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Intelligent Data Analysis in Medicine and Pharmacology

Overview of attention for book
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
Attention for Chapter 12: Diterpene Structure Elucidation from 13C NMR-Spectra with Machine Learning
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Citations

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Chapter title
Diterpene Structure Elucidation from 13C NMR-Spectra with Machine Learning
Chapter number 12
Book title
Intelligent Data Analysis in Medicine and Pharmacology
Published by
Springer US, January 1997
DOI 10.1007/978-1-4615-6059-3_12
Book ISBNs
978-1-4613-7775-7, 978-1-4615-6059-3
Authors

Sašo Džeroski, Steffen Schulze-Kremer, Karsten R. Heidtke, Karsten Siems, Dietrich Wettschereck

Editors

Nada Lavrač, Elpida T. Keravnou, Blaž Zupan

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 33%
Unknown 2 67%
Readers by discipline Count As %
Computer Science 1 33%
Unknown 2 67%

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