@inproceedings{54d6f68f2ff247f393d9cd19bae75d9d,
title = "Predicting Mortality in the Intensive Care Using Episodes",
abstract = "Patient outcome prediction lies at the heart of various medically relevant tasks such as quality assessment and decision support, and is an important research issue in medical informatics and AI in medicine. In the Intensive Care (IC) there are various prognostic models in use today that predict patient mortality. These are logistic regression models that predict the probability of death of an IC patient based on severity of illness scores that are calculated from information that is collected within the first 24 hours of patient admission. For example the SAPS (simplified acute physiology score) quantifies the patient's condition at admission and is used as the only covariate in the SAPS logistic regression model.",
author = "Tudor Toma and Ameen Abu-Hanna and Bosman, {Robert Jan}",
year = "2006",
language = "English",
series = "Belgian/Netherlands Artificial Intelligence Conference",
booktitle = "Predicting Mortality in the Intensive Care Using Episodes",
note = "18th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC 2006 ; Conference date: 05-10-2006 Through 06-10-2006",
}