000 03972nam a22005895i 4500
001 978-3-319-50478-0
003 DE-He213
005 20240423130140.0
007 cr nn 008mamaa
008 161209s2016 sz | s |||| 0|eng d
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
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
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.
250 _a1st ed. 2016.
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 Artificial Intelligence,
_x2945-9141 ;
_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 _aData mining.
650 0 _aMedical informatics.
650 0 _aAlgorithms.
650 0 _aComputer vision.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aHealth Informatics.
650 2 4 _aAlgorithms.
650 2 4 _aComputer Vision.
700 1 _aHolzinger, Andreas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319504773
776 0 8 _iPrinted edition:
_z9783319504797
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v9605
856 4 0 _uhttps://doi.org/10.1007/978-3-319-50478-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
999 _c185576
_d185576