Intelligent Data Analysis in Medicine and Pharmacology / Edition 1

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Overview

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 Intelligent Data Analysis in Medicine and Pharmacology 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.
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Editorial Reviews

Booknews
Compiles selected and thoroughly revised research papers presented at the August 1996 Workshop held in Budapest as part of the 12th European Conference on Artificial Intelligence, and includes additional contributions by invited authors. Presents available techniques and methods for intelligent data analysis in medicine and pharmacology along with case studies of their application, the purpose being to narrow the gap between excessive amounts of data stored in databases and the interpretation, understanding, and effective use of stored data. Primarily addressed to researchers and practitioners, but parts can be used as advanced readings for university courses in medical informatics. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

Table of Contents

Preface
Contributing Authors
1 Intelligent data analysis in medicine and pharmacology: An overview 1
2 Time-oriented analysis of high-frequency data in ICU monitoring 17
3 Context-sensitive temporal abstraction of clinical data 37
4 Temporal abstraction of medical data: Deriving periodicity 61
5 Cooperative intelligent data analysis: An application to diabetic patients management 81
6 Ptah: A system for supporting nosocomial infection therapy 99
7 Prognosing the survival time of patients with anaplastic thyroid carcinoma using machine learning 115
8 Data analysis of patients with severe head injury 131
9 Dementia screening with machine learning methods 149
10 Experiments with machine learning in the prediction of coronary artery disease progression 167
11 Noise elimination applied to early diagnosis of rheumatic diseases 187
12 Diterpene structure elucidation from [superscript 13]C NMR-spectra with machine learning 207
13 Using Inductive Logic Programming to learn rules that identify glaucomatous eyes 227
14 Carcinogenesis predictions using Inductive Logic Programming 243
15 Concept discovery by decision table decomposition and its application in neurophysiology 261
16 Classification of human brain waves using self-organizing maps 279
17 Applying a neural network to prostate cancer survival data 295
Index 307
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