Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants
- Resource Type
- article
- Authors
- Faiza Khurshid; Helen Coo; Amal Khalil; Jonathan Messiha; Joseph Y. Ting; Jonathan Wong; Prakesh S. Shah
- Source
- Frontiers in Pediatrics, Vol 9 (2021)
- Subject
- bronchopulmonary dysplasia
chronic lung disease
prediction
machine learning
discrimination
calibration
Pediatrics
RJ1-570
- Language
- English
- ISSN
- 2296-2360
Bronchopulmonary dysplasia (BPD) is the most prevalent and clinically significant complication of prematurity. Accurate identification of at-risk infants would enable ongoing intervention to improve outcomes. Although postnatal exposures are known to affect an infant's likelihood of developing BPD, most existing BPD prediction models do not allow risk to be evaluated at different time points, and/or are not suitable for use in ethno-diverse populations. A comprehensive approach to developing clinical prediction models avoids assumptions as to which method will yield the optimal results by testing multiple algorithms/models. We compared the performance of machine learning and logistic regression models in predicting BPD/death. Our main cohort included infants