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
Prognosis
Abhishek, A. ., Grainge, M. ., Card, T. ., Williams, H. C., Taal, M. W., Aithal, G. P., … Riley, R. . (2024). Risk-stratified monitoring for sulfasalazine toxicity: prognostic model development and validation. RMD Open, 10. http://doi.org/10.1136/rmdopen-2023-003980
Bellanca, L. ., Linden, S. ., & Farmer, R. . (2023). Incidence and prevalence of heart failure in England: a descriptive analysis of linked primary and secondary care data - the PULSE study. BMC Cardiovasc Disord, 23, 374. http://doi.org/10.1186/s12872-023-03337-1
Nakafero, G. ., Grainge, M. J., Williams, H. C., Card, T. ., Taal, M. W., Aithal, G. P., … Abhishek, A. . (2023). Risk stratified monitoring for methotrexate toxicity in immune mediated inflammatory diseases: prognostic model development and validation using primary care data from the UK. Bmj, 381, e074678. http://doi.org/10.1136/bmj-2022-074678
Chudasama, Y. V., Khunti, K. ., Coles, B. ., Gillies, C. L., Islam, N. ., Rowlands, A. V., … Zaccardi, F. . (2023). Life expectancy following a cardiovascular event in individuals with and without type 2 diabetes: A UK multi-ethnic population-based observational study. Nutr Metab Cardiovasc Dis. http://doi.org/10.1016/j.numecd.2023.04.003
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
Wambua, S. ., Crowe, F. ., Thangaratinam, S. ., O’Reilly, D. ., McCowan, C. ., Brophy, S. ., … Riley, R. . (2022). Protocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model incorporating reproductive and pregnancy-related candidate predictors. Diagn Progn Res, 6, 23. http://doi.org/10.1186/s41512-022-00137-7
Archer, L. ., Koshiaris, C. ., Lay-Flurrie, S. ., Snell, K. I. E., Riley, R. D., Stevens, R. ., … Sheppard, J. P. (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
Rapsomaniki, E. ., Shah, A. ., Perel, P. ., Denaxas, S. ., George, J. ., Nicholas, O. ., … Hemingway, H. . (2014). Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients. Eur Heart J, 35, 844–52. http://doi.org/10.1093/eurheartj/eht533