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