ArticleJournal of medical Internet research2019
Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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.
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Who cites it
31 citing papers in PubMed.
- Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance.Briefings in bioinformatics · 2026Article
- Review
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction.ACS omega · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.Scientific reports · 2024Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.Communications biology · 2023Article
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- Evaluation of four machine learning models for signal detection.Therapeutic advances in drug safety · 2023Article
- IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.Computational intelligence and neuroscience · 2023Article
- Patient Preferences and Their Influence on Chronic Hepatitis B-A Review.Patient preference and adherence · 2023Review
- Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis.British journal of clinical pharmacology · 2022Article
- Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network.Journal of integrative bioinformatics · 2022Article
- A Narrative Review of Adverse Event Detection, Monitoring, and Prevention in Indian Hospitals.Cureus · 2022Review
- SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.Bioinformatics (Oxford, England) · 2022Article
- Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning.Healthcare (Basel, Switzerland) · 2022Article
- HIDTI: integration of heterogeneous information to predict drug-target interactions.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug reactions (ADRs) are common and are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance.
objectiveThe objective of this study was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN).
methodsWe designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfill two main purposes: identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships using the word-embedding approach to process substantial biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset.
resultsUsing the drug information and the ADRs reported up to 2009, we predicted the ADRs of drugs recorded up to 2012. There were 746 drugs and 232 new drugs, which were only recorded in 2012 with 1325 ADRs. The experimental results showed that the overall performance of our model with mean average precision at top-10 achieved is 0.523 and the rea under the receiver operating characteristic curve (AUC) score achieved is 0.844 for ADR prediction on the dataset.
conclusionsOur model is effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past.
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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 Socratic graph.