ArticleJournal of biomedical semantics2011
Automatic extraction of semantic relations between medical entities: a rule based approach.
Article in Journal of biomedical semantics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Towards discovery: an end-to-end system for uncovering novel biomedical relations.Database : the journal of biological databases and curation · 2024Article
- Synthetic data for annotation and extraction of family history information from clinical text.Journal of biomedical semantics · 2021Article
- Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation.JMIR medical informatics · 2020Article
- Distant supervision for treatment relation extraction by leveraging MeSH subheadings.Artificial intelligence in medicine · 2019Article
- Comparison of Natural Language Processing Techniques in Analysis of Sparse Clinical Data: Insulin Decline by Patients.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Linked open data-based framework for automatic biomedical ontology generation.BMC bioinformatics · 2018Article
- Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.Journal of biomedical informatics · 2018Article
- Identifying genotype-phenotype relationships in biomedical text.Journal of biomedical semantics · 2017Article
- Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.Biomedical informatics insights · 2017Article
- Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016Article
- Identifying Plant-Human Disease Associations in Biomedical Literature: A Case Study.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- CD-REST: a system for extracting chemical-induced disease relation in literature.Database : the journal of biological databases and curation · 2016Article
- Extraction of a group-pair relation: problem-solving relation from web-board documents.SpringerPlus · 2016Article
- Automating the generation of lexical patterns for processing free text in clinical documents.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.IET systems biology · 2015Article
- A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set.Computational and mathematical methods in medicine · 2015Article
- Sequential pattern mining for discovering gene interactions and their contextual information from biomedical texts.Journal of biomedical semantics · 2015Article
- Ranking biomedical annotations with annotator's semantic relevancy.Computational and mathematical methods in medicine · 2014Article
- Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.Journal of biomedical informatics · 2013Article
- An exploratory study of user-centered indexing of published biomedical images.Journal of the Medical Library Association : JMLA · 2013Article
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2 authors.
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Abstract
backgroundInformation extraction is a complex task which is necessary to develop high-precision information retrieval tools. In this paper, we present the platform MeTAE (Medical Texts Annotation and Exploration). MeTAE allows (i) to extract and annotate medical entities and relationships from medical texts and (ii) to explore semantically the produced RDF annotations.
resultsOur annotation approach relies on linguistic patterns and domain knowledge and consists in two steps: (i) recognition of medical entities and (ii) identification of the correct semantic relation between each pair of entities. The first step is achieved by an enhanced use of MetaMap which improves the precision obtained by MetaMap by 19.59% in our evaluation. The second step relies on linguistic patterns which are built semi-automatically from a corpus selected according to semantic criteria. We evaluate our system's ability to identify medical entities of 16 types. We also evaluate the extraction of treatment relations between a treatment (e.g. medication) and a problem (e.g. disease): we obtain 75.72% precision and 60.46% recall.
conclusionsAccording to our experiments, using an external sentence segmenter and noun phrase chunker may improve the precision of MetaMap-based medical entity recognition. Our pattern-based relation extraction method obtains good precision and recall w.r.t related works. A more precise comparison with related approaches remains difficult however given the differences in corpora and in the exact nature of the extracted relations. The selection of MEDLINE articles through queries related to known drug-disease pairs enabled us to obtain a more focused corpus of relevant examples of treatment relations than a more general MEDLINE query.
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