Enhance health evidence quality in classification tasks: A triangulation approach utilizing case-based reasoning and process features.

ABSTRACT:

Objective

Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.This study proposed an integrated approach to enhance the quality of health evidence from a classification task for predicting Medicare's Diagnosis-Related Groups of ischemic heart disease (IHD) patients.

Methods

Eligible participants were identified from the Medical Information Mart for Intensive Care IV (MIMIC IV), a publicly available hospital database. S

SUBMITTER: Guo R 

PROVIDER: S-EPMC11748077 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature