From Signals to Symptoms: An Abstract-Level Systematic Mapping Review of Machine Learning for Preeclampsia Prediction

Background: Preeclampsia remains a major cause of maternal and perinatal morbidity and mortality, and machine learning (ML) and artificial intelligence (AI) models have increasingly been proposed for risk prediction, diagnosis, monitoring, and prognosis. However, the extent to which key methodological details are visible from abstracts and bibliographic metadata remains unclear. Methods: We conducted an abstract-level systematic mapping review of ML/AI studies for preeclampsia (PE), hypertensive disorders of pregnancy (HDP), and directly related complications. Records were identified from ACM Digital Library, Elsevier/ScienceDirect, IEEE Xplore, PubMed, Scopus, SpringerLink, and snowballing. After DOI-based deduplication, abstract-availability checks, technical metadata filtering, and a two-stage eligibility audit, 29 primary ML/AI studies were retained for the final synthesis. Two authors independently re-audited the 145 technically retained records for eligibility. Initial agreement was 131/145 records (90.3%); for the binary include/exclude decision, Cohen’s kappa was approximately 0.764, indicating substantial agreement. Extraction was limited to title, abstract, year, DOI, and bibliographic metadata using a predefined RQ-aligned schema. Therefore, all frequencies represent information explicitly extractable at the abstract level and should be interpreted as lower-bound reporting estimates rather than full-text prevalence estimates. Results: Among the 29 eligible studies, tree-based models were the most frequently identifiable family (17/29), followed by linear/generalized linear models (15/29), support vector machines (9/29), and neural networks (8/29). The most frequently identifiable input modalities were EHR/clinical variables (21/29), demographics (10/29), laboratory variables (7/29), vital signs/blood pressure (7/29), and ultrasound/Doppler information (6/29). AUC was mentioned in 20/29 abstracts, but a numeric AUC value was extractable into the structured field in 14/29. These values were summarized descriptively and were not pooled because outcomes, prediction horizons, populations, predictors, and validation strategies were heterogeneous. External validation was explicitly identifiable in 8/29 abstracts, explainability-related methods in 5/29, and repository or dataset-source information in 1/29. Conclusions: The abstract-level evidence shows an active but heterogeneous ML/AI literature for PE/HDP prediction and related outcomes. However, abstracts often provide insufficient detail for robust comparative assessment of model validity, calibration, clinical readiness, reproducibility, and implementation. Future work should prioritize full-text evidence synthesis, standardized reporting, external and temporal validation, calibration, equity assessment, clinically meaningful explainability, and reusable data/model resources.