@article{Houthooft2015191,
title = "Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores ",
journal = "Artificial Intelligence in Medicine ",
volume = "63",
number = "3",
pages = "191 - 207",
year = "2015",
note = "",
issn = "0933-3657",
doi = "http://dx.doi.org/10.1016/j.artmed.2014.12.009",
url = "http://www.sciencedirect.com/science/article/pii/S093336571400147X",
author = "Rein Houthooft and Joeri Ruyssinck and Joachim van der Herten and Sean Stijven and Ivo Couckuyt and Bram Gadeyne and Femke Ongenae and Kirsten Colpaert and Johan Decruyenaere and Tom Dhaene and Filip De Turck",
keywords = "Mortality prediction",
keywords = "Length of stay modeling",
keywords = "Support vector machines",
keywords = "Critical care",
keywords = "Sequential organ failure score ",
abstract = "AbstractIntroduction The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient \{ICU\} resource usage and varies considerably. Planning of postoperative \{ICU\} admissions is important as \{ICUs\} often have no nonoccupied beds available. Problem statement Estimation of the \{ICU\} bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate. For this reason, predictive models have much potential for improving planning for \{ICU\} patient admission. Objective Our goal is to develop and optimize models for patient survival and \{ICU\} length of stay (LOS) based on monitored \{ICU\} patient data. Furthermore, these models are compared on their use of sequential organ failure (SOFA) scores as well as underlying raw data as input features. Methodology Different machine learning techniques are trained, using a 14,480 patient dataset, both on \{SOFA\} scores as well as their underlying raw data values from the first five days after admission, in order to predict (i) the patient LOS, and (ii) the patient mortality. Furthermore, to help physicians in assessing the prediction credibility, a probabilistic model is tailored to the output of our best-performing model, assigning a belief to each patient status prediction. A two-by-two grid is built, using the classification outputs of the mortality and prolonged stay predictors to improve the patient \{LOS\} regression models. Results For predicting patient mortality and a prolonged stay, the best performing model is a support vector machine (SVM) with GA,D = 65.9% (area under the curve (AUC) of 0.77) and GS,L = 73.2% (AUC of 0.82). In terms of \{LOS\} regression, the best performing model is support vector regression, achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay. Conclusion Using a classification grid based on the predicted patient mortality and prolonged stay, allows more accurate modeling of the patient LOS. The detailed models allow to support the decisions made by physicians in an \{ICU\} setting. "
}