Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.

ABSTRACT:

Importance

Patient-reported symptom burden was recently found to be associated with emergency department use and unplanned hospitalization (ED/Hosp) in patients with head and neck cancer. It was hypothesized that symptom scores could be combined with administrative health data to accurately risk stratify patients.

Objective

To develop and validate a machine learning approach to predict future ED/Hosp in patients with head and neck cancer.

Design, setting, and participants

This was a population-based predictive modeling study of patients in Ontario, Canada, diagnosed with head and neck cancer from January 2007 through March 2018. All outpatient clinical encounters were identified. Edmonton Symptom Assessment System (ESAS) scores and clinical and demographic factors wer

SUBMITTER: Noel CW 

PROVIDER: S-EPMC9247860 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature