A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method.

Journal: Computer methods and programs in biomedicine

PMID: 31972347

Abstract

BACKGROUND AND OBJECTIVE: The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelligence-based nonlinear approach, if the estimation of ITS data could be facilitated, in addition to providing a computationally explicit equation.

Authors

Hossein Bonakdari

Department of Civil Engineering, Razi University, Kermanshah, Iran and Water and Wastewater Research Center, Razi University, Kermanshah, Iran E-mail: bonakdari@yahoo.com.
Jean-Pierre Pelletier

Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), Montreal, Quebec, Canada.
Johanne Martel-Pelletier

Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), Montreal, Quebec, Canada. jm@martelpelletier.ca.

Keywords

Arthritis, Rheumatoid Arthroplasty, Replacement, Hip Arthroplasty, Replacement, Knee Artificial Intelligence Hip Humans Incidence Interrupted Time Series Analysis Knee Linear Models Outcome Assessment, Health Care Research Design

External Resources

View on PubMed Access via DOI PubMed (31972347)

A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method.

Abstract

Authors

Keywords

External Resources

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