---
title: "Mother‑Child AI Agent (MoChiAgent) predicts maternal and infant outcomes from longitudinal EHRs"
id: "nature-0-prediction-of-maternal-and-infant-outcomes-from-longitudinal-electronic-health"
canonical_url: "https://medichelpline.com/clinical-feed/nature-0-prediction-of-maternal-and-infant-outcomes-from-longitudinal-electronic-health"
content_type: "clinical_feed_article"
specialty: "Pediatrics"
source_name: "Nature Medicine"
source_url: "https://www.nature.com/articles/s41591-026-04694-y"
published_at: "2026-09-04T12:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Mother‑Child AI Agent (MoChiAgent) predicts maternal and infant outcomes from longitudinal EHRs
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/nature-0-prediction-of-maternal-and-infant-outcomes-from-longitudinal-electronic-health
- **Specialty:** [Pediatrics](https://medichelpline.com/clinical-feed/pediatrics.md)
- **Primary Source:** Nature Medicine
- **Source URL:** [Original Journal Publication](https://www.nature.com/articles/s41591-026-04694-y)
- **Published At:** 2026-09-04T12:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Researchers developed the **Mother‑Child AI Agent (MoChiAgent)**, an LLM‑based clinical assistant that integrates longitudinal electronic health record (EHR) data to forecast maternal and infant diseases. - The core predictive engine, **MoChiFormer**, was trained on 4,401,599 longitudinal clinical visits and externally validated on independent maternal and infant cohorts with 263,452 and 23,192 visits, respectively. - MoChiFormer reconstructs missing laboratory values, mitigates batch effects and learns EHR representations to support gestational, fetal and infant age estimation, health‑trajectory modelling and risk stratification. - For maternal outcomes, MoChiFormer achieved high discrimination for key gestational conditions: **AUROC** 0.89 for placental abruption, 0.89 for premature rupture of membranes and 0.91 for preterm labour. - Paired mother–infant analyses identified transgenerational risk clusters; infants born to mothers in specific clusters had markedly increased risks of neonatal jaundice (hazard ratio **HR** 2.81, 95% CI 2.60–3.03) and haematological diseases (HR 2.83, 95% CI 2.62–3.05). - Integrating maternal gestational EHRs with infant records improved prediction of infant conditions including chromosomal abnormalities and respiratory disorders. - A separate Knowledge Search Tool uses MoChiAgent forecasts to retrieve intervention and treatment recommendations from curated literature and authoritative guidelines. - The article reports that MoChiAgent can provide clinically relevant, actionable decision‑support to enable risk‑stratified care for mothers and infants. Specific implementation details, prospective clinical utility testing and regulatory considerations were not reported in the source.
## Clinical Analysis & Structured Key Points
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[articles](https://www.nature.com/nm/articles?type=article) 4. article Prediction of maternal and infant outcomes from longitudinal electronic health records with a Mother-Child AI agent [ Download PDF ](https://www.nature.com/articles/s41591-026-04694-y_reference.pdf) * Article * [Open access](https://www.springernature.com/gp/open-science/about/the-fundamentals-of-open-access-and-open-research) * Published: 04 September 2026 # Prediction of maternal and infant outcomes from longitudinal electronic health records with a Mother-Child AI agent * [Sian Liu](https://www.nature.com/articles/s41591-026-04694-y#auth-Sian-Liu-Aff1-Aff2)[1](https://www.nature.com/articles/s41591-026-04694-y#Aff1),[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Wenxin Zheng](https://www.nature.com/articles/s41591-026-04694-y#auth-Wenxin-Zheng-Aff3)[3](https://www.nature.com/articles/s41591-026-04694-y#Aff3) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Jin Kang](https://www.nature.com/articles/s41591-026-04694-y#auth-Jin-Kang-Aff4)[4](https://www.nature.com/articles/s41591-026-04694-y#Aff4) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Tianyi Xu](https://www.nature.com/articles/s41591-026-04694-y#auth-Tianyi-Xu-Aff2)[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Siming Chen](https://www.nature.com/articles/s41591-026-04694-y#auth-Siming-Chen-Aff3)[3](https://www.nature.com/articles/s41591-026-04694-y#Aff3) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Gen Li](https://www.nature.com/articles/s41591-026-04694-y#auth-Gen-Li-Aff2)[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Junlong Li](https://www.nature.com/articles/s41591-026-04694-y#auth-Junlong-Li-Aff1)[1](https://www.nature.com/articles/s41591-026-04694-y#Aff1) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Hang Wong](https://www.nature.com/articles/s41591-026-04694-y#auth-Hang-Wong-Aff2-Aff4)[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2),[4](https://www.nature.com/articles/s41591-026-04694-y#Aff4) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Meihao Wang](https://www.nature.com/articles/s41591-026-04694-y#auth-Meihao-Wang-Aff5)[5](https://www.nature.com/articles/s41591-026-04694-y#Aff5) [na2](https://www.nature.com/articles/s41591-026-04694-y#na2), * [Xiaokai