Construction of a Quetiapine Blood Concentration Prediction Model for Patients with Anxiety Disorders Based on Machine Learning

OBJECTIVE To construct a machine learning-based prediction model for predicting quetiapine plasma concentration in patients with anxiety disorders, providing a reference for primary healthcare institutions lacking therapeutic drug monitoring(TDM) conditions.

METHODS Clinical data and laboratory indicators of 337 patients with anxiety disorders who received quetiapine treatment from January 2020 to December 2022 at Urumqi Fourth People’s Hospital(Xinjiang Mental Health Center) were collected. Feature selection was performed using univariate analysis and Lasso regression. The selected variables were incorporated into five machine learning models(Random Forest, Support Vector Machine, Decision Tree, Light Gradient Boosting Machine, and Extreme Gradient Boosting). Optimize the model hyperparameters via five-fold cross-validation, evaluate the model performance on the test set, and meanwhile assess the model generalization ability on the independent time validation set(January 2025 to February 2025). The model was interpreted using the Shapley additive explanations(SHAP) method, and the contribution of each feature to the prediction results was analyzed.

RESULTS Univariate analysis showed that dose, triglycerides, alanine aminotransferase, aspartate aminotransferase, red blood cell count, white blood cell count, neutrophil count, and triiodothyronine significantly affected quetiapine plasma concentration(P<0.05). Lasso regression identified six variables. Among the five machine learning models, the Decision Tree model performed the best, with a coefficient of determination(R²) of 0.746, mean absolute percentage error(MAPE) of 50.81%, mean absolute error(MAE) of 10.0, root mean squared error(RMSE) of 16.1, and accuracy of 55.78% and maintained good predictive ability on the temporal validation set(R²=0.694, MAE=11.05).

CONCLUSION The Decision Tree model established in this study demonstrates good predictive performance and provides a reference for clinical individualized drug use.