aceToward Clinically Interpretable Perioperative Decision Support: Explainable Machine Learning for 30-Day Postoperative Mortality Prediction
Postoperative mortality prediction remains a critical challenge in healthcare, demanding robust and interpretable predictive methods. This study developed and evaluated machine learning (ML) models using the novel Informative Surgical Patient dataset for Innovative Research Environment (INSPIRE), a comprehensive perioperative dataset, to predict 30-day postoperative mortality. Patient-specific features such as ASA physical status, BMI, and temporal medical parameters were incorporated. Data imbalance was address…
Machine learning models trained on perioperative data achieved high discrimination for 30-day postoperative mortality and identified clinically meaningful predictors, supporting more transparent surgical risk assessment and patient counseling.
Evidence
- Peer-reviewedThe American Surgeon™2026-09-23
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Truvace Impact Record TRV-2026-1207, v1: “Toward Clinically Interpretable Perioperative Decision Support: Explainable Machine Learning for 30-Day Postoperative Mortality Prediction.” Truvace, 2026-09-28. /record/TRV-2026-1207 (accessed at citation time). sha256 24b11f5f0212c7c0…
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