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Intelligent Data Analysis in Medicine and Pharmacology

  • Book
  • © 1997

Overview

Editors:
  1. Nada Lavrač

    1. J. Stefan Institute, Ljubljana, Slovenia

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  2. Elpida T. Keravnou

    1. University of Cyprus, Nicosia, Cyprus

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  3. Blaž Zupan

    1. J. Stefan Institute, Ljubljana, Slovenia

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 414)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Intelligent Data Analysis In Medicine And Pharmacology: An Overview

    1. Intelligent Data Analysis In Medicine And Pharmacology: An Overview

      • Nada Lavrač, Elpida T. Keravnou, Blaž Zupan
      Pages 1-13

  3. Data Abstraction

    1. Front Matter

      Pages 15-15
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    2. Time-Oriented Analysis of High-Frequency Data in ICU Monitoring

      • Silvia Miksch, Werner Horn, Christian Popow, Franz Paky
      Pages 17-36

    3. Context-Sensitive Temporal Abstraction of Clinical Data

      • Yuval Shahar
      Pages 37-59

    4. Temporal Abstraction of Medical Data: Deriving Periodicity

      • Elpida T. Keravnou
      Pages 61-79

    5. Cooperative Intelligent Data Analysis: An Application to Diabetic Patients Management

      • Riccardo Bellazzi, Cristiana Larizza, Alberto Riva
      Pages 81-98

    6. PTAH: A System for Supporting Nosocomial Infection Therapy

      • Marko Bohanec, Miran Rems, Smiljana Slavec, Božo Urh
      Pages 99-111

  4. Data Mining

    1. Front Matter

      Pages 113-113
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    2. Prognosing the Survival Time of Patients with Anaplastic Thyroid Carcinoma using Machine Learning

      • Matjaž Kukar, Nikola Bešič, Igor Kononenko, Marija Auersperg, Marko Robnik-Šikonja
      Pages 115-129

    3. Data Analysis of Patients with Severe Head Injury

      • Iztok A. Pilih, Dunja Mladenić, Nada Lavrač, Tine S. Prevec
      Pages 131-148

    4. Dementia Screening with Machine Learning Methods

      • William R. Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth
      Pages 149-165

    5. Experiments with Machine Learning in the Prediction of Coronary Artery Disease Progression

      • Branko Šter, Matjaž Kukar, Andrej Dobnikar, Igor Kranjec, Igor Kononenko
      Pages 167-185

    6. Noise Elimination Applied to Early Diagnosis of Rheumatic Diseases

      • Nada Lavrač, Dragan Gamberger, Sašo Džeroski
      Pages 187-205

    7. Diterpene Structure Elucidation from 13C NMR-Spectra with Machine Learning

      • Sašo Džeroski, Steffen Schulze-Kremer, Karsten R. Heidtke, Karsten Siems, Dietrich Wettschereck
      Pages 207-225

    8. Using Inductive Logic Programming to Learn Rules that Identify Glaucomatous Eyes

      • Fumio Mizoguchi, Hayato Ohwada, Makiko Daidoji, Shiroaki Shirato
      Pages 227-242

    9. Carcinogenesis Predictions Using Inductive Logic Programming

      • Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg
      Pages 243-260

    10. Concept Discovery by Decision Table Decomposition and its Application in Neurophysiology

      • Blaž Zupan, John A. Halter, Marko Bohanec
      Pages 261-278

    11. Classification of Human Brain Waves Using Self-Organizing Maps

      • Udo Heuser, Josef Göppert, Wolfgang Rosenstiel, Andreas Stevens
      Pages 279-294

    12. Applying a Neural Network to Prostate Cancer Survival Data

      • Michael W. Kattan, Haku Ishida, Peter T. Scardino, J. Robert Beck
      Pages 295-306
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Keywords

About this book

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.

Editors and Affiliations

  • J. Stefan Institute, Ljubljana, Slovenia

    Nada Lavrač, Blaž Zupan

  • University of Cyprus, Nicosia, Cyprus

    Elpida T. Keravnou

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