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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

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Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.

David Seong¹, Camilo Espinosa², Nima Aghaeepour³

¹Immunology 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.

Clinics in Perinatology

|May 5, 2024

Summary

Artificial intelligence (AI) can analyze complex data to improve understanding of preterm birth (PTB). This review explores AI

Keywords:

Computational modeling Multimodal Neonatal outcomes Preterm birth

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Published on: October 4, 2015

Area of Science:

Reproductive Health
Medical Informatics
Artificial Intelligence in Medicine

Background:

Preterm birth (PTB) is a major cause of infant mortality and morbidity.
Understanding PTB's multifactorial causes requires advanced analytical approaches.
Current predictive models for PTB and its morbidities need improvement.

Purpose of the Study:

To review the application of artificial intelligence (AI) in analyzing multimodal data for preterm birth research.
To highlight the potential of AI in gaining novel insights into the complex factors contributing to PTB.
To assess the integration of diverse data sources for enhanced PTB prediction and understanding.

Main Methods:

Review of studies utilizing AI for preterm birth analysis.
Analysis of AI applications across electronic health records (EHRs).
Examination of AI's role in interpreting biological omics data and social determinants of health (SDOH) metrics.

Main Results:

AI offers powerful tools for analyzing high-dimensional, multimodal datasets relevant to PTB.
Integration of EHR, omics, and SDOH data with AI can reveal complex PTB-associated patterns.
AI facilitates a deeper biological and clinical understanding of PTB.

Conclusions:

AI-driven analysis of multimodal data holds significant promise for advancing preterm birth research.
Improved predictive models and biological insights can be achieved through AI.
Future research should focus on leveraging AI for comprehensive PTB risk assessment and prevention strategies.