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190802 ||| eng |
| 020 |
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|a 9783319504780
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| 100 |
1 |
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|a Holzinger, Andreas
|e [editor]
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| 245 |
0 |
0 |
|a Machine Learning for Health Informatics
|h Elektronische Ressource
|b State-of-the-Art and Future Challenges
|c edited by Andreas Holzinger
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| 250 |
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|a 1st ed. 2016
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2016, 2016
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| 300 |
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|a XXII, 481 p. 98 illus
|b online resource
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| 505 |
0 |
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|a 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.
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| 653 |
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|a Health Informatics
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| 653 |
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|a Computer vision
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| 653 |
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|a Medical informatics
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| 653 |
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|a Algorithms
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| 653 |
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|a Computer Vision
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| 653 |
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|a Data mining
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Artificial Intelligence
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-50478-0
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-50478-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.312
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| 520 |
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|a 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
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