A machine learning-based predictive model for stem cell therapy outcomes in plastic surgery.

ABSTRACT:

Objective

Stem cell therapy has emerged as a promising approach in plastic surgery, yet its efficacy varies markedly among individuals and lacks reliable predictive assessment tools. This study aimed to construct and validate a predictive model for assessing the therapeutic efficacy of stem cell therapy in plastic surgery by identifying key influencing factors through clinical data analysis and machine learning.

Methods

Patients who underwent stem cell therapy in the Department of Plastic Surgery from June 2021 to July 2024 were retrospectively included and randomly divided into a training set and a validation set at a 7:3 ratio. Baseline clinical data were collected, and independent influencing factors were screened via univariate analysis, followed by multivariate logistic

SUBMITTER: Xu L 

PROVIDER: S-EPMC12753976 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature