Cross-Linguistic Disease and Drug Detection in Cardiology Clinical Texts: Methods and Outcomes

Abstract

This paper presents our approach to the MultiCardioNER lab at CLEF2024, focusing on disease detection in Spanish texts and drug detection in Italian, Spanish, and English texts. We enhance model performance through several strategies: (1) fine-tuning on automatically translated TREC Clinical Trials admission notes using Masked Language Modeling (MLM); (2) data augmentation with translated MTSamples processed through a Spanish medical lexicon (MedLexSp) for accurate vocabulary matching; and (3) employing sliding windows with overlap to improve data capture. Additionally, we use transfer learning with a clinical trials corpus (CT-EMB-SP) to refine the outcomes. We further fine-tune several already established disease and drug extraction models to leverage their extensive vocabulary and compare their performance to models trained from scratch. Our methods and experiments demonstrate notable improvements in multilingual clinical NER, as evidenced by our track results.