Large-scale Text Mining of Suicide Attempt improves Identification of Distinct Suicidal Events in Electronic Health Records.

In this study, we explore a natural language processing (NLP) algorithm's capacity to identify proximal but distinct suicide attempt (SA) events compared to diagnostic code-based approaches. This study used an NLP algorithm with high precision in identifying SA events, which processes clinical notes for suicide-related text expressions and generates SA outcome relevance scores on mentioned dates. We chart reviewed all SA visit pairs less than 15 days apart. Despite sample size limitations, our NLP method surpassed the code-based model's performance (0.85 [95% CI: 0.74 - 0.92] vs. 0.78 [95% CI: 0.56 - 0.92], p = 0.71). More importantly, NLP detected three times more SA visit pairs <15 days compared to the code-based approach (71 vs. 23), with only 3 overlaps. This study demonstrates NLP's efficacy in identifying distinct SA visit pairs. Given minimal overlap, we suggest leveraging both clinical notes and diagnostic codes for a comprehensive SA event detection.