Predicting Postpartum Hemorrhage Using Clinical Features Extracted With Large Language Models
Abstract
Objective: To evaluate whether large language models (LLMs) applied to prenatal clinical notes can predict postpartum hemorrhage (PPH) before the onset of labor and to compare model performance across outcome definitions, including a novel intervention-based definition.
Methods: We conducted a retrospective cohort study within a large regional health network. Two outcome definitions for PPH were used: 1) estimated or quantitative blood loss (EBL-QBL) extracted from clinical notes; and 2) a clinical intervention-based PPH definition (cPPH) designed to capture significant hemorrhage requiring intervention, including transfusion, uterotonics, Bakri balloon, or hysterectomy. We evaluated three PPH prediction pipelines: 1) structured data only-supervised machine learning that used structured electronic medical record data; 2) LLM-direct-direct prediction that used a fine-tuned LLM applied to clinical notes; and 3) LLM-extract-interpretable models that used LLM-extracted features combined with structured data. Model performance was evaluated using an area under the receiver operating characteristic curve (AUROC) on a temporally held-out test set.
Results: Among 19,992 deliveries, 1,156 patients (5.8%) met the EBL-QBL definition of PPH, 321 (1.6%) met the cPPH definition, and 309 (1.5%) met both definitions. The LLM-based direct prediction model achieved the highest AUROC for both PPH definitions (AUROC 0.79-0.80), followed by interpretable models that combined LLM-extracted…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,156 participants or records.
Read the paper
Related studies
- Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects
- Information quality of videos related to esophageal cancer on tiktok, kwai, and bilibili: a cross-sectional study
- The quality and reliability of short videos about hypertension on TikTok: a cross-sectional study
- Quality and reliability evaluation of pancreatic cancer-related video content on social short video platforms: a cross-sectional study
- Quality and reliability of pediatric pneumonia related short videos on mainstream platforms: cross-sectional study
- Association between metabolic score for insulin resistance (METS-IR) and hypertension: a cross-sectional study based on NHANES 2007–2018
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.