RESUMO
Multi-label classification according to the International Classification of Diseases (ICD) is an Extreme Multi-label Classification task aiming to categorise health records according to a set of relevant ICD codes. We implemented PlaBERT, a new multi-label text classification head with per-label attention, on top of a BERT model. The model assessment is conducted on Electronic Health Records, conveying Discharge Summaries in three languages - English, Spanish, and Swedish. The study focuses on 157 diagnostic codes from the ICD. We additionally measure the labelling noise to estimate the consistency of the gold standard. Our specialised attention mechanism computes attention weights for each input token and label pair, obtaining the specific relevance of every word concerning each ICD code. The PlaBERT model outputs the computed attention importance for each token and label, allowing for visualisation. Our best results are 40.65, 38.36, and 41.13 F1-Score points on the English, Spanish and Swedish datasets, respectively, for the 157 gastrointestinal codes. Besides, Precision is the metric that most significantly improves owing to the attention mechanism of PlaBERT, with an increase of 44.63, 40.93, and 12.92 points, respectively, for the Spanish, Swedish and English datasets.