Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit
Joint Authors
Ruyssinck, Joeri
van der Herten, Joachim
Houthooft, Rein
Ongenae, Femke
Couckuyt, Ivo
Gadeyne, Bram
Colpaert, Kirsten
Decruyenaere, Johan
De Turck, Filip
Dhaene, Tom
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-13
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task.
The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures.
In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy.
As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years.
We compare the performance of our system with a baseline performance and a standard Random Forest regression approach.
Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem.
Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed.
American Psychological Association (APA)
Ruyssinck, Joeri& van der Herten, Joachim& Houthooft, Rein& Ongenae, Femke& Couckuyt, Ivo& Gadeyne, Bram…[et al.]. 2016. Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100181
Modern Language Association (MLA)
Ruyssinck, Joeri…[et al.]. Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1100181
American Medical Association (AMA)
Ruyssinck, Joeri& van der Herten, Joachim& Houthooft, Rein& Ongenae, Femke& Couckuyt, Ivo& Gadeyne, Bram…[et al.]. Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100181
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100181