Objectives We examine recent published research on the extraction of information from
textual documents in the Electronic Health Record (EHR). Methods Literature review of the …

This article reviews recent advances in applying natural language processing (NLP) to
Electronic Health Records (EHRs) for computational phenoty**. NLP-based …

Named entity recognition is a crucial component of biomedical natural language processing,
enabling information extraction and ultimately reasoning over and knowledge discovery …

Background The human genome has been extensively annotated with Gene Ontology for
biological functions, but minimally computationally annotated for diseases. Results We used …

Background Information extraction is a complex task which is necessary to develop high-
precision information retrieval tools. In this paper, we present the platform MeTAE (Medical …

Abstract Medical Entity Recognition is a crucial step towards efficient medical texts analysis.
In this paper we present and compare three methods based on domain-knowledge and …

The volume of data on the social media is huge and even keeps increasing. The need for
efficient processing of this extensive information resulted in increasing research interest in …

Objective This paper describes the approaches the authors developed while participating in
the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate …

OBJECTIVE: The majority of clinical symptoms are stored as free text in the clinical record,
and this information can inform clinical decision support and automated surveillance efforts if …

Emergency department (ED) visits due to allergic reactions are common. Allergy information
is often recorded in free-text provider notes; however, this domain has not yet been widely …