Please use this identifier to cite or link to this item:
https://hdl.handle.net/2445/215452
Title: | Automated clinical coding of medical notes into the SNOMED CT Medical terminology structuring system |
Author: | Cantón Simó, Sergi |
Director/Tutor: | Sumoy Van Dyck, Lauro Igual Muñoz, Laura |
Keywords: | Llenguatge mèdic Glossaris Informàtica mèdica Treballs de fi de màster Aprenentatge automàtic Medical language Glossaries Medical informatics Master's thesis Machine learning |
Issue Date: | 1-Sep-2024 |
Abstract: | Automated 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. |
Note: | Treballs 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 Muñoz |
URI: | https://hdl.handle.net/2445/215452 |
Appears in Collections: | Màster Oficial - Fonaments de la Ciència de Dades Programari - Treballs de l'alumnat |
Files in This Item:
File | Description | Size | Format | |
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tfm_canton_simo_sergi.pdf | Memòria | 897.03 kB | Adobe PDF | View/Open |
SNOMED-CT-AutomatedClinicalCoding-main.zip | Codi font | 999.52 kB | zip | View/Open |
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