This study explores lymphocyte profiles as non-invasive biomarkers for classification of pediatric nephrotic syndrome (NS). Using retrospective clinical and immunological data from 205 patients, the aim is to develop a predictive model based on Long Short-Term Memory to identify NS subtypes. By comparing models with and without immunological data, the study will assess the value of immune profiles. The goal is to support personalized management while reducing the need for invasive procedures.
Ricci, G., Capuzzi, S., Riganati, M., Tozzi, A.e., Vivarelli, M., Ferro, D., et al. (2025). Deep learning on longitudinal clinical and immunological data for the stratification of nephrotic syndrome|Deep learning su dati clinici longitudinali e immunologici per la stratificazione della sindrome nefrosica. In Recenti Progressi in Medicina (pp.573-574). Il Pensiero Scientifico Editore [10.1701/4573.45781].
Deep learning on longitudinal clinical and immunological data for the stratification of nephrotic syndrome|Deep learning su dati clinici longitudinali e immunologici per la stratificazione della sindrome nefrosica
Riganati M.;
2025-01-01
Abstract
This study explores lymphocyte profiles as non-invasive biomarkers for classification of pediatric nephrotic syndrome (NS). Using retrospective clinical and immunological data from 205 patients, the aim is to develop a predictive model based on Long Short-Term Memory to identify NS subtypes. By comparing models with and without immunological data, the study will assess the value of immune profiles. The goal is to support personalized management while reducing the need for invasive procedures.| File | Dimensione | Formato | |
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