Exploiting ICD Hierarchy for Classification of EHRs in Spanish Through Multi-Task Transformers.

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.