Chamseddine Barki
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Personalized Hearing Loss Care Using SNOMED CT-Aligned Ontology and Random Forest Machine Learning: A Hybrid Decision-Support Framework
Submitted: 29 December 2025 You are already at the latest version Background: Hearing loss affects over 466 million individuals globally and is recognized as a major risk factor for Alzheimer's disease, yet treatment personalization remains limited due to the complexity and diversity of underlying causes. Current diagnostic and therapeutic approaches lack standardized methods to accurately predict the most appropriate intervention for individual patients.
Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning Approach
Submitted: 02 December 2025 You are already at the latest version anatomopathological report ; BioClinicalBERT ; code mapping ; deep learning ; ICD-11 ; LOINC ; named entity recognition ; natural language processing ; SNOMED CT ; transformer architecture This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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