Computational Approaches for Predicting Preterm Birth and Newborn Outcomes

Clin Perinatol. 2024 Jun;51(2):461-473. doi: 10.1016/j.clp.2024.02.005. Epub 2024 Mar 8.


Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.

Keywords: Computational modeling; Multimodal; Neonatal outcomes; Preterm birth.

Publication types

  • Review
  • Research Support, N.I.H., Extramural

MeSH terms

  • Artificial Intelligence*
  • Electronic Health Records*
  • Female
  • Humans
  • Infant Mortality
  • Infant, Newborn
  • Pregnancy
  • Premature Birth* / epidemiology
  • Social Determinants of Health