Development of an optimized risk evaluation system for cardiovascular-kidney-metabolic syndrome-associated coronary heart disease based on tabular prior-data fitted network.

ABSTRACT:

Background

The innovative concept of cardiovascular-kidney-metabolic (CKM) syndrome and tabular prior-data fitted network (TabPFN) offers opportunities for optimizing coronary heart disease (CHD) risk evaluation. This study compared TabPFN with traditional machine learning (ML) methods in medical small-sample data, aiming to construct and validate a risk model for coronary stenosis in CKM-CHD patients.

Methods

The research strictly adheres to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + artificial intelligence (TRIPOD + AI). A total of 296 inpatients from the Main Campus of Jiangsu Province Hospital of Chinese Medicine (June 2023-August 2024) and Zidong Branch (June 2024-December 2024) were screened. The data of the Main C

SUBMITTER: Zhu S 

PROVIDER: S-EPMC12437168 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature