Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/89850

TítuloIdentifying diabetic patient profile through machine learning-based clustering analysis
Autor(es)Gomes, João
Lopes, João
Guimarães, Tiago André Saraiva
Santos, Manuel
Palavras-chaveClustering
Diabetes
Machine learning
Data2023
EditoraElsevier 1
RevistaProcedia Computer Science
Resumo(s)Given the rapid growth over the past 15 years, Diabetes is currently a key issue in medical science and healthcare administration. Considering the importance of the health sector in our society, it is critical to correctly diagnose and treat Diabetes in order to avoid immediate difficulties and reduce the chance of long-term issues. The analysis of vast amounts of data that are available in organizations is an important factor to describing their internal factors, predicting future trends, and prescribing the best course of action to improve their performance in light of the increasing technological evolution and the emergence of Artificial Intelligence (AI). The main objective of this project, which is being carried out in collaboration with the Unidade Local de Saúde do Alto Minho (ULSAM), is to define a typology of diabetic patients by building Machine Learning (ML) models from registered clinical information, medication, complementary diagnostic tools, therapeutic and monitoring data, and registered medication data.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/89850
DOI10.1016/j.procs.2023.03.116
ISSN1877-0509
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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Este trabalho está licenciado sob uma Licença Creative Commons Creative Commons

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