Using LASSO Regression to Predict Rheumatoid Arthritis Treatment Efficacy

David J Odgers, Natalie Tellis, Heather Hall, Michel Dumontier

Research output: Contribution to journalArticleAcademicpeer-review


Rheumatoid arthritis (RA) accounts for one-fifth of the deaths due to arthritis, the leading cause of disability in the United States. Finding effective treatments for managing arthritis symptoms are a major challenge, since the mechanisms of autoimmune disorders are not fully understood and disease presentation differs for each patient. The American College of Rheumatology clinical guidelines for treatment consider the severity of the disease when deciding treatment, but do not include any prediction of drug efficacy. Using Electronic Health Records and Biomedical Linked Open Data (LOD), we demonstrate a method to classify patient outcomes using LASSO penalized regression. We show how Linked Data improves prediction and provides insight into how drug treatment regimes have different treatment outcome. Applying classifiers like this to decision support in clinical applications could decrease time to successful disease management, lessening a physical and financial burden on patients individually and the healthcare system as a whole.

Original languageEnglish
Pages (from-to)176-83
Number of pages8
JournalAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Publication statusPublished - 2016
Externally publishedYes


  • Journal Article

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