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Intelligent Data Analysis in Medicine and Pharmacology

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Cover of 'Intelligent Data Analysis in Medicine and Pharmacology'

Table of Contents

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    Book Overview
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    Chapter 1 Intelligent Data Analysis In Medicine And Pharmacology: An Overview
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    Chapter 2 Time-Oriented Analysis of High-Frequency Data in ICU Monitoring
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    Chapter 3 Context-Sensitive Temporal Abstraction of Clinical Data
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    Chapter 4 Temporal Abstraction of Medical Data: Deriving Periodicity
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    Chapter 5 Cooperative Intelligent Data Analysis: An Application to Diabetic Patients Management
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    Chapter 6 PTAH: A System for Supporting Nosocomial Infection Therapy
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    Chapter 7 Prognosing the Survival Time of Patients with Anaplastic Thyroid Carcinoma using Machine Learning
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    Chapter 8 Data Analysis of Patients with Severe Head Injury
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    Chapter 9 Dementia Screening with Machine Learning Methods
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    Chapter 10 Experiments with Machine Learning in the Prediction of Coronary Artery Disease Progression
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    Chapter 11 Noise Elimination Applied to Early Diagnosis of Rheumatic Diseases
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    Chapter 12 Diterpene Structure Elucidation from 13C NMR-Spectra with Machine Learning
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    Chapter 13 Using Inductive Logic Programming to Learn Rules that Identify Glaucomatous Eyes
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    Chapter 14 Carcinogenesis Predictions Using Inductive Logic Programming
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    Chapter 15 Concept Discovery by Decision Table Decomposition and its Application in Neurophysiology
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    Chapter 16 Classification of Human Brain Waves Using Self-Organizing Maps
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    Chapter 17 Applying a Neural Network to Prostate Cancer Survival Data
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Title
Intelligent Data Analysis in Medicine and Pharmacology
Published by
Springer US, January 1997
DOI 10.1007/978-1-4615-6059-3
ISBNs
978-1-4613-7775-7, 978-1-4615-6059-3
Editors

Nada Lavrač, Elpida T. Keravnou, Blaž Zupan

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The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Professor 2 17%
Researcher 2 17%
Student > Master 2 17%
Other 1 8%
Other 0 0%
Unknown 3 25%
Readers by discipline Count As %
Computer Science 5 42%
Engineering 3 25%
Neuroscience 1 8%
Unknown 3 25%