ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 15 citations in OpenAlex.
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- Hyperdimensional computing in biomedical sciences: a brief review.PeerJ. Computer science · 2025Article
- Fast polypharmacy side effect prediction using tensor factorization.Bioinformatics (Oxford, England) · 2024Article
- Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.PLoS computational biology · 2024Review
- Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care.Frontiers in big data · 2022Article
- Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation.JMIR medical informatics · 2021Article
- SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.Bioinformatics (Oxford, England) · 2021Article
- Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.PLoS computational biology · 2021Article
- Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Emerging approaches to polypharmacy among older adults.Nature aging · 2021Review
- Identifying side effects of commonly used drugs in the treatment of Covid 19.Scientific reports · 2020Review
- Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic PredicationsAMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
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
Abstract
The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predicting individual drug side effects. In the current paper we extend this work by applying ESP to the problem of predicting polypharmacy side-effects for particular drug combinations, building on a recent reconceptualization of this problem as a network of drug nodes connected by side effect edges. We evaluate ESP embeddings derived from the resulting graph on a side-effect prediction task against a previously reported graph convolutional neural network approach, using the same data and evaluation methods. We demonstrate that ESP models perform better, while being faster to train, more re-usable, and significantly simpler.
Indexed as
Identifiers
32308896PMC7153048W3026528364What 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.