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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis

Author

Listed:
  • Shang-Ming Zhou
  • Fabiola Fernandez-Gutierrez
  • Jonathan Kennedy
  • Roxanne Cooksey
  • Mark Atkinson
  • Spiros Denaxas
  • Stefan Siebert
  • William G Dixon
  • Terence W O’Neill
  • Ernest Choy
  • Cathie Sudlow
  • UK Biobank Follow-up and Outcomes Group
  • Sinead Brophy

Abstract

Objectives: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. Methods: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. Results: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. Conclusion: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

Suggested Citation

  • Shang-Ming Zhou & Fabiola Fernandez-Gutierrez & Jonathan Kennedy & Roxanne Cooksey & Mark Atkinson & Spiros Denaxas & Stefan Siebert & William G Dixon & Terence W O’Neill & Ernest Choy & Cathie Sudlow, 2016. "Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
  • Handle: RePEc:plo:pone00:0154515
    DOI: 10.1371/journal.pone.0154515
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    References listed on IDEAS

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    1. Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
    2. Jeffrey S. Racine, 2012. "RStudio: A Platform‐Independent IDE for R and Sweave," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 167-172, January.
    3. Shang-Ming Zhou & Ronan A Lyons & Owen G Bodger & Ann John & Huw Brunt & Kerina Jones & Mike B Gravenor & Sinead Brophy, 2014. "Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-14, November.
    4. Shang-Ming Zhou & Ronan A Lyons & Sinead Brophy & Mike B Gravenor, 2012. "Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-14, December.
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    1. Francesco Inchingolo & Angelo Michele Inchingolo & Maria Celeste Fatone & Pasquale Avantario & Gaetano Del Vecchio & Carmela Pezzolla & Antonio Mancini & Francesco Galante & Andrea Palermo & Alessio D, 2024. "Management of Rheumatoid Arthritis in Primary Care: A Scoping Review," IJERPH, MDPI, vol. 21(6), pages 1-35, May.

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