用BERT模型从荷兰临床文本中提取患者的生活方式特征
Hielke Muizelaar1,2, Marcel Haas3, Koert van Dortmont4
1LIACS, Leiden University, P.O. Box 9512, Leiden, 2300RA, The Netherlands. h.j.muizelaar@lumc.nl.
BMC medical informatics and decision making
|June 3, 2024
概括
这项研究表明,MedRoBERTa.nl-HAGA模型擅长从荷兰临床笔记中对患者的生活方式指标 (如吸烟和吸毒) 进行分类. 它还强调了翻译文本在非英语研究中培训临床BERT模型的潜力.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 机器学习 机器学习
背景情况:
- 伯特模型广泛用于临床文本,但很少用于患者的生活方式分类,特别是在荷兰语中.
- 现有的研究缺乏基于生活方式指标 (如吸烟,酒精和药物使用) 的非结构化临床笔记分类的重点.
- 这项研究解决了荷兰临床文本分析对患者生活方式分类的差距.
研究的目的:
- 评估深度BERT模型的可行性,用于在荷兰临床笔记中对患者生活方式指标进行分类.
- 为了在这个领域引入一个可重复的框架,为未来的研究.
- 为了比较各种BERT模型和生活方式分类的传统方法的性能.
主要方法:
- 利用来自HagaZiekenhuis的超过148,000个非结构化临床笔记,自动标记吸烟,酒精和药物使用.
- 使用宏F1分数,比较了字符串匹配,SGD和几个BERT模型 (荷兰语和翻译英语).
- 经过验证的自动标签与手工标签输入以证明分类可行性.
主要成果:
- 在MedRoBERTa.nl-HAGA模型中,在吸烟 (0.93) 和吸毒 (0.77) 的分类方面取得了卓越的表现.
- 对翻译文本进行微调的ClinicalBERT模型显示出酒精分类 (0.80) 的最佳结果.
- t-SNE可视化证实了 MedRoBERTa.nl-HAGA 在嵌入空间内区分生活方式类别的有效性.
结论:
- MedRoBERTa.nl-HAGA被提议作为未来荷兰自由文本患者生活方式分类研究的基准.
- 由于有希望的结果,强烈建议对非英语临床BERT研究的文本翻译进行进一步的探索.
- 这项研究为改善患者生活方式分类的基础,在多语言临床环境中使用先进的NLP技术.
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