Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Objective: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.

Methods: This retrospective study used claims data from Truven databases from January 2006 to September 2015 (Segment 1) and October 2015 to February 2018 (Segment 2). Machine learning identified features differentiating patients with AS from matched controls; selected features were used as inputs in developing Model A/B to identify patients likely to have AS. Model A/B was trained and developed in Segment 1, and patients predicted to have AS in Segment 1 were followed up in Segment 2 to evaluate the predictive capability of Model A/B.

Results: Of 228,471 patients in Segment 1 without any history of AS, Model A/B predicted 1923 patients to have AS. Ultimately, 1242 patients received an AS diagnosis in Segment 2; 120 of these were correctly predicted by Model A/B, yielding a positive predictive value (PPV) of 6.24%. The diagnostic accuracy of Model A/B compared favorably with that of a clinical model (PPV, 1.29%) that predicted AS based on spondyloarthritis features described in the Assessment of SpondyloArthritis international Society classification criteria. A simplified linear regression model created to test the operability of Model A/B yielded a lower PPV (2.55%).

Conclusions: Model A/B performed better than a clinically based model in predicting a diagnosis of AS among patients in a large claims database; its use may contribute to early recognition of AS and a timely diagnosis.