ArticleNature medicine2026
Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent.
Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06791486 (Predicting Biological Age Using Electronic Health Records), which is not on this map. Not yet cited in PubMed.
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The trial behind it
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Predicting Biological Age Using Electronic Health Records: An AI-Based Approach
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47 authors.
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Abstract
Current predictive models for pregnancy and infant outcomes often focus on limited endpoints and rely on costly tests or imaging. Here we developed the Mother-Child Artificial Intelligence Agent (MoChiAgent)-a large-language-model-based clinical assistant that orchestrates several tools to integrate sequential electronic health record (EHR) data, including routine laboratory tests, for forecasting maternal and infant diseases. MoChiAgent's core predictive engine, MoChiFormer, was developed and evaluated internally using 4,401,599 longitudinal clinical visits and validated externally using independent maternal and infant cohorts consisting of 263,452 and 23,192 visits, respectively. MoChiFormer reconstructs missing laboratory values, reduces batch effects and learns EHR representations that support gestational, fetal and infant age estimation, health-trajectory modeling and stratification of current and future disease risk. Subsequently, a Knowledge Search Tool utilizes these forecasts to retrieve evidence-based intervention and treatment recommendations from curated medical literature and authoritative guidelines. For maternal health, MoChiFormer accurately identified key gestational conditions, achieving areas under the receiver operating characteristic curves of 0.89 for placental abruption, 0.89 for premature rupture of membranes and 0.91 for preterm labor. Analysis of paired mother-infant data further revealed transgenerational risk associations, with infants born to mothers in specific clusters showing substantially elevated risks of neonatal jaundice (hazard ratio = 2.81; 95% confidence interval, 2.60-3.03) and hematological diseases (hazard ratio = 2.83; 95% confidence interval, 2.62-3.05). Integrating maternal gestational EHRs with infant records improved prediction of infant conditions, including chromosomal abnormalities and respiratory disorders. These findings indicate that MoChiAgent can provide clinically relevant, actionable decision support information to enhance risk-stratified care for mothers and infants. ClinicalTrials.gov identifier: NCT06791486 .
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Registered trials
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