Identifying chemotherapy regimens in electronic health record data using interval-encoded sequence alignment

Abstract

Electronic health records (EHRs) play an essential role in patient management and guideline-based care. However, EHRs often do not encode therapy protocols directly, and instead only catalog the individual drug agents patients receive. In this paper, we present an automated approach for protocol identification using EHR data. We introduce a novel sequence alignment method based on the Needleman-Wunsch algorithm that models variation in treatment gaps. Using data on 178 breast cancer patients that included manually annotated chemotherapy protocols, our method successfully matched 93% of regimens based on the top score and had 98% accuracy using the top two scored regimens. These results indicate that our sequence alignment approach can accurately find chemotherapy plans in patient event logs while measuring temporal variation in treatment administration.