title: Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium. Electronic address: rein.houthooft@intec.ugent.be.
authors.forename: Rein
authors.initials: R
authors.lastname: Houthooft
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Joeri
authors.initials: J
authors.lastname: Ruyssinck
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Joachim
authors.initials: J
authors.lastname: van der Herten
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Sean
authors.initials: S
authors.lastname: Stijven
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Ivo
authors.initials: I
authors.lastname: Couckuyt
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium; Department of Intensive Care Medicine, Ghent University Hospital, De Pintelaan 185, 2 K12 IC, B-9000 Ghent, Belgium.
authors.forename: Bram
authors.initials: B
authors.lastname: Gadeyne
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Femke
authors.initials: F
authors.lastname: Ongenae
authors.affiliation: Department of Intensive Care Medicine, Ghent University Hospital, De Pintelaan 185, 2 K12 IC, B-9000 Ghent, Belgium.
authors.forename: Kirsten
authors.initials: K
authors.lastname: Colpaert
authors.affiliation: Department of Intensive Care Medicine, Ghent University Hospital, De Pintelaan 185, 2 K12 IC, B-9000 Ghent, Belgium; Department of Internal Medicine, Ghent University, De Pintelaan 185, B-9000 Ghent, Belgium.
authors.forename: Johan
authors.initials: J
authors.lastname: Decruyenaere
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Tom
authors.initials: T
authors.lastname: Dhaene
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, B-9050 Ghent, Belgium.
authors.forename: Filip
authors.initials: F
authors.lastname: De Turck
mesh_terms: D001530:Belgium; D016638:Critical Illness / Q000401:mortality*; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay; D008297:Male; D063305:Organ Dysfunction Scores; D060388:Support Vector Machine; D016019:Survival Analysis
keywords: Critical care; Length of stay modeling; Mortality prediction; Sequential organ failure score; Support vector machines