Developing and Evaluating a Computable Phenotype for Treatment-Resistant Schizophrenia
2021 Award: $41,302
Treatment-Resistant Schizophrenia affects about 30% of Schizophrenia patients. Reliable identification of TRS patients within an Electronic Health Record (EHR) system will improve patient care and enhance clinical research. We will develop a computable phenotyping algorithm by combining several information technologies to characterize TRS patients from an EHR system.
Need/Problem: Treatment-Resistant Schizophrenia (TRS) affects about 30% of schizophrenia patients. However, the utilization rate of clozapine, the only approved antipsychotic for TRS, remains low. Characterization of TRS patients from Electronic Health Records will facilitate early detection of TRS patients and subsequently increase the use of clozapine.
Grant Summary: We will use an array of information technologies (database query, temporal medication mining, and natural language processing) to develop an algorithm that could quickly characterize TRS patients in an Electronic Health Records Systems. The performance of the algorithm will be systematically assessed.
Goals and Projected Outcomes: The goal of the project to generate a computable phenotyping algorithm that could be used to mine Electronic Health Records to identify TRS patients in an automated manner. We will use data from UNC EHR to train and assess the algorithm. We expect the final version of the algorithm to be shared among the computable phenotyping community to be used in other EHR settings.