Abstract Background and Objectives: Named Entity Recognition (NER) and Relation
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …

Background Text mining and natural language processing of clinical text, such as notes from
electronic health records, requires specific consideration of the specialized characteristics of …

The advances achieved in Natural Language Processing make it possible to automatically
mine information from electronically created documents. Many Natural Language …

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 …

In this work we describe the Waiting List Corpus consisting of de-identified referrals for
several specialty consultations from the waiting list in Chilean public hospitals. A subset of …

Background and objectives The 10th version of International Classification of Diseases (ICD-
10) codification system has been widely adopted by the health systems of many countries …

This work focuses on adverse drug reaction extraction tackling the class imbalance problem.
Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is …

Objective The goal of this study is to investigate entity recognition within Electronic Health
Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust …

This work focuses on the detection of adverse drug reactions (ADRs) in electronic health
records (EHRs) written in Spanish. The World Health Organization underlines the …

Objective: To tackle the extraction of adverse drug reaction events in electronic health
records. The challenge stands in inferring a robust prediction model from highly unbalanced …