S. Denaxas

First name
S.
Last name
Denaxas
Rapsomaniki, E., Shah, A., Perel, P., Denaxas, S., George, J., Nicholas, O., et al. (2014). Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients. Eur Heart J, 35, 844-52. http://doi.org/10.1093/eurheartj/eht533
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Elkheder, M., Gonzalez-Izquierdo, A., Arfeen, Q. U., Kuan, V., Lumbers, R. T., Denaxas, S., & Shah, A. D. (2022). Translating and evaluating historic phenotyping algorithms using SNOMED CT. J Am Med Inform Assoc. http://doi.org/10.1093/jamia/ocac158
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. Y. Tan, Y., Papez, V., Chang, W. H., Mueller, S. H., Denaxas, S., & Lai, A. G. (2022). Comparing clinical trial population representativeness to real-world populations: an external validity analysis encompassing 43 895 trials and 5 685 738 individuals across 989 unique drugs and 286 conditions in England. Lancet Healthy Longev. http://doi.org/10.1016/s2666-7568(22)00186-6
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Chung, S. C., Providencia, R., Sofat, R., Pujades-Rodriguez, M., Torralbo, A., Fatemifar, G., et al. (2022). Incidence, morbidity, mortality and disparities in dementia: A population linked electronic health records study of 4.3 million individuals. Alzheimers Dement. http://doi.org/10.1002/alz.12635
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Dashtban, A., Mizani, M. A., Denaxas, S., Nitsch, D., Quint, J., Corbett, R., et al. (2022). A retrospective cohort study measured predicting and validating the impact of the COVID-19 pandemic in individuals with chronic kidney disease. Kidney Int. http://doi.org/10.1016/j.kint.2022.05.015
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Jordan, K. P., Rathod-Mistry, T., Bailey, J., Chen, Y., Clarson, L., Denaxas, S., et al. (2022). Long-Term Cardiovascular Risk and Management of Patients Recorded in Primary Care With Unattributed Chest Pain: An Electronic Health Record Study. J Am Heart Assoc, 11, e023146. http://doi.org/10.1161/jaha.121.023146
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Clarke, C. S., Williamson, E., Denaxas, S., Carpenter, J. R., Thomas, M., Blackshaw, H., et al. (2022). Observational retrospective study calculating health service costs of patients receiving surgery for chronic rhinosinusitis in England, using linked patient-level primary and secondary care electronic data. Bmj Open, 12, e055603. http://doi.org/10.1136/bmjopen-2021-055603
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Warren-Gash, C., Hayward, A. C., Hemingway, H., Denaxas, S., Thomas, S. L., Timmis, A. D., et al. (2012). Influenza infection and risk of acute myocardial infarction in England and Wales: a CALIBER self-controlled case series study. J Infect Dis. http://doi.org/10.1093/infdis/jis597
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Wang, Z., Shah, A. D., Tate, A. R., Denaxas, S., Shawe-Taylor, J., & Hemingway, H. (2012). Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. PLoS One. http://doi.org/10.1371/journal.pone.0030412
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Herrett, E., George, J., Denaxas, S., Bhaskaran, K., Timmis, A., Hemingway, H., & Smeeth, L. (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. http://doi.org/10.1177/2048872613487495
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