Evidence map›Paper›PMID 35415408›Full record

ArticleJournal of healthcare informatics research2018

Pattern Discovery from High-Order Drug-Drug Interaction Relations.

Wen-Hao Chiang, Titus Schleyer, Li Shen, Lang Li, Xia Ning

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

5 authors.

Wen-Hao ChiangDepartment of Computer & Information Science, Indiana University - Purdue University Indianapolis, Indianapolis, IN 46202 USA.
Titus SchleyerCenter for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202 USA.
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Pennsylvania, PA 19104 USA.
Lang LiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA.
Xia NingDepartment of Computer & Information Science, Indiana University - Purdue University Indianapolis, Indianapolis, IN 46202 USA.ORCID 0000-0002-6842-1165

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd's) and directional DDI relations (DDI-d's), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its stochastic algorithm

Indexed as

ConvolutionDrug-drug interactionsDrug-drug similaritiesGraph representationStochastic algorithm

Identifiers

PMID35415408
PMCPMC8982853

What OpenQuestion holds

Textmetadata
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Registered trials

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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.