Machine learning to predict late respiratory support in preterm infants: a retrospective cohort study

Tsung Yu Wu, Wei Ting Lin, Yen Ju Chen, Yu Shan Chang, Chyi Her Lin, Yuh Jyh Lin

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Bronchopulmonary dysplasia (BPD) has been a critical morbidity in preterm infants. To improve our definition and prediction of BPD is challenging yet indispensable. We aimed to apply machine learning (ML) to investigate effective models by using the recently-proposed and data-driven definition to predict late respiratory support modalities at 36 weeks’ post menstrual age (PMA). We collected data on very-low-birth-weight infants born between 2016 and 2019 from the Taiwan Neonatal Network database. Twenty-four attributes associated with their early life and seven ML algorithms were used in our analysis. The target outcomes were overall mortality, death before 36 weeks’ PMA, and severity of BPD under the new definition, which served as a proxy for respiratory support modalities. Of the 4103 infants initially considered, 3200 were deemed eligible. The logistic regression algorithm yielded the highest area under the receiver operating characteristic curve (AUROC). After attribute selection, the AUROC of the simplified models remain favorable (e.g., 0.801 when predicting no BPD, 0.850 when predicting grade 3 BPD or death before 36 weeks’ PMA, and 0.881 when predicting overall mortality). By using ML, we developed models to predict late respiratory support. Estimators were developed for clinical application after being simplified through attribute selection.

原文English
文章編號2839
期刊Scientific reports
13
發行號1
DOIs
出版狀態Published - 2023 12月

All Science Journal Classification (ASJC) codes

  • 多學科

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