Work

Development of a machine learning model to predict venous thromboembolism risk in women using combined oral contraceptives

Year: 2026

Type: article

Abstract: Objective Venous thromboembolism (VTE) is a major cause of morbidity and mortality, with combined oral contraceptives (COCPs) being a modifiable risk factor. Existing clinical tools have limited ability to capture complex predictor interactions. This study aimed to develop and evaluate machine learning (ML) models to predict VTE risk among COCP users.Methods This study was conducted using data from the Saudi Real-World Evidence Network (2016–2023). The primary outcome was the first VTE event within 2 years of COCP initiation. Following data partitioning, feature selection was performed on the training set. Elastic-net logistic regression, random forest (RF), extreme gradient boosting (XGBoost), one-class support vector machine and Isolation-Forest were trained on original and resampled datasets. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 score.Results The cohort included 24 518 treatment episodes (n=17 512, VTE events=52). After handling class imbalance, RF and XGBoost were the best-performing models (F1: 84.3% and 81.6%; AUROC: 59.6% and 69.2%, respectively), compared with other models (F1: 2.6%–55.3%, AUROC: 54.2%–68.4%). Key predictors included age, glucocorticoid use and rheumatic, renal and thyroid diseases.Discussion ML shows potential for VTE risk detection despite low event rate and class imbalance. However, findings suggest synthetic sample overfitting, and external validation is warranted before clinical deployment. Identified predictors reinforce the role of metabolic and inflammatory factors in VTE susceptibility among COCP users.Conclusion ML models, particularly XGBoost and RF, demonstrated moderate predictive ability for VTE among COCP users, supporting their use in individualised risk assessments, though external validation is warranted.

Source: BMJ Health & Care Informatics

DOI: 10.1136/bmjhci-2025-102020

Authors: Almaha Alfakhri, Ohoud Almadani, Hanan Alsehli, Ibrahim Asiri

Institutions: Saudi Food and Drug Authority, King Saud bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, King Abdullah International Medical Research Center, National Guard Health Affairs

Language: English

Topic: Venous Thromboembolism Diagnosis and Management

Subfield: Internal Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,207