Suicide is a leading cause of death in the US. Patients with pain conditions have higher suicidal risks. In a systematic review searching observational studies from multiple sources (e.g., MEDLINE) from 1 January 2000–12 September 2020, we evaluated existing suicide prediction models’ (SPMs) performance and identified risk factors and their derived data sources among patients with pain conditions. The suicide-related outcomes included suicidal ideation, suicide attempts, suicide deaths, and suicide behaviors. Among the 87 studies included (with 8 SPM studies), 107 suicide risk factors (grouped into 27 categories) were identified. The most frequently occurring risk factor category was depression and their severity (33%). Approximately 20% of the risk factor categories would require identification from data sources beyond structured data (e.g., clinical notes). For 8 SPM studies (only 2 performing validation), the reported prediction metrics/performance varied: C-statistics (n = 3 studies) ranged 0.67–0.84, overall accuracy(n = 5): 0.78–0.96, sensitivity(n = 2): 0.65–0.91, and positive predictive values(n = 3): 0.01–0.43. Using the modified Quality in Prognosis Studies tool to assess the risk of biases, four SPM studies had moderate-to-high risk of biases. This systematic review identified a comprehensive list of risk factors that may improve predicting suicidal risks for patients with pain conditions. Future studies need to examine reasons for performance variations and SPM’s clinical utility.
Ambiguity and misunderstanding of free-text clinical trial eligibility can affect the accuracy of translating trial investigators' mental model of the study population to the correct cohort identification queries. In this pilot study, to eliminate the ambiguity when parsing eligibility criteria, we built ontology-based representations to standardize clinical trial eligibility criteria. We analyzed 10 Alzheimer's disease (AD) trials' eligibility criteria and categorized them into general query patterns using an annotation schema borrowed from the literature on constructing knowledge graphs. Then, for each pattern, we built the corresponding ontological representations, linked them to real-word electronic health record (EHR) data, and constructed cohort identification queries using the neo4j graph database. Our evaluation results of these cohort queries verified the accuracy of our ontology representation; and interestingly, we found that graph-queries achieved better runtime performance for complex study traits. These results indicated that our approach is feasible and potentially beneficial; nevertheless, more systematic and comprehensive investigations are warranted.
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