Machine Learning for Health Informatics (Record no. 52498)

000 -LEADER
fixed length control field 03790nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-3-319-50478-0
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200420221249.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 161209s2016 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319504780
-- 978-3-319-50478-0
082 04 - CLASSIFICATION NUMBER
Call Number 006.312
245 10 - TITLE STATEMENT
Title Machine Learning for Health Informatics
Sub Title State-of-the-Art and Future Challenges /
300 ## - PHYSICAL DESCRIPTION
Number of Pages XXII, 481 p. 98 illus.
490 1# - SERIES STATEMENT
Series statement Lecture Notes in Computer Science,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Machine Learning for Health Informatics -- Bagging Soft Decision Trees -- Grammars for Discrete Dynamics -- Empowering Bridging Term Discovery for Cross-domain Literature Mining in the TextFlows Platform -- Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice -- Deep learning trends for focal brain pathology segmentation in MRI -- Differentiation between Normal and Epileptic EEG using K-Nearest-Neighbors Technique -- Survey on Feature Extraction and Applications of Biosignals -- Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning -- Machine Learning and Data mining Methods for Managing Parkinson's Disease -- Challenges of Medical Text and Image Processing: Machine Learning Approaches -- Visual Intelligent Decision Support Systems in the medical field: design and evaluation. .
520 ## - SUMMARY, ETC.
Summary, etc Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
700 1# - AUTHOR 2
Author 2 Holzinger, Andreas.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-50478-0
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Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2016.
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-- text
-- txt
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer science.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Health informatics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Algorithms.
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-- Data mining.
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-- Image processing.
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-- Computer Science.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Health Informatics.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Algorithm Analysis and Problem Complexity.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image Processing and Computer Vision.
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-- 0302-9743 ;
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