» Articles » PMID: 23467469

Web-scale Pharmacovigilance: Listening to Signals from the Crowd

Overview
Date 2013 Mar 8
PMID 23467469
Citations 74
Authors
Affiliations
Soon will be listed here.
Abstract

Adverse drug events cause substantial morbidity and mortality and are often discovered after a drug comes to market. We hypothesized that Internet users may provide early clues about adverse drug events via their online information-seeking. We conducted a large-scale study of Web search log data gathered during 2010. We pay particular attention to the specific drug pairing of paroxetine and pravastatin, whose interaction was reported to cause hyperglycemia after the time period of the online logs used in the analysis. We also examine sets of drug pairs known to be associated with hyperglycemia and those not associated with hyperglycemia. We find that anonymized signals on drug interactions can be mined from search logs. Compared to analyses of other sources such as electronic health records (EHR), logs are inexpensive to collect and mine. The results demonstrate that logs of the search activities of populations of computer users can contribute to drug safety surveillance.

Citing Articles

Making Sense of Aging with Data Big and Small.

Dodge H, Estrin D Bridge (Wash D C). 2024; 49(1):39-46.

PMID: 39449830 PMC: 11500021.


A large-scale observational comparison of antidepressants and their effects.

Heinz M, Yom-Tov E, Mackin D, Matsumura R, Jacobson N J Psychiatr Res. 2024; 178:219-224.

PMID: 39163659 PMC: 11398883. DOI: 10.1016/j.jpsychires.2024.08.001.


How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review.

Schulte T, Bohnet-Joschko S Int J Integr Care. 2022; 22(2):23.

PMID: 35756337 PMC: 9205381. DOI: 10.5334/ijic.5543.


Detecting Adverse Drug Reactions on Twitter with Convolutional Neural Networks and Word Embedding Features.

Masino A, Forsyth D, Fiks A J Healthc Inform Res. 2022; 2(1-2):25-43.

PMID: 35415401 PMC: 8982795. DOI: 10.1007/s41666-018-0018-9.


Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography.

Ponomariov V, Chirila L, Apipie F, Abate R, Rusu M, Wu Z Discoveries (Craiova). 2020; 5(3):e76.

PMID: 32309594 PMC: 6941587. DOI: 10.15190/d.2017.6.


References
1.
Coloma P, Trifiro G, Schuemie M, Gini R, Herings R, Hippisley-Cox J . Electronic healthcare databases for active drug safety surveillance: is there enough leverage?. Pharmacoepidemiol Drug Saf. 2012; 21(6):611-21. DOI: 10.1002/pds.3197. View

2.
Avorn J, Schneeweiss S . Managing drug-risk information--what to do with all those new numbers. N Engl J Med. 2009; 361(7):647-9. DOI: 10.1056/NEJMp0905466. View

3.
Johnson J, Bootman J . Drug-related morbidity and mortality. A cost-of-illness model. Arch Intern Med. 1995; 155(18):1949-56. View

4.
Ginsberg J, Mohebbi M, Patel R, Brammer L, Smolinski M, Brilliant L . Detecting influenza epidemics using search engine query data. Nature. 2008; 457(7232):1012-4. DOI: 10.1038/nature07634. View

5.
Schneeweiss S, Avorn J . A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005; 58(4):323-37. DOI: 10.1016/j.jclinepi.2004.10.012. View