Efficient Chemical-Disease Identification and Relationship Extraction Using Wikipedia to Improve Recall

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Efficient Chemical-Disease Identification and Relationship Extraction Using Wikipedia to Improve Recall
Authors
Daniel M. Lowe
Noel M. O’Boyle
Roger A. Sayle
Publication date
2016
DOI
10.1093/database/baw039
Links
Original

Efficient Chemical-Disease Identification and Relationship Extraction Using Wikipedia to Improve Recall - scientific work related to Wikipedia quality published in 2016, written by Daniel M. Lowe, Noel M. O’Boyle and Roger A. Sayle.

Overview

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. Authors extended 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, authors developed a simple pattern-based system to find relationships within the same sentence. Authors 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 system was over an order of magnitude faster than other solutions evaluated on the task, authors were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs.

Embed

Wikipedia Quality

Lowe, Daniel M.; O’Boyle, Noel M.; Sayle, Roger A.. (2016). "[[Efficient Chemical-Disease Identification and Relationship Extraction Using Wikipedia to Improve Recall]]". Oxford University Press. DOI: 10.1093/database/baw039.

English Wikipedia

{{cite journal |last1=Lowe |first1=Daniel M. |last2=O’Boyle |first2=Noel M. |last3=Sayle |first3=Roger A. |title=Efficient Chemical-Disease Identification and Relationship Extraction Using Wikipedia to Improve Recall |date=2016 |doi=10.1093/database/baw039 |url=https://wikipediaquality.com/wiki/Efficient_Chemical-Disease_Identification_and_Relationship_Extraction_Using_Wikipedia_to_Improve_Recall |journal=Oxford University Press}}

HTML

Lowe, Daniel M.; O’Boyle, Noel M.; Sayle, Roger A.. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Efficient_Chemical-Disease_Identification_and_Relationship_Extraction_Using_Wikipedia_to_Improve_Recall">Efficient Chemical-Disease Identification and Relationship Extraction Using Wikipedia to Improve Recall</a>&quot;. Oxford University Press. DOI: 10.1093/database/baw039.