Electronic Health Records (EHRs) convey valuable information. Experts in clinical documentation read the report, understand the prior work, procedures, tests carried out, and encode the EHRs according to the International Classification of Diseases (ICD). Assigning these codes to the EHRs helps to share information, and extract statistics. In this paper, we explore computer-aided multi-label classification approaches. While Natural Language Understanding has evolved for clinical text mining, there is still a gap for languages other than English. Language-modeling aware Transformers has demonstrated state of the art approaches through exploiting contextual dependencies. Here we focus on EHRs written in Spanish, and try to benefit from the Language Model itself, with unannotated corpus with less data but in-house, in-domain and closely-related EHRs to that of the downstream task. The International Classification of Diseases coding scheme is hierarchical, but its synergies among hierarchical levels are rarely exploited. In this work, we implement and release a hierarchical head for multi-label classification, which benefits from the hierarchy of the ICD via multi-task classification.

中文翻译：

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通过多任务转换器利用 ICD 层次结构对西班牙语中的 EHR 进行分类

电子健康记录 (EHR) 传达有价值的信息。临床文档专家阅读报告，了解之前的工作、程序、进行的测试，并根据国际疾病分类 (ICD) 对 EHR 进行编码。将这些代码分配给 EHR 有助于共享信息和提取统计数据。在本文中，我们探索了计算机辅助的多标签分类方法。虽然自然语言理解已经发展到临床文本挖掘，但对于英语以外的语言仍然存在差距。具有语言建模意识的 Transformers 通过利用上下文依赖关系展示了最先进的方法。在这里，我们专注于用西班牙语编写的 EHR，并尝试从语言模型本身中受益，使用未注释的语料库，数据较少，但在内部，域内和与下游任务密切相关的 EHR。国际疾病分类编码方案是分级的，但其分级之间的协同作用很少被利用。在这项工作中，我们实现并发布了一个用于多标签分类的分层头，它通过多任务分类从 ICD 的层次结构中受益。