Academic Journal
Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients
| Title: | Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients |
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| Authors: | Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh |
| Source: | Scientific Reports, Vol 15, Iss 1, Pp 1-14 (2025) |
| Publisher Information: | Nature Portfolio, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Survival analysis, Machine learning, Liver transplantation, Mortality, Postoperative complications, Biliary complications, Medicine, Science |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-025-89570-4 |
| Access URL: | https://doaj.org/article/5d7cc36c60994c9a88728c86ded9c90d |
| Accession Number: | edsdoj.5d7cc36c60994c9a88728c86ded9c90d |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20452322 |
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| DOI: | 10.1038/s41598-025-89570-4 |
| Published in: | Scientific Reports |
| Language: | English |