Wang, H. I., Doran, T. ., Crooks, M. G., Khunti, K. ., Heightman, M. ., Gonzalez-Izquierdo, A. ., … Van Der Feltz-Cornelis, C. . (2024). Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England. J Infect, 89, 106235. http://doi.org/10.1016/j.jinf.2024.106235
A. Banerjee
First name
A.
Last name
Banerjee
. Y. Wong, A. ., Warren-Gash, C. ., Bhaskaran, K. ., Leyrat, C. ., Banerjee, A. ., Smeeth, L. ., & Douglas, I. J. (2024). Potential interactions between medications for rate control and direct oral anticoagulants: population-based cohort and case-crossover study. Heart Rhythm. http://doi.org/10.1016/j.hrthm.2024.06.033
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
Davidson, J. A., Banerjee, A. ., Strongman, H. ., Herrett, E. ., Smeeth, L. ., Breuer, J. ., & Warren-Gash, C. . (2023). Acute Cardiovascular Events After COVID-19 in England in 2020: A Self-Controlled Case Series Study. Clin Epidemiol, 15, 911–921. http://doi.org/10.2147/clep.s421062
Nakao, Y. M., Nakao, K. ., Nadarajah, R. ., Banerjee, A. ., Fonarow, G. C., Petrie, M. C., … Gale, C. P. (2023). Prognosis, characteristics, and provision of care for patients with the unspecified heart failure electronic health record phenotype: a population-based linked cohort study of 95262 individuals. EClinicalMedicine, 63, 102164. http://doi.org/10.1016/j.eclinm.2023.102164
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
Koshiaris, C. ., Archer, L. ., Lay-Flurrie, S. ., Snell, K. I., Riley, R. D., Stevens, R. ., … Sheppard, J. P. (2023). Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI. Br J Gen Pract. http://doi.org/10.3399/bjgp.2022.0389
Sheppard, J. P., Koshiaris, C. ., Stevens, R. ., Lay-Flurrie, S. ., Banerjee, A. ., Bellows, B. K., … McManus, R. J. (2023). The association between antihypertensive treatment and serious adverse events by age and frailty: A cohort study. PLoS Med, 20, e1004223. http://doi.org/10.1371/journal.pmed.1004223
Warren-Gash, C. ., Davidson, J. A., Strongman, H. ., Herrett, E. ., Smeeth, L. ., Breuer, J. ., & Banerjee, A. . (2023). Severe COVID-19 outcomes by cardiovascular risk profile in England in 2020: a population-based cohort study. Lancet Reg Health Eur, 27, 100604. http://doi.org/10.1016/j.lanepe.2023.100604
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