Machine learning models for predicting survival in patients with ampullary adenocarcinoma.

ABSTRACT:

Objective

The aim of this study was to predict the long-term survival probability of patients with ampullary adenocarcinoma (AAC), which would provide a theoretical basis for the long-term care of these patients.

Methods

Data on patients with AAC during 2004-2015 were obtained from the Surveillance, Epidemiology, and End Results database, which were split at a 7:3 ratio into two independent cohorts: training and testing cohorts. Differences in survival between the two groups were tested using the Kaplan-Meier estimator and log-rank test methods. We constructed six survival analysis methods: the American Joint Committee on Cancer TNM stage, Cox Proportional Hazards regression, CoxTime, DeepSurv, XGBoost Survival Embeddings, and Random Survival Forest. The performances of thes

SUBMITTER: Huang T 

PROVIDER: S-EPMC9583040 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature