目的 构建基于误差反向传播神经网络（BPNN）的重症感染患者利奈唑胺血药浓度预测模型，为利奈唑胺个体化给药提供依据。方法 纳入2022年7月—2024年12月天津市第四中心医院重症医学科（ICU）接受利奈唑胺治疗的113例重症感染患者，采用液相色谱-串联质谱（LC-MS/MS）法测定利奈唑胺血药浓度，通过Boruta算法筛选与利奈唑胺血药浓度相关的特征变量，构建BPNN模型并开发可视化预测操作界面，结合SHAP法对模型的特征变量进行解释分析。结果BPNN模型验证结果显示，预测值与测定值相关系数（R²）为0.85，平均绝对误差（MAE）为1.065 mg·L^-1，84.2%样本预测误差≤2 mg·L^-1。SHAP分析显示，血清丙氨酸转氨酶（ALT）、总胆红素（TB）、白蛋白（ALB）及联用P-糖蛋白（P-gp）抑制剂对利奈唑胺血药浓度呈正向贡献，而肌酐清除率（CLCr）、身体质量指数（BMI）、C反应蛋白（CRP）呈负向贡献；可视化预测操作界面实现输入特征变量后一键生成血药浓度预测值。结论 构建的BPNN模型对利奈唑胺血药浓度预测能力良好，SHAP提示多因素影响利奈唑胺血药浓度，可视化预测界面实现对利奈唑胺血药浓度的实时预测。

Objective To construct a prediction model for linezolid blood concentration in critically ill patients based on the Back Propagation Neural Network (BPNN), to support personalized medication.Methods A total of 113 critically ill patients treated with linezolid in the Intensive Care Unit (ICU) of Tianjin 4th Center Hospital from July 2022 to December 2024 were included in the study. Linezolid blood concentration was determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The Boruta algorithm was used to select feature variables related to linezolid blood concentration, and a BPNN model was constructed along with a visual prediction interface. The SHAP (Shapley Additive Explanations) method was used for interpretative analysis of the model's feature variables.Results The validation results of the BPNN model showed that the correlation coefficient (R²) between the predicted and measured values was 0.85, with a mean absolute error (MAE) of 1.065 mg·L^-1, and 84.2% of the samples had prediction errors of ≤ 2 mg·L^-1. SHAP analysis showed that serum alanine aminotransferase (ALT), total bilirubin (TB), albumin (ALB), and the use of P-glycoprotein (P-gp) inhibitors had a positive impact on linezolid blood concentration, while creatinine clearance rate (CLCr), body mass index (BMI), and C-reactive protein (CRP) negatively impacted it; The visual prediction interface allows for one-click generation of blood concentration predictions after inputting feature variables.Conclusion The constructed BPNN model shows strong predictive ability for linezolid blood concentration. SHAP analysis suggests that multiple factors influence linezolid blood concentration, and the visual prediction interface allows for real-time predictions of linezolid blood concentration.

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天津市卫生健康科技项目（TJWJ2022QN038）