从使用自然语言处理的临床叙述中识别 Delirium 的症状
Aokun Chen1, Daniel Paredes1, Zehao Yu1
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
这项研究开发了一种新的自然语言处理 (NLP) 系统,从临床笔记中提取妄想症状,改善这种急性认知状况的识别和诊断.
科学领域:
- 医疗信息学 医疗信息学
- 临床自然语言处理 临床自然语言处理
- 计算语言学 计算语言学
背景情况:
- Delirium是一种急性认知障碍,由于其短暂和多样化的呈现,在电子健康记录 (EHR) 中经常无法识别.
- 准确识别妄症状对于及时诊断和管理至关重要,但目前的方法有限.
- 自然语言处理 (NLP) 为从非结构化的临床文本中提取复杂的医疗信息提供了一个有希望的途径.
研究的目的:
- 开发和评估先进的NLP方法,以准确地从临床叙述中提取各种 Delirium 症状.
- 建立一个强大的系统,以帮助诊断和表型 Delirium.
- 为了为可计算的表型和自动化妄诊断奠定基础.
主要方法:
- 一个专家小组策划了妄想症状,制定了注释指南,并创建了一个专门的妄想体.
- 他们比较了五种最先进的变压器模型,包括一般 (BERT,RoBERTa) 和临床 (BERT_MIMIC,RoBERTa_MIMIC,GatorTron) 领域.
- 进行了NLP模型的训练和评估,在精心策划的妄体上进行了症状提取.
主要成果:
- 盖特朗车型表现出卓越的性能,获得最高的严格F1分数0.8055和宽松F1分数0.8759.
- 错误分析确定了在妄想症状注释和NLP系统开发中的关键挑战.
- 这代表了第一个大型基于语言模型的系统,用于妄症状提取.
结论:
- 开发的NLP系统显著提高了从临床笔记中提取妄想症状的能力.
- 这项工作为未来可计算妄表型和诊断工具的进步提供了基础.
- 改进NLP驱动的妄想检测可以带来更好的患者结果和更有效的医疗保健.
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