Computational Approaches for Predicting Preterm Birth and Newborn Outcomes - 03/05/24

Doi : 10.1016/j.clp.2024.02.005

David Seong, BA ^a,^b,^c,^d, Camilo Espinosa, MPhil ^a,^d,^e,^f, Nima Aghaeepour, PhD ^d,^e,^f,^⁎
Aghaeepour Laboratory^a,^b,^c,^d,^e,^f,^g,^¹
The members of the Aghaeepour Laboratory that contributed as co-authors to the writing of various sections throughout this manuscript are listed in the acknowledgment section.
^a Immunology Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
^b Medical Scientist Training Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
^c Department of Microbiology and Immunology, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
^d Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
^e Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
^f Department of Biomedical Data Science, Stanford University, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
^g Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA

^∗Corresponding author. 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117.300 Pasteur DriveGrant S280StanfordCA94305-5117

Résumé

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.

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Keywords : Preterm birth, Computational modeling, Multimodal, Neonatal outcomes

Plan

Key points

Introduction

Discussion

Use of Electronic Health Records to Predict Maternal and Neonatal Health

Utilization of High-Dimensional Biological Data in Predictive Models

Importance of Considering Social Determinants of Health in Predictive Models

Ethical Model Development and Deployment

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Vol 51 - N° 2

P. 461-473 - juin 2024 Retour au numéro

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Computational Approaches for Predicting Preterm Birth and Newborn Outcomes - 03/05/24

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