Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/89850
Título: | Identifying diabetic patient profile through machine learning-based clustering analysis |
Autor(es): | Gomes, João Lopes, João Guimarães, Tiago André Saraiva Santos, Manuel |
Palavras-chave: | Clustering Diabetes Machine learning |
Data: | 2023 |
Editora: | Elsevier 1 |
Revista: | Procedia 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/89850 |
DOI: | 10.1016/j.procs.2023.03.116 |
ISSN: | 1877-0509 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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1-s2.0-S1877050923006518-main.pdf | 706,53 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons