Abstract：
Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to analyze high-suicide risk Veterans Affairs patients\' unstructured electronic health records. Our sample included all high-risk patients that died (cases) or did not (controls) by suicide in 2017 and 2018. Cases and controls shared the same risk, location, and treatment intervals and received nine months of mental health care during the year before the relevant end date. Each case was matched with five controls. We analyzed case records from diagnosis until death and control records from diagnosis until matched case\'s death date. Our final sample included 218 cases and 943 controls. We analyzed the corpus using a Python-based Dynamic Topic Modeling algorithm. We identified five distinct topics, \"Medication,\" \"Intervention,\" \"Treatment Goals,\" \"Suicide,\" and \"Treatment Focus.\" We observed divergent change patterns over time, with pathology-focused care increasing for cases and supportive care increasing for controls. The case topics tended to fluctuate more than the control topics, suggesting the importance of monitoring lability. Our study provides a method for monitoring risk fluctuation and strengthens the groundwork for time-sensitive risk measurement.
摘要：
衡量自杀风险波动仍然很困难，特别是高自杀风险患者。我们的研究通过利用动态主题建模来解决这个问题，一种自然语言处理方法，用于评估主题随时间的变化，分析高自杀风险退伍军人事务部患者的非结构化电子健康记录。我们的样本包括2017年和2018年因自杀死亡（病例）或未死亡（对照）的所有高危患者。案例和控制共享相同的风险，location,和治疗间隔，并在相关结束日期之前的一年内接受了9个月的精神健康护理。每个病例与5个对照相匹配。我们分析了从诊断到死亡的病例记录和从诊断到匹配病例死亡日期的对照记录。我们的最终样本包括218例病例和943例对照。我们使用基于Python的动态主题建模算法对语料库进行了分析。我们确定了五个不同的主题，\"药物治疗,\"\"干预,\"\"治疗目标,\"\"自杀,“和”治疗重点。“我们观察到随着时间的推移不同的变化模式，病例以病理学为重点的护理增加，对照组的支持性护理增加。案例主题往往比对照主题波动更大，这表明了监测不稳定的重要性。我们的研究提供了一种监测风险波动的方法，并为时间敏感风险度量奠定了基础。
