ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Hierarchy-aware Adverse Reaction Embeddings for Signal Detection.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 1 citations in OpenAlex.
- Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance.Briefings in bioinformatics · 2026Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-market drug surveillance monitors new and evolving treatments for their effectiveness and safety in real-world conditions. A large amount of drug safety surveillance data is captured by spontaneous reporting systems such as the FAERS. Developing automated methods to identify actionable safety signals from these databases is an active area of research. In this paper, we propose two novel network representation learning methods (HARE and T-HARE) for signal detection that jointly utilize association information between drugs and medical outcomes from the FAERS and ancestral information in medical ontologies. We evaluate these methods using two publicly available reference datasets, EU-ADR and OMOP corpus. Experimental results showed that the proposed methods significantly outper-formed standard methodologies based on disproportionality metrics and the existing state-of-the-art aer2vec method with statistically significant improvements on both EU-ADR and OMOP datasets. Through quantitative and qualitative analysis, we demonstrate the potential of the proposed methods for effective signal detection.
Indexed as
Identifiers
37128452PMC10148317W4367668827What 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.