Machine Learning for Clinical Data Processing

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Author(s): Guo-Zheng Li (Tongji University, China)
Copyright: 2011
Pages: 23
Source title: Digital Forensics for the Health Sciences: Applications in Practice and Research
Source Author(s)/Editor(s): Andriani Daskalaki (Max Planck Institute for Molecular Genetics, Germany)
DOI: 10.4018/978-1-60960-483-7.ch009

Keywords: Digital Crime & Forensics / Health Information Systems / Medical Information Science Reference / Security & Forensics

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Abstract

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.

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