Interpretable/Explainable Predictive Modeling with Perioperative Dataset

This study focuses on analyzing a perioperative dataset to extract knowledge and gain valuable insights for developing predictive models that enable real-time risk assessment and early intervention strategies for patients. We performed our analysis on several fronts, including K-means Clustering, Association Rule Mining, and SHAP analysis. Moreover, we developed two deep Learning models based on MambaNet and TabNet, achieving high AUROC scores of 0.946 and 0.923, respectively. The model’s objective is to predict 30-day mortality after surgery.