S. Denaxas

First name
S.
Last name
Denaxas
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
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
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
. 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
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
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
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
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
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
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