Bibliography

Research using CPRD data has informed drug safety guidance and clinical practice and resulted in over 2,300 peer-reviewed publications.

The CPRD bibliography is updated on a monthly basis (last updated 4 November 2019) 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.

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(PDF, 3MB, 192 pages)

 

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Author Title [ Type(Desc)] Year
Filters: Author is Shah, A. D.  [Clear All Filters]
Journal Article
A. D. Shah and Martinez, C., An algorithm to derive a numerical daily dose from unstructured text dosage instructions, Pharmacoepidemiol Drug Saf, vol. 15, pp. 161-6, 2006.
E. Herrett, Shah, A. D., Boggon, R., Denaxas, S., Smeeth, L., Van Staa, T., Timmis, A., and Hemingway, H., Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study, Bmj, vol. 346, p. f2350, 2013.
S. C. Denaxas, George, J., Herrett, E., Shah, A. D., Kalra, D., Hingorani, A. D., Kivimaki, M., Timmis, A. D., Smeeth, L., and Hemingway, H., Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER), Int J Epidemiol, vol. 41, pp. 1625-38, 2012.
Z. Wang, Shah, A. D., Tate, A. R., Denaxas, S., Shawe-Taylor, J., and Hemingway, H., Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning, PLoS One, vol. 7, p. e30412, 2012.
A. D. Shah, Martinez, C., and Hemingway, H., The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records, BMC Med Inform Decis Mak, vol. 12, p. 88, 2012.
A. D. Shah, Nicholas, O., Timmis, A. D., Feder, G., Abrams, K. R., Chen, R., Hingorani, A. D., and Hemingway, H., Threshold haemoglobin levels and the prognosis of stable coronary disease: two new cohorts and a systematic review and meta-analysis, PLoS Med, vol. 8, p. e1000439, 2011.
S. Denaxas, Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., Dobson, R. J. B., Howe, L. J., Kuan, V., Lumbers, R. T., Pasea, L., Patel, R. S., Shah, A. D., Hingorani, A. D., Sudlow, C., and Hemingway, H., UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER, J Am Med Inform Assoc, 2019.
M. Asaria, Walker, S., Palmer, S., Gale, C. P., Shah, A. D., Abrams, K. R., Crowther, M., Manca, A., Timmis, A., Hemingway, H., and Sculpher, M., Using electronic health records to predict costs and outcomes in stable coronary artery disease, Heart, vol. 102, pp. 755-62, 2016.
[Page last reviewed 4 November 2019]