| Autor: |
Chin, Yen Po Harvey, Song, Wenyu, Lien, Chia En, Yoon, Chang Ho, Wang, Wei-Chen, Liu, Jennifer, Nguyen, Phung Anh, Feng, Yi Ting, Zhou, Li, Li, Yu Chuan Jack, Bates, David Westfall |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
JMIR Medical Informatics, Vol 9, Iss 1, p e23454 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2291-9694 |
| DOI: |
10.2196/23454 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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