BACKGROUND: Increasing demand on health care systems requires innovative, transformative solutions for efficient, high-quality care. One promising approach is digital twin (DT) technology, which leverages real-time data to create dynamic, virtual rep...
OBJECTIVE: As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. This study examines the eGDR-frailty link, develops a machine learning predictive model to add...
This study presents a novel deep learning (DL) framework, the Deep Neural Frailty Competing Risks (DNFCR) model, which simultaneously integrates frailty and competing risks (CR) for mortality prediction in heart failure (HF). While existing models li...
BACKGROUND: Due to its association with multimorbidity, frailty gives rise to multidimensional needs for different services. Too often, patient preferences and service encounter information are not adequately shared.
INTRODUCTION: Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is crucial for implementing timely interventions. However...
There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable ma...
Falls are a critical concern in older adults with cognitive frailty (CF). However, previous studies have not fully examined whether machine learning models can predict falls in older individuals with CF. The 2-year longitudinal data set from the Kore...
PURPOSE: This artificial intelligence (AI)-driven scientometric analysis, conducted using the Mynd discovery platform, explores research trends in lower urinary tract symptoms (LUTS) among older patients. By applying its novel recency metric, the stu...
OBJECTIVE: To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced decision-making and targeted health management in integrat...
BACKGROUND: White matter hyperintensities (WMHs) are closely associated with cognitive frailty (CF). This study aims to explore the potential diagnostic value of WMHs for CF based on radiomics approaches, thereby providing a novel methodology for the...
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