Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma

Abstract Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepatectomy, and 30% of the samples were util...

Full description

Saved in:
Bibliographic Details
Main Authors: Tianzhi Tang, Tianyu Guo, Bo Zhu, Qihui Tian, Yang Wu, Yefu Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Post-hepatectomy liver failure
Hepatocellular carcinoma
Machine learning
Prediction model
Online Access:https://doi.org/10.1038/s41598-025-97878-4
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items