Assessment of disease named entity recognition on a corpus of annotated sentences

Abstract

Background: In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions.

Bootstrapping a Romanian Corpus for Medical Named Entity Recognition

“…In particular, biomedical named entity recognition (BioNER) tools aim to detect biomedical terms such as human anatomical parts (Xu et al, 2014), drug names (Liu et al, 2015), gene and protein mentions (Tanabe and Wilbur, 2002), chemical compounds (Eltyeb and Salim, 2014), diseases (Jimeno et al, 2008) and to assign them the correct categories.…”

Unsupervised mapping of sentences to biomedical concepts based on integrated information retrieval model and clustering

“…We construct a disease lexicon by extracting disease-related concepts from UMLS according to J. Antonio et al [17], and use their evaluation data. The data consists of 600 sentences and 924 disease terms.…”

“…It has also been shown that for the task of identifying concepts from annotations of high-throughput datasets, simple methods perform as well or better than MetaMap [12,13,14,15]. In previous work, some approaches of S. Gaudan et al [16,17] are based on the identification of weighted words that compose terms denoting ontology concepts. They integrate two new aspects in their scoring method: the proximity between words in text and the amount of information carried by each individual word.…”

Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments

“…A strong focus in biomedical information extraction has long been on named entity recognition, for which machine-learning solutions such as conditional random fields (Lafferty et al, 2001) or dictionary-based systems (Schuemie et al, 2007;Hanisch et al, 2005;Hakenberg et al, 2011) are available which tackle the respective problem with decent performance and for specific entity classes such as organisms (Pafilis et al, 2013) or symptoms (Savova et al, 2010;Jimeno et al, 2008). A detailed overview on named entity recognition, covering other domains as well, can be found in Nadeau and Sekine (2007).…”