Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.

Alison Deatsch, Michael McKenna, Jonathan Palumbo, Qu Tian, Eleanor Simonsick, Luigi Ferrucci, Robert Jeraj, Richard G Spencer

Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America. · pubmed

Paper

Abstract

Identification of accelerated aging and its biomarkers can lead to more timely therapeutic interventions and decision-making. Therefore, we sought to predict aging-related slow gait, a known predictor of accelerated aging, and its determinants.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper claims to predict aging-related slow gait and its determinants using deep learning and logistic regression. This research is relevant as it addresses a specific aspect of aging (slow gait) that can serve as a biomarker for accelerated aging, potentially leading to better interventions.

Author of this paper? Send a correction.