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    Ranking Adverse Drug Reactions With Crowdsourcing

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    Type
    Article
    Authors
    Gottlieb, Assaf
    Hoehndorf, Robert cc
    Dumontier, Michel
    Altman, Russ B
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2015-03-23
    Permanent link to this record
    http://hdl.handle.net/10754/550418
    
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    Abstract
    Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.
    Citation
    Ranking Adverse Drug Reactions With Crowdsourcing 2015, 17 (3):e80 Journal of Medical Internet Research
    Publisher
    JMIR Publications Inc.
    Journal
    Journal of Medical Internet Research
    DOI
    10.2196/jmir.3962
    PubMed ID
    25800813
    PubMed Central ID
    PMC4387295
    Additional Links
    http://www.jmir.org/2015/3/e80/
    ae974a485f413a2113503eed53cd6c53
    10.2196/jmir.3962
    Scopus Count
    Collections
    Articles; Bio-Ontology Research Group (BORG); Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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