ArticleJournal of the American Medical Informatics Association : JAMIA2017
Learning disease relationships from clinical drug trials.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset.Scientific reports · 2021Article
- Article
- Analysis of disease organ as a novel phenotype towards disease genetics understanding.Journal of biomedical informatics · 2019Article
- Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data.BMC bioinformatics · 2018Article
- Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.Assay and drug development technologies · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOur objective is to test the limits of the assumption that better learning from data in medicine requires more granular data. We hypothesize that clinical trial metadata contains latent scientific, clinical, and regulatory expert knowledge that can be accessed to draw conclusions about the underlying biology of diseases. We seek to demonstrate that this latent information can be uncovered from the whole body of clinical trials. MATERIALS AND
methodsWe extract free-text metadata from 93 654 clinical drug trials and introduce a representation that allows us to compare different trials. We then construct a network of diseases using only the trial metadata. We view each trial as the summation of expert knowledge of biological mechanisms and medical evidence linking a disease to a drug believed to modulate the pathways of that disease. Our network representation allows us to visualize disease relationships based on this underlying information.
resultsOur disease network shows surprising agreement with another disease network based on genetic data and on the Medical Subject Headings (MeSH) taxonomy, yet also contains unique disease similarities. DISCUSSION AND
conclusionThe agreement of our results with other sources indicates that our premise regarding latent expert knowledge holds. The disease relationships unique to our network may be used to generate hypotheses for future biological and clinical research as well as drug repurposing and design. Our results provide an example of using experimental data on humans to generate biologically useful information and point to a set of new and promising strategies to link clinical outcomes data back to biological research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.