Application of machine learning in the diagnosis of axial spondyloarthritis. Issue 4 (July 2019)

• Record Type: Journal Article
• Authors: Walsh, Jessica A.; Rozycki, Martin; Yi, Esther; Park, Yujin
• Is Part Of: Current opinion in rheumatology. Volume 31:Issue 4(2019:Jul.)
• Publication Date: 2019-07
• Subjects: ankylosing spondylitis -- axial spondyloarthritis -- machine learning; Rheumatology -- Periodicals; Arthritis -- Periodicals; Review Literature -- Periodicals; Rheumatic Diseases -- Periodicals; 616.723
• DOI: 10.1097/BOR.0000000000000612
• Languages: English
• ISSNs: 1040-8711

Abstract

Abstract : Purpose of review: In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA). Recent findings: In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6–91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing. Summary: We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.