Developing an automated mechanism to identify medical articles from wikipedia for knowledge extraction

International Journal of Medical Informatics
Lishan Yu, Sheng Yu

Abstract

Wikipedia contains rich biomedical information that can support medical informatics studies and applications. Identifying the subset of medical articles of Wikipedia has many benefits, such as facilitating medical knowledge extraction, serving as a corpus for language modeling, or simply making the size of data easy to work with. However, due to the extremely low prevalence of medical articles in the entire Wikipedia, articles identified by generic text classifiers would be bloated by irrelevant pages. To control the false discovery rate while maintaining a high recall, we developed a mechanism that leverages the rich page elements and the connected nature of Wikipedia and uses a crawling classification strategy to achieve accurate classification. Structured assertional knowledge in Infoboxes and Wikidata items associated with the identified medical articles were also extracted. This automatic mechanism is aimed to run periodically to update the results and share them with the informatics community.

References

May 22, 2002·JAMA : the Journal of the American Medical Association·Gunther EysenbachEun-Ryoung Sa
Oct 28, 2008·American Journal of Human Genetics·Peter N RobinsonStefan Mundlos
Mar 6, 2015·Journal of Medical Internet Research·James M Heilman, Andrew G West
May 2, 2015·Journal of the American Medical Informatics Association : JAMIA·Sheng YuTianxi Cai
Sep 15, 2016·Academic Medicine : Journal of the Association of American Medical Colleges·Amin AzzamJack D McCue
May 18, 2018·Proceedings of the VLDB Endowment·Alexander RatnerChristopher Ré

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Software Mentioned

NILE
TextCNN
Wikidata
DISNET
Wikipedia
ANAT
NaïveB
UMLS
Infobox

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