Frontiers in medicine · 2026
Development and validation of a postpartum hemorrhage risk prediction model for advanced maternal age women undergoing vaginal delivery: a comparison of multiple machine learning algorithms.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhou Yun, Yao Xiao, Zhou Jue, Duan Yaoxiang, Yu Yi, Ma Xueer
Аннотация
BACKGROUND: Postpartum hemorrhage (PPH) is the leading cause of maternal mortality in women of advanced maternal age (AMA). Accurate antenatal prediction remains challenging. This study aimed to develop and compare the performance of multiple machine learning (ML) algorithms for predicting PPH risk following vaginal delivery in AMA parturients.
METHODS: A retrospective cohort study was conducted using data from 5,369 women who delivered vaginally at Shanghai First Maternity and Infant Hospital. LASSO regression was used to identify key predictors. Seven machine learning algorithms-logistic regression (LR), decision tree (DT), random forest (RF), XGBoost, LightGBM, support vector machine (SVM), and artificial neural network (ANN)-were employed to build prediction models. Model performance was evaluated on an independent validation set using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score. The optimal model was selected based on a composite criterion prioritizing the highest sensitivity and best F1 score, while requiring a competitive AUC.
RESULTS: Eight predictors were selected for model construction. Among the seven models, the SVM model achieved the highest sensitivity (0.482) and the highest F1 score (0.504), and was therefore selected as the clinically preferred model, while maintaining a competitive AUC of 0.782 (95% CI: 0.758-0.806). SVM achieved a favorable balance between sensitivity and specificity, indicating better clinical utility for early screening and risk stratification of postpartum hemorrhage.
CONCLUSION: Among seven machine learning models, the SVM model was selected as the clinically preferred predictor due to its highest sensitivity and F1-score. Its good discriminative ability and balanced performance make it suitable for clinical application as a screening tool. The integration of SHAP analysis can further enhance model interpretability and facilitate individualized risk assessment.
METHODS: A retrospective cohort study was conducted using data from 5,369 women who delivered vaginally at Shanghai First Maternity and Infant Hospital. LASSO regression was used to identify key predictors. Seven machine learning algorithms-logistic regression (LR), decision tree (DT), random forest (RF), XGBoost, LightGBM, support vector machine (SVM), and artificial neural network (ANN)-were employed to build prediction models. Model performance was evaluated on an independent validation set using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score. The optimal model was selected based on a composite criterion prioritizing the highest sensitivity and best F1 score, while requiring a competitive AUC.
RESULTS: Eight predictors were selected for model construction. Among the seven models, the SVM model achieved the highest sensitivity (0.482) and the highest F1 score (0.504), and was therefore selected as the clinically preferred model, while maintaining a competitive AUC of 0.782 (95% CI: 0.758-0.806). SVM achieved a favorable balance between sensitivity and specificity, indicating better clinical utility for early screening and risk stratification of postpartum hemorrhage.
CONCLUSION: Among seven machine learning models, the SVM model was selected as the clinically preferred predictor due to its highest sensitivity and F1-score. Its good discriminative ability and balanced performance make it suitable for clinical application as a screening tool. The integration of SHAP analysis can further enhance model interpretability and facilitate individualized risk assessment.