Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …

Background Most state-of-the-art biomedical entity normalization systems, such as rule-
based systems, merely rely on morphological information of entity mentions, but rarely …

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

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 …

In the last five years there has been a flurry of work on information extraction from clinical
documents, that is, on algorithms capable of extracting, from the informal and unstructured …

Mental health is an increasingly important problem in healthcare. Psychiatric stressors are
one of the major contributors of mental disorders. Very few studies have investigated …

Information extraction from textual documents such as hospital records and healthrelated
user discussions has become a topic of intense interest. The task of medical concept coding …

Named entity recognition (NER) from text is an important task for several applications,
including in the biomedical domain. Supervised machine learning based systems have …