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Updated: Jun 9, 2025

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification.

Adane Nega Tarekegn^1,2, Krzysztof Michalak³, Giuseppe Costa⁴

¹Department of Information Science and Media Studies, University of Bergen, Bergen, Norway.

Journal of Healthcare Informatics Research

|October 28, 2024

Summary

This summary is machine-generated.

This study introduces a novel hybrid resampling method to predict multiple adverse outcomes associated with frailty syndrome in older adults. The approach effectively handles imbalanced data, achieving an 83% average precision score.

Keywords:

Frailty prediction Hybrid resampling Imbalanced data Multi-label classification Resampling algorithm

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Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Gerontology
Computational Medicine
Machine Learning

Background:

Frailty syndrome is common in the elderly, often associated with chronic diseases and poor health.
Previous research primarily focused on predicting single frailty outcomes.
Predicting multiple adverse outcomes simultaneously presents a significant challenge.

Purpose of the Study:

To frame frailty prediction as a multi-label learning problem.
To develop and evaluate a hybrid resampling approach for imbalanced multi-label frailty data.
To simultaneously predict multiple adverse health outcomes in elderly individuals.

Main Methods:

A novel hybrid resampling technique was developed to address label imbalance in multi-label classification.
The method was applied to a high-dimensional medical dataset of individuals aged 65 and above.
Multiple multi-label algorithms were tested and evaluated using established metrics.

Main Results:

The proposed hybrid resampling approach demonstrated effectiveness in handling imbalanced multi-label data.
The best-performing prediction model achieved an average precision score of 83%.
The study successfully predicted multiple frailty-related outcomes from complex medical data.

Conclusions:

The developed hybrid resampling method is effective for multi-label frailty prediction.
This approach offers a promising solution for analyzing complex and imbalanced elderly health data.
Simultaneous prediction of multiple adverse outcomes improves understanding of frailty syndrome.