ArticleBMC medical informatics and decision making2021
Prediction of adverse drug reactions based on knowledge graph embedding.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- Machine learning models for adverse drug reaction prediction. A systematic review and three-level multilevel meta-analysis.Frontiers in pharmacology · 2026Pooled it
- A systematic review on sex differences in adverse drug reactions related to psychotropic, cardiovascular, and analgesic medications.Frontiers in pharmacology · 2023Pooled it
- Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance.Briefings in bioinformatics · 2026Article
- 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
- AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Informative relational learning for adverse reaction prediction with enhanced generalization to novel drugs.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- DrugForm-TAS: Target-Agnostic Selectivity as Proteome-wide Binding Propensity Estimation.Computational and structural biotechnology journal · 2026Article
- Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach.JMIR medical informatics · 2025Article
- Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction.Molecules (Basel, Switzerland) · 2023Article
- Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.Communications biology · 2023Article
- Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB.Healthcare (Basel, Switzerland) · 2023Article
- A Science-Based Methodology Framework for the Assessment of Combination Safety Risks in Clinical Trials.Pharmaceutical medicine · 2023Article
- Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.Frontiers in pharmacology · 2023Article
- Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of big data · 2023Article
- iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development.International journal of molecular sciences · 2022Article
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data.
methodBased on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs.
resultFirst, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863.
conclusionIn this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.
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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.