Machine Learning Predicts 30‐Day Readmission and Mortality After Surgical Resection of Head and Neck Cancer

Abstract Objective To develop and validate a machine learning model to identify patients at high risk of 30‐day mortality and hospital readmission using routinely collected health care data. Study Design Prognostic predictive modeling and retrospective cohort study. The study was conducted in 2024 using data from 2006 to 2018, with at least a 30‐day follow‐up. Setting The 2006 to 2018 National Cancer Database (NCDB). Methods The study used deidentified NCDB data on 103,891 head and neck squamous cell carcinoma (HNSCC) patients who underwent surgical resection. Machine learning models were trained on 80% of the data, tested on the remaining 20%, and evaluated using the area under the curve (AUC) and SHapley Additive exPlanations (SHAP) analysis to identify key predictors for 30‐day mortality and readmission. Results Among 103,891 patients, 5838 (5.6%) were readmitted, and 829 (0.8%) died within 30 days. The median age was 62, 69% male, and 89% white. Predictors included demographic and clinical data from the NCDB. Five machine learning models were combined and achieved an AUC of 0.80 (95% CI: 0.77‐0.83) for mortality prediction and 0.67 (95% CI: 0.65‐0.68) for readmission prediction. SHAP analysis identified sex and urban‐rural index as key predictors of mortality and readmission, respectively. Conclusion Machine learning models can accurately predict mortality and readmission risks, offering insights into the most influential factors. With further validation, these models may enhance clinical decision‐making in postsurgical care for HNSCC patients.