Hafida Tiaiba2025-09-292025MD/40https://dspace.univ-bba.dz/handle/123456789/869The healthcare sector generates a massive volume of heterogeneous information every day, a large portion of which takes a narrative form, such as medical records, hospitalisation reports, and patient testimonies. In the context of exponential growth in textual medical data, it has become essential to transform this information into machine-readable knowledge to support personalised diagnoses, therapeutic monitoring, and the practice of precision medicine. This thesis proposes an interdisciplinary approach that combines symbolic arti f icial intelligence, machine learning—including deep learning techniques—and medical on tologies to enhance the representation, organisation, and utilisation of healthcare knowledge. Ontologies structure and provide meaning to specific terms, abbreviations, and contextual dependencies present in medical texts, while advanced machine learning techniques facilitate their automatic classification into disease categories, treatment plans, or predefined diag nostic groups. The integration of these methods provides a robust framework for managing narrative knowledge in healthcare, optimising the retrieval of relevant information, support ing clinical decision-making, and enabling treatment planning tailored to individual patient needs. Thus, combining narrative formalisms, statistical methods, and ontological resources offers a powerful means to fully exploit the potential of textual medical data.enMedical unstructured texttext miningontologyclassificationembeddingmachine learningnatural language processing.Thesis