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H. Hemingway
First name
H.
Last name
Hemingway
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
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
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
Mizani, M. A., Dashtban, A. ., Pasea, L. ., Lai, A. G., Thygesen, J. ., Tomlinson, C. ., … Banerjee, A. . (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. ., … Hemingway, H. . (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
Dashtban, A. ., Mizani, M. A., Denaxas, S. ., Nitsch, D. ., Quint, J. ., Corbett, R. ., … Banerjee, A. . (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
Jordan, K. P., Rathod-Mistry, T. ., Bailey, J. ., Chen, Y. ., Clarson, L. ., Denaxas, S. ., … Mamas, M. A. (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