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已发表论文
预测第三代头孢菌素治疗期间血小板增多症风险的可解释机器学习模型的开发与验证
Received 15 September 2025
Accepted for publication 8 January 2026
Published 11 February 2026 Volume 2026:17 567773
DOI https://doi.org/10.2147/JBM.S567773
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Chang Kim
Kailei Du,1 Maofeng Wang,2 Ping Yu3
1Intensive Care Medicine, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China; 2Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China; 3Department of Gynecology, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China
Correspondence: Maofeng Wang, Email wzmcwmf@wmu.edu.cn Ping Yu, Email 13505897866@163.com
Objective: Third-generation cephalosporins are widely used for severe infections but carry thrombocythemia risks complicating therapeutic decisions. Current predictive tools lack accuracy and clinical interpretability. This study aimed to develop an interpretable machine learning (ML) model for thrombocythemia risk stratification during cephalosporin therapy.
Methods: A retrospective cohort of 45,779 adults treated with third-generation cephalosporins (2019– 2023) was analyzed. After exclusions (age < 18, missing data, baseline platelet anomalies), 25,707 patients were included. Thrombocythemia was defined as platelet count > 400× 109/L within 30 days post-treatment. Predictors encompassed demographics, comorbidities, medications, and laboratory parameters. Data preprocessing included multiple imputation and stratified partitioning (70% training, 30% testing). Three ML algorithms (XGBoost, Random Forest, LightGBM) were evaluated using ROC-AUC, Brier score, and clinical utility metrics. SHAP analysis provided model interpretability.
Results: XGBoost demonstrated superior performance, achieving the highest test-set discrimination (AUC=0.858, 95% CI:0.814– 0.902) and calibration (Brier score=0.0088). SHAP analysis identified Baseline platelet count (PLT), red blood cell count (RBC), creatinine (CRE), daily usage frequency, and sex as key drivers. PLT was the strongest predictor (SHAP range: − 1.67 to +1.48), with lower PLT exerting protective effects. RBC and CRE ranked second and third in importance, showing nonlinear risk relationships. Key clinical interactions included amplified risk from malignancies (SHAP=− 0.215) and protective effects of female sex (SHAP=− 0.194).
Conclusion: This interpretable ML framework enables precise thrombocythemia risk prediction during cephalosporin therapy, balancing algorithmic performance with clinical actionability.
Keywords: thrombocythemia, risk prediction, machine learning, third-generation cephalosporin, XGBoost
1Intensive Care Medicine, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China; 2Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China; 3Department of Gynecology, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China
Correspondence: Maofeng Wang, Email wzmcwmf@wmu.edu.cn Ping Yu, Email 13505897866@163.com
Objective: Third-generation cephalosporins are widely used for severe infections but carry thrombocythemia risks complicating therapeutic decisions. Current predictive tools lack accuracy and clinical interpretability. This study aimed to develop an interpretable machine learning (ML) model for thrombocythemia risk stratification during cephalosporin therapy.
Methods: A retrospective cohort of 45,779 adults treated with third-generation cephalosporins (2019– 2023) was analyzed. After exclusions (age < 18, missing data, baseline platelet anomalies), 25,707 patients were included. Thrombocythemia was defined as platelet count > 400× 109/L within 30 days post-treatment. Predictors encompassed demographics, comorbidities, medications, and laboratory parameters. Data preprocessing included multiple imputation and stratified partitioning (70% training, 30% testing). Three ML algorithms (XGBoost, Random Forest, LightGBM) were evaluated using ROC-AUC, Brier score, and clinical utility metrics. SHAP analysis provided model interpretability.
Results: XGBoost demonstrated superior performance, achieving the highest test-set discrimination (AUC=0.858, 95% CI:0.814– 0.902) and calibration (Brier score=0.0088). SHAP analysis identified Baseline platelet count (PLT), red blood cell count (RBC), creatinine (CRE), daily usage frequency, and sex as key drivers. PLT was the strongest predictor (SHAP range: − 1.67 to +1.48), with lower PLT exerting protective effects. RBC and CRE ranked second and third in importance, showing nonlinear risk relationships. Key clinical interactions included amplified risk from malignancies (SHAP=− 0.215) and protective effects of female sex (SHAP=− 0.194).
Conclusion: This interpretable ML framework enables precise thrombocythemia risk prediction during cephalosporin therapy, balancing algorithmic performance with clinical actionability.
Keywords: thrombocythemia, risk prediction, machine learning, third-generation cephalosporin, XGBoost