Machine Learning Models for Predicting Response to Percutaneous Transhepatic Biliary Drainage in Malignant Obstructive Jaundice: A Retrospective Cohort Study.
Pre-procedural ML-based prediction of PTBD response is feasible with a modest AUC, and logistic regression generalises most reliably in this small, class-imbalanced dataset.
A nomogram based on routinely available clinical, in fl ammatory, and procedural variables showed acceptable internal performance and may assist in the early identi fi cation and targeted management of patients at high risk.
Xin Wang, Ji-Xian Zang, Xiao-Yang Su et al.· 0 citations
Accurate severity grading of acute cholangitis (AC) according to the Tokyo Guidelines 2018 (TG18) is essential for guiding biliary drainage decisions and intensive care allocation, yet bedside application of the multi-parameter grading system remains challenging. Existing prediction models are predominantly binary...
Yunxiao Lyu, Bin Wang, Z. Lyu· Frontiers in Medicine· 0 citations
The random survival forest demonstrated favorable discrimination and calibration, highlighting the potential value of ensemble learning in intrahepatic cholangiocarcinoma.
Guo-Teng Qiu, Shi-Zheng Mi, Jia-Li Chen et al.· Cancer Management and Resear...· 0 citations
A machine learning prediction model developed and dual-center validated a robust SHPT recurrence prediction model and deployed it as an online calculator, enabling convenient individualized risk assessment, optimizing postoperative monitoring.
Run-Min Cao, Yu-Run Zhang, Ling Cao et al.· International Journal of Med...· 0 citations
This model provides a reference for surgical risk stratification, which helps establish personalized postoperative monitoring strategies and demonstrates an interpretable machine learning model to effectively predict POF following fURL.
Xiao-Fei Lu, Chun-Ping Yu, Zhi-Yong Ding et al.· Frontiers in Medicine· 0 citations
SHAP analysis revealed that stone burden, preoperative serum creatinine, preoperative white blood cell count, stone complexity, and hydronephrosis were the most critical predictors of SFS.
Yunxun Liu, Wen Hu, Peixiong He et al.· World journal of urology· 0 citations
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