Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.

Journal: Clinics in perinatology
PMID: 38705652

Abstract

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.

Authors

  • David Seong
    Immunology Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Medical Scientist Training Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA.
  • Camilo Espinosa
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Nima Aghaeepour
    Departments of Anesthesiology, Pain, and Peri-operative Medicine and Biomedical Data Sciences, Stanford University, Stanford, CA, USA.

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