machine learning

Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. M. (2023). Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine. Bmc Med Inform Decis Mak, 23, 110. http://doi.org/10.1186/s12911-023-02207-2

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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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Yang, F., Meng, T., Torben-Nielsen, B., Magnus, C., Liu, C., & Dejean, E. (2023). A machine learning approach to support triaging of primary versus secondary headache patients using complete blood count. Plos One, 18, e0282237. http://doi.org/10.1371/journal.pone.0282237

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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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Briggs, E., de Kamps, M., Hamilton, W., Johnson, O., McInerney, C. D., & Neal, R. D. (2022). Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools. Cancers (Basel), 14. http://doi.org/10.3390/cancers14205023

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