Electronic Health Records

Vestesson, E. M., De Corte, K. L. A., Crellin, E., Ledger, J., Bakhai, M., & Clarke, G. M. (2023). Consultation Rate and Mode by Deprivation in English General Practice From 2018 to 2022: Population-Based Study. Jmir Public Health Surveill, 9, e44944. http://doi.org/10.2196/44944
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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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Little, D. J., Arnold, M., Hedman, K., Sun, P., Haque, S. A., & James, G. (2023). Rates of adverse clinical events in patients with chronic kidney disease: analysis of electronic health records from the UK clinical practice research datalink linked to hospital data. Bmc Nephrol, 24, 91. http://doi.org/10.1186/s12882-023-03119-z
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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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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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Jordan, K. P., Rathod-Mistry, T., van der Windt, D. A., Bailey, J., Chen, Y., Clarson, L., et al. (2023). Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study. Eur J Prev Cardiol. http://doi.org/10.1093/eurjpc/zwad055
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Nadarajah, R., Wu, J., Hogg, D., Raveendra, K., Nakao, Y. M., Nakao, K., et al. (2023). Prediction of short-term atrial fibrillation risk using primary care electronic health records. Heart. http://doi.org/10.1136/heartjnl-2022-322076
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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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Ling, S., Zaccardi, F., Issa, E., Davies, M. J., Khunti, K., & Brown, K. (2023). Inequalities in cancer mortality trends in people with type 2 diabetes: 20 year population-based study in England. Diabetologia. http://doi.org/10.1007/s00125-022-05854-8
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Jani, M., Yimer, B. B., Selby, D., Lunt, M., Nenadic, G., & Dixon, W. G. (2023). "Take up to eight tablets per day": Incorporating free-text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology. Pharmacoepidemiol Drug Saf. http://doi.org/10.1002/pds.5595
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