Machine Learning Methods for Neonatal Mortality and Morbidity Classification

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Jaskari , J , Myllärinen , J , Leskinen , M , Rad , A B , Hollmen , J , Andersson , S & Särkkä , S 2020 , ' Machine Learning Methods for Neonatal Mortality and Morbidity Classification ' , IEEE Access , vol. 8 , pp. 123347-123358 . https://doi.org/10.1109/ACCESS.2020.3006710

Title: Machine Learning Methods for Neonatal Mortality and Morbidity Classification
Author: Jaskari, Joel; Myllärinen, Janne; Leskinen, Markus; Rad, Ali Bahrami; Hollmen, Jaakko; Andersson, Sture; Särkkä, Simo
Contributor: University of Helsinki, Children's Hospital
University of Helsinki, Clinicum
Date: 2020
Language: eng
Number of pages: 12
Belongs to series: IEEE Access
ISSN: 2169-3536
URI: http://hdl.handle.net/10138/320160
Abstract: Preterm birth is the leading cause of mortality in children under the age of five. In particular, low birth weight and low gestational age are associated with an increased risk of mortality. Preterm birth also increases the risks of several complications, which can increase the risk of death, or cause long-term morbidities with both individual and societal impacts. In this work, we use machine learning for prediction of neonatal mortality as well as neonatal morbidities of bronchopulmonary dysplasia, necrotizing enterocolitis, and retinopathy of prematurity, among very low birth weight infants. Our predictors include time series data and clinical variables collected at the neonatal intensive care unit of Children's Hospital, Helsinki University Hospital. We examine 9 different classifiers and present our main results in AUROC, similar to our previous studies, and in F1-score, which we propose for classifier selection in this study. We also investigate how the predictive performance of the classifiers evolves as the length of time series is increased, and examine the relative importance of different features using the random forest classifier, which we found to generally perform the best in all tasks. Our systematic study also involves different data preprocessing methods which can be used to improve classifier sensitivities. Our best classifier AUROC is 0.922 in the prediction of mortality, 0.899 in the prediction of bronchopulmonary dysplasia, 0.806 in the prediction of necrotizing enterocolitis, and 0.846 in the prediction of retinopathy of prematurity. Our best classifier F1-score is 0.493 in the prediction of mortality, 0.704 in the prediction of bronchopulmonary dysplasia, 0.215 in the prediction of necrotizing enterocolitis, and 0.368 in the prediction of retinopathy of prematurity.
Subject: Bronchopulmonary dysplasia
classification
machine learning
necrotizing enterocolitis
neonatal intensive care unit
neonatal mortality
neonatology
NICU
retinopathy of prematurity
LOW-BIRTH-WEIGHT
DIABETIC-RETINOPATHY
ILLNESS SEVERITY
SNAPPE-II
PATHOGENESIS
VALIDATION
SCORE
RISK
113 Computer and information sciences
217 Medical engineering
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