Bai](https://www.nature.com/articles/s41591-026-04694-y#auth-Xiaokai-Bai-Aff6) [ORCID: orcid.org/0009-0002-8382-2976](https://orcid.org/0009-0002-8382-2976)[6](https://www.nature.com/articles/s41591-026-04694-y#Aff6), * [Changxi Hu](https://www.nature.com/articles/s41591-026-04694-y#auth-Changxi-Hu-Aff2)[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2), * [Cheng Tang](https://www.nature.com/articles/s41591-026-04694-y#auth-Cheng-Tang-Aff2)[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2), * [Shengwei Jin](https://www.nature.com/articles/s41591-026-04694-y#auth-Shengwei-Jin-Aff5)[5](https://www.nature.com/articles/s41591-026-04694-y#Aff5), * [Zixing Zou](https://www.nature.com/articles/s41591-026-04694-y#auth-Zixing-Zou-Aff4-Aff7)[4](https://www.nature.com/articles/s41591-026-04694-y#Aff4),[7](https://www.nature.com/articles/s41591-026-04694-y#Aff7), * [Ieng Chong](https://www.nature.com/articles/s41591-026-04694-y#auth-Ieng-Chong-Aff4) [ORCID: orcid.org/0009-0000-7801-1488](https://orcid.org/0009-0000-7801-1488)[4](https://www.nature.com/articles/s41591-026-04694-y#Aff4), * [Yuxing Lu](https://www.nature.com/articles/s41591-026-04694-y#auth-Yuxing-Lu-Aff8) [ORCID: orcid.org/0000-0002-8207-4411](https://orcid.org/0000-0002-8207-4411)[8](https://www.nature.com/articles/s41591-026-04694-y#Aff8), * [Io Nam Wong](https://www.nature.com/articles/s41591-026-04694-y#auth-Io_Nam-Wong-Aff4) [ORCID: orcid.org/0000-0002-4500-1758](https://orcid.org/0000-0002-4500-1758)[4](https://www.nature.com/articles/s41591-026-04694-y#Aff4), * [Hui Xu](https://www.nature.com/articles/s41591-026-04694-y#auth-Hui-Xu-Aff2)[2](https://www.nature.com/articles/s41591-026-04694-y#Aff2), * [Charlotte L. 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It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. ## 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 AI Agent (MoChiAgent), an LLM-based clinical assistant that orchestrates multiple 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 internally evaluated using 4,401,599 longitudinal clinical visits and externally validated 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 modelling 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 AUROCs of 0.89 for placental abruption, 0.89 for premature rupture of membranes, and 0.91 for preterm labour. 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 (HR = 2.81, 95% CI 2.60-3.03) and haematological diseases (HR = 2.83, 95% CI 2.62-3.05). Integrating maternal gestational EHRs with infant records improved prediction of infant conditions, including chromosomal abnormalities and respiratory disorders. These findings suggest that MoChiAgent can provide clinically relevant, actionable decision-support information to enhance risk-stratified care for mothers and infants. [ Download PDF ](https://www.nature.com/articles/s41591-026-04694-y_reference.pdf) ### Explore related subjects Discover the latest articles and news in related subjects. * [Computational models](https://www.nature.com/subjects/computational-models) * [Population screening](https://www.nature.com/subjects/population-screening) ## Author information Author notes 1. These authors contributed equally: Sian Liu, Wenxin Zheng, Jin Kang, Tianyi Xu, Siming Chen, Gen Li, Junlong Li, Hang Wong, Meihao Wang. ### Authors and Affiliations 1. Center for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University; Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission and Chongqing Translational Medicine Center, Chongqing, China Sian Liu, Junlong Li, Jingman Shi, Li Yang, Huanhuan Huang, Sicheng Pan & Hao Wu 2. State Key Lab of Eye Health and Clinical Data Science Institute, Institute for Advanced Study on Eye Health and Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China Sian Liu, Tianyi Xu, Gen Li, Hang Wong, Changxi Hu, Cheng Tang, Hui Xu, Charlotte L. Zhang, Yuan Zhang, Xian Zhu, Xinyu Lu, Sicheng Pan, Ning Sun, Yun Yin & Kang Zhang 3. Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China Wenxin Zheng, Siming Chen, Erhu Feng, Jinyu Gu, Haibo Chen & Yubin Xia 4. Artificial Intelligence Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Macau, China Jin Kang, Hang Wong, Zixing Zou, Ieng Chong, Io Nam Wong, Charlotte L. Zhang, Zhuo Sun, Zhao Zhenhui, Ngaman Cheng, John E. J. Rasko, Kai Wang, Kang Zhang & Zhenhui Zhao 5. Department of Radiology, Department of Anesthesia and Critical Care, Key Laboratory of Pediatric Anesthesiology , Ministry of Education, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China Meihao Wang & Shengwei Jin 6. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China Xiaokai Bai 7. Guangzhou National Laboratory, Guangzhou, China Zixing Zou & Kang Zhang 8. Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing, China Yuxing Lu 9. College of Computer Science, Sichuan University, Chengdu, China Xiuyuan Xu 10. Department of Clinical Research Center, Dazhou Central Hospital and Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Nanchong, China Xue Li & Fanxin Zeng 11. Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China Hongbo Qi 12. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA Michelle Willi
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