Survival path model outperforms conventional static machine learning models in long-term dynamic prognosis prediction for patients with intermediate stage hepatocellular carcinoma.

ABSTRACT:

Motivation

Patients with intermediate stage hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment, and treatment planning. A novel machine learning model called survival path mapping (SP) model was developed, while its performance as compared with conventional machine learning models remains unknown. Between January 2007 and December 2018, the time-series data of 2644 intermediate stage HCC patients from four medical centers in China were reviewed and included. Static machine learning models by Gaussian Naive Bayes (GNB), support vector machine (SVM), and random forest (RF) for the prediction of survivorship were built based on data at initial admission. Longitudinal data divided into different time slices were utilized for the construction of the

SUBMITTER: Shen L 

PROVIDER: S-EPMC11978388 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature