PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data

[Display omitted] •PK-RNN is an AI-based pharmacokinetic (PK) model for therapeutic drug monitoring.•PK-RNN uses RNN to makes personalized prediction integrating individual EHR data.•PK-RNN integrates RNN and pharmacokinetic equations into end-to-end model.•PK-RNN-V outperformed Bayesian based PK model in predicting vancomycin levels.•PK-RNN is generalizable to other drugs, enabling new models for precision dosing. Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, however, although using carefully designed lab observations, were often developed in limited patient populations. The increasing availability of electronic health record (EHR) data offers an opportunity to develop TDM models for real-world patient populations. Here, we present a deep learning-based pharmacokinetic prediction model for vancomycin (PK-RNN-V E) using a large EHR dataset of 5,483 patients with 55,336 vancomycin administrations. PK-RNN-V E takes the patient’s real-time sparse and irregular observations and offers dynamic predictions. Our results show that RNN-PK-V E offers a root mean squared error (RMSE) of 5.39 and outperforms the traditional Bayesian model (VTDM model) with an RMSE of 6.29. We believe that PK-RNN-V E can provide a pharmacokinetic model for vancomycin and other antimicrobials that require TDM.