title: Predictive data mining on monitoring data from the intensive care unit.
abstract: The widespread implementation of computerized medical files in intensive care units (ICUs) over recent years has made available large databases of clinical data for the purpose of developing clinical prediction models. The typical intensive care unit has several information sources from which data is electronically collected as time series of varying time resolutions. We present an overview of research questions studied in the ICU setting that have been addressed through the automatic analysis of these large databases. We focus on automatic learning methods, specifically data mining approaches for predictive modeling based on these time series of clinical data. On the one hand we examine short and medium term predictions, which have as ultimate goal the development of early warning or decision support systems. On the other hand we examine long term outcome prediction models and evaluate their performance with respect to established scoring systems based on static admission and demographic data.
authors.affiliation: Department of Intensive Care Medicine, University of Leuven (KU Leuven), Belgium Herestraat 49, 3000, Leuven, Belgium. fabian.guiza@med.kuleuven.be
authors.forename: Fabian
authors.initials: F
authors.lastname: Güiza
authors.forename: Jelle
authors.initials: J
authors.lastname: Van Eyck
authors.forename: Geert
authors.initials: G
authors.lastname: Meyfroidt
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003422:Critical Care / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D006801:Humans; D007362:Intensive Care Units; D016347:Medical Records Systems, Computerized; D008991:Monitoring, Physiologic / Q000379:methods*; D010343:Patient Admission; D011379:Prognosis; D012306:Risk; D016896:Treatment Outcome