Evidence map›Paper›PMID 24713590›Full record

ArticleClinical pharmacology and therapeutics2014

Toward enhanced pharmacovigilance using patient-generated data on the internet.

R W White, R Harpaz, N H Shah, W DuMouchel, E Horvitz

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Medication-indication knowledge bases: a systematic review and critical appraisal.Journal of the American Medical Informatics Association : JAMIA · 2015
    Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions.P & T : a peer-reviewed journal for formulary management · 2018
    Article
  19. Toward multimodal signal detection of adverse drug reactions.Journal of biomedical informatics · 2017
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

R W WhiteMicrosoft Research, Redmond, Washington, USA.
R HarpazCenter for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
N H ShahCenter for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
W DuMouchel1] Oracle Health Sciences, Burlington, Massachusetts, USA [2] Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, Maryland, USA.
E HorvitzMicrosoft Research, Redmond, Washington, USA.

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
Mining health data for drug safety profilesR01GM101430 · NIGMS · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2013 to 2017
$2.9M
NHGRI NIH HHS U54 HG004028NHGRI NIH HHS U54-HG004028NIGMS NIH HHS GM101430-01A1NIGMS NIH HHS R01 GM101430
6 · The paper itself

Abstract

The promise of augmenting pharmacovigilance with patient-generated data drawn from the Internet was called out by a scientific committee charged with conducting a review of the current and planned pharmacovigilance practices of the US Food and Drug Administration (FDA). To this end, we present a study on harnessing behavioral data drawn from Internet search logs to detect adverse drug reactions (ADRs). By analyzing search queries collected from 80 million consenting users and by using a widely recognized benchmark of ADRs, we found that the performance of ADR detection via search logs is comparable and complementary to detection based on the FDA's adverse event reporting system (AERS). We show that by jointly leveraging data from the AERS and search logs, the accuracy of ADR detection can be improved by 19% relative to the use of each data source independently. The results suggest that leveraging nontraditional sources such as online search logs could supplement existing pharmacovigilance approaches.

Indexed as

PharmacovigilanceAdverse Drug Reaction Reporting SystemsDatabases, FactualData MiningElectronic Health RecordsHumansInternet

Identifiers

PMID24713590
PMCPMC4111778

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.