Data Mining Models for Automatic Problem Identification in Intensive Medicine.
Procedia Comput Sci
; 210: 218-223, 2022.
Article
in English
| MEDLINE | ID: covidwho-2132118
ABSTRACT
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 António 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
/
Reviews
Language:
English
Journal:
Procedia Comput Sci
Year:
2022
Document Type:
Article
Affiliation country:
J.procs.2022.10.140
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