Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial.

Journal: The Journal of arthroplasty

Published Date: Oct 1, 2019

Abstract

BACKGROUND: Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PGHD in TJA to predict patient-reported outcome measures (PROMs).

Authors

Stefano A Bini

Department of Orthopaedics, University of California, San Francisco, San Francisco, California.
Romil F Shah

Department of Orthopedic Surgery, University of California-San Francisco, San Francisco, CA.
Ilya Bendich

Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA.
Joseph T Patterson

Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA.
Kevin M Hwang

Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA.
Musa B Zaid

Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA.

Keywords

Aged Algorithms Arthroplasty, Replacement, Hip Arthroplasty, Replacement, Knee Female Humans Knee Joint Machine Learning Male Middle Aged Monitoring, Ambulatory Osteoarthritis, Hip Osteoarthritis, Knee Outcome Assessment, Health Care Patient Reported Outcome Measures Pilot Projects Postoperative Period Prospective Studies Range of Motion, Articular Signal Processing, Computer-Assisted Wearable Electronic Devices

External Resources

View on PubMed Access via DOI PubMed (31439405)

Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial.

Abstract

Authors

Keywords

External Resources

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