An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.

ABSTRACT:

Background

Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges remain in choosing the most appropriate method, deciding how many top-ranked variables to include, and ensuring these selections are meaningful from a medical perspective.

Methods

We developed a practical multi-step feature selection (FS) framework that integrates data-driven statistical inference with a knowledge verification strategy. This framework was validated using two distinct EMR datasets targeting different clinical outcomes. The first cohort, sourced from the Medical Information Mart for Intensive Care III (MIMIC-III), foc

SUBMITTER: Wang H 

PROVIDER: S-EPMC11834488 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature