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話題の人工知能の粋を集めた学会誌 話題の人工知能の粋を集めた学会誌人工知能に関する専門家からのさまざまな研究結果、レポートを載せ、この分野における最新の情報を掲載しています。 ★このような方におすすめ 人工知能に関心のある一般の方、人工知能の研究者 ●主要目次● 巻頭言 特集：「超高齢化社会とAI─社会生活支援編─」 特集：「超高齢化社会とAI─健康増進支援編─」 特集：「国内人工知能研究拠点紹介」〔第1回〕 小特集：「ミンスキー博士を偲んで」 アーティクル レクチャーシリーズ：「つながりが創発するイノベーション」〔第7回〕 SFショート・ショート〔第21回〕 私のブックマーク 会議報告 書評

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks.General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com.From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013', describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.For this reason we produced a 100 page cookbook, entitled'Machine Learning in Medicine - Cookbook One', with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled'Machine Learning in Medicine - Cookbook Two'is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care.Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machinelearning methods, that are, likewise, based on the three major machine learning methodologies:Cluster methodologies (Chaps. 1-3)Linear methodologies (Chaps. 4-11)Rules methodologies (Chaps. 12-20)In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS'data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8.We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing'general purposes','main scientific questions'and'conclusions'are given in place.Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

Advanced conceptual modeling techniques serve as a powerful tool for those in the medical field by increasing the accuracy and efficiency of the diagnostic process. The application of artificial intelligence assists medical professionals to analyze and comprehend a broad range of medical data, thus eliminating the potential for human error. Medical Diagnosis Using Artificial Neural Networks introduces effective parameters for improving the performance and application of machine learning and pattern recognition techniques to facilitate medical processes. This book is an essential reference work for academicians, professionals, researchers, and students interested in the relationship between artificial intelligence and medical science through the use of informatics to improve the quality of medical care.

Computational Intelligence in Biomedical Imaging is a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients'medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians'decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.

Enormous advances in information technology have permeated essentially all facets of life in the past two decades. Formidable challenges remain in fostering tools that enhance productivity but are sensitive to work practices. Cognitive Informatics (CI) is the multidisciplinary study of cognition, information and computational sciences that investigates all facets of human computing including design and computer-mediated intelligent action, thus is strongly grounded in methods and theories from cognitive science. As an applied discipline, it has a close affiliation with human factors and human-computer interaction, and provides a framework for the analysis and modeling of complex human performance in technology-mediated settings and contributes to the design and development of better information systems. In recent years, CI has emerged as a distinct area with special relevance to biomedicine and health care. In addition, it has become a foundation for education and training of health informaticians, the Office of the National Coordinator for Health Information Technology initiating a program including CI as one of its critical elements to support health IT curriculum development. This book represents a first textbook on cognitive informatics and will focus on key examples drawn from the application of methods and theories from CI to challenges pertaining to the practice of critical-care medicine (CCM). Technology is transforming critical care workflows and re-organizing patient care management processes. CCM has proven to be a fertile test bed for theories and methods of cognitive informatics. CI, in turn, has contributed much to our understanding of the factors that result in complexity and patient errors. The topic is strongly interdisciplinary and will be important for individuals from a range of academic and professional backgrounds, including critical care specialists, psychologists, computer scientists, medical informaticians, and anthropologists.​​

Computer technology has impacted the practice of medicine in dramatic ways. Imaging techniques provide noninvasive tools which alter the di­ agnostic process. Sophisticated monitoring equipment presents new levels of detail for both patient management and research. In most of these tech­ nology applications, the computer is embedded in the device; its presence is transparent to the user. There is also a growing number of applications in which the health care provider directly interacts with a computer. In many cases, these appli­ cations are limited to administrative functions, e.g., office practice man­ agement, location of hospital patients, appointments, and scheduling. Nevertheless, there also are instances of patient care functions such as results reporting, decision support, surveillance, and reminders. This series, Computers and Medicine, will focus upon the direct use of information systems as it relates to the medical community. After twenty-five years of experimentation and experience, there are many tested applications which can be implemented economically using the current generation of computers. Moreover, the falling cost of computers suggests that there will be even more extensive use in the near future. Yet there is a gap between current practice and the state-of-the-art.

A detailed description of a new approach to perceptual analysis and processing of medical images is given. Instead of traditional pattern recognition a new method of image analysis is presented, based on a syntactic description of the shapes selected on the image and graph-grammar parsing algorithms. This method of'Image Understanding'can be found as a model of mans'cognitive image understanding processes. The usefulness for the automatic understanding of the merit of medical images is demonstrated as well as the ability for giving useful diagnostic descriptions of the illnesses. As an application, the production of a content-based, automatically generated index for arranging and for searching medical images in multimedia medical databases is presented.

