ArticleIEEE journal of biomedical and health informatics2015
SimConcept: a hybrid approach for simplifying composite named entities in biomedical text.
Article in IEEE journal of biomedical and health informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An overview of biomedical entity linking throughout the years.Journal of biomedical informatics · 2023Review
- HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning.Frontiers in genetics · 2022Article
- NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition.Journal of biomedical informatics · 2021Article
- A graph-based method for reconstructing entities from coordination ellipsis in medical text.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability.PLoS biology · 2020Article
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.BMC medical informatics and decision making · 2018Article
- Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.PLoS computational biology · 2016Article
- TaggerOne: joint named entity recognition and normalization with semi-Markov Models.Bioinformatics (Oxford, England) · 2016Article
- Mining chemical patents with an ensemble of open systems.Database : the journal of biological databases and curation · 2016Article
- A Comparison between Human and NLP-based Annotation of Clinical Trial Eligibility Criteria Text Using The OMOP Common Data Model.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
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3 authors.
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Abstract
One particular challenge in biomedical named entity recognition (NER) and normalization is the identification and resolution of composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2). Previous NER and normalization studies have either ignored composite mentions, used simple ad hoc rules, or only handled coordination ellipsis, making a robust approach for handling multitype composite mentions greatly needed. To this end, we propose a hybrid method integrating a machine-learning model with a pattern identification strategy to identify the individual components of each composite mention. Our method, which we have named SimConcept, is the first to systematically handle many types of composite mentions. The technique achieves high performance in identifying and resolving composite mentions for three key biological entities: genes (90.42% in F-measure), diseases (86.47% in F-measure), and chemicals (86.05% in F-measure). Furthermore, our results show that using our SimConcept method can subsequently improve the performance of gene and disease concept recognition and normalization. SimConcept is available for download at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/SimConcept/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.