Evidence map›Paper›PMID 25250527›Full record

ArticleCPT: pharmacometrics & systems pharmacology2014

Similarity-based modeling applied to signal detection in pharmacovigilance.

S Vilar, P B Ryan, D Madigan, P E Stang, M J Schuemie, C Friedman, N P Tatonetti, G Hripcsak

Open access · goldAbstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.0field-weighted citation impact, top 18% of its field
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

16 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. In silico methods for drug repurposing and pharmacology.Wiley interdisciplinary reviews. Systems biology and medicine · 2016
    Review
  11. Article
  12. Observational
  13. Article
  14. Database: A New Article Type in CPT: Pharmacometrics & Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2015
    Article
  15. Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

S Vilar1] Department of Biomedical Informatics, Columbia University, New York, New York, USA [2] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA.
P B Ryan1] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA [2] Janssen Research and Development, Titusville, New Jersey, USA.
D Madigan1] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA [2] Department of Statistics, Columbia University, New York, New York, USA.
P E Stang1] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA [2] Janssen Research and Development, Titusville, New Jersey, USA.
M J Schuemie1] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA [2] Janssen Research and Development, Titusville, New Jersey, USA.
C Friedman1] Department of Biomedical Informatics, Columbia University, New York, New York, USA [2] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA.
N P Tatonetti1] Department of Biomedical Informatics, Columbia University, New York, New York, USA [2] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA [3] Department of Systems Biology, Columbia University Medical Center, New York, New York, USA [4] Department of Medicine, Columbia University Medical Center, New York, New York, USA.
G Hripcsak1] Department of Biomedical Informatics, Columbia University, New York, New York, USA [2] Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA.
Columbia University · USJanssen (United States) · USColumbia University Irving Medical Center · US

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
Pharmacovigilence using Natural Language Processing, Statistics, and the EHRR01LM010016 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FRIEDMAN, CAROL · 2009 to 2016
$3.4M
Drug Effect Discovery Through Data Mining and Integrative Chemical BiologyR01GM107145 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TATONETTI, NICHOLAS P · 2014 to 2018
$2.5M
A Biomedical Natural Language Processing ResourceR01LM008635 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FRIEDMAN, CAROL · 2005 to 2008
$2.1M
NIGMS NIH HHS R01 GM107145NLM NIH HHS R01 LM006910NLM NIH HHS R01 LM008635NLM NIH HHS R01 LM010016
6 · The paper itself

Abstract

One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.

Identifiers

PMID25250527
PMCPMC4211266
OpenAlexW2035035706

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.