Automated Identification of Radiotherapy Courses From US Department of Veterans Affairs Administrative Data.

Abstract

Radiotherapy is a critically important cancer treatment; however, its details are often not well represented in electronic health record data sets. US Veterans' radiation courses are further distributed across a range of medical centers, both internal and external to the Veterans Health Administration (VHA), inhibiting analysis of radiotherapy treatment across this population. We train and test a suite of supervised machine learning models for the accurate prediction of radiation course dates using billing and diagnostic codes from a combination of VHA and Centers for Medicare and Medicaid Services (CMS) databases. We use a separate heuristic algorithm to assemble course date predictions into complete radiation treatments. Our top model predicts radiation course dates with compelling accuracy (macro-average of 0.914 across classes). The retrospective application of our model and assembly algorithm to radiation procedure dates for 1,331,342 patients identified 1,526,660 predicted courses of radiotherapy. The identified courses were collected into a shared resource to facilitate future VHA-based studies, and our predictive model is available for application to a wider range of non-VHA data sets, particularly those leveraging CMS data.