Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer

Abstract

Statistics can provide inferences within an overall system, while ML is a novel methodology that can make predictions. We can identify patients who are "high risk" for delayed radiation using information from >75,000 patient experiences, which has the potential for a direct impact on clinical care. Our inability to achieve greater accuracy is due to limitations of the data captured by the NCDB, and we need to continue to identify new variables that are correlated with delayed radiation therapy. ML will prove to be a valuable clinical tool in years to come, but its utility is limited by available data.