自然语言处理用于识别严重抑郁症患者的自杀念头和无情感
L Alexander Vance1, Leslie Way2, Deepali Kulkarni3
1Holmusk Technologies, Inc, 54 Thompson St, New York, NY, 10012, USA. alex.vance@holmusk.com.
BMC medical informatics and decision making
|January 13, 2025
概括
自然语言处理 (NLP) 准确地识别了使用临床笔记的大型抑郁症 (MDD) 患者的无情和自杀念头. 这种方法通过提供MDD进展的全面视图来增强心理健康护理.
科学领域:
- 计算语言学应用于心理健康.
- 开发人工智能模型用于临床数据分析.
背景情况:
- 厌倦和自杀念头是主要抑郁症 (MDD) 的关键症状.
- 这些症状通常不会被结构化的临床尺度所捕捉,而是存在于非结构化的电子健康记录 (EHR) 注释中.
- 自然语言处理 (NLP) 为从临床笔记中提取这些症状的纵向数据提供了一种方法.
研究的目的:
- 评估NLP技术在MDD患者的EHR中识别无情和自杀症状的准确性.
- 评估NLP从非结构化的临床笔记中提取纵向症状数据的能力.
主要方法:
- 使用了来自NeuroBlu数据库的非识别的EHR数据.
- 通过让心理健康临床医生注释无欲症和自杀症状的情况,建立了一个基本真理.
- 训练了一种基于新型变压器架构的NLP模型,将句子分类为无情,有/没有意图的自杀念头或缺乏症状.
- 使用积极预测值 (PPV),F1评分和评分间可靠性 (IRR) 等指标评估模型性能.
主要成果:
- 实现了0.80.8的高间隔可靠性 (IRR).
- NLP模型表现出强的表现,验证的正预测值 (PPV) 为无情感的0.99,有意自杀的0.95,无意自杀的0.87.
- 验证F1分数为0.99,0.95和0.89对于各自的类别.
结论:
- NLP有效地利用EHR中的上下文信息来识别MDD患者的临界无意识和自杀症状.
- 通过NLP整合结构化和非结构化数据,可以提供MDD的整体视图,从而实现及时和有效的干预.
- 对NLP模型的进一步改进将改善临床特征的提取,支持个性化和积极的心理健康护理.
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