ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.Frontiers in research metrics and analytics · 2021Article
- Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance.Drug safety · 2020Article
- Article
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
- Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Predicting drug-disease associations by using similarity constrained matrix factorization.BMC bioinformatics · 2018Article
- Rediscovering Don Swanson: the Past, Present and Future of Literature-Based Discovery.Journal of data and information science (Warsaw, Poland) · 2017Article
- Embedding of semantic predications.Journal of biomedical informatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An important aspect of post-marketing drug surveillance involves identifying potential side-effects utilizing adverse drug event (ADE) reporting systems and/or Electronic Health Records. These data are noisy, necessitating identified drug/ADE associations be manually reviewed - a human-intensive process that scales poorly with large numbers of possibly dangerous associations and rapid growth of biomedical literature. Recent work has employed Literature Based Discovery methods that exploit implicit relationships between biomedical entities within the literature to estimate the plausibility of drug/ADE connections. We extend this work by evaluating machine learning classifiers applied to high-dimensional vector representations of relationships extracted from the literature as a means to identify substantiated drug/ADE connections. Using a curated reference standard, we show applying classifiers to such representations improves performance (+≈37%AUC) over previous approaches. These trained systems reproduce outcomes of the manual literature review process used to create the reference standard, but further research is required to establish their generalizability.
Indexed as
Identifiers
28269953PMC5333205What 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.