SynthesisThe Journal of international medical research2024
Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.
Synthesis in The Journal of international medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.The Journal of international medical research · 2024Pooled it
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Article
- Augmenting Electronic Health Records for Adverse Event Detection.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence-driven risk prediction of polypharmacy in older adults: current advances, clinical applications, and future perspectives.Frontiers in public health · 2026Review
- Predicting clinically significant medication errors: Development and validation of a risk stratification model using incident reporting data.Frontiers in medicine · 2026Article
- Review
- Development of a warning model for drug-induced liver injury in the older patients.Frontiers in pharmacology · 2025Article
- Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis systematic review aimed to provide a comprehensive overview of the application of machine learning (ML) in predicting multiple adverse drug events (ADEs) using electronic health record (EHR) data.
methodsSystematic searches were conducted using PubMed, Web of Science, Embase, and IEEE Xplore from database inception until 21 November 2023. Studies that developed ML models for predicting multiple ADEs based on EHR data were included.
resultsTen studies met the inclusion criteria. Twenty ML methods were reported, most commonly random forest (RF, n = 9), followed by AdaBoost (n = 4), eXtreme Gradient Boosting (n = 3), and support vector machine (n = 3). The mean area under the summary receiver operator characteristics curve (AUC) was 0.76 (95% confidence interval [CI] = 0.26-0.95). RF combined with resampling-based approaches achieved high AUCs (0.9448-0.9457). The common risk factors of ADEs included the length of hospital stay, number of prescribed drugs, and admission type. The pooled estimated AUC was 0.72 (95% CI = 0.68-0.75).
conclusionsFuture studies should adhere to more rigorous reporting standards and consider new ML methods to facilitate the application of ML models in clinical practice.
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.