NDLI: Modelling the development of late onset sepsis and length of stay using discrete conditional survival models with a classification tree component

Content Provider	IEEE Xplore Digital Library
Author	Marshall, A.H. Payne, K. Cairns, K.J. Craig, S. McCall, E.
Copyright Year	2012
Description	Author affiliation: Centre for Statistical Science and Operational Research (CenSSOR), Queen's University Belfast, Northern Ireland, BT7 1NN (Marshall, A.H.; Payne, K.; Cairns, K.J.) \|\| Regional Neonatal Unit, Royal Maternity Hospital, Belfast, Northern Ireland (Craig, S.) \|\| School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Northern Ireland (McCall, E.)
Abstract	This paper introduces a discrete conditional survival model (DC-S) with a classification component for predicting patient outcome and survival component for predicting length of stay in hospital. The DC-S model consists of two components; the conditional component which utilises a classification tree and the survival component which models the survival distribution. The survival component of the model is conditioned on the discrete conditional component, the classification tree. The DC-S model with classification tree is applied to a healthcare scenario where the length of stay of babies in neonatal wards in Northern Ireland (United Kingdom) is modelled using the baby characteristics known on the first day of admission. The resulting model can accurately predict length of stay of babies and thus has the potential to be used in bed planning. Hospitals could use such good estimates for the length of stay of patients (determined on the day of arrival) to plan ahead to make the correct provisions available during their stay. Not only does this have resource implications, it can also help patient families. The resulting model can also predict the occurrence (or otherwise) of late onset sepsis, which has implications on a patients stay.
Starting Page	1
Ending Page	6
File Size	550066
Page Count	6
File Format	PDF
ISBN	9781467320498
ISSN	10637125
e-ISBN	9781467320511
DOI	10.1109/CBMS.2012.6266407
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Predictive models Data models Hospitals Regression tree analysis Biological system modeling Educational institutions
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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