
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)
“Allergic disease, corticosteroid use and risk of Hodgkin's lymphoma: A UK Nationwide case-control study”, J Allergy Clin Immunol, 2019.
, “Antibiotic usage in chronic rhinosinusitis: analysis of national primary care electronic health records”, Rhinology, 2019.
, , “A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service”, The Lancet Digital Health, 2019.
, “Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records”, BMC Med Inform Decis Mak, vol. 19, p. 86, 2019.
, “Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death”, J Biomed Semantics, vol. 10, p. 20, 2019.
, “Risk factors for incident heart failure in age- and sex-specific strata: a population-based cohort using linked electronic health records”, Eur J Heart Fail, 2019.
, , “Socioeconomic deprivation and regional variation in Hodgkin's lymphoma incidence in the UK: a population-based cohort study of 10 million individuals”, BMJ Open, vol. 9, p. e029228, 2019.
, , “UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER”, J Am Med Inform Assoc, 2019.
, “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.
, “Type and timing of heralding in ST-elevation and non-ST-elevation myocardial infarction: an analysis of prospectively collected electronic healthcare records linked to the national registry of acute coronary syndromes”, Eur Heart J Acute Cardiovasc Care, vol. 2, pp. 235-45, 2013.
, “Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning”, PLoS One, vol. 7, p. e30412, 2012.
, “Influenza infection and risk of acute myocardial infarction in England and Wales: a CALIBER self-controlled case series study”, J Infect Dis, vol. 206, pp. 1652-9, 2012.
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