@inproceedings{e241bce837ff433fb6a12160eaaef84b,
title = "A Deep-Learning-based Two-Compartment Predictive Model (PKRNN-2CM) for Vancomycin Therapeutic Drug Monitoring",
abstract = "This study developed a two-compartment deep learning model (PKRNN-2CM) for therapeutic drug monitoring (TDM) of vancomycin (VAN), a commonly used antibiotic. The model, which uses irregularly sampled electronic health record (EHR) data, outperformed a one-compartment model (PKRNN) in predicting VAN concentration. Simulation results also demonstrated the superiority of the PKRNN-2CM model, suggesting that it could improve the accuracy and effectiveness of personalized VAN TDM, leading to better clinical outcomes.",
keywords = "Compartmental model, Deep learning, Pharmacokinetics, Recurrent neural network, Vancomycin",
author = "Bingyu Mao and Ziqian Xie and Laila Rasmy and Masayuki Nigo and Degui Zhi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th IEEE International Conference on Healthcare Informatics, ICHI 2023 ; Conference date: 26-06-2023 Through 29-06-2023",
year = "2023",
doi = "10.1109/ICHI57859.2023.00075",
language = "English (US)",
series = "Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "484",
booktitle = "Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023",
address = "United States",
}