识别自杀企图风险时期的特征,使用电子健康记录中的文档频率和语言使用
Rina Dutta1,2, George Gkotsis1, Sumithra U Velupillai1
1King's College London, IoPPN, London, United Kingdom.
Frontiers in psychiatry
|December 28, 2023
概括
电子健康记录 (EHR) 和机器学习可以识别自杀企图的风险. 然而,许多有风险的患者缺乏最近的文档,这突出了心理健康监测的差距.
科学领域:
- 心理健康护理 心理健康护理
- 临床信息学 临床信息学
- 医疗保健中的机器学习
背景情况:
- 个性化心理健康护理需要确定可修改的自杀风险因素.
- 目前的风险评估依赖于静态因素,需要专注于动态的患者健康轨迹.
- 电子健康记录 (EHR) 和机器学习为开发自杀风险早期预警指标提供了潜力.
研究的目的:
- 确定EHR的临床自由文本数据是否可以将近自杀企图的时期与风险较低的时期区分开来.
- 调查电子健康记录记录率与自杀企图之间的关联.
- 通过使用机器学习来比较EHRs中的词语使用和n-gram频率与自杀企图的近距离和远距离.
主要方法:
- 通过临床记录交互搜索 (CRIS) 系统,利用了来自南伦敦和莫德斯利NHS基金会信托 (SLaM) 的非身份化EHR数据.
- 将CRIS数据与入院患者护理的医院病例统计 (HES) 相关联.
- 分析了2006年4月至2013年3月期间住院自杀企图的患者的文件和事件数据,重点关注ICD-10代码X60-X84,Y10-Y34,Y87.0/Y87.2.2.
主要成果:
- 在8247名住院自杀企图的患者中,有3167名 (39.8%) 患者先前有EHR文件.
- 在自杀未遂之前的60天内,观察到监测的快速增加 (35.1%至45.0%).
- 提到药物/药物/过量服用的文件 (OR 1.88) 表示风险更高,而引用护理计划的文件 (OR 0.22) 表示风险更低. 函数词和代名词的使用在近位和远位的尝试中有所不同.
结论:
- 电子心脏病记录的频率和语言模式可以区分自杀企图附近的时期.
- 具有先前记录的患者中有很大一部分 (55.0%) 在第一次尝试之前的30天内缺乏记录,这表明在关键时期可能存在护理差距.
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