Investigate the Phenotypical Characterization for Osteoarthritis Pain Relief and Benefits of Combination Therapy Using Real World Big Data and Machine Learning Technologies
Osteoarthritis (OA) is a common disease associated with a substantial and growing health and economic burden. OA is the most widespread musculoskeletal disease and is the global leading cause of disability. In the UK, one third of persons over 45 years of age have sought treatment for OA, and X-ray studies have shown that at least 50% of people older than 65 have evidence of osteoarthritis. OA has extraordinary interpatient variability, that is, the manner in which OA manifests itself in patients varies considerably. This interpatient variability does not lend itself to one-fits-all therapeutic strategies. Hence, the aims of this project is are to identify characteristics that allow identification of distinct grouping of OA patients, determine how OA progresses in these groups, and how these unique OA groups respond to treatments. From the UK Biobank database we will be examining patients with: self-reported OA, hospital-diagnosed OA, and pain at joints. We will employ innovative real world big data and machine learning (ML) technologies to identify these distinct groups. The overall project goal is to assist in customizing the right treatment at the right time for specific OA groups. By matching OA groups with appropriate and timely treatment, substantial unnecessary pain, suffering, and loss of productivity can be reduced. The project duration is expected to be less than twelve months.