使用深度学习和自然语言处理检测个人和家庭自杀思想和行为的历史:一项多站点研究
Prakash Adekkanattu1, Al'ona Furmanchuk2, Yonghui Wu3
1Weill Cornell Medicine, New York, NY, USA.
Research square
|April 1, 2024
概括
深度学习 (DL) 自然语言处理 (NLP) 工具有效地从临床笔记中识别自杀思想和行为 (PSH和FSH) 的个人和家庭历史,优于传统方法. 这些先进的NLP工具提高了医疗保健系统的检测准确性和适应性.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 临床心理学 临床心理学
背景情况:
- 自杀思想和行为 (PSH和FSH) 的个人和家庭史是自杀事件的关键风险因素.
- 这些病史通常被记录在电子健康记录 (EHR) 中的非结构化临床笔记中.
- 准确识别PSH和FSH对于自杀风险评估和预防至关重要.
结论:
- 基于DL的NLP工具为从非结构化的临床笔记中提取PSH和FSH提供了强大的解决方案.
- 这些工具比基于规则的方法更容易适应当地文档的变化.
- 开发的DL工具可以为临床医生提供重要的信息,以识别风险患者,否则可能无法诊断.
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