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Postpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide, accounting for 25–30% of deaths, particularly in low-resource settings, and early identification of high-risk patients during labor could enable timely interventions such as uterotonic administration, blood preparation, and escalation of care; however, current risk stratification models rely mainly on static antepartum factors and fail to incorporate dynamic intrapartum physiological changes. Existing tools, including those from the California Maternal Quality Care Collaborative, use baseline maternal characteristics such as prior PPH, BMI, parity, and comorbidities, but do not capture continuously evolving labor data, despite intrapartum signals like fetal heart rate patterns, maternal vital sign trends, and labor progression metrics containing rich predictive information that remains underused in real-time decision-making, while clinical judgment is limited by inter-observer variability and inability to integrate complex temporal trends. To address this gap, we propose an explainable gradient boosting machine framework for real-time PPH risk prediction that integrates electronic fetal monitoring parameters (baseline rate, variability, decelerations), maternal vital signs (heart rate, blood pressure, temperature, oxygen saturation), and labor progression features (cervical dilation, contraction frequency, stage duration, and oxytocin use), producing continuously updated risk scores throughout labor. The system combines a gradient boosting model (XGBoost or LightGBM), a SHAP-based explainability module, a real-time feature extraction pipeline, and a clinician-facing dashboard that displays risk scores and key contributing factors, where SHAP provides both global and patient-specific interpretability by identifying how features such as tachysystole or prolonged labor stages influence predictions, thereby improving transparency and clinical trust. Overall, this framework enables dynamic, interpretable PPH risk assessment using routinely collected intrapartum data, combining predictive accuracy with explainability to support earlier detection of hemorrhage risk and more timely, targeted interventions.

Anticoagulation management requires balancing multiple factors such as bleeding risk, thromboembolic risk, drug interactions, and renal function. Deep learning can assist in risk prediction, but its effectiveness relies on clinicians' ability to understand and verify the recommendations. Black-box models may recommend actions without providing clear explanations. In contrast, clinical guidelines are rule-based but not directly executable by neural models. This article introduces a neuro-symbolic XAI framework that combines deep learning predictions with explicit clinical guidelines. It includes a neural prediction module, a symbolic reasoning engine, and an integration layer for traceable justifications. The neuro-symbolic approach connects data-driven predictions to clinical rules, improving auditability and trustworthiness in decision support. This framework aims to enhance anticoagulation management by providing verifiable, clinician-understandable decision support, focusing on explainability-by-design.