Abstracts tagged "machine learning"

Abstract Number: 0210 • ACR Convergence 2021
Using Machine Learning to Predict Medial Knee Cartilage Worsening over 2 Years Using Gait and Physical Activity: The MOST Study
Kerry Costello¹, David Felson¹, S. Reza Jafarzadeh², Neil Segal³, Cora Lewis⁴, Michael Nevitt⁵, Cara Lewis⁶, Vijay Kolachalama² and Deepak Kumar¹, ¹Boston University, Boston, MA, ²Boston University School of Medicine, Boston, MA, ³University of Kansas Medical Center, Iowa City, IA, ⁴University of Alabama Birmingham, Birmingham, AL, ⁵University of California San Francisco, Orinda, CA, ⁶Boston University, Brookline, MA
Background/Purpose: Identifying knees at risk of worsening osteoarthritis (OA) could help to identify individuals in need of interventions. While gait and physical activity are considered…
Abstract Number: 0264 • ACR Convergence 2021
Joint Acoustic Emissions as a Biomarker to Differentiate Between Active and Inactive Juvenile Idiopathic Arthritis via 2-stage Machine Learning Classifier
Luis Rosa¹, Sevda Gharehbaghi¹, Omer Inan¹, Daniel Whittingslow², Lori Ponder³ and Sampath Prahalad⁴, ¹Georgia Institute of Technology, Atlanta, GA, ²Emory University School of Medicine, Atlanta, GA, ³Children's Healthcare of Atlanta, Atlanta, GA, ⁴Emory + Children's Pediatric Institute, Atlanta, GA
Background/Purpose: Juvenile Idiopathic Arthritis (JIA) is the most prevalent chronic rheumatic disease and can result in disability in children. JIA most commonly affects the knee.…
Abstract Number: 1215 • ACR Convergence 2021
Development of a Multivariable Prediction Model for Treatment Response to Etanercept in a Multi-centre Cohort of Patients with Established RA
Michael Stadler¹, Stephanie Ling², Nisha Nair², John Isaacs³, Kimme Hyrich², Ann Morgan⁴, Anthony Wilson⁵, Darren Plant², Anne Barton² and John Bowes², ¹University of Manchester, Preston, United Kingdom, ²University of Manchester, Manchester, United Kingdom, ³Newcastle University, Newcastle upon Tyne, United Kingdom, ⁴University of Leeds, Leeds, United Kingdom, ⁵Center for Arthritis Research, Conway Institute, University College Dublin, Dublin, Ireland
Background/Purpose: RA patients who respond inadequately to first-line DMARDs usually progress to a biologic DMARD. Treatment response to both DMARDs and biologics is heterogeneous within…
Abstract Number: 1227 • ACR Convergence 2021
Clinical Predictors of Response to Methotrexate in Patients with Rheumatoid Arthritis: A Machine Learning Approach Using Clinical Trial Data
Stephanie Duong¹, Cynthia Crowson², Elizabeth Atkinson¹, Arjun Athreya¹, John Davis¹, Eric Matteson³, Richard Weinshilboum¹, Liewei Wang¹ and Elena Myasoedova¹, ¹Mayo Clinic, Rochester, MN, ²Mayo Clinic, Eyota, MN, ³Mayo Clinic College of Medicine and Science, Rochester, MN
Background/Purpose: Methotrexate (MTX) is the preferred initial disease-modifying drug (DMARD) for rheumatoid arthritis (RA). However, up to 50% of patients respond inadequately to MTX. Clinically…
Abstract Number: 365 • 2019 ACR/ARP Annual Meeting
Classifying Pseudogout Using Machine Learning Approaches with Electronic Health Record Data
Sara K. Tedeschi¹, Tianrun Cai ², Zeling He ³, Yuri Ahuja ⁴, Chuan Hong ⁵, Katherine Yates ⁴, Kumar Dahal ², Chang Xu ³, Houchen Lyu ², Kazuki Yoshida ⁶, Daniel Solomon ⁷, Tianxi Cai ⁵ and Katherine Liao ², ¹Brigham and Women's Hospital, Div. of Rheumatology, Immunology and Allergy, Boston, MA, ²Brigham and Women's Hospital, Boston, ³Brigham and Women's Hospital, Boston, MA, ⁴Harvard Medical School, Boston, ⁵Harvard T.H. Chan School of Public Health, Boston, ⁶Brigham and Women's Hospital, Div. of Rheumatology, Immunology and Allergy, Boston, ⁷Brigham and Women´s Hospital, Div. of Rheumatology, Immunology and Allergy, Boston, MA
Background/Purpose: Identifying pseudogout in large administrative datasets has been difficult due to lack of specific billing codes for this acute subtype of calcium pyrophosphate (CPP)…

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