医学编码系统的短暂学习:用BERT和Mistral对死亡证明的案例研究
Mihai Horia Popescu1, Vincenzo Della Mea1, Kevin Roitero1
1University of Udine, Department of Mathematics, Computer Science and Physics.
Studies in health technology and informatics
|May 17, 2025
概括
在自动化从死亡证明中对死亡原因进行编码方面,Mistral语言模型显著优于BERT,特别是在具有有限数据的不太常见的ICD代码方面.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医学编码 医学编码
背景情况:
- 准确识别潜在的死亡原因对于公共卫生政策和规划至关重要.
- 世界卫生组织 (WHO) 倡导使用国际疾病分类第10版 (ICD-10) 进行标准化的死亡证明编码.
- 半自动化系统越来越多地用于支持ICD-10编码的复杂任务.
研究的目的:
- 评估BERT和Mistral语言模型在自动化死亡原因分类方面的性能.
- 评估ICD代码在不同实例密度的模型有效性,模拟新的和已建立的代码.
- 为了比较微调Mistral与BERT在处理数据稀缺方面的能力.
主要方法:
- 对BERT和微调的Mistral语言模型进行比较实验分析.
- 在具有不同ICD代码实例密度 (1至100) 的数据集上测试模型性能.
- 在每个代码数据有限的场景中,专注于自动分类准确性.
主要成果:
- 与BERT相比,微调的米斯特拉尔模型表现出明显优异的性能.
- 米斯特拉尔显示出增强的有效性,特别是在数据不足的情况下,模仿罕见或新引入的ICD代码.
- 对于具有有限培训实例的ICD代码,绩效增长最为明显.
结论:
- 微调的Mistral模型在自动ICD-10编码方面比BERT具有显著的优势.
- 由于Mistral在有限的数据中具有强大性能,这表明它可以提高死亡原因分类系统的准确性和效率.
- 这些发现支持像Mistral这样的先进语言模型的整合,以增强公共卫生数据基础设施.
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