Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning

Background This study aimed to create a deep learning model for predicting phenotypic physical frailty from electronic medical record information in patients with cardiovascular disease. Methods and Results This single-center retrospective study enrolled patients who could be assessed for physical frailty according to cardiovascular health study criteria (25.5% [691/2,705] of the patients were frail). Patients were randomly separated for training (Train set: 80%) and validation (Test set: 20%) of the deep learning model. Multiple models were created using LightGBM, random forest, and logistic regression for deep learning, and their predictive abilities were compared. The LightGBM model had the highest accuracy (in a Test set: F1 score 0.561; accuracy 0.726; area under the curve of the receiver operating characteristics [AUC] 0.804). These results using only commonly used blood biochemistry test indices (in a Test set: F1 score 0.551; accuracy 0.721; AUC 0.793) were similar. The created models were consistently and strongly associated with physical functions at hospital discharge, all-cause death, and heart failure-related readmission. Conclusions Deep learning models derived from large sample sizes of phenotypic physical frailty have shown good accuracy and consistent associations with prognosis and physical functions.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning

Cited by 3 publications

References 34 publications

Advances of artificial intelligence in predicting frailty using real-world data: A scoping review

Advances of artificial intelligence in predicting frailty using real-world data: A scoping review

An offbeat bolstered swarm integrated ensemble learning (BSEL) model for heart disease diagnosis and classification

Artificial Intelligence (AI)-Driven Frailty Prediction Using Electronic Health Records in Hospitalized Patients With Cardiovascular Disease

Contact Info

Product

Resources

About