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.
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.
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.
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.
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.