Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

ABSTRACT:

Importance

Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Objective

To develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm.

Design, setting, and participants

This retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regre

SUBMITTER: Tian D 

PROVIDER: S-EPMC10163387 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature