White, B. ., Zakkak, N. ., Renzi, C. ., Rafiq, M. ., Gonzalez-Izquierdo, A. ., Denaxas, S. ., … Barclay, M. . (2024). Underlying disease risk among fatigued patients: a population-based cohort study in primary care. Br J Gen Pract. http://doi.org/10.3399/bjgp.2024.0093
S. Denaxas
First name
S.
Last name
Denaxas
Barclay, M. ., Renzi, C. ., Antoniou, A. ., Denaxas, S. ., Harrison, H. ., Ip, S. ., … Lyratzopoulos, G. . (2023). Phenotypes and rates of cancer-relevant symptoms and tests in the year before cancer diagnosis in UK Biobank and CPRD Gold. PLOS Digit Health, 2, e0000383. http://doi.org/10.1371/journal.pdig.0000383
Graul, E. L., Stone, P. W., Massen, G. M., Hatam, S. ., Adamson, A. ., Denaxas, S. ., … Quint, J. K. (2023). Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists. JAMIA Open, 6, ooad078. http://doi.org/10.1093/jamiaopen/ooad078
Prugger, C. ., Perier, M. C., Gonzalez-Izquierdo, A. ., Hemingway, H. ., Denaxas, S. ., & Empana, J. P. (2023). Incidence of 12 common cardiovascular diseases and subsequent mortality risk in the general population. Eur J Prev Cardiol. http://doi.org/10.1093/eurjpc/zwad192
Josephson, C. B., Gonzalez-Izquierdo, A. ., Denaxas, S. ., Sajobi, T. T., Klein, K. M., & Wiebe, S. . (2023). Independent Associations of Incident Epilepsy and Enzyme-Inducing and Non-Enzyme-Inducing Antiseizure Medications With the Development of Osteoporosis. JAMA Neurol. http://doi.org/10.1001/jamaneurol.2023.1580
Banerjee, A. ., Dashtban, A. ., Chen, S. ., Pasea, L. ., Thygesen, J. H., Fatemifar, G. ., … Hemingway, H. . (2023). Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study. Lancet Digit Health, 5, e370-e379. http://doi.org/10.1016/s2589-7500(23)00065-1
Voss, E. A., Shoaibi, A. ., Lai, Y. H., Blacketer, C. ., Alshammari, T. ., Makadia, R. ., … Ryan, P. B. (2023). Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study. EClinicalMedicine, 58, 101932. http://doi.org/10.1016/j.eclinm.2023.101932
Jordan, K. P., Rathod-Mistry, T. ., van der Windt, D. A., Bailey, J. ., Chen, Y. ., Clarson, L. ., … Mamas, M. A. (2023). Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study. Eur J Prev Cardiol. http://doi.org/10.1093/eurjpc/zwad055
Dashtban, A. ., Mizani, M. A., Pasea, L. ., Denaxas, S. ., Corbett, R. ., Mamza, J. B., … Banerjee, A. . (2023). Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals. EBioMedicine, 89, 104489. http://doi.org/10.1016/j.ebiom.2023.104489
Kuan, V. ., Denaxas, S. ., Patalay, P. ., Nitsch, D. ., Mathur, R. ., Gonzalez-Izquierdo, A. ., … Hingorani, A. D. (2022). Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digit Health. http://doi.org/10.1016/s2589-7500(22)00187-x