Sumoy Van Dyck, LauroIgual Muñoz, LauraCantón Simó, Sergi2024-09-302024-09-302024-09-01https://hdl.handle.net/2445/215452Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2023-2024. Tutor: Lauro Sumoy Van Dyck i Laura Igual MuñozAutomated clinical coding is the computational process of annotating healthcare free-text data by detecting relevant medical concepts and linking them to a structured medical terminology system. One of the most significant of these systems is SNOMED CT, which contains a vast array of specific medical terms, each identified by a unique ID. This work focuses on the automatic clinical coding of medical notes within the SNOMED CT system. The study presents a comprehensive review of state-of-the-art methods in this field, followed by a detailed examination of two specific approaches, each tested and their results discussed. The first method employs a classical dictionary-based approach, while the second utilizes a deep learning BERT-based model. Additionally, the work introduces a novel contribution to one of these methods and demonstrates a practical application where automatic clinical coding facilitates the extraction of specific numerical values from medical discharge summaries.46 p.application/pdfengcc-by-nc-nd (c) Sergi Cantón Simó, 2023codi: GPL (c) Sergi Cantón Simó, 2023http://creativecommons.org/licenses/by-nc-nd/3.0/es/http://www.gnu.org/licenses/gpl-3.0.ca.htmlLlenguatge mèdicGlossarisInformàtica mèdicaTreballs de fi de màsterAprenentatge automàticMedical languageGlossariesMedical informaticsMaster's thesisMachine learninginfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccess