通过基于规则的方法,机器学习和预训练的语言模型来识别精神病入院笔记中的精神病情节
Yining Hua1,2,3, Suzanne V Blackley4, Ann K Shinn5,6
1Departmetn of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.
medRxiv : the preprint server for health sciences
|April 2, 2024
概括
自然语言处理 (NLP) 在精神病学笔记中显著改善了精神病的检测. 预选关键词可以增强机器学习和语言模型,优于早期诊断的传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 精神病学是一个精神病学.
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.
- 电子健康记录 (EHR) 往往缺乏详细的临床信息,以追溯识别精神病.
- 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:
- 为了评估NLP算法用于精神病诊断精神病入院笔记.
- 为了比较基于规则,机器学习和预训练的语言模型.
- 评估关键词预选对模型性能的影响.
主要方法:
- 分析了4,617份精神病医院的入院记录 (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).
- 对关键词预选策略的评估,以简化NLP模型的数据.
主要成果:
- 使用关键字选择的TF-IDF特征的XGBoost获得了F1得分0.8881.
- 蓝伯特获得了0.8841.1的F1比较得分.
- 这两种NLP模型的表现都明显优于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,特别是与关键词预选择相结合时,提供了一种强有力的方法来增强精神病检测.
- 这项研究表明NLP有潜力提高精神病治疗的诊断准确性和效率.
- 这些发现为未来的研究提供了基础,将NLP应用于EHR数据中的精神病识别.
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