A. Banerjee

First name
A.
Last name
Banerjee
Banerjee, A., Dashtban, A., Chen, S., Pasea, L., Thygesen, J. H., Fatemifar, G., et al. (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
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Koshiaris, C., Archer, L., Lay-Flurrie, S., Snell, K. I., Riley, R. D., Stevens, R., et al. (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
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Sheppard, J. P., Koshiaris, C., Stevens, R., Lay-Flurrie, S., Banerjee, A., Bellows, B. K., et al. (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
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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
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Dashtban, A., Mizani, M. A., Pasea, L., Denaxas, S., Corbett, R., Mamza, J. B., et al. (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
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Davidson, J. A., Banerjee, A., Douglas, I., Leyrat, C., Pebody, R., McDonald, H. I., et al. (2022). Primary prevention of acute cardiovascular events by influenza vaccination: an observational study. Eur Heart J. http://doi.org/10.1093/eurheartj/ehac737
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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
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Archer, L., Koshiaris, C., Lay-Flurrie, S., Snell, K. I. E., Riley, R. D., Stevens, R., et al. (2022). Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study. Bmj, 379, e070918. http://doi.org/10.1136/bmj-2022-070918
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Idris, I., Zhang, R., Mamza, J. B., Ford, M., Morris, T., Banerjee, A., & Khunti, K. (2021). Lower risk of hospitalization for heart failure, kidney disease and death with sodium-glucose co-transporter-2 inhibitors compared with dipeptidyl peptidase-4 inhibitors in type 2 diabetes regardless of prior cardiovascular or kidney disease. Diabetes Obes Metab, 23, 2207-2214. http://doi.org/10.1111/dom.14437
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Birkeland, K. I., Bodegard, J., Banerjee, A., Kim, D. J., Norhammar, A., Eriksson, J. W., et al. (2021). Lower cardiorenal risk with sodium-glucose cotransporter-2 inhibitors versus dipeptidyl peptidase-4 inhibitors in patients with type 2 diabetes without cardiovascular and renal diseases: A large multinational observational study. Diabetes Obes Metab, 23, 75-85. http://doi.org/10.1111/dom.14189
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