S. Denaxas

First name
Last name
Barclay, M., Renzi, C., Antoniou, A., Denaxas, S., Harrison, H., Ip, S., et al. (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., et al. (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., et al. (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., et al. (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., et al. (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., et al. (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
Mizani, M. A., Dashtban, A., Pasea, L., Lai, A. G., Thygesen, J., Tomlinson, C., et al. (2022). Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study. J R Soc Med, 1410768221131897. http://doi.org/10.1177/01410768221131897
Kuan, V., Denaxas, S., Patalay, P., Nitsch, D., Mathur, R., Gonzalez-Izquierdo, A., et al. (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