挪威临床文本中的代码预测:模型开发和评估研究
Phuong Dinh Ngo1,2, Miguel Ángel Tejedor Hernández1,3, Taridzo Chomutare1,4
1Norwegian Centre for E-health Research, University Hospital of Northern Norway, P.O. Box 35, N-9038, Tromsø, Norway, 47 92699162.
JMIR AI
|August 25, 2025
概括
新的挪威BERT模型NorDeClin-BERT显著提高了国际疾病统计分类第十版 (ICD-10) 的编码精度. 对于挪威临床文本来说,特定领域的预培训提高了性能.
科学领域:
- 自然语言处理 (NLP)
- 机器学习
- 卫生信息学
背景情况:
- 准确的国际疾病统计分类第十次修订 (ICD-10) 编码对于医疗保健运作至关重要,但手动流程容易出现错误和效率低下.
- 目前用于ICD-10编码的NLP模型主要集中在英语,为挪威临床文本创造了研究缺口.
- 需要针对挪威医疗保健系统的自动化ICD-10编码解决方案.
研究的目的:
- 介绍NorDeClin-BERT,这是一个针对特定领域的挪威BERT模型,用于增强医学语言理解.
- 评估特定领域预训练和模型大小对ICD-10代码分类性能的影响.
- 将Nordeclin-BERT与挪威ICD-10编码的通用和跨语言BERT模型进行比较.
主要方法:
- 在ClinCode Gastro Corpus的880万个挪威临床笔记上预先训练了两个版本的NorDeClin-BERT (基本和大).
- 微调了ICD-10诊断码预测的模型.
- 与SweDeClin-BERT,ScandiBERT,NorBERT3-base和NorBERT3-large进行比较,使用准确度,精度,回忆和F1分数.
主要成果:
- 在分类ICD-10代码方面,NorDeClin-BERT版本的表现都超过了挪威的一般BERT模型和瑞典的临床BERT模型.
- 在所有评估指标中,NorDeClin-BERT-large取得了最高的表现,证明了特定领域预培训和模型能力的好处.
- 瑞典的临床模型显示出有限的可转移性,强调了对挪威的临床预训的需要.
结论:
- 在挪威胃肠病学中,NorDeClin-BERT具有显著的潜力,可以改善ICD-10代码分类,简化文档和减少行政负担.
- 这项研究确立了NorDeClin-BERT作为挪威医学NLP和ICD-10编码的最先进模型,为研究奠定了新的基础.
- 未来的研究应探索先进的领域适应,外部知识整合和跨医院通用性,以获得更广泛的临床应用.
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