ArticleScientific reports2017
Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 50 papers, 3 of them syntheses that pooled it.
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
50 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.Pharmaceutical medicine · 2022Pooled it
- Graph-Representation of Patient Data: a Systematic Literature Review.Journal of medical systems · 2020Pooled it
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective.Journal of medical Internet research · 2026Article
- Identification of Moonlighting Proteins from Published Literature Using Natural Language Processing and AI.The protein journal · 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
- Integrating multi-type features and knowledge graph for graded prediction of drug-induced liver injury in humans.PLoS computational biology · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.Scientific reports · 2025Article
- KGG: a fully automated workflow for creating disease-specific knowledge graphs.Bioinformatics (Oxford, England) · 2025Article
- Hepatotoxicity Evaluation of Levornidazole and Its Three Main Impurities: Based on Structure-Toxicity Classification Prediction Combined with Zebrafish Toxicity Assessment.Molecules (Basel, Switzerland) · 2025Article
- A novel weighted pseudo-labeling framework based on matrix factorization for adverse drug reaction prediction.BMC bioinformatics · 2025Article
- Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration.Therapeutic advances in drug safety · 2025Review
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Perspective review: Will generative AI make common data models obsolete in future analyses of distributed data networks?Therapeutic advances in drug safety · 2025Review
- A patient safety knowledge graph supporting vaccine product development.BMC medical informatics and decision making · 2024Article
- Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph.Bioinformatics (Oxford, England) · 2024Article
- The future of medicine: an outline attempt using state-of-the-art business and scientific trends.Frontiers in medicine · 2024Review
- Artificial intelligence in the field of pharmacy practice: A literature review.Exploratory research in clinical and social pharmacy · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials.
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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.