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

Journal: PloS one
Published Date: Jan 1, 2016

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.

Authors

  • Shang-Ming Zhou
    Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
  • Fabiola Fernandez-Gutierrez
    Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
  • Jonathan Kennedy
    Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
  • Roxanne Cooksey
    Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
  • Mark Atkinson
    Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
  • Spiros Denaxas
    UCL Institute of Health Informatics and Farr Institute of Health Informatics Research, London, United Kingdom.
  • Stefan Siebert
    Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom.
  • William G Dixon
    Arthritis Research UK Centre for Epidemiology, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.
  • Terence W O'Neill
    Arthritis Research UK Centre for Epidemiology, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.
  • Ernest Choy
    Arthritis Research UK CREATE Centre and Welsh Arthritis Research Network, School of Medicine, Cardiff University, Cardiff, United Kingdom.
  • Cathie Sudlow
    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
  • Sinead Brophy
    Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.

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