Predicting interval from diagnosis to delivery in preeclampsia using electronic health records
Year: 2025
Type: article
Abstract: Preeclampsia is a major cause of maternal and perinatal mortality with no known cure. Delivery timing is critical to balancing maternal and fetal risks. We develop and externally validate PEDeliveryTime, a class of clinically informative models which resulted from deep-learning models, to predict the time from PE diagnosis to delivery using electronic health records. We build the models on 1533 PE cases from the University of Michigan and validate it on 2172 preeclampsia cases from the University of Florida. PEDeliveryTime full model contains only 12 features yet achieves high c-index of 0.79 and 0.74 on the Michigan and Florida data set respectively. For the early-onset preeclampsia subset, the full model reaches 0.76 and 0.67 on the Michigan and Florida test sets. Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources. Preeclampsia poses significant risks in pregnancy, and accurately predicting delivery timing is crucial for optimal care. Here, the authors show that deep-learning models, using limited electronic health record features, can effectively predict the time from preeclampsia diagnosis to delivery, potentially offering a tool for better resource allocation.
Source: Nature Communications
Authors: Xiaotong Yang, Hailey K. Ballard, Aditya D. Mahadevan, Ke Xu, David G. Garmire, Elizabeth S. Langen, Dominick J. Lemas, Lana X. Garmire
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: U.S. Department of Health and Human Services, National Institutes of Health, University of Florida Health, Medical School, University of Michigan, Clinical and Translational Science Institute, University of Florida, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: DR. EPS: Drug Repurposing for Extended Patient Survival, G1546313279, G1831379592, G2132730271, G2554551371, G3183524957, G3345260134, An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer Risks, G3753832015, G398914654, G4066467814, Human milk metabolomics and microbe-host interactions associated with pediatric obesity, Biomedical Informatics and Data Science Training Program (BIDS-TP), G432001851, G4591767391, G4738176638, G5081352788, G5720752015, J. NRSA Training Core, An Integrative Bioinformatics Platform with Application in Single Cancer Cells, G7576132855, G9000035999, G9000039892, G9000132165, G9000193465
OpenAlex ID: W4409390769