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 …

Having in mind that different languages might present different challenges, this paper
presents the following contributions to the area of Information Extraction from clinical text …

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is
unstructured, often appearing as free text. This format restricts its potential utility in clinical …

Abstract Named Entity Recognition (NER) or the extraction of concepts from clinical text is
the task of identifying entities in text and slotting them into categories such as problems …

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 …

Biomedical named entity recognition and entity linking are important building blocks for
various clinical applications and downstream NLP tasks. In the clinical domain, language …

We present our participation in Task 1a of the 2013 CLEF-eHEALTH Challenge, whose goal
was the identification of disorder named entities from electronic medical records. We …

Abstract Clinical Named Entity Recognition (NER) is a critical natural language processing
(NLP) task to extract important concepts (named entities) from clinical narratives …

A primary focus of the healthcare industry is to improve patient experience and quality of
service. Practitioners and health workers are generating large volumes of text that are …