Evidence map›Paper›PMID 34541578›Full record

ArticleACM transactions on computing for healthcare2021

Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.

Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu

Abstract read
In one paragraph

Article in ACM transactions on computing for healthcare, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Tung TranUniversity of Kentucky, United States.
Ramakanth KavuluruUniversity of Kentucky, United States.
Halil KilicogluNational Library of Medicine, United States.

Funding

Kentucky Center for Clinical and Translational ScienceUL1TR001998 · NCATS · UNIVERSITY OF KENTUCKY · PI HARTMANN, KATHERINE E, KERN, PHILIP A · 2016 to 2025
$34.2M
From Syntactic Relations to Semantic Predications: Porting Open Information Extraction to BiomedicineR21LM012274 · NLM · UNIVERSITY OF KENTUCKY · PI KAVULURU, VENKATA NAGA RAMAKANTH · 2016 to 2017
$374k
NCATS NIH HHS UL1 TR001998NLM NIH HHS R21 LM012274
6 · The paper itself

Abstract

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. Herein, we tackle the problem of jointly extracting mentions of drugs and their interactions, including interaction

Indexed as

drug-drug interactionsmulti-task learningNeural networksrelation extraction

Identifiers

PMID34541578
PMCPMC8445229

What OpenQuestion holds

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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.