LCDL:基于疾病标签共发生依赖性和LongFormer与医学知识的ICD代码的分类
Yumeng Yang1, Hongfei Lin1, Zhihao Yang1
1School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
Artificial intelligence in medicine
|December 12, 2024
概括
这项研究引入了自动化医疗编码的新模型,以提高准确性和效率. 长尾疾病标签 (LCDL) 模型增强了患者记录中常见和罕见疾病的识别.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 医疗保健数据科学 数据科学
背景情况:
- 手动医学编码临床自由文本是耗时的,容易出现错误.
- 疾病编码中的长尾现象,频繁的常见代码和不频繁的罕见代码,构成了重大挑战.
- 自动化医疗编码旨在提高效率,准确性,并减少医疗保健专业人员的工作量.
研究的目的:
- 开发和评估一个针对长尾现象的自动化医疗编码模型.
- 增强医学知识的表现,以提高编码性能.
- 为了展示一个新的长尾疾病标签 (LCDL) 模型.
主要方法:
- 使用了LongFormer预训练语言模型和疾病标签共发生地图.
- 纳入医学知识层次,同义词和缩写来改善表达.
- 使用多标签分类方法来分配临床代码.
主要成果:
- 拟议的LCDL模型在MIMIC-III基准数据集上取得了竞争性表现.
- 在自动化医疗编码中证明了提高准确性和效率.
- 有效地解决了疾病编码中长尾现象所带来的挑战.
结论:
- 该LCDL模型为增强自动化医疗编码提供了一个有前途的解决方案.
- 整合医学知识和先进的语言模型可以提高罕见疾病的编码精度.
- 这种方法有可能显著有利于医疗保健数据管理和分析.
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