Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/89898
Título: | Data Mining models for automatic problem identification in intensive medicine |
Autor(es): | Quesado, Inês Duarte, Júlio Miguel Marques Silva, Alvaro Manuel, Maria Quintas, Cesar |
Palavras-chave: | Classification Data Mining Intensive Care Unit Intensive Medicine Vital Signs |
Data: | 2022 |
Editora: | Elsevier 1 |
Revista: | Procedia Computer Science |
Citação: | Quesado, I., Duarte, J., Silva, Á., Manuel, M., & Quintas, C. (2022). Data Mining Models for Automatic Problem Identification in Intensive Medicine. Procedia Computer Science. Elsevier BV. http://doi.org/10.1016/j.procs.2022.10.140 |
Resumo(s): | This paper aims to support medical decision making on predicting the diagnosis of COVID-19. Thus, a set of Data Mining (DM) models was developed using prediction techniques and classification models. These models try to understand whether the vital signs of patients have a correlation with a diagnosis. To achieve the objective of the paper, initially, the data was acquired and collected from several data sources such as bedside monitors and electronic nursing records from the Intensive Care Unit of the Santo Antonio Hospital. Secondly, the data was transformed so that it could be used in DM models. The models were induced using the following algorithms: Decision Trees, Random Forest, Naive Bayes, and Support Vector Machine. The analysis of the sensitivity, specificity, and accuracy were the metrics used to identify the most relevant measures to predict COVID-19 diagnosis. This work demonstrates that the models created had promising results. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/89898 |
DOI: | 10.1016/j.procs.2022.10.140 |
ISSN: | 1877-0509 |
Versão da editora: | http://doi.org/10.1016/j.procs.2022.10.140 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
hodii22_julio.pdf | 521,66 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons