Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients

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...

Full description

Saved in:
Bibliographic Details
Main 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
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Survival analysis
Machine learning
Liver transplantation
Mortality
Postoperative complications
Biliary complications
Online Access:https://doi.org/10.1038/s41598-025-89570-4
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-89570-4

Similar Items