ArticleJMIR medical informatics2021
Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning for medication error detection: a scoping review.Research square · 2026Article
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
- Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.JMIR formative research · 2025Article
- Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises.NPJ digital medicine · 2024Article
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11 authors.
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Abstract
backgroundAlthough most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan's local databases (TLD) to address this issue. However, the international transferability of this model is unclear.
objectiveThis study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model.
methodsThe study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance.
resultsThe interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model.
conclusionsOur ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.
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