Bibliographic Details
| Title: |
Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients. |
| Authors: |
Andishgar, Aref, Bazmi, Sina, Lankarani, Kamran B., Taghavi, Seyed Alireza, Imanieh, Mohammad Hadi, Sivandzadeh, Gholamreza, Saeian, Samira, Dadashpour, Nazanin, Shamsaeefar, Alireza, Ravankhah, Mahdi, Deylami, Hamed Nikoupour, Tabrizi, Reza, Imanieh, Mohammad Hossein |
| Source: |
Scientific Reports; 2/8/2025, Vol. 15 Issue 1, p1-14, 14p |
| Subject Terms: |
MACHINE learning, FEATURE selection, MORTALITY risk factors, SURVIVAL rate, SURVIVAL analysis (Biometry) |
| Abstract: |
Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. Optimal hyperparameters were determined based on average C-index. Features importance was assessed using standardized regression coefficients and permutation importance for top models. Stability was evaluated using 5-fold cross-validation standard deviation. Finally, 1799 observations with 40 outcome predictors were included. RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient's BMI, recipient's history of PVT, and previous LT history. For mortality, they were post-transplant AST, creatinine, recipient's age, post-transplant ALT, and tacrolimus consumption. We identified BC and mortality risk factors, improving decision-making and outcomes. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
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