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Showing 1–2 of 2 results
Advanced filters: Author: Symeon V. Savvopoulos Clear advanced filters

  • Savvopoulos, Scheffner et al. develop a simple model that learns each kidney transplant recipient’s usual kidney function from routine tests and defines a personal failure threshold. This individualized threshold predicts future transplant loss more accurately than fixed cut-offs, helping to identify high-risk patients earlier.

    • Symeon V. Savvopoulos
    • Irina Scheffner
    • Haralampos Hatzikirou
    ResearchOpen Access
    Communications Medicine
    P: 1-14

  • Mascheroni et al. develop a method for individual clinical predictions by combining mathematical modelling and machine learning in a Bayesian framework (BaM3). By using both synthetic and real clinical datasets, they show the potential of the method to predict tumour growth in the context of clinical data sparsity and limited knowledge of disease mechanisms.

    • Pietro Mascheroni
    • Symeon Savvopoulos
    • Haralampos Hatzikirou
    ResearchOpen Access
    Communications Medicine
    Volume: 1, P: 1-14