Temporal Information Systems in Medicine introduces the engineering of information systems for medically-related problems and applications. The chapters are organized into four parts; fundamentals, temporal reasoning & maintenance in medicine, time in clinical tasks, and the display of time-oriented clinical information. The chapters are self-contained with pointers to other relevant chapters or sections in this book when necessary. Time is of central importance and is a key component of the engineering process for information systems. This book is designed as a secondary text or reference book for upper -undergraduate level students and graduate level students concentrating on computer science, biomedicine and engineering. Industry professionals and researchers working in health care management, information systems in medicine, medical informatics, database management and AI will also find this book a valuable asset.

Temporal Information Systems in Medicine introduces the engineering of information systems for medically-related problems and applications. The chapters are organized into four parts; fundamentals, temporal reasoning & maintenance in medicine, time in clinical tasks, and the display of time-oriented clinical information. The chapters are self-contained with pointers to other relevant chapters or sections in this book when necessary. Time is of central importance and is a key component of the engineering process for information systems. This book is designed as a secondary text or reference book for upper -undergraduate level students and graduate level students concentrating on computer science, biomedicine and engineering. Industry professionals and researchers working in health care management, information systems in medicine, medical informatics, database management and AI will also find this book a valuable asset.

The European Society for Arti?cial Intelligence in Medicine (AIME) was est- lishedin1986withtwomaingoals:1)tofosterfundamentalandappliedresearch in the application of Arti?cial Intelligence (AI) techniques to medical care and medical research, and 2) to providea forum at biennial conferences for reporting signi?cant results achieved. Additionally, AIME assists medical industrialists to identify newAItechniqueswithhighpotentialforintegrationintonewproducts. Amajoractivityofthissocietyhasbeenaseriesofinternationalconferencesheld biennially over the last 18 years: Marseilles, France (1987), London, UK (1989), Maastricht, Netherlands (1991), Munich, Germany (1993), Pavia, Italy (1995), Grenoble, France (1997), Aalborg, Denmark (1999), Cascais, Portugal (2001), Protaras, Cyprus (2003). The AIME conference provides a unique opportunity to present and improve the international state of the art of AI in medicine from both a research and an applications perspective. For this purpose, the AIME conference includes invited lectures, contributed papers, system demonstrations, a doctoral cons- tium, tutorials, and workshops. The present volume contains the proceedings of AIME 2005, the 10th conference on Arti?cial Intelligence in Medicine, held in Aberdeen, Scotland, July 23-27, 2005. In the AIME 2005 conference announcement, we encouraged authors to s- mit original contributions to the development of theory, techniques, and - plications of AI in medicine, including the evaluation of health care programs. Theoretical papers were to include presentation or analysis of the properties of novelAImethodologiespotentiallyusefultosolvingmedicalproblems.Technical papers were to describe the novelty of the proposed approach, its assumptions, bene?ts, and limitations compared with other alternative techniques. Appli- tion papers were to present su?cient information to allow the evaluation of the practical bene?ts of the proposed system or methodology

The European Society for Arti?cial Intelligence in Medicine (AIME) was - tablished in 1986 with two main goals: 1) to foster fundamental and applied research in the application of Arti?cial Intelligence (AI) techniques to medical care and medical research, and 2) to provide a forum for reporting signi?cant results achieved at biennial conferences. Additionally, AIME assists medical - dustrials to identify new AI techniques with high potential for integration into new products. A major activity of this society has been a series of international conferences, fromMarseille(FR)in1987toCascais(PT)in2001, heldbiennially over the last 16 years. The AIME conference provides a unique opportunity to present and improve the international state of the art of AI in medicine from both a research and an applicationsperspective.Forthispurpose, theAIMEconferenceincludesinvited lectures, contributed papers, system demonstrations, tutorials and workshops. The present volume contains the proceedings of the AIME 2003 conference, the ninthconferenceonArti?cialIntelligenceinMedicineinEurope, heldinCyprus, October 18-22, 2003. In the AIME 2003 conference announcement, we encouraged authors to s- mit original contributions to the development of theory, techniques, and - plications of AI in medicine, including the evaluation of health care programs. Theoretical papers should include a prospective part about possible applications to medical problems solving. Technical papers should describe the novelty of the proposed approach, its assumptions and pros and cons compared to other alt- native techniques. Application papers should present su?cient information to allow the evaluation of the practical bene?ts of the proposed system or meth- ol

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.

This book constitutes the refereed proceedings of the 6th Conference on Artificial Intelligence in Medicine Europe, AIME'97, held in Grenoble, France, in March 1997.
The volume presents 33 revised full papers selected from a total of 82 submissions; also included are three invited presentations and 25 posters. The papers are organized in topical sections on protocols and guidelines, knowledge acquisition and learning, decision-support theories, diagnostic problem solving, probabilistic models and fuzzy logic, temporal reasoning and planning, natural language and terminology, image and signal processing, and hybrid and cooperative systems.

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.