Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study.

ABSTRACT:

Background

Health care budgets are limited, requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources.

Objective

We assessed the applicability of selected ML tools to evaluate the contribution of known risk markers for prognosis of coronary artery disease to predict health care costs for all reasons in patients with a recent acute coronary syndrome (n = 65, aged 65 ± 9 years) for 1-year follow-up.

Methods

Risk markers were assessed at baseline, and health care costs were collected from electronic health registries. The Cross-decomposition algorithms were used to rank the considered risk markers based on their impacts on variances. Then regression analysis was performed to predict cos

SUBMITTER: Hautala AJ 

PROVIDER: S-EPMC10435951 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature