Research progress of machine learning in the assessment of frailty in patients with heart failure
ZHENG Bowen
WANG Xiaolei
HUA Musen
Abstract：Patients with heart failure are often comorbid with frailty symptoms.Early assessment of frailty status in these patients can effectively improve their prognosis.However,traditional frailty assessment tools have issues such as strong subjectivity and insufficient specificity for heart failure patients.As an important branch of artificial intelligence,machine learning has demonstrated clinical value in the precise assessment of frailty in heart failure patients.This study re-views the current application status of machine learning in this field,specifically including the construction of frailty assess-ment indices,prediction of frailty assessment scale scores,evaluation of different frailty phenotypes,and identification of frailty by combining wearable devices and voice biomarkers.It also discusses the challenges in its application,aiming to provide a reference for promoting multimodal data fusion and establishing a precise and intelligent assessment system for patients comorbid with heart failure and frailty in the future.
Keywords：machine learningheart failurefrailtyreview
Publication Date：2025-12-25
Online Publishing Date：2026-01-09(First online date of this platform, not the publication date of the document)
Pages：5( 33-37 )