ICB-UMA at CLEF e-Health 2020 Task 1: Automatic ICD-10 coding in Spanish with BERT

. This working notes paper presents our contribution to the CLEF eHealth 2020 Task 1. Our team has participated in the CodiEsp-D subtask, the first shared task consisted in the automatic clinical coding of medical cases in Spanish, annotated with ICD-10-CM codes. We tackled the task as a multi-label classification problem using BERT model [4]. With the aim of leveraging all the language modeling capacities of the deep bidirectional encoder architecture of BERT, we developed a tailored approach to annotate short fragments of text extracted from the long clinical cases present in the CodiEsp corpus and use them as input to the model. Two publicly available Spanish versions of BERT, namely BETO [3] and BERT-SciELO [1], were fine-tuned on the CodiEsp-D corpus extended by a set of abstracts annotated with ICD-10 codes, following our fragment-based classification approach. BERT-SciELO, a BERT-Base model pre-trained from scratch on an unlabeled corpus of biomedical articles in Spanish, achieved the best results among our three submitted systems, obtaining a final Mean Average Precision (MAP) metric score of 0.482 on the evaluation set.