Gaussian process classification for prediction of in-hospital mortality among preterm infants

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http://hdl.handle.net/10138/302053

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Rinta-Koski , O-P , Särkkä , S , Hollmén , J , Leskinen , M & Andersson , S 2018 , ' Gaussian process classification for prediction of in-hospital mortality among preterm infants ' , Neurocomputing , vol. 298 , pp. 134-141 . https://doi.org/10.1016/j.neucom.2017.12.064

Title: Gaussian process classification for prediction of in-hospital mortality among preterm infants
Author: Rinta-Koski, Olli-Pekka; Särkkä, Simo; Hollmén, Jaakko; Leskinen, Markus; Andersson, Sture
Contributor: University of Helsinki, HUS Children and Adolescents
Date: 2018-07-12
Language: eng
Number of pages: 8
Belongs to series: Neurocomputing
ISSN: 0925-2312
URI: http://hdl.handle.net/10138/302053
Abstract: We present a method for predicting preterm infant in-hospital mortality using Bayesian Gaussian process classification. We combined features extracted from sensor measurements, made during the first 72 h of care for 598 Very Low Birth Weight infants of birth weight <1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores. (C) 2018 Elsevier B.V. All rights reserved.
Subject: BABIES SCORE
BIRTH-WEIGHT INFANTS
CLINICAL RISK INDEX
CRIB-II
Gaussian process classification
MODELS
NEONATAL INTENSIVE-CARE
Neonatal intensive care
SEVERITY
SNAPPE-II
Time series prediction
Very low birth weight infants
113 Computer and information sciences
3123 Gynaecology and paediatrics
3126 Surgery, anesthesiology, intensive care, radiology
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