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01094637 Lavra\v{c}, Nada Keravnou, Elpida T. Zupan, Bla\v{z} Intelligent data analysis in medicine and pharmacology. 1st international workshop, IDAMAP-96, Budapest, Hungary, August 1996. The Kluwer International Series in Engineering and Computer Science. 414. Dordrecht: Kluwer Academic Publishers. xxi, 310 p. Dfl 270.00; \$ 129.00; \sterling 87.75 (1997). 1997 Dordrecht: Kluwer Academic Publishers EN Budapest (Hungary) Workshop Proceedings IDAMAP-96 Intelligent data analysis Medicine Pharmacology monitoring diabetic patients antibiotics effectiveness decision trees machine learning intelligent data analysis data mining knowledge discovery abstraction of clinical data abstraction of medical data patients management [The articles of this volume will not be reviewed individually.] 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. Hence, the main purpose of this book is to increase the awareness of the various techniques and methods that are available for intelligent data analysis in medicine and pharmacology, and to present case studies of their application. The expected readership of the book are researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those that aim at 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. (From the preface) Contents: 1. {\it N. Lavra\v{c}}, {\it E. T. Keravnou} and {\it B. Zupan}, Intelligent data analysis in medicine and pharmology: An overview; 2. {\it S. Miksch}, {\it W. Horn}, {\it Ch. Popow} and {\it F. Paky}, Time-oriented analysis of high-frequency data in ICU monitoring; 3. {\it Y. Shahar}, Context-sensitive temporal abstraction of clinical data; 4. {\it E. T. Keravnou}, Temporal abstraction of medical data: Deriving periodicity; 5. {\it R. Bellazzi}, {\it C. Larizza} and {\it A. Riva}, Cooperative intelligent data analysis: An application to diabetic patients management; 6. {\it M. Bohanec}, {\it M. Rems}, {\it S. Slavec} and {\it B. Urh}, Ptah: A system for supporting nosocomial infection therapy; 7. {\it M. Kukar}, {\it N. Be\v{s}i\v{c}}, {\it I. Kononenko}, {\it M. Auersperg} and {\it M. Robnik-\v{S}ikonja}, Prognosing the survival time of patients with anaplastic thyroid carcinoma using machine learning; 8. {\it I. A. Pilih}, {\it D. Mladeni\'c}, {\it N. Lavra\v{c}} and {\it T. S. Prevec}, Data analysis of patients with severe head injury; 9. {\it W. R. Shankle}, {\it S. Mani}, {\it M. J. Pazzani} and {\it P. Smyth}, Dementia screening with machine learning methods; 10. {\it B. \v{S}ter}, {\it M. Kukar}, {\it A. Dobnikar}, {\it I. Kranjec} and {\it I. Kononenko}, Experiments with machine learning in the prediction of coronary artery disease progression; 11. {\it N. Lavra\v{c},} {\it D. Gamberger} and {\it S. D\v{z}eroski}, Noise elimination applied to early diagnosis of rheumatic diseases; 12. {\it S. D\v{z}eroski}, {\it S. Schulze-Kremer}, {\it K. R. Heidtke}, {\it K. Siems} and {\it D. Wettschereck}, Diterpene structure elucidation from ${}^{13}\text{C NMR}$-spectra with machine learning; 13. {\it F. Mizoguchi}, {\it H. Ohwada}, {\it M. Daidoji} and {\it S. Shirato}, Using inductive logic programming to learn rules that identify glaucomatous eyes; 14. {\it A. Srinivasan}, {\it R. D. King}, {\it S. H. Muggleton} and {\it M. J. Sternberg}, Carcinogenesis predictions using inductive logic programming; 15. {\it B. Zupan}, {\it J. A. Halter} and {\it M. Bohanec}, Concept discovery by decision table decomposition and its application in neurophysiology; 16. {\it U. Heuser}, {\it J. G\"oppert}, {\it W. Rosenstiel} and {\it A. Stevens}, Classification of human brain waves using self-organizing maps; 17. {\it M. W. Kattan}, {\it H. Ishida}, {\it P. T. Scardino} and {\it J. R. Beck}, Applying a neural network to prostate cancer survival data.