Machine learning techniques to examine large patient databases

被引:65
作者
Meyfroidt, Geert [1 ]
Guiza, Fabian [2 ]
Ramon, Jan [2 ]
Bruynooghe, Maurice [2 ]
机构
[1] Katholieke Univ Leuven, Dept Intens Care Med, UZ Leuven Campus Gasthuisberg,Herestr 49, B-3000 Leuven, Belgium
[2] Katholieke Univ Leuven, Fac Engn, Dept Comp Sci, Leuven, Belgium
关键词
intensive care unit; operating room; computerization; patient data management system; machine learning; data mining; decision tree learning; Bayesian networks; Support Vector Machines; Gaussian processes;
D O I
10.1016/j.bpa.2008.09.003
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:127 / 143
页数:17
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