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自主国际疾病分类编码使用预训练的语言模型和先进的快速学习技术:使用医学文本评估自动化分析系统

Yan Zhuang¹, Junyan Zhang¹, Xiuxing Li²

¹Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.

JMIR medical informatics

|January 6, 2025

概括

这项研究引入了一种使用Key-BERT和即时学习的自动化管道,用于实时国际疾病分类 (ICD) 编码医疗记录. 该方法通过将自由文本转换为标准化的ICD代码,显著提高了诊断和治疗的准确性和效率.

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.
机器学习机器学习

背景情况:

机器学习模型旨在自动化国际疾病分类 (ICD) 从医疗记录编码,以提高诊断和治疗效率.
现有的方法面临着诸如小数据集,多样化的写作风格,非结构化文本等挑战,需要手动预处理,导致高错误率.
处理缺失值和上下文理解的局限性阻碍了传统模型的性能,如天真的贝叶斯,Word2Vec和CNNs.

研究的目的:

提出使用快速学习和预训练语言模型的实时框架,用于心血管疾病医疗记录的自动ICD-10编码.
消除在将长长的自由文本医疗数据转换为标准化的ICD代码时需要半自动预处理.

主要方法:

开发了一个完全自动化的管道,集成医疗预训练的BERT,关键字过BERT,微调和特定任务的快速学习,使用混合模板和软语言化器.
在584,969个心血管疾病记录的多中心数据集上验证了框架,并将其与其他基于BERT的微调管道进行了比较.
进行了几次学习实验,以评估小到中型数据集的性能.

主要成果:

提出的基于Key-BERT的快速学习框架实现了0.838的微F1得分和0.958的宏观AUC,超过了传统方法的10%.
在快速学习设置中,使用混合模板和软语言化器的组合实现了最佳性能.
几次射击学习实验表明性能稳定,AUC在500个实例处达到峰值.

结论:

预先训练的语言模型中的快速学习和微调对于ICD编码等医疗子任务非常有效.
开发的实时ICD编码管道有效地标准化了医学自由文本,显示了临床决策支持的潜力.
该系统可以帮助不熟悉ICD编码的临床医生,加速医疗过程,提高诊断和治疗效率.

关键词:

贝尔特 (BERT) 公司在ICD的基础上,ICD是ICD.疾病的国际分类.来自变压器的双向编码器表示心血管疾病心血管疾病几次射击的学习学习多中心医疗数据预先训练的语言模型快速学习快速学习

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