Community-Based Service Use After Clozapine Treatment: A Mirror-Image Analysis of Public Claims Data.

Although clozapine demonstrates unique efficacy for treatment-resistant schizophrenia, its impact on community-based services remains largely underexplored. The authors examined changes in use of community-based services after clozapine treatment among a sample of 163 patients with schizophrenia by using public claims data in Allegheny County, Pennsylvania. Mirror-image analyses of service utilization were used to compare the 180-day period before treatment initiation with the 180-day period that began after 6 months of adherent treatment, accounting for age, race, and gender. Across demographic variables, clozapine treatment was associated with increased use of community-based services and decreased use of psychiatric inpatient services (p<0.05, Bonferroni corrected), suggesting that clozapine treatment shifts service needs from emergency care to community-based care and recovery.

Hybrid bag of approaches to characterize selection criteria for cohort identification.

OBJECTIVE: The 2018 National NLP Clinical Challenge (2018 n2c2) focused on the task of cohort selection for clinical trials, where participating systems were tasked with analyzing longitudinal patient records to determine if the patients met or did not meet any of the 13 selection criteria. This article describes our participation in this shared task. MATERIALS AND METHODS: We followed a hybrid approach combining pattern-based, knowledge-intensive, and feature weighting techniques. After preprocessing the notes using publicly available natural language processing tools, we developed individual criterion-specific components that relied on collecting knowledge resources relevant for these criteria and pattern-based and weighting approaches to identify "met" and "not met" cases. RESULTS: As part of the 2018 n2c2 challenge, 3 runs were submitted. The overall micro-averaged F1 on the training set was 0.9444. On the test set, the micro-averaged F1 for the 3 submitted runs were 0.9075, 0.9065, and 0.9056. The best run was placed second in the overall challenge and all 3 runs were statistically similar to the top-ranked system. A reimplemented system achieved the best overall F1 of 0.9111 on the test set. DISCUSSION: We highlight the need for a focused resource-intensive effort to address the class imbalance in the cohort selection identification task. CONCLUSION: Our hybrid approach was able to identify all selection criteria with high F1 performance on both training and test sets. Based on our participation in the 2018 n2c2 task, we conclude that there is merit in continuing a focused criterion-specific analysis and developing appropriate knowledge resources to build a quality cohort selection system.