Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring

Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. T...

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Main Authors: Sooyoung Lee (Author), Moonsik Song (Author), Jongdae Han (Author), Donghwan Lee (Author), Bo-Hyung Kim (Author)
Format: Book
Published: MDPI AG, 2022-05-01T00:00:00Z.
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001 doaj_c7863c1c89f04a6a94de0318aa3fc9f6
042 |a dc 
100 1 0 |a Sooyoung Lee  |e author 
700 1 0 |a Moonsik Song  |e author 
700 1 0 |a Jongdae Han  |e author 
700 1 0 |a Donghwan Lee  |e author 
700 1 0 |a Bo-Hyung Kim  |e author 
245 0 0 |a Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring 
260 |b MDPI AG,   |c 2022-05-01T00:00:00Z. 
500 |a 10.3390/pharmaceutics14051023 
500 |a 1999-4923 
520 |a Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. Therefore, we aimed to develop a classifier using machine learning (ML) to select a more suitable vancomycin PK model for TDM in a patient. In our study, nine vancomycin PK studies were selected, and a classifier was created to choose suitable models among them for patients. The classifier was trained using 900,000 virtual patients, and its performance was evaluated using 9000 and 4000 virtual patients for internal and external validation, respectively. The accuracy of the classifier ranged from 20.8% to 71.6% in the simulation scenarios. TDM using the ML classifier showed stable results compared with that using single models without the ML classifier. Based on these results, we have discussed further development of TDM using ML. In conclusion, we developed and evaluated a new method for selecting a PK model for TDM using ML. With more information, such as on additional PK model reporting and ML model improvement, this method can be further enhanced. 
546 |a EN 
690 |a population pharmacokinetics 
690 |a simulation 
690 |a Bayesian method 
690 |a XGBoost 
690 |a classifier 
690 |a Pharmacy and materia medica 
690 |a RS1-441 
655 7 |a article  |2 local 
786 0 |n Pharmaceutics, Vol 14, Iss 5, p 1023 (2022) 
787 0 |n https://www.mdpi.com/1999-4923/14/5/1023 
787 0 |n https://doaj.org/toc/1999-4923 
856 4 1 |u https://doaj.org/article/c7863c1c89f04a6a94de0318aa3fc9f6  |z Connect to this object online.