Lee Hyunjoon, Bejan Cosmin A, Walsh Colin G
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
AMIA Annu Symp Proc. 2025 May 22;2024:648-654. eCollection 2024.
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.
在本研究中，我们探索了一种自然语言处理（NLP）算法与基于诊断代码的方法相比，识别近期但不同的自杀未遂（SA）事件的能力。本研究使用了一种在识别SA事件方面具有高精度的NLP算法，该算法处理临床记录中的自杀相关文本表达，并在提及的日期生成SA结果相关性分数。我们对间隔时间小于15天的所有SA就诊对进行了图表回顾。尽管样本量有限，但我们的NLP方法超过了基于代码的模型的性能（0.85 [95%置信区间：0.74 - 0.92] 对 0.78 [95%置信区间：0.56 - 0.92]，p = 0.71）。更重要的是，与基于代码的方法相比，NLP检测到的间隔时间<15天的SA就诊对多两倍（71对 23对），仅有3对重叠。本研究证明了NLP在识别不同的SA就诊对方面的有效性。鉴于重叠极少，我们建议同时利用临床记录和诊断代码进行全面的SA事件检测。