Advances in research have led to the use of robotics in a range of surgical applications. Medical robotics: Minimally invasive surgery provides authoritative coverage of the core principles, applications and future potential of this enabling technology.Beginning with an introduction to robot-assisted minimally invasive surgery (MIS), the core technologies of the field are discussed, including localization and tracking technologies for medical robotics. Key applications of robotics in laparoscopy, neurology, cardiovascular interventions, urology and orthopaedics are considered, as well as applications for ear, nose and throat (ENT) surgery, vitreoretinal surgery and natural orifice transluminal endoscopic surgery (NOTES). Microscale mobile robots for the circulatory system and mesoscale robots for the gastrointestinal tract are investigated, as is MRI-based navigation for in vivo magnetic microrobots. Finally, the book concludes with a discussion of ethical issues related to the use of robotics in surgery.With its distinguished editor and international team of expert contributors, Medical robotics: Minimally invasive surgery is a comprehensive guide for all those working in the research, design, development and application of medical robotics for surgery. It also provides an authoritative introduction for academics and medical practitioners working in this field. - Provides authoritative coverage of the core principles, applications and future potential of medical robotics - Introduces robot-assisted minimally invasive surgery (MIS), including the core technologies of the field and localization and tracking technologies for medical robotics - Considers key applications of robotics in laparoscopy, neurology, cardiovascular interventions, urology and orthopaedics

'This book explores the potential of utilizing medical data through the implementation of developed models in practical applications'--Provided by publisher.

This book introduces zero-effort technologies (ZETs), an emerging class of technology that requires little or no effort from the people who use it. ZETs use advanced techniques, such as computer vision, sensor fusion, decision-making and planning, and machine learning to autonomously operate through the collection, analysis, and application of data about the user and his/her context. This book gives an overview of ZETs, presents concepts in the development of pervasive intelligent technologies and environments for health and rehabilitation, along with an in-depth discussion of the design principles that this approach entails. The book concludes with a discussion of specific ZETs that have applied these design principles with the goal of ensuring the safety and well-being of the people who use them, such as older adults with dementia and provides thoughts regarding future directions of the field. Table of Contents: Lecture Overview / Introduction to Zero Effort Technologies / Designing ZETs / Building and Evaluating ZETs / Examples of ZETs / Conclusions and Future Directions

This book constitutes the refereed proceedings of the 13th Conference on Artificial Intelligence in Medicine, AIME 2011, held in Bled, Slovenia, in July 2011. The 42 revised full and short papers presented together with 2 invited talks were carefully reviewed and selected from 113 submissions. The papers are organized in topical sections on knowledge-based systems; data mining; special session on AI applications; probabilistic modeling and reasoning; terminologies and ontologies; temporal reasoning and temporal data mining; therapy planning, scheduling and guideline-based care; and natural language processing.

'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

L'informatique envahit notre vie quotidienne, contribue à son accélération, et modifie les relations interhumaines. Cet ouvrage montre qu'une rencontre fructueuse est possible entre la clinique psychiatrique et l'informatique. Ces apports qui dépassent les seuls aspects techniques, permettent de mieux préciser les rapports de l'esprit et de ses automatismes, les risques encourus en se soumettant aveuglément à ces derniers, et les moyens d'y remédier.

'This book presents recent advancements in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, it will be a highly valued addition to libraries in the neuroscience, molecular biology, and behavioral science spheres'--Provided by publisher.

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-

Multi-agent systems are one of the most exciting research areas in Artificial Intelligence. In the last ten years there has been a growing interest in the application of agent-based systems in health care. Moreover, a growing European community of researchers interested in the application of intelligent agents in health care emerged as a result of the activities within the AgentCities.NET European network and the AgentLink III Technical Forum Group on Healthcare Applications of Intelligent Agents. This book reports on the results achieved in this area, discusses the benefits (and drawbacks) that agent-based systems may bring to medical domains and society, and also provides a list of the research topics that should be tackled in the near future to make the deployment of health-care agent-based systems a reality.

This book presents some of the most recent research results on the applications of computational intelligence in healthcare. The contents include: Information model for management of clinical content State-based model for management of type II diabetes Case-based reasoning in medicine Assessing the quality of care in artificial intelligence environment Electronic medical record to examine physician decisions Multi-agent systems for the management of community healthcare Assistive wheelchair navigation Modelling treatment processes using information extraction Neonatal pain detection using face classification techniques Medical education interfaces using virtual patients The book is directed to the computer scientists, medical practitioners, scientists, professors and students of health science, computer science and related disciplines.

Intelligent systems try to achieve, through the use of computers, what we associate with intelligence - flexible, learning and adaptive activity like we find in the human brain. For the first time, this groundbreaking resource provides a detailed understanding of the analysis, design, and application of new intelligent systems in the biomedical industry. The book covers the three major areas of application in biomedicine, including the modeling and control in human anaesthesia, decision support for critically ill patients in intensive care units, and modeling of humans who are subjected to physiological stress. The culmination of more than 18 years of research, this cutting-edge reference offers practical modeling and control guidance by presenting a combination of simulations, real-time experiments, and actual patient data.

The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primaril

The main aim of this book is to present a sample of recent research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages — such as adaptation, fault tolerance, learning and human-like behavior — over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis.This book is the first of its kind on the topic of artificial intelligence in breast cancer. It presents the applications of artificial intelligence in breast cancer diagnosis and prognosis, and includes state-of-the-art concepts in the field. It contains contributions from Australia, Germany, Italy, UK and the USA.

Ce livre établit les fondements de la vie artificielle puis dresse un état de la recherche actuelle. Les aspects théoriques, philosophiques et pratiques sont abordés. Des automates auto-reproducteurs aux algorithmes génétiques, l'ouvrage permet de mieux appréhender les développements récents de la vie artificielle et les questions fondamentales qu'elle suscite : Qu'est-ce que la vie? Quelles sont les origines de la vie? La vie peut-elle exister indépendamment de la matière organique? Peut-on concevoir une machine vivante?