D. Canoy

First name
D.
Last name
Canoy
Wamil, M., Hassaine, A., Rao, S., Li, Y., Mamouei, M., Canoy, D., et al. (2023). Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis. Sci Rep, 13, 11478. http://doi.org/10.1038/s41598-023-38251-1
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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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Nazarzadeh, M., Bidel, Z., Mohseni, H., Canoy, D., Pinho-Gomes, A. C., Hassaine, A., et al. (2022). Blood pressure and risk of venous thromboembolism: a cohort analysis of 5.5 million UK adults and Mendelian randomization studies. Cardiovasc Res. http://doi.org/10.1093/cvr/cvac135
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Rahimian, F., Salimi-Khorshidi, G., Payberah, A. H., Tran, J., Solares, A., Raimondi, F., et al. (2018). Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records. PLoS Med. http://doi.org/10.1371/journal.pmed.1002695
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Tran, J., Norton, R., Conrad, N., Rahimian, F., Canoy, D., Nazarzadeh, M., & Rahimi, K. (2018). Patterns and temporal trends of comorbidity among adult patients with incident cardiovascular disease in the UK between 2000 and 2014: A population-based cohort study. PLoS Med. http://doi.org/10.1371/journal.pmed.1002513
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Conrad, N., Judge, A., Canoy, D., Tran, J., Pinho-Gomes, A. C., Millett, E. R. C., et al. (2019). Temporal Trends and Patterns in Mortality After Incident Heart Failure: A Longitudinal Analysis of 86000 Individuals. JAMA Cardiol. http://doi.org/10.1001/jamacardio.2019.3593
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Solares, J. R. A., Canoy, D., Raimondi, F. E. D., Zhu, Y., Hassaine, A., Salimi-Khorshidi, G., et al. (2019). Long-Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large-Scale Routine Electronic Health Records. J Am Heart Assoc. http://doi.org/10.1161/jaha.119.012129
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Conrad, N., Judge, A., Canoy, D., Tran, J., O\textquoterightDonnell, J., Nazarzadeh, M., et al. (2019). Diagnostic tests, drug prescriptions, and follow-up patterns after incident heart failure: A cohort study of 93,000 UK patients. PLoS Med. http://doi.org/10.1371/journal.pmed.1002805
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Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., et al. (2020). BEHRT: Transformer for Electronic Health Records. Sci Rep. http://doi.org/10.1038/s41598-020-62922-y
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Solares, J. R. A., Raimondi, F. E. D., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., et al. (2020). Deep learning for electronic health records: A comparative review of multiple deep neural architectures. J Biomed Inform. http://doi.org/10.1016/j.jbi.2019.103337
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