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

SUBMITTER: Fu D 

PROVIDER: S-EPMC11924807 | biostudies-literature | 2025 Jan-Mar

REPOSITORIES: biostudies-literature