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 constitutes thoroughly refereed revised selected papers from the BPM 2012 Joint Workshop on Process-Oriented Information Systems and Knowledge Representation in Health Care, ProHealth 2012/KR4HC 2012, held in Tallinn, Estonia, in September 2012. The 9 papers presented were carefully reviewed and selected from 19 submissions. In addition the book contains 1 keynote paper and 2 invited contributions. The papers are organized in topical sections named: guidelines and summarization; archetypes and cooperation; and process mining and temporal analysis.
Computational intelligence techniques are gaining momentum in the medical prognosis and diagnosis. This volume presents advanced applications of machine intelligence in medicine and bio-medical engineering. Applied methods include knowledge bases, expert systems, neural networks, neuro-fuzzy systems, evolvable systems, wavelet transforms, and specific internet applications. The volume is written in view of explaining to the practitioner the fundamental issues related to computational intelligence paradigms and to offer a fast and friendly-managed introduction to the most recent methods based on computer intelligence in medicine.
AI planning is a broad research topic, linked with such issues as robotics, control theory, operations research and learning. The purpose of EWSP '93 was twofold. Planning under certainty, or classical search-based planning is one direction in the submitted papers, with approaches ranging from the introduction of conditional actions to methods based on statistics and decision theory.
Intelligent data analysis, data mining and knowledge discovery in databases have recently gained the attention of a large number of researchers and practitioners. This is witnessed by the rapidly increasing number of submissions and participants at related conferences and workshops, by the emergence of new journals in this area (e.g., Data Mining and Knowledge Discovery, Intelligent Data Analysis, etc.), and by the increasing number of new applications in this field. In our view, the awareness of these challenging research fields and emerging technologies has been much larger in industry than in medicine and pharmacology. The main purpose of this book is to present the various techniques and methods that are available for intelligent data analysis in medicine and pharmacology, and to present case studies of their application. Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96), IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of the book is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.
It is a pleasure for us to present the contributions of the First International Symposium on Medical Data Analysis. Traditionally, the eld of medical data analysis can be devided into classical topics such as medical statistics, sur- val analysis, biometrics and medical informatics. Recently, however, time series analysis by physicists, machine learning and data mining with methods such as neural networks, Bayes networks or fuzzy computing by computer scientists have contributed important ideas to the led of medical data analysis. Although all these groups have similar intentions, there was nearly no exchange or discussion between them. With the growing possibilities for storing and ana- zing patient data, even in smaller health care institutions, the need for a rational treatment of all these data emerged as well. Therefore, the need for data exchange and presentation systems grew also. The goal of the symposium is to collect all these relevant aspects together. It provides an international forum for the sharing and exchange of original re- arch results, ideas and practical experiences among researchers and application developers from di erent areas related to medical applications dealing with the analysis of medical data. After a thorough reviewing process, 33 high quality papers were selected from the 45 international submissions. These contributions provided the di erent - pects of the eld in order to represent us with an exciting program.
The Handbook of Applied Expert Systems is a landmark work dedicated solely to this rapidly advancing area of study. Edited by Jay Liebowitz, a professor, author, and consultant known around the world for his work in the field, this authoritative source covers the latest expert system technologies, applications, methodologies, and practices. The book features contributions from more than 40 of the world's foremost expert systems authorities in industry, government, and academia. The Handbook is organized into two major sections. The first section explains expert systems technologies while the second section focuses on applied examples in a wide variety of industries. Key topics covered include fuzzy systems, genetic algorithm development, machine learning, knowledge representation, and much more.
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001. The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and neural net based learning; learning for handwriting recognition; statistical and evolutionary learning; and content-based image retrieval.
Clinical Infomation Systems are increasingly important in Medical Practice. This work is a two-part book detailing the importance, selection and implementation of information systems in the health care setting. Volume One discusses the technical, organizational, clinical and administrative issues pertaining to EMR implementation. Highlighted topics include: infrastructure of the electronic patient records for administrators and clinicians, understanding processes and outcomes, and preparing for an EMR. The second workbook is filled with sample charts and questions, guiding the reader through the actual EMR implementation process.
