通过基于规则的方法,机器学习和预训练的语言模型,通过精神病学入院笔记识别精神病发作
Yining Hua1,2,3, Suzanne V Blackley4, Ann K Shinn5,6
1Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. yininghua@g.harvard.edu.
Translational psychiatry
|November 29, 2025
概括
自然语言处理 (NLP) 模型显著改善了精神病学笔记中的精神病检测,优于传统方法. 关键词预选可以提高NLP模型的性能,以便在电子健康记录中更准确地识别精神病.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗信息学医学信息学
- 自然语言处理自然语言处理.
背景情况:
- 准确的精神病诊断至关重要,但由于症状的变化和报告不足而具有挑战性.
- 使用结构化电子健康记录 (EHR) 数据的现有方法通常不足以识别精神病.
- 耻辱和洞察力下降进一步使精神病发作的早期和准确诊断复杂化.
研究的目的:
- 评估自然语言处理 (NLP) 算法的有效性,以识别精神病在精神病房的入院笔记.
- 为了比较基于规则,机器学习和预训练的语言模型来检测精神病.
- 评估关键词预选对NLP模型在EHR数据中的表现的影响.
主要方法:
- 分析了来自16-35岁患者的4629张精神病学入院笔记 (2005-2019).
- 应用基于规则的算法,机器学习 (XGBoost与TF-IDF) 和预训练语言模型 (BlueBERT).
- 评估关键词预选策略,在模型培训和分析之前精制笔记.
主要成果:
- 具有TF-IDF功能和关键字预选择的XGBoost分类器获得了最高的F1分数0.8881.
- 蓝伯特 (BlueBERT) 的表现也相似,F1得分为0.8841.
- 这两种NLP模型的表现都明显优于传统的ICD基于代码的检测 (F1得分为0.7608),关键字预选择提高了性能.
结论:
- 在临床笔记中,NLP技术,特别是关键词预选择,提供了一种强有力的方法来改善精神病的检测.
- 与传统方法相比,机器学习和预训练的语言模型在EHR中表现优异,用于识别精神病.
- 这项研究为利用NLP在电子健康记录中增强精神病诊断和患者护理提供了基础.
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