Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients

IF 3.7 2区 医学 Q1 PSYCHIATRY Journal of psychiatric research Pub Date : 2024-09-24 DOI:10.1016/j.jpsychires.2024.09.031
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Abstract

Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk. This high-risk population accounts for less than 10% of VA patient suicide deaths. We previously evaluated epidemiological patterns among VA patients that had lower classified suicide risk and derived moderate- and low-risk groupings. Expanding upon VA's leading suicide prediction model, this study uses national VA data to refine high-, moderate-, and low-risk specific suicide prediction methods. We selected all VA patients who died by suicide in 2017 or 2018 (n = 4584), matching each case with five controls who remained alive during treatment year and shared suicide risk percentiles. We extracted all sample unstructured electronic health record notes, analyzed them using natural language processing, and applied machine-learning classification algorithms to develop risk-tier-specific predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy and analyzed derived words.

Results

Our high-risk model (AUC = 0.621 (95% CI: 0.55–0.68)), moderate-risk (AUC = 0.669 (95% CI: 0.64–0.71)), and low-risk (AUC = 0.673 (95% CI: 0.63–0.72)) models offered significant predictive accuracy over VA's leading suicide prediction algorithm. Derived words varied considerably, the high-risk model including chronic condition service words, moderate-risk model including outpatient care, and low-risk model including acute condition care.
Study suggests benefit of leveraging unstructured electronic health records and expands prediction resources for non-high-risk suicide decedents, an historically underserved population.
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利用自然语言处理技术为退伍军人事务患者开发风险等级特定的自杀预测模型
自杀是导致死亡的主要原因之一。退伍军人事务部(VA)病人的自杀率尤其高。虽然退伍军人事务部在预防自杀方面取得了有影响力的进展,但主要针对的是有自杀风险记录的高危患者。在退伍军人事务部的自杀死亡患者中,这类高危人群所占比例不到 10%。我们之前评估了退伍军人事务部自杀风险分类较低的患者的流行病学模式,并得出了中度和低度风险分组。在退伍军人事务部领先的自杀预测模型的基础上,本研究使用退伍军人事务部的全国数据来完善高、中、低风险的特定自杀预测方法。我们选取了所有在 2017 年或 2018 年自杀身亡的退伍军人事务部患者(n = 4584),将每个病例与五个在治疗年度内仍然存活且自杀风险百分位数相同的对照组进行匹配。我们提取了所有非结构化电子健康记录笔记样本,使用自然语言处理对其进行了分析,并应用机器学习分类算法开发了风险等级特异性预测模型。结果我们的高风险模型(AUC = 0.621 (95% CI: 0.55-0.68))、中度风险模型(AUC = 0.669 (95% CI: 0.64-0.71))和低风险模型(AUC = 0.673 (95% CI: 0.63-0.72))与退伍军人事务部领先的自杀预测算法相比具有显著的预测准确性。研究表明,利用非结构化电子健康记录很有益处,并为非高风险自杀死者这一历来服务不足的人群扩展了预测资源。
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来源期刊
Journal of psychiatric research
Journal of psychiatric research 医学-精神病学
CiteScore
7.30
自引率
2.10%
发文量
622
审稿时长
130 days
期刊介绍: Founded in 1961 to report on the latest work in psychiatry and cognate disciplines, the Journal of Psychiatric Research is dedicated to innovative and timely studies of four important areas of research: (1) clinical studies of all disciplines relating to psychiatric illness, as well as normal human behaviour, including biochemical, physiological, genetic, environmental, social, psychological and epidemiological factors; (2) basic studies pertaining to psychiatry in such fields as neuropsychopharmacology, neuroendocrinology, electrophysiology, genetics, experimental psychology and epidemiology; (3) the growing application of clinical laboratory techniques in psychiatry, including imagery and spectroscopy of the brain, molecular biology and computer sciences;
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