Leveraging ChatGPT for Vancomycin Therapeutic Drug Monitoring: Simulation Using Bayesian Estimation and Hyperparameter Optimization

Abstract

The usefulness of ChatGPT, a large language model, has recently been explored in medical research. However, no studies have examined its reproducibility or applicability to therapeutic drug monitoring (TDM), a core task of clinical pharmacists. In this simulation study, we evaluated the feasibility of using ChatGPT for vancomycin (VCM) TDM based on Bayesian estimation. A total of 1000 virtual patients were generated by Monte Carlo simulations using a population pharmacokinetic model of VCM. Bayesian-estimated pharmacokinetic parameters and predicted concentrations were input into ChatGPT, and dosage regimens were compared among the three conditions, using temperature as a hyperparameter (T = 0.1, 0.5, and 1.0). Reproducibility was evaluated using the mode percentage in repeated runs. The reproducibility of the ChatGPT output was higher at T = 0.1 than at T = 0.5 and T = 1.0. When ChatGPT simulated the mode-recommended regimen (T = 0.1), the target attainment rate of the area under the serum concentration (AUC) (400–600 mg·h/L) improved from 25.5% (pre-optimization AUC (fixed-dose regimen)) to 71.5% (post-optimization AUC (ChatGPT-guided regimen)). These findings demonstrate that ChatGPT-based TDM using Bayesian estimation can enhance dose optimization. Adjusting the hyperparameter temperature to 0.1 improved reproducibility, suggesting that a reliable ChatGPT-assisted TDM support system may be clinically useful.

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