*Electronic Health Records

Nakao, Y. M., Nadarajah, R., Shuweihdi, F., Nakao, K., Fuat, A., Moore, J., et al. (2024). Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study. Bmj Open, 14, e073455. http://doi.org/10.1136/bmjopen-2023-073455
Bolt, H., Suffel, A., Matthewman, J., Sandmann, F., Tomlinson, L., & Eggo, R. (2023). Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records. Bmc Nephrol, 24, 234. http://doi.org/10.1186/s12882-023-03269-0
Joseph, R. M., Knaggs, R. D., Coupland, C. A. C., Taylor, A., Vinogradova, Y., Butler, D., et al. (2023). Frequency and impact of medication reviews for people aged 65 years or above in UK primary care: an observational study using electronic health records. Bmc Geriatr, 23, 435. http://doi.org/10.1186/s12877-023-04143-2
Ford, E., Rooney, P., Hurley, P., Oliver, S., Bremner, S., & Cassell, J. (2020). Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data. Front Public Health, 8, 54. http://doi.org/10.3389/fpubh.2020.00054
Meffen, A., Sayers, R. D., Gillies, C. L., Khunti, K., & Gray, L. J. (2022). Are major lower extremity amputations well recorded in primary care electronic health records?: Insights from primary care electronic health records in England. Prim Health Care Res Dev, 23, e77. http://doi.org/10.1017/s1463423622000718
Tyrer, F., Bhaskaran, K., & Rutherford, M. J. (2022). Immortal time bias for life-long conditions in retrospective observational studies using electronic health records. Bmc Med Res Methodol, 22, 86. http://doi.org/10.1186/s12874-022-01581-1