Artificial intelligence in triage of COVID-19 patients
In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1495074/full |
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| author | Yuri Oliveira Iêda Rios Paula Araújo Alinne Macambira Marcos Guimarães Lúcia Sales Marcos Rosa Júnior André Nicola Mauro Nakayama Hermeto Paschoalick Francisco Nascimento Carlos Castillo-Salgado Vania Moraes Ferreira Hervaldo Carvalho |
| author_facet | Yuri Oliveira Iêda Rios Paula Araújo Alinne Macambira Marcos Guimarães Lúcia Sales Marcos Rosa Júnior André Nicola Mauro Nakayama Hermeto Paschoalick Francisco Nascimento Carlos Castillo-Salgado Vania Moraes Ferreira Hervaldo Carvalho |
| author_sort | Yuri Oliveira |
| collection | DOAJ |
| description | In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone. This data was collected during a prospective, multicenter cohort that followed patients with respiratory syndrome during the pandemic. We aimed to predict which patients would present mild cases of COVID-19 and which would develop severe cases. Severe cases were defined as those requiring access to the Intensive Care Unit, endotracheal intubation, or even progressing to death. The system achieved an accuracy of 80%, with Area Under Receiver Operating Characteristic Curve (AUC) of 91%, Positive Predictive Value of 87% and Negative Predictive Value of 82%. Considering that only data from hospital admission was used, and that this data came from low-cost clinical examination and laboratory testing, the low false positive rate and acceptable accuracy observed shows that it is feasible to implement prediction systems based on artificial intelligence as an effective triage method. |
| format | Article |
| id | doaj-art-b356ccca9f204767b14960ca7a3c2b51 |
| institution | OA Journals |
| issn | 2624-8212 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-b356ccca9f204767b14960ca7a3c2b512025-08-20T01:57:55ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-12-01710.3389/frai.2024.14950741495074Artificial intelligence in triage of COVID-19 patientsYuri Oliveira0Iêda Rios1Paula Araújo2Alinne Macambira3Marcos Guimarães4Lúcia Sales5Marcos Rosa Júnior6André Nicola7Mauro Nakayama8Hermeto Paschoalick9Francisco Nascimento10Carlos Castillo-Salgado11Vania Moraes Ferreira12Hervaldo Carvalho13School of Medicine, University of Brasilia, Brasilia, BrazilSchool of Health Sciences, University of Brasilia, Brasilia, BrazilUniversity Hospital of Brasilia, University of Brasilia, Brasilia, BrazilHospital of Tropical Diseases, Federal University of Tocantins, Araguaína, BrazilUniversity Hospital, Federal University of Vale do São Francisco, Petrolina, BrazilInstitute of Health Sciences, Federal University of Pará, Belém, BrazilUniversity Hospital Cassiano Antônio de Moraes, Federal University of Espírito Santo, Vitória, BrazilSchool of Medicine, University of Brasilia, Brasilia, BrazilUniversity Hospital, Federal University of Grande Dourados, Dourados, BrazilUniversity Hospital, Federal University of Grande Dourados, Dourados, BrazilDepartment of Electrical Engineering, University of Brasilia, Brasilia, Brazil0School of Public Health, Johns Hopkins University, Baltimore, MD, United StatesSchool of Medicine, University of Brasilia, Brasilia, BrazilSchool of Medicine, University of Brasilia, Brasilia, BrazilIn 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone. This data was collected during a prospective, multicenter cohort that followed patients with respiratory syndrome during the pandemic. We aimed to predict which patients would present mild cases of COVID-19 and which would develop severe cases. Severe cases were defined as those requiring access to the Intensive Care Unit, endotracheal intubation, or even progressing to death. The system achieved an accuracy of 80%, with Area Under Receiver Operating Characteristic Curve (AUC) of 91%, Positive Predictive Value of 87% and Negative Predictive Value of 82%. Considering that only data from hospital admission was used, and that this data came from low-cost clinical examination and laboratory testing, the low false positive rate and acceptable accuracy observed shows that it is feasible to implement prediction systems based on artificial intelligence as an effective triage method.https://www.frontiersin.org/articles/10.3389/frai.2024.1495074/fullartificial intelligencemachine learningclinical dataCOVID-19outcome predictionprediction algorithms |
| spellingShingle | Yuri Oliveira Iêda Rios Paula Araújo Alinne Macambira Marcos Guimarães Lúcia Sales Marcos Rosa Júnior André Nicola Mauro Nakayama Hermeto Paschoalick Francisco Nascimento Carlos Castillo-Salgado Vania Moraes Ferreira Hervaldo Carvalho Artificial intelligence in triage of COVID-19 patients Frontiers in Artificial Intelligence artificial intelligence machine learning clinical data COVID-19 outcome prediction prediction algorithms |
| title | Artificial intelligence in triage of COVID-19 patients |
| title_full | Artificial intelligence in triage of COVID-19 patients |
| title_fullStr | Artificial intelligence in triage of COVID-19 patients |
| title_full_unstemmed | Artificial intelligence in triage of COVID-19 patients |
| title_short | Artificial intelligence in triage of COVID-19 patients |
| title_sort | artificial intelligence in triage of covid 19 patients |
| topic | artificial intelligence machine learning clinical data COVID-19 outcome prediction prediction algorithms |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1495074/full |
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