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

Masayuki Nigo, Hong Thoai Nga Tran, Ziqian Xie, Han Feng, Bingyu Mao, Laila Rasmy, Hongyu Miao, Degui Zhi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number104166
JournalJournal of Biomedical Informatics
Volume133
DOIs
StatePublished - Sep 2022

Keywords

  • Bayesian model
  • Deep learning
  • Pharmacokinetics
  • Recurrent neural network
  • Vancomycin

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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