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Automated Identification of Radiotherapy Courses From US Department of Veterans Affairs Administrative Data
William Schreyer1,2, Ryan Melson1, Christopher Anderson1
1Portland VA Research Foundation, Portland, OR.
Purpose:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
