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
A. Dashtban
First name
A.
Last name
Dashtban
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
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