Assessing the international transferability of a machine learning model for detecting medication error in the general internal medicine clinic: Multicenter preliminary validation study

Yen Po Harvey Chin, Wenyu Song, Chia En Lien, Chang Ho Yoon, Wei Chen Wang, Jennifer Liu, Phung Anh Nguyen, Yi Ting Feng, Li Zhou, Yu Chuan Jack Li, David Westfall Bates

研究成果: 雜誌貢獻文章同行評審

摘要

Background: Although 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. Objective: This 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. Methods: The 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. Results: The 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. Conclusions: Our 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.
原文英語
文章編號e23454
期刊JMIR Medical Informatics
9
發行號1
DOIs
出版狀態已發佈 - 一月 2021

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management

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