Bibliography

Research using CPRD data has informed drug safety guidance and clinical practice and resulted in over 2,500 peer-reviewed publications. The CPRD bibliography is updated on a monthly basis (last updated 7 September 2020) and papers are listed below and in the PDF below.

If you have published papers using CPRD data which are not included in this list, please contact us at enquiries@cprd.com so that we can update the bibliography.

Download:

 (PDF, 3MB, 209 pages) 

This work uses data provided by patients and collected by the NHS as part of their care and support. CPRD encourages researchers to use this citation in all publications using CPRD data. Find out more about acknowledging the use of patient data at the Understanding Patient Data website

 

 

Export 5 results:
Author Title [ Type(Asc)] Year
Filters: Author is Nazarzadeh, M.  [Clear All Filters]
Journal Article
F. Rahimian, Salimi-Khorshidi, G., Payberah, A. H., Tran, J., R. Solares, A., Raimondi, F., Nazarzadeh, M., Canoy, D., and Rahimi, K., Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records, PLoS Med, vol. 15, p. e1002695, 2018.
J. Tran, Norton, R., Conrad, N., Rahimian, F., Canoy, D., Nazarzadeh, M., and Rahimi, K., Patterns and temporal trends of comorbidity among adult patients with incident cardiovascular disease in the UK between 2000 and 2014: A population-based cohort study, PLoS Med, vol. 15, p. e1002513, 2018.
K. Rahimi, Mohseni, H., Otto, C. M., Conrad, N., Tran, J., Nazarzadeh, M., Woodward, M., Dwyer, T., and MacMahon, S., Elevated blood pressure and risk of mitral regurgitation: A longitudinal cohort study of 5.5 million United Kingdom adults, PLoS Med, vol. 14, p. e1002404, 2017.
N. Conrad, Judge, A., Canoy, D., Tran, J., O'Donnell, J., Nazarzadeh, M., Salimi-Khorshidi, G., Hobbs, F. D. R., Cleland, J. G., McMurray, J. J. V., and Rahimi, K., Diagnostic tests, drug prescriptions, and follow-up patterns after incident heart failure: A cohort study of 93,000 UK patients, PLoS Med, vol. 16, p. e1002805, 2019.
J. R. Ayala Solares, Raimondi, F. E. Diletta, Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Gomes, A. C. Pinho, Payberah, A. H., Zottoli, M., Nazarzadeh, M., Conrad, N., Rahimi, K., and Salimi-Khorshidi, G., Deep learning for electronic health records: A comparative review of multiple deep neural architectures, J Biomed Inform, vol. 101, p. 103337, 2020.
[Page last reviewed 7 September 2020]