Interpretable machine-learning-based prediction of postpartum haemorrhage in normal vaginal births in Shanghai, China

Aug 5, 2026

Postpartum haemorrhage remains the most common complication after vaginal birth and a major cause of maternal mortality. This original research develops and validates an interpretable machine-learning predictive model for postpartum haemorrhage using multidimensional demographic, antenatal, and intrapartum data from pregnant women delivering vaginally in Shanghai between July 2023 and August 2024. The SHAP analysis clarifies key contributors, highlighting variables related to midwife experience, childbirth fear, labour duration, and related intrapartum factors for clinical risk stratification.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Background

Clinical problem and rationale

Aim

Methods

Study setting and participants

Data collection and model building

Interpretability with SHapley Additive exPlanations

Findings

Sample characteristics and variable selection

Best-performing model and key predictive factors

Conclusion

OPEN ACCESS EDITED BY Ali Çetin, University of Health Sciences, Türkiye REVIEWED BY Kingsley Wong, University of Western Australia, Australia Joshua Guedalia, Hadassah Medical Center, Israel *CORRESPONDENCE Rong Huang [huangrong_1986@hotmail.com](huangrong_1986@hotmail.com)[ ](huangrong_1986@hotmail.com)Hao Ying [stephenying_2011@163.com](stephenying_2011@163.com)[ ](stephenying_2011@163.com)†These authors share first authorship RECEIVED 22 July 2025 ACCEPTED 19 September 2025 PUBLISHED 15 October 2025 CITATION Yao X, Bao Y, Wu N, Shan S, Xu Y, Huo K, Huang R and Ying H (2025) Interpretable machine-learning-based prediction of postpartum haemorrhage in normal vaginal births in Shanghai, China. Front. Med. 12:1670987. doi: 10.3389/fmed.2025.1670987 COPYRIGHT © 2025 Yao, Bao, Wu, Shan, Xu, Huo, Huang and Ying. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 15 October 2025 DOI 10.3389/fmed.2025.1670987 Interpretable machine-learning-based prediction of postpartum haemorrhage in normal vaginal births in Shanghai, China Xiao Yao†, Yirong Bao†, Na Wu, Shanshan Shan, Yiting Xu, Keying Huo, Rong Huang * and Hao Ying * Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China Background: Postpartum haemorrhage is the most common complication associated with vaginal birth and a principal cause of maternal mortality. While clinical guidelines suggest that the majority of postpartum haemorrhage cases can be averted through precise prediction and scientific management that utilise assessment tools, existing tools for predicting postpartum haemorrhage in vaginal births have demonstrated inadequacies. Aim: To develop a predictive model for postpartum haemorrhage in vaginal births based on machine-learning algorithms. Methods: We selected pregnant women who gave birth vaginally at a tertiarylevel obstetrics and gynaecology hospital in Shanghai, China, from July 2023 to August 2024. Multidimensional data were collected on demographic factors of pregnant women and midwives, along with their antenatal factors (e. g., previous medical history, current medical history, laboratory indicators, and psychosocial factors) and intrapartum factors (e. g., induction techniques; the first, second, and third stages of labour; and other factors) . Five predictive models were constructed using machine-learning algorithms, and these models were subsequently validated and evaluated for performance. We applied the SHapley Additive exPlanations tool to conduct an interpretative analysis of the optimal model. Findings: A total of 1,225 women who underwent vaginal births were included in our final analysis, and following univariate analysis and least absolute shrinkage and selection operator regression, 13 predictive variables were incorporated into the model. The eXtreme Gradient Boosting model exhibited the most superior performance. A midwife’s years of service, degree of a woman’s fear of childbirth, parity, duration of the second stage of labour, episiotomy, and companionship during labour and childbirth were identified as significant predictive factors. Moreover, the midwife’s years of service and their companionship during childbirth had a moderating effect, which could effectively reduce the impact of childbirth fear and prolonged labour on the risk of postpartum haemorrhage. Conclusion: The postpartum haem

FAQs

What is the main goal of the study?

To develop a predictive model for postpartum haemorrhage in vaginal births using machine-learning algorithms, with interpretability to support clinical use.

What data and time range were used to build the models?

Women who delivered vaginally in Shanghai were selected from July 2023 to August 2024, with multidimensional data covering demographic, antenatal, and intrapartum factors.

How was model interpretability achieved and which factors mattered most?

The study applied SHAP to interpret the optimal model. Key predictors included midwife years of service, a woman’s fear of childbirth, parity, second-stage labour duration, episiotomy, and companionship during labour and childbirth.

Aug 5, 2026

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