Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning

Kim S. Betts; Steve Kisely; Rosa Alati

doi:10.1016/j.jbi.2020.103651

Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning

Kim S. Betts, Steve Kisely, Rosa Alati

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	103651
Journal	Journal of Biomedical Informatics
Volume	114
DOIs	https://doi.org/10.1016/j.jbi.2020.103651
Publication status	Published - Feb 2021
Externally published	Yes

ASJC Scopus Subject Areas

Health Informatics
Computer Science Applications

Keywords

Administrative data linkage
Machine learning
Neonatal outcomes
Predictive models

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10.1016/j.jbi.2020.103651

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Betts, K. S., Kisely, S., & Alati, R. (2021). Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning. Journal of Biomedical Informatics, 114, Article 103651. https://doi.org/10.1016/j.jbi.2020.103651

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keywords = "Administrative data linkage, Machine learning, Neonatal outcomes, Predictive models",

author = "Betts, {Kim S.} and Steve Kisely and Rosa Alati",

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AU - Alati, Rosa

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KW - Predictive models

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Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning

ASJC Scopus Subject Areas

Keywords

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