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020 _a9783319504780
_9978-3-319-50478-0
024 7 _a10.1007/978-3-319-50478-0
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
245 1 0 _aMachine Learning for Health Informatics
_h[electronic resource] :
_bState-of-the-Art and Future Challenges /
_cedited by Andreas Holzinger.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXII, 481 p. 98 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v9605
505 0 _aMachine 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 _aMachine 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.
650 0 _aComputer science.
650 0 _aHealth informatics.
650 0 _aAlgorithms.
650 0 _aData mining.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aHealth Informatics.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aImage Processing and Computer Vision.
700 1 _aHolzinger, Andreas.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319504773
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v9605
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-50478-0
912 _aZDB-2-SCS
912 _aZDB-2-LNC
942 _cEBK
999 _c52498
_d52498