Web App for prediction of hospitalisation in Intensive Care Unit by covid-19
ABSTRACT Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Associação Brasileira de Enfermagem
2023-12-01
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| Series: | Revista Brasileira de Enfermagem |
| Subjects: | |
| Online Access: | http://revodonto.bvsalud.org/scielo.php?script=sci_arttext&pid=S0034-71672023001001200&lng=en&tlng=en |
| _version_ | 1797404438799843328 |
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| author | Greici Capellari Fabrizzio Alacoque Lorenzini Erdmann Lincoln Moura de Oliveira |
| author_facet | Greici Capellari Fabrizzio Alacoque Lorenzini Erdmann Lincoln Moura de Oliveira |
| author_sort | Greici Capellari Fabrizzio |
| collection | DOAJ |
| description | ABSTRACT Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668). Results: Based on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. Final considerations: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making. |
| first_indexed | 2024-03-09T02:54:58Z |
| format | Article |
| id | doaj.art-51a1bf1bca914fa99290fa7727d0555c |
| institution | Directory Open Access Journal |
| issn | 1984-0446 |
| language | English |
| last_indexed | 2024-03-09T02:54:58Z |
| publishDate | 2023-12-01 |
| publisher | Associação Brasileira de Enfermagem |
| record_format | Article |
| series | Revista Brasileira de Enfermagem |
| spelling | doaj.art-51a1bf1bca914fa99290fa7727d0555c2023-12-05T07:37:28ZengAssociação Brasileira de EnfermagemRevista Brasileira de Enfermagem1984-04462023-12-0176610.1590/0034-7167-2022-0740Web App for prediction of hospitalisation in Intensive Care Unit by covid-19Greici Capellari Fabrizziohttps://orcid.org/0000-0002-3848-5694Alacoque Lorenzini Erdmannhttps://orcid.org/0000-0003-4845-8515Lincoln Moura de Oliveirahttps://orcid.org/0000-0001-6016-745XABSTRACT Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668). Results: Based on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. Final considerations: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making.http://revodonto.bvsalud.org/scielo.php?script=sci_arttext&pid=S0034-71672023001001200&lng=en&tlng=enInventionsForecastingArtificial IntelligenceCovid-19Precision Medicine |
| spellingShingle | Greici Capellari Fabrizzio Alacoque Lorenzini Erdmann Lincoln Moura de Oliveira Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 Revista Brasileira de Enfermagem Inventions Forecasting Artificial Intelligence Covid-19 Precision Medicine |
| title | Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 |
| title_full | Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 |
| title_fullStr | Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 |
| title_full_unstemmed | Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 |
| title_short | Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 |
| title_sort | web app for prediction of hospitalisation in intensive care unit by covid 19 |
| topic | Inventions Forecasting Artificial Intelligence Covid-19 Precision Medicine |
| url | http://revodonto.bvsalud.org/scielo.php?script=sci_arttext&pid=S0034-71672023001001200&lng=en&tlng=en |
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