Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.

ABSTRACT:

Introduction

Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF.

Methods

In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolut

SUBMITTER: Yilmaz R 

PROVIDER: S-EPMC11023638 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature