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 Web-scale pharmacovigilance Maggie Mahan 16 April 2013.

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Presentation on theme: " Web-scale pharmacovigilance Maggie Mahan 16 April 2013."— Presentation transcript:

1  Web-scale pharmacovigilance Maggie Mahan 16 April 2013

2 Motivation  Adverse drug events cause morbidity & mortality  Typically discovered after drug marketed  Increased internet searches of health information (~60% of American adults)  Mining web search history to identify unreported side effects of drugs or drug combinations  Logs are inexpensive to collect & mine  Drug safety surveillance

3 Background (1/2)  Drug side effects reported but incomplete and biased  Leads to delayed reporting of adverse events  Compounded with multiple drugs  Previous research on tracking seasonal influenza  Search logs can be used for health monitoring  Health-seeking activity captured in queries to web search services mirrors trends gathered by traditional surveillance

4 Background (2/2)  Present study used online health-seeking search activity to identify adverse drug events associated with drug interactions  Paroxetine: anti-depressant  Pravastatin: cholesterol-lowering drug  Interaction reported to create hyperglycemia  Hypothesis: patients taking these two drugs might experience symptoms of hyperglycemia and may have conducted internet searches on these symptoms and concerns related to hyperglycemia before the association was reported

5 Methods  12 months of search logs  Word used in user queries  Pravastatin & brand names  Paroxetine & brand names  Hyperglycemia-associated words  Disproportionality analysis  Assess increased chance of search for hyperglycemia- related terms given search for both drugs  Reporting ratios based on observed versus expected

6 Results – user groups & prevalence  Searching both drugs = more likely to search hyperglycemia- associated terms  Difference between groups is consistent

7 Results – disproportionality analysis

8 Results - disproportionality analysis for known drug–drug interactions

9 Conclusions  Log analysis valuable for identifying drug pairs linked to hyperglycemia  Method generalizable, similar to a prediction task  Majority of TP identified provides validation for the set of terms used  Valuable signal even though search logs are unstructured, not necessarily related to health, and include any words entered by users  More in-depth analysis is needed  Patient search behavior directly can complement traditional sources of data for pharmacovigilance

10 References  White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E (2013) Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Infom Assoc. 20(3): 404-408.  http://scopeblog.stanford.edu/2013/03/06/researchers-mine- internet-search-data-to-identify-unreported-side-effects-of- drugs/


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