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Full-Text Articles in Medicine and Health Sciences
Development Of An Adverse Drug Reaction Corpus From Consumer Health Posts, Maryam Zolnoori, Timothy B. Patrick, Kin Wah Fung, Anthony Faiola, Yi Shuan Shirley Wu, Jiaxi Zhu, Christina Eldredge
Development Of An Adverse Drug Reaction Corpus From Consumer Health Posts, Maryam Zolnoori, Timothy B. Patrick, Kin Wah Fung, Anthony Faiola, Yi Shuan Shirley Wu, Jiaxi Zhu, Christina Eldredge
School of Information Faculty Publications
UWM-Adverse Drug Events Corpus (UWM-ADEC) is an annotated corpus that has been developed from consumer drug review posts in social media. In this corpus, we identified four types of Adverse Drug Reactions (ADRs) including physiological, psychological, cognitive, and functional problems. Additionally, we mapped the ADRs to corresponding concepts in Unified medical language Systems (UMLS). The quality of the corpus was measured using well-defined guidelines, double coding, high inter-annotator agreement, and final reviews by pharmacists and clinical terminologists. This corpus is a valuable source for research in the area of text mining and machine learning for ADRs identifications from consumer health …
Characterization Of The Mechanism Of Drug-Drug Interactions From Pubmed Using Mesh Terms, Yin Lu, Bryan Figler, Hong Huang, Yi-Cheng Tu, Ju Wang, Feng Cheng
Characterization Of The Mechanism Of Drug-Drug Interactions From Pubmed Using Mesh Terms, Yin Lu, Bryan Figler, Hong Huang, Yi-Cheng Tu, Ju Wang, Feng Cheng
School of Information Faculty Publications
Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant …