Machine learning methods for predicting adverse drug events: A systematic review
Bibliographic record
Abstract
Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high-risk patients and reducing health-care costs. While traditional methods struggle with the complexity of health-care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ADE prediction remains unclear. This systematic review evaluated ML algorithms used for this purpose, analysing studies that focussed on outpatient care or utilized large-scale data sources (e.g. electronic health records, administrative claims and spontaneous reporting systems) that primarily represent the outpatient continuum. We systematically searched MEDLINE and Embase up to December 2024 to identify studies developing or validating ML models for ADE prediction. Study characteristics, ML methods, ADE types, model performance and risk of bias were assessed using the PROBAST tool. From 59 included studies comprising 191 ML implementations, Logistic regression, Random forest and XGBoost emerged as the most commonly used algorithms. The majority of studies (67.8%) reported area under the curve (AUC), with 85% demonstrating moderate to high performance (AUC > 0.70) for internal validation. However, only 33.9% of studies addressed class imbalance, and merely 18.6% conducted external validation, raising concerns about methodological rigour, particularly in missing data handling and validation procedures. Our findings indicate that ML models, especially ensemble methods, show promise in predicting ADEs, although challenges with class imbalance and limited external validation currently hinder their clinical applicability. Future research should focus on adopting more rigorous methodologies and developing specialized frameworks for ML-based ADE prediction that build upon established pharmacovigilance practices to ensure models are accurate, generalizable, and seamlessly integrated into clinical workflows for ongoing monitoring and improved medication safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".