修订：Developing a machine learning-based predictive model for the analgesic effectiveness of transdermal fentanyl in cancer patients: an interpretable approach (Mar, 10.1007/s11096-024-01860-5, 2025)
In the original version of this article, the term “BMI” in the
Results subsection of the Abstract was incorrectly written
as “BMI2
”. The correct Results subsection of the abstract
should have been “Among 151 patients, 27.2% reported
inefectiveness of transdermal fentanyl. Logistic regression
identifed key factors of NRS, transdermal fentanyl dosage,
BMI, and ALT. Among the nine models, RF Model 8
exhibited the best performance, achieving a ROC-AUC
of 0.984 (95% CI: [0.968, 0.999]). This performance was
further validated by the confusion matrix metrics and
visualization results. The SHAP analysis highlighted BMI,
lower doses, NRS, and ALT as predictors of transdermal
fentanyl inefectiveness”.
第一作者机构：[1]Chongqing Jiulongpo Peoples Hosp, Dept Pharm, Chongqing, Peoples R China
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Hu Xiaogang,Chen Ya,Tang Yuelu,et al.修订：Developing a machine learning-based predictive model for the analgesic effectiveness of transdermal fentanyl in cancer patients: an interpretable approach (Mar, 10.1007/s11096-024-01860-5, 2025)[J].INTERNATIONAL JOURNAL OF CLINICAL PHARMACY.2025,1120-1120.doi:10.1007/s11096-025-01921-3.
APA：
Hu, Xiaogang,Chen, Ya,Tang, Yuelu,Wang, Xiaoxiao,Li, Lixian...&Chen, Wanyi.(2025).修订：Developing a machine learning-based predictive model for the analgesic effectiveness of transdermal fentanyl in cancer patients: an interpretable approach (Mar, 10.1007/s11096-024-01860-5, 2025).INTERNATIONAL JOURNAL OF CLINICAL PHARMACY,,
MLA：
Hu, Xiaogang,et al."修订：Developing a machine learning-based predictive model for the analgesic effectiveness of transdermal fentanyl in cancer patients: an interpretable approach (Mar, 10.1007/s11096-024-01860-5, 2025)".INTERNATIONAL JOURNAL OF CLINICAL PHARMACY .(2025):1120-1120
