L. Pasea

First name
L.
Last name
Pasea
Mizani, M. A., Dashtban, A., Pasea, L., Zeng, Q., Khunti, K., Valabhji, J., et al. (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
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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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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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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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Chung, S. C., Pujades-Rodriguez, M., Duyx, B., Denaxas, S. C., Pasea, L., Hingorani, A., et al. (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
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Gho, J., Schmidt, A. F., Pasea, L., Koudstaal, S., Pujades-Rodriguez, M., Denaxas, S., et al. (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
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Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., et al. (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
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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
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Lai, A. G., Pasea, L., Banerjee, A., Hall, G., Denaxas, S., Chang, W. H., et al. (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
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Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., et al. (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
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