title: Machine learning techniques to examine large patient databases.
abstract: 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.
authors.affiliation: Department of Intensive Care Medicine, UZ Leuven--Campus Gasthuisberg, Catholic University of Leuven, Herestraat 49, 3000 Leuven, Belgium. geert.meyfroidt@uzleuven.be
authors.forename: Geert
authors.initials: G
authors.lastname: Meyfroidt
authors.forename: Fabian
authors.initials: F
authors.lastname: Güiza
authors.forename: Jan
authors.initials: J
authors.lastname: Ramon
authors.forename: Maurice
authors.initials: M
authors.lastname: Bruynooghe
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D007362:Intensive Care Units; D016347:Medical Records Systems, Computerized; D016571:Neural Networks, Computer; D009873:Operating Rooms / Q000379:methods; D012984:Software