Mizani, M. A., Dashtban, A. ., Pasea, L. ., Zeng, Q. ., Khunti, K. ., Valabhji, J. ., … Banerjee, A. . (2024). Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals. BMJ Open Diabetes Res Care, 12. http://doi.org/10.1136/bmjdrc-2024-004191
L. Pasea
First name
L.
Last name
Pasea
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
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
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
Chung, S. C., Pujades-Rodriguez, M. ., Duyx, B. ., Denaxas, S. C., Pasea, L. ., Hingorani, A. ., … Hemingway, H. . (2018). Time spent at blood pressure target and the risk of death and cardiovascular diseases. PLoS One. http://doi.org/10.1371/journal.pone.0202359
Gho, J. ., Schmidt, A. F., Pasea, L. ., Koudstaal, S. ., Pujades-Rodriguez, M. ., Denaxas, S. ., … Asselbergs, F. W. (2018). An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open. http://doi.org/10.1136/bmjopen-2017-018331
Denaxas, S. ., Gonzalez-Izquierdo, A. ., Direk, K. ., Fitzpatrick, N. K., Fatemifar, G. ., Banerjee, A. ., … Hemingway, H. . (2019). UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. J Am Med Inform Assoc. http://doi.org/10.1093/jamia/ocz105
Katsoulis, M. ., Pasea, L. ., Lai, A. G., Dobson, R. J. B., Denaxas, S. ., Hemingway, H. ., & Banerjee, A. . (2020). Obesity during the COVID-19 pandemic: both cause of high risk and potential effect of lockdown? A population-based electronic health record study. Public Health. http://doi.org/10.1016/j.puhe.2020.12.003
Lai, A. G., Pasea, L. ., Banerjee, A. ., Hall, G. ., Denaxas, S. ., Chang, W. H., … Hemingway, H. . (2020). Estimated impact of the COVID-19 pandemic on cancer services and excess 1-year mortality in people with cancer and multimorbidity: near real-time data on cancer care, cancer deaths and a population-based cohort study. BMJ Open. http://doi.org/10.1136/bmjopen-2020-043828
Banerjee, A. ., Pasea, L. ., Harris, S. ., Gonzalez-Izquierdo, A. ., Torralbo, A. ., Shallcross, L. ., … Hemingway, H. . (2020). Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet. http://doi.org/10.1016/s0140-6736(20)30854-0