumer needs and provider realities with equal efficiency and effectiveness, and improved quality and patient outcomes. Further, contributors tackle the gamut of technological, medical, cultural, and business issues, among them: Initiatives of service-oriented architecture towards performance improvement Adapted lean thinking for emergency departments Lean thinking in dementia care through smart assistive technology Supporting preventive healthcare with persuasive services Value stream mapping for lean healthcare A technology mediated solution to reduce healthcare disparities Geared toward both how lean ideas can be carried out and how they are being used successfully in the real world, Lean Principles for Healthcare not only brings expert knowledge to healthcare managers and health services researchers but to all who have an interest in superior healthcare delivery.
"This book covers state-of-the-art applications in many areas of medicine and healthcare"--Provided by publisher.
This book features high-quality research papers presented at the 3rd International Conference on Sustainable Expert Systems (ICSES 2022), held in Nepal during September 9–10, 2022. The book focuses on the research information related to artificial intelligence, sustainability and expert systems applied in almost all the areas of industries, government sectors and educational institutions worldwide. The main thrust of the book is to publish the conference papers that deal with the design, implementation, development, testing and management of intelligent and sustainable expert systems and also to provide both theoretical and practical guidelines for the deployment of these systems.
In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.
MIE 2002 is the XVIIth international conference of the European Federation of Medical Informatics. Today, mankind builds up the information society, enabled by the underlying rapid development in computer technology. The significance of the spread of the internet is comparable to the significance of Gutenberg's invention. On one hand it both helps dissemination of data and knowledge and sharing of ideas. On the other hand the achievements may divide the society, as did non-literacy deprive many people from knowledge throughout centuries. Today millions of people are isolated from an incredibly large amount of information because of "computer non-literacy," and a new elite mastering the information society has appeared. However, the ease of production and dissemination of information may foster thoughtless communication, and has lead to a flood of information and disinformation. We have to learn how to behave in this new situation, in which the dissemination of information - at an international level - is totally uncontrolled. In the area of medical or health informatics these questions are more serious. Lack of information, false or inadequate information, as well as improper interpretation of accurate information may seriously harm patients. And the process may go out of control of the physician, i.e. patients can "treat" themselves just by visiting some health sites on the net. Everybody may throw a message in a bottle in information flood, and everybody may pick up messages at any time. Can we do anything to ensure that all messages are valid? Can we guarantee that our messages reach the intended audience? Can we secure that content has not changed on its way? Do we know that people getting our messages will interpret them correctly? Are we able to understand the intention of a sender, when we get a message totally out of context? These questions build up the framework of MIE2002.
In the last two decades the artificial neural networks have been refined and widely used by the researchers and application engineers. We have not witnessed such a large degree of evolution in any other artificial neural network as in the Adaptive Resonance Theory (ART) neural network. The ART network remains plastic, or adaptive, in response to significant events and yet remains stable in response to irrelevant events. This stability-plasticity property is a great step towards realizing intelligent machines capable of autonomous learning in real time environment. The main aim of this book is to report a very small sample of the research on the evolution of ART neural network and its applications. Interested readers may refer literature for many more innovations in ART such as Fuzzy ART, ART2, ART2-a, ARTMAP, ARTMAP-PI, ARTMAP-DS, Gaussian ARTMAP, EXACT ART, and ART-EMAP.
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.
"This book provides theoretical frameworks and the latest empirical research findings used by medical professionals in the implementation of multi-agent systems"--Provided by publisher.