There is a pressing need for advanced semantic annotation technologies of medical content,
in particular medical publications, clinical trials and clinical records. Search engines and …

Detecting negation and uncertainty is crucial for medical text mining applications; otherwise,
extracted information can be incorrectly identified as real or factual events. Although several …

Here we describe a new clinical corpus rich in nested entities and a series of neural models
to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean …

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 …

Background The high volume of research focusing on extracting patient information from
electronic health records (EHRs) has led to an increase in the demand for annotated …

Background The exploration of clinically relevant information in the free text of electronic
health records (EHRs) holds the potential to positively impact clinical practice as well as …

Las listas de espera no cubiertas por el Plan de Garantías Explícitas en Salud para nueva
consulta de especialidad en Chile se han visto incrementadas por los efectos de la …

We sought to craft a drug safety signalling pipeline associating latent information in clinical
free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and …

Biomedical communication is an area that increasingly benefits from natural language
processing (NLP) work. Biomedical named entity recognition (NER) in particular provides a …

In Natural Language Processing (NLP), the scarcity of linguistic resources (labeled corpus,
parallel corpus, pre-trained models, etc.) can lead to poor performance when applying …