ArticlePharmaceuticals (Basel, Switzerland)2024
Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.
Article in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.The Journal of international medical research · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Review
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- 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
- Characterization of adverse reactions to four common targeted drugs for hepatocellular carcinoma in WHO-VigiAccess.Scientific reports · 2025Article
- Pharmacovigilance signal detection of psychiatric adverse events induced by third-generation antiepileptic drugs in children.Frontiers in pharmacology · 2025Article
- Mobile-Based Medication Management Applications: A Scoping Review of Functional and Non-Functional Requirements.Healthcare technology lettersReview
- Applying Machine Learning Techniques to Predict Drug-Related Side Effect: A Policy Brief.Inquiry : a journal of medical care organization, provision and financingArticle
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.
Funding
Abstract
backgroundDrug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features.
methodsThis was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023.
resultsThe results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects.
conclusionsThis review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.
Indexed as
Identifiers
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.