Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall

• Published in: Database : the journal of biological databases and curation Vol. 2016; p. baw039
• Main Authors: Lowe, Daniel M, O'Boyle, Noel M, Sayle, Roger A
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 2016
• Subjects: Animals; Computational Biology - methods; Data Mining - methods; Databases, Chemical; Databases, Factual; Disease - etiology; Disease Models, Animal; Drug-Related Side Effects and Adverse Reactions; Hazardous Substances - toxicity; Humans; Internet; Medical Subject Headings; Original; Pattern Recognition, Automated; Search Engine
• ISSN: 1758-0463; 1758-0463
• DOI: 10.1093/database/baw039

Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical-Disease Relation task and achieved very good results for both disease concept ID recognition (F1-score: 86.12%) and CIDs (F1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs.