Mining Drugs and Indications for Suicide-Related Adverse Events.

Tiffany Ding, Elizabeth S Chen

Open access · greenAbstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact, top 84% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

Deliberate Self-Poisoning: Real-Time Characterization of Suicidal Habits and Toxidromes in the Food and Drug Administration Adverse Event Reporting System.Drug safety · 2023
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

2 authors at 1 institution in 1 country.

Tiffany DingCenter for Biomedical Informatics, Brown University, Providence, RI.

Elizabeth S ChenCenter for Biomedical Informatics, Brown University, Providence, RI.

Brown University · US

Funding

Tracking and Evaluation CoreU54GM115677 · NIGMS · BROWN UNIVERSITY · PI ROUNDS, SHARON IRENE SMITH · 2016 to 2025

$45.0M

NIGMS NIH HHS U54 GM115677

6 · The paper itself

Abstract

There has been a significant increase in suicide rates in the United States (U.S.) over the past two decades. Studies have highlighted the need for further exploration of suicide risk factors, particularly combinations of factors. In this study, a pharmacovigilance analysis was conducted to better understand drugs and indications as risk factors for suicide using data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and Adverse Event Open Learning through Universal Standardization (AEOLUS), a standardized version of FAERS. Association rule mining techniques were applied to 85,071 cases involving suicide-related adverse reactions and demographic subsets of these cases. Preliminary results reveal combinations of drugs and indications that may increase the likelihood of suicide, with certain combinations potentially affecting some demographic groups more than others. Further work is needed to validate the initial findings, explore subpopulations, and determine the broader implications for suicide prevention.

Indexed as

Data MiningDrug-Related Side Effects and Adverse ReactionsAdolescentAdultAdverse Drug Reaction Reporting SystemsAgedDatasets as TopicFemaleHumansMaleMiddle AgedPharmacovigilanceRisk FactorsSuicidal IdeationSuicideSuicide, Attempted

Identifiers

PMID32308898

PMCPMC7153138

OpenAlexW3027771804

What OpenQuestion holds

Textmetadata

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.