Predictive Analytics in Bronchopulmonary Dysplasia: Past, Present, and Future

Authors

Bryan G McOmber
Alvaro G Moreira
Kelsey Kirkman
Sebastian Acosta
Craig Rusin
Binoy Shivanna

Publication Date

1-1-2024

Journal

Frontiers in Pediatrics

DOI

10.3389/fped.2024.1483940

PMID

39633818

PMCID

PMC11615574

PubMedCentral® Posted Date

11-20-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

bronchopulmonary dysplasia, predictive analytics, artificial intelligence, machine learning, personalized medicine, bioinformatics

Abstract

Bronchopulmonary dysplasia (BPD) remains a significant complication of prematurity, impacting approximately 18,000 infants annually in the United States. Advances in neonatal care have not reduced BPD, and its management is challenged by the rising survival of extremely premature infants and the variability in clinical practices. Leveraging statistical and machine learning techniques, predictive analytics can enhance BPD management by utilizing large clinical datasets to predict individual patient outcomes. This review explores the foundations and applications of predictive analytics in the context of BPD, examining commonly used data sources, modeling techniques, and metrics for model evaluation. We also highlight bioinformatics' potential role in understanding BPD's molecular basis and discuss case studies demonstrating the use of machine learning models for risk prediction and prognosis in neonates. Challenges such as data bias, model complexity, and ethical considerations are outlined, along with strategies to address these issues. Future directions for advancing the integration of predictive analytics into clinical practice include improving model interpretability, expanding data sharing and interoperability, and aligning predictive models with precision medicine goals. By overcoming current challenges, predictive analytics holds promise for transforming neonatal care and providing personalized interventions for infants at risk of BPD.