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ORIGINAL ARTICLE
Artificial intelligence to predict bed bath time in Intensive Care Units
Revista Brasileira de Enfermagem. 2024;77(1):e20230201
02-26-2024
Resumo
ORIGINAL ARTICLEArtificial intelligence to predict bed bath time in Intensive Care Units
Revista Brasileira de Enfermagem. 2024;77(1):e20230201
02-26-2024DOI 10.1590/0034-7167-2023-0201
Visualizações0Ver maisABSTRACT
Objectives:
to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.
Methods:
a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed.
Results:
among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%.
Conclusions:
the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.
Visualizações0
Resumo
ORIGINAL ARTICLEArtificial intelligence to predict bed bath time in Intensive Care Units
Revista Brasileira de Enfermagem. 2024;77(1):e20230201
02-26-2024DOI 10.1590/0034-7167-2023-0201
Visualizações0Ver maisABSTRACT
Objectives:
to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.
Methods:
a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed.
Results:
among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%.
Conclusions:
the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.
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TECHNOLOGICAL INNOVATION
Web App for prediction of hospitalisation in Intensive Care Unit by covid-19
Revista Brasileira de Enfermagem. 2023;76(6):e20220740
12-04-2023
Resumo
TECHNOLOGICAL INNOVATIONWeb App for prediction of hospitalisation in Intensive Care Unit by covid-19
Revista Brasileira de Enfermagem. 2023;76(6):e20220740
12-04-2023DOI 10.1590/0034-7167-2022-0740
Visualizações0Ver maisABSTRACT
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.
Visualizações0Resumo
TECHNOLOGICAL INNOVATIONWeb App for prediction of hospitalisation in Intensive Care Unit by covid-19
Revista Brasileira de Enfermagem. 2023;76(6):e20220740
12-04-2023DOI 10.1590/0034-7167-2022-0740
Visualizações0Ver maisABSTRACT
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.
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ORIGINAL ARTICLE
Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?
- Juliane de Souza Scherer
, - Jéssica Silveira Pereira ,
- Mariana Severo Debastiani ,
- Claudia Giuliano Bica
05-09-2022
Resumo
ORIGINAL ARTICLEBeyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?
Revista Brasileira de Enfermagem. 2022;75(5):e20210586
05-09-2022DOI 10.1590/0034-7167-2021-0586
- Juliane de Souza Scherer ,
- Jéssica Silveira Pereira ,
- Mariana Severo Debastiani ,
- Claudia Giuliano Bica
Visualizações0Ver maisABSTRACT
Objective:
To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex.
Methods:
An observational retrospective cohort study. The Machine Learning (ML) tool, Robot Laura®, scores changes in vital parameters and lab tests, classifying them by severity. Inpatients and patients over 18 years of age were included.
Results:
A total of 122,703 alarms were extracted from the platform, classified as 2 to 9. The pre-selection of critical alarms (6 to 9) indicated 263 urgent alerts (0.2%), from which, after filtering exclusion criteria, 254 alerts were delimited for 61 inpatients. Patient mortality from sepsis was 75%, of which 52% was due to sepsis related to the new coronavirus. After the alarms were answered, 82% of the patients remained in the sectors.
Conclusions:
Far beyond technology, ML models can speed up assertive clinical decisions by nurses, optimizing time and specialized human resources.
Visualizações0Resumo
ORIGINAL ARTICLEBeyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?
Revista Brasileira de Enfermagem. 2022;75(5):e20210586
05-09-2022DOI 10.1590/0034-7167-2021-0586
- Juliane de Souza Scherer ,
- Jéssica Silveira Pereira ,
- Mariana Severo Debastiani ,
- Claudia Giuliano Bica
Visualizações0Ver maisABSTRACT
Objective:
To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex.
Methods:
An observational retrospective cohort study. The Machine Learning (ML) tool, Robot Laura®, scores changes in vital parameters and lab tests, classifying them by severity. Inpatients and patients over 18 years of age were included.
Results:
A total of 122,703 alarms were extracted from the platform, classified as 2 to 9. The pre-selection of critical alarms (6 to 9) indicated 263 urgent alerts (0.2%), from which, after filtering exclusion criteria, 254 alerts were delimited for 61 inpatients. Patient mortality from sepsis was 75%, of which 52% was due to sepsis related to the new coronavirus. After the alarms were answered, 82% of the patients remained in the sectors.
Conclusions:
Far beyond technology, ML models can speed up assertive clinical decisions by nurses, optimizing time and specialized human resources.
- Juliane de Souza Scherer
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EXPERIENCE REPORT
Implementation of an Artificial Intelligence Algorithm for sepsis detection
- Luciana Schleder Gonçalves ,
- Maria Luiza de Medeiros Amaro ,
- Andressa de Lima Miranda Romero ,
- Fernanda Karoline Schamne ,
- Jacson Luiz Fressatto , [ ... ],
- Carolina Wrobel Bezerra
04-09-2020
Resumo
EXPERIENCE REPORTImplementation of an Artificial Intelligence Algorithm for sepsis detection
Revista Brasileira de Enfermagem. 2020;73(3):e20180421
04-09-2020DOI 10.1590/0034-7167-2018-0421
- Luciana Schleder Gonçalves ,
- Maria Luiza de Medeiros Amaro ,
- Andressa de Lima Miranda Romero ,
- Fernanda Karoline Schamne ,
- Jacson Luiz Fressatto ,
- Carolina Wrobel Bezerra
Visualizações0Ver maisABSTRACT
Objectives:
to present the nurses’ experience with technological tools to support the early identification of sepsis.
Methods:
experience report before and after the implementation of artificial intelligence algorithms in the clinical practice of a philanthropic hospital, in the first half of 2018.
Results:
describe the motivation for the creation and use of the algorithm; the role of the nurse in the development and implementation of this technology and its effects on the nursing work process.
Final Considerations:
technological innovations need to contribute to the improvement of professional practices in health. Thus, nurses must recognize their role in all stages of this process, in order to guarantee safe, effective and patient-centered care. In the case presented, the participation of the nurses in the technology incorporation process enables a rapid decision-making in the early identification of sepsis.
Visualizações0Resumo
EXPERIENCE REPORTImplementation of an Artificial Intelligence Algorithm for sepsis detection
Revista Brasileira de Enfermagem. 2020;73(3):e20180421
04-09-2020DOI 10.1590/0034-7167-2018-0421
- Luciana Schleder Gonçalves ,
- Maria Luiza de Medeiros Amaro ,
- Andressa de Lima Miranda Romero ,
- Fernanda Karoline Schamne ,
- Jacson Luiz Fressatto ,
- Carolina Wrobel Bezerra
Visualizações0Ver maisABSTRACT
Objectives:
to present the nurses’ experience with technological tools to support the early identification of sepsis.
Methods:
experience report before and after the implementation of artificial intelligence algorithms in the clinical practice of a philanthropic hospital, in the first half of 2018.
Results:
describe the motivation for the creation and use of the algorithm; the role of the nurse in the development and implementation of this technology and its effects on the nursing work process.
Final Considerations:
technological innovations need to contribute to the improvement of professional practices in health. Thus, nurses must recognize their role in all stages of this process, in order to guarantee safe, effective and patient-centered care. In the case presented, the participation of the nurses in the technology incorporation process enables a rapid decision-making in the early identification of sepsis.
- Luciana Schleder Gonçalves
Artificial Intelligence Archives - Revista Brasileira de Enfermagem
