| Titre : | Machine Learning-Based Prediction of One Year Post-Transplant Outcomes in Allogeneic Hematopoietic Stem Cell Transplantation Patients with Malignant Hemopathies |
| Auteurs : | Fangchen Xia ; Ecole des hautes études en santé publique (EHESP) (Rennes, FRA) |
| Type de document : | Mémoire |
| Année de publication : | 2025 |
| Description : | 54p. / tabl. |
| Langues: | Anglais |
| Classement : | MPH/ (Mémoires MPH à partir de 2024) |
| Mots-clés : | Intelligence artificielle ; Hémopathie ; Greffe ; Transplantation organe ; Evénement indésirable ; Evènement indésirable associé aux soins ; Anticipation ; Etude prospective ; E-santé |
| Résumé : | Allogeneic hematopoietic stem cell transplantation (Allo-HSCT) is a curative treatment for hematological malignancies. However, patients remain at risk for serious complications, including non-relapse mortality (NRM), relapse, rejection, acute graft-versus host disease (aGvHD), and chronic GVHD (cGvHD). Accurate early prediction of these outcomes can support clinical decision-making and improve long-term prognosis. This study uses pre-transplant data from 16,427 patients with malignancies recorded in the European Society for Blood and Marrow Transplantation registry (EBMT) between 2013 and 2023. This study aims to use machine learning algorithms to predict the probability of NRM, rejection, relapse, aGvHD and cGvHD in patients within one year after Allo-HSCT. (R.A.) |
| Diplôme : | Master MPH of public health |
| Plan de classement simplifié : | Master of Public Health - master international de Santé Publique (MPH) |
| En ligne : | https://documentation.ehesp.fr/memoires/2025/mph/fangchen_wia.pdf |
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