Evidence
Clin Perinatol. 2024 Jun;51(2):461-473. doi: 10.1016/j.clp.2024.02.005. Epub 2024 Mar 8.
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.
PMID:38705652 | DOI:10.1016/j.clp.2024.02.005
Add to Google Keep
Post to X
Share on Linkedin
Estimated reading time: 2 minute(s)
Latest: Psychiatryai.com #RAISR4D
Cool Evidence: Engaging Young People and Students in Real-World Evidence ☀️
Real-Time Evidence Search [Psychiatry]
AI Research [Andisearch.com]
Computational Approaches for Predicting Preterm Birth and Newborn Outcomes
🌐 90 Days
VR Related Evidence Matrix
Evidence Blueprint
Computational Approaches for Predicting Preterm Birth and Newborn Outcomes
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence
Computational Approaches for Predicting Preterm Birth and Newborn Outcomes
🌐 365 Days
VR Related Evidence Matrix
