自杀本体学的三步理论的发展,以促进从临床进展笔记中提取3ST因子
Esther L Meerwijk1, Gabrielle A Jones1, Asqar S Shotqara1
1VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
Journal of biomedical informatics
|December 31, 2023
概括
这项研究开发了一个本体学和受控的词汇,从临床笔记中提取自杀风险因素,改善退伍军人卫生管理局 (VHA) 患者的预测算法. 该系统准确地识别了自杀的三步理论 (3ST) 的关键元素.
科学领域:
- 计算语言学计算语言学
- 精神病学是一个精神病学.
- 医疗信息学 医疗信息学
背景情况:
- 退伍军人卫生管理局 (VHA) 自杀风险预测模型缺乏关键的心理因素.
- 自杀的三步理论 (3ST) 确定了诸如绝望和心理疼痛等关键因素.
- 这些3ST因素存在于非结构化的临床文本中,而不是结构化的EHR数据.
研究的目的:
- 在临床笔记中为3ST因子创建一个本体学和受控的词汇.
- 从电子健康记录中自动提取与3ST相关的术语.
- 评估自动化3ST因子识别的准确性.
主要方法:
- 语言注释3万个临床进展记录自试杀或已故的退伍军人.
- 开发一个受控的词汇,包含33个子类中的8486个术语.
- 机器标记1400万份临床笔记,并通过随机抽样进行准确性评估.
主要成果:
- 控制的词汇成功地将术语映射到3ST因子和极性.
- 自动提取实现了高精度 (0.73-1.00) 和中度回忆 (0.65-0.91).
- 开发的本体论通过BioPortal.com公开提供.
结论:
- 成功开发了对3ST因子的本体学和受控词汇.
- 从临床笔记中自动提取可以识别关键的自杀风险指标.
- 这种方法可以追踪自杀事件之前的3ST因素.
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