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Barreto TDO, Veras NVR, Cardoso PH, Fernandes FRDS, Medeiros LPDS, Bezerra MV, de Andrade FMQ, Pinheiro CDO, Sánchez-Gendriz I, Silva GJPC, Rodrigues LF, de Morais AHF, dos Santos JPQ, Paiva JC, de Andrade IGM, Valentim RADM. Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil. Front Artif Intell 2023; 6:1290022. [PMID: 38145230 PMCID: PMC10748397 DOI: 10.3389/frai.2023.1290022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.
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Affiliation(s)
- Tiago de Oliveira Barreto
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Nícolas Vinícius Rodrigues Veras
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Pablo Holanda Cardoso
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Felipe Ricardo dos Santos Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | | | - Maria Valéria Bezerra
- Secretary of Public Health of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | | | | | - Ignacio Sánchez-Gendriz
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Gleyson José Pinheiro Caldeira Silva
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Leandro Farias Rodrigues
- Brazilian Company of Hospital Services (EBSERH), University Hospital of Pelotas, Federal University of Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brazil
| | - Antonio Higor Freire de Morais
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - João Paulo Queiroz dos Santos
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Jailton Carlos Paiva
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Ion Garcia Mascarenhas de Andrade
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
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de Souza Mareco TC, de Moura Santos Lima TGF, Ramos MNP, Dos Santos MM, da Silva JA, Priamo V, de Brito CMG, Dos Santos Pereira ED, de Oliveira CAP, Cortez LR, de Andrade IGM, de Almeida MCD, de Medeiros Valentim RA. Analyzing a national health surveillance strategy to reduce mother-to-child transmission of syphilis: The case of Brazilian investigation committees. IJID Reg 2023; 8:164-171. [PMID: 37694221 PMCID: PMC10482742 DOI: 10.1016/j.ijregi.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/24/2023] [Accepted: 07/29/2023] [Indexed: 09/12/2023]
Abstract
Objectives This study aimed to analyze the relevance of investigation committees in eliminating mother-to-child transmission of syphilis in Brazil. Methods Questionnaires and interviews were conducted with health managers of 25 Brazilian Federative Units and Brazil's Federal District. Data were analyzed using Bardin's content analysis technique and subsequently compared with the global prescriptions for syphilis response of the Pan American Health Organization, World Health Organization, and recent research publications examining the course of syphilis in Brazil, in Brazilian regions, and globally. Results While the investigation committees drew on the successful experience of those in reducing maternal mortality, which helped the country achieve the Millennium Development Goals, they are not demonstrated to be sufficient for preventing mother-to-child transmission of syphilis. The committees' systematic and bureaucratic agenda has not been efficient in managing avoidable factors for syphilis, nor do they operate in the scope of the integration of surveillance and care actions, as recommended by the health policy. Conclusion The committees' model needs to be reviewed in the context of Brazil's National Health System. The research process should be rescaled in order to remain a cornerstone for the induction of health policy that integrates surveillance and healthcare across Brazilian Federative Units. The advancement toward an automated case management model becomes relevant for the country to meet global commitments to eliminate congenital syphilis transmission and achieve the goals outlined in the 2030 Agenda.
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Affiliation(s)
- Thereza Cristina de Souza Mareco
- Laboratory for Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
- University of Brasília, Brasília, Federal District, Brazil
| | | | | | | | - José Adailton da Silva
- Laboratory for Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
- Graduate Program in Health Management and Innovation, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Vania Priamo
- Laboratory for Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Cintia Michele Gondim de Brito
- Laboratory for Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
- University of Pernambuco, Recife, Brazil
| | | | | | - Lyane Ramalho Cortez
- Laboratory for Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
- Graduate Program in Health Management and Innovation, Federal University of Rio Grande do Norte, Natal, Brazil
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