Supervision : Jean-François PRADAT-PEYRE

Co-supervision : DELBOT François

Assistance in the management and follow-up of cranio-cerebral trauma

The medical and social world is neither informed nor aware of the disorders caused by cranio-cerebral trauma, such as neurological after-effects, and consequently they are poorly supported and badly taken care of. The objective is to develop a prognostic algorithm and a medical protocol to better organize the care and medical-social support of people who have suffered a cranio-cerebral trauma by identifying, for example, the features that are relevant to the need or not to follow up on a trauma thanks to techniques and tools from the field of operational research and machine learning.

2022-2024 Publications

• 2024
  • Th. Anani, J.‑F. Pradat‑Peyre, F. Delbot, P.‑F. Pradat : “Machine Learning and Feature Selection Methods to Predict 1-Year Disease Progression for Amyotrophic Lateral Sclerosis”, (2024)
• 2022
  • Th. Anani, J.‑F. Pradat‑Peyre, F. Delbot, P.‑F. Pradat : “Use of metaheuristics for machine learning in the context of ALS”, 8^th ALS and MND research meeting, Paris, France (2022)
  • Th. Anani, F. Delbot, J.‑F. Pradat‑Peyre : “Experimental Comparison of Metaheuristics for Feature Selection in Machine Learning in the Medical Context”, Artificial Intelligence Applications and Innovations, vol. AICT-647 (Part II), IFIP Advances in Information and Communication Technology, Hersonissos, Greece, pp. 194-205, (Springer International Publishing), (ISBN: 978-3-031-08336-5) (2022)
  • Th. Anani, F. Delbot, J.‑F. Pradat‑Peyre : “Comparaison expérimentale de métaheuristiques pour la sélection de variables dans le cadre de l’apprentissage automatique appliqué au domaine médical”, 23^e congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Villeurbanne - Lyon, France (2022)