Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation.

Journal: Gait & posture

PMID: 39260073

Abstract

INTRODUCTION: The advent of digital and mobile health innovations, especially use of wearables for passive data collection, allows remote monitoring and creates an abundance of data. For this information to be interpretable, machine learning (ML) processes are necessary.

Authors

Karl Surmacz

ZBAI, Zimmer Biomet London, UK.
Roberta E Redfern

Clinical Affairs, Zimmer Biomet, Warsaw, IN, USA.
David C Van Andel

Clinical Affairs, Zimmer Biomet, Warsaw, IN, USA.
Atul F Kamath

Machine Learning Arthroplasty Lab, Cleveland Clinic, Cleveland, OH.

Keywords

Aged Arthroplasty, Replacement, Hip Arthroplasty, Replacement, Knee Female Humans Machine Learning Male Middle Aged Smartphone Walking Speed

External Resources

View on PubMed Access via DOI PubMed (39260073)

Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation.

Abstract

Authors

Keywords

External Resources

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