多粒度标签预测模型用于疾病自动国际分类编码在临床文本中的编码
Ying Yu1,2, Tian Qiu1, Junwen Duan1
1Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R. China.
概括
这项研究引入了一种新的多任务学习模型,用于国际疾病分类 (ICD) 编码. 它有效地使用ICD代码的等级结构来提高疾病预测的准确性,优于基线模型.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 国际疾病分类 (ICD) 编码对于全球疾病统计至关重要.
- 当前的ICD编码方法与ICD代码的庞大,分层性质相斗争.
- 现有的研究往往忽略了ICD代码级别之间的等级关系.
研究的目的:
- 开发一个改进的ICD编码预测模型.
- 为了提高准确性,利用ICD代码的层次结构.
- 为了解决仅关注子类预测的模型的局限性.
主要方法:
- 提出了一种多任务学习模型,用于不同ICD代码级别的多个分类器.
- 整合了一种强化机制,以捕捉粗和细粒度标签之间的关系.
- 使用注意力机制对英语和中文基准数据集进行模型评估.
主要成果:
- 与基线模型相比,实现了竞争性表现,特别是在宏观F1得分方面.
- 证明有效利用ICD代码层次结构,以改善预测.
- 注意力分析揭示了多重细分性注意力捕获关键文本特征用于解释.
结论:
- 建议的多任务学习方法有效地利用了ICD代码层次结构.
- 该模型显示了提高自动化ICD编码的准确性和可解释性的前景.
- 这种方法为疾病分类和统计分析提供了更强大的解决方案.
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