Prediction of COVID-19 diagnosis based on openEHR artefacts
Abstract Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in re...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15968-z |
| _version_ | 1818192896213385216 |
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| author | Daniela Oliveira Diana Ferreira Nuno Abreu Pedro Leuschner António Abelha José Machado |
| author_facet | Daniela Oliveira Diana Ferreira Nuno Abreu Pedro Leuschner António Abelha José Machado |
| author_sort | Daniela Oliveira |
| collection | DOAJ |
| description | Abstract Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems. |
| first_indexed | 2024-12-12T00:37:47Z |
| format | Article |
| id | doaj.art-7c982fa8b19d479ebcb295647ccdc886 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T00:37:47Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7c982fa8b19d479ebcb295647ccdc8862022-12-22T00:44:20ZengNature PortfolioScientific Reports2045-23222022-07-0112111310.1038/s41598-022-15968-zPrediction of COVID-19 diagnosis based on openEHR artefactsDaniela Oliveira0Diana Ferreira1Nuno Abreu2Pedro Leuschner3António Abelha4José Machado5Algoritmi Research Center, University of MinhoAlgoritmi Research Center, University of MinhoCentro Hospitalar Universitário do PortoCentro Hospitalar Universitário do PortoAlgoritmi Research Center, University of MinhoAlgoritmi Research Center, University of MinhoAbstract Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems.https://doi.org/10.1038/s41598-022-15968-z |
| spellingShingle | Daniela Oliveira Diana Ferreira Nuno Abreu Pedro Leuschner António Abelha José Machado Prediction of COVID-19 diagnosis based on openEHR artefacts Scientific Reports |
| title | Prediction of COVID-19 diagnosis based on openEHR artefacts |
| title_full | Prediction of COVID-19 diagnosis based on openEHR artefacts |
| title_fullStr | Prediction of COVID-19 diagnosis based on openEHR artefacts |
| title_full_unstemmed | Prediction of COVID-19 diagnosis based on openEHR artefacts |
| title_short | Prediction of COVID-19 diagnosis based on openEHR artefacts |
| title_sort | prediction of covid 19 diagnosis based on openehr artefacts |
| url | https://doi.org/10.1038/s41598-022-15968-z |
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