Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In JMIR medical informatics ; h5-index 23.0

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.

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


clinical decision support, electronic health records, machine learning, medication alert systems, patient safety