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Information Technology in Bio- and Medical Informatics

Overview of attention for book
Cover of 'Information Technology in Bio- and Medical Informatics'

Table of Contents

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    Book Overview
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    Chapter 1 From Literature to Knowledge: Exploiting PubMed to Answer Biomedical Questions in Natural Language
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    Chapter 2 Using Twitter Data and Sentiment Analysis to Study Diseases Dynamics
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    Chapter 3 An Open Data Approach for Clinical Appropriateness
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    Chapter 4 A Logistic Regression Approach for Identifying Hot Spots in Protein Interfaces
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    Chapter 5 The Discovery of Prognosis Factors Using Association Rule Mining in Acute Myocardial Infarction with ST-Segment Elevation
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    Chapter 6 Data Mining Techniques in Health Informatics: A Case Study from Breast Cancer Research
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    Chapter 7 Artificial Neural Networks in Diagnosis of Liver Diseases
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    Chapter 8 How to Increase the Effectiveness of the Hepatitis Diagnostics by Means of Appropriate Machine Learning Methods
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    Chapter 9 Ant-Inspired Algorithms for Decision Tree Induction
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    Chapter 10 Microsleep Classifier Using EOG Channel Recording: A Feasibility Study
Attention for Chapter 2: Using Twitter Data and Sentiment Analysis to Study Diseases Dynamics
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24 Mendeley
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Chapter title
Using Twitter Data and Sentiment Analysis to Study Diseases Dynamics
Chapter number 2
Book title
Information Technology in Bio- and Medical Informatics
Published by
Springer International Publishing, September 2015
DOI 10.1007/978-3-319-22741-2_2
Book ISBNs
978-3-31-922740-5, 978-3-31-922741-2
Authors

Vincenza Carchiolo, Alessandro Longheu, Michele Malgeri

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 29%
Student > Bachelor 4 17%
Researcher 3 13%
Lecturer 3 13%
Student > Master 2 8%
Other 2 8%
Unknown 3 13%
Readers by discipline Count As %
Computer Science 11 46%
Environmental Science 2 8%
Nursing and Health Professions 2 8%
Engineering 2 8%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 5 21%

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