在电子医疗记录中识别与腰部疼痛相关的危险因素 免费文本:使用临床笔记注释的深度学习方法
Aman Jaiswal1, Alan Katz2, Marcello Nesca2
1Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
JMIR medical informatics
|August 16, 2023
概括
包括BERT在内的深度学习模型可以有效地在临床笔记中识别腰部疼痛的危险因素. 这种方法有助于检测潜在的潜在疾病,改善患者护理,减少医疗负担.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 腰部疼痛是一种广泛的疾病,导致残疾和经济负担.
- 人工智能和NLP的进步为识别腰部疼痛风险因素提供了新的途径.
- 早期识别风险因素对于及时干预和管理至关重要.
研究的目的:
- 开发一种深度学习模型,用于在临床笔记中检测腰部疼痛风险因素.
- 解决使用NLP用于电子健康记录自由文本分析的挑战.
- 为这个领域的未来研究提供建议.
主要方法:
- 对六种风险因素的临床注释手册注释.
- 使用语义文本相似性和正则表达式来增强数据捕获.
- 采用诸如减量采样,多标签分类和无监督预训等技术.
主要成果:
- 从变压器 (BERT) 的双向编码器表示模型显著改善了风险因素的识别 (15%的宏观AUROC).
- 对较长文本的BERT卷积神经网络 (CNN) 模型显示了进一步的改善 (4%宏观AUROC).
- 域调整和多任务学习增强了模型稳定性和性能.
结论:
- 初级医疗保健的临床笔记需要预处理才能进行有效的自由文本分析.
- 在多标签分类中,BERT模型对于识别腰部疼痛风险因素的多标签分类是有价值的.
- 这种由人工智能驱动的方法可以帮助检测腰部疼痛患者的成像指示.
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