• Tudor Toma, Ameen Abu-Hanna, and Robert-Jan Bosman.
• Department of Medical Informatics, Academic Medical Center, Universiteit van Amsterdam, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands. t.toma@amc.uva.nl
• J Biomed Inform. 2007 Dec 1; 40 (6): 649-60.

AbstractPredicting the survival status of Intensive Care patients at the end of their hospital stay is useful for various clinical and organizational tasks. Current models for predicting mortality use logistic regression models that rely solely on data collected during the first 24h of patient admission. These models do not exploit information contained in daily organ failure scores which nowadays are being routinely collected in many Intensive Care Units. We propose a novel method for mortality prediction that, in addition to admission-related data, takes advantage of daily data as well. The method is characterized by the data-driven discovery of temporal patterns, called episodes, of the organ failure scores and by embedding them in the familiar logistic regression framework for prediction. Our method results in a set of D logistic regression models, one for each of the first D days of Intensive Care Unit stay. A model for day d

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