Development and validation of machine learning models to predict frailty risk for elderly

CONCLUSION: Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly.IMPACT: The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions.IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life.REPORTING METHOD: The study has adhered to STROBE guidelines.PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.PMID:38605460 | DOI:10.1111/jan.16192
Source: Adv Data - Category: Epidemiology Authors: Source Type: research