Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.

BACKGROUND: A majority (>80%) of maternal deaths in the United States are preventable. Using machine learning (ML) models that are generated from electronic medical records (EMRs) may be a promising approach to predict the risk of adverse maternal outcomes and enable proactive intervention to prevent maternal mortality. Current evidence syntheses of such ML approaches either focus only on specific maternal outcomes, aspects other than risk prediction, or do not consider the full pipeline of studies from the development to implementation in clinical practice. OBJECTIVE: The goal of this scoping review is to document evidence for the use of ML models for predicting the risk of maternal morbidity and mortality outcomes (research objective [RO1]), the translation of such models into applications for clinical use by providers (RO2), and factors associated with the implementation of clinical applications in practice (RO3). METHODS: The review was limited to studies in health care settings, using data from EMRs. A detailed search string was developed in collaboration with a health sciences librarian and implemented on February 20, 2023, on PubMed, CINAHL Plus, Scopus, Embase, and IEEE Xplore. Two reviewers independently reviewed titles and abstracts for inclusion, and a third reviewer resolved conflicts. Only full-length journal articles published in English were included. Studies using non-EMR data exclusively were excluded. Two reviewers independently reviewed full texts for inclusion, and a third reviewer resolved conflicts. A structured template was used for data extraction, and findings were summarized descriptively. RESULTS: From 480 deduplicated studies identified from the search, 142 studies were included for full-text review, and 39 studies were included in the review. More than half of the included studies were conducted in 2022, and 34 studies were from just 3 countries (United States, China, and Israel). More studies focused on identifying the risk of pregnancy and delivery outcomes compared with postpartum outcomes. The top 3 most common outcomes for risk prediction were cardiovascular risks and hypertensive disorders of pregnancy (9 studies), gestational diabetes (7 studies), and postpartum hemorrhage (6 studies). Data were labeled with computable phenotypes in 30 studies, and the most often used method in ML models was boosting methods (18 studies). The most common metric used to assess model performance was area under the precision-recall curve (AUPRC; 33 studies). No studies described clinical applications of ML models for providers (RO2) or associated implementation factors (RO3). CONCLUSIONS: Key recommendations for future research and practice include expanding efforts to study maternal morbidity and mortality outcomes in the postpartum period, increasing transparency and reproducibility of studies through use of reporting checklists, and expanding efforts to implement ML models in clinical practice.

Duke Scholars

Author David Page Biostatistics & Bioinformatics, Division of Biostatistics