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

Authors

Shang Ming Zhou, School of Nursing and Midwifery
Fabiola Fernandez-Gutierrez, Swansea University
Jonathan Kennedy, Swansea University
Roxanne Cooksey, Swansea University
Mark Atkinson, Swansea University
Spiros Denaxas, University College London
Stefan Siebert, University of Glasgow
William G. Dixon, University of Manchester
TW O’Neill
Ernest Choy, Cardiff University
Cathie Sudlow, University of Edinburgh
Sinead Brophy, Swansea University

ORCID

• Shang Ming Zhou: 0000-0002-0719-9353

DOI Link

10.1371/journal.pone.0154515

Publication Date

2016-05-02

Publication Title

PLoS ONE

Volume

11

Issue

5

ISSN

1932-6203

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

First Page

154515

Last Page

154515

Recommended Citation

Zhou, S., Fernandez-Gutierrez, F., Kennedy, J., Cooksey, R., Atkinson, M., Denaxas, S., Siebert, S., Dixon, W., O’Neill, T., Choy, E., Sudlow, C., & Brophy, S. (2016) 'Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis', PLoS ONE, 11(5), pp. 154515-154515. Available at: 10.1371/journal.pone.0154515