Machine Learning Methods for Neonatal Mortality and Morbidity Classification

Joel Jaskari, Janne Myllarinen, Markus Leskinen, Ali Bahrami Rad, Jaakko Hollmén, Sture Andersson, Simo Sarkka*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Article number9131772
Pages (from-to)123347-123358
Number of pages12
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2 Jul 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Bronchopulmonary dysplasia
  • Classification
  • Machine learning
  • Necrotizing enterocolitis
  • Neonatal intensive care unit
  • Neonatal mortality
  • Neonatology
  • NICU
  • Retinopathy of prematurity

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