tetano
Editor, Senior Moderator
J Clin Med
. 2022 Mar 5;11(5):1437.
doi: 10.3390/jcm11051437.
Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance
Ying-Chuan Wang[SUP] 1 [/SUP], Dung-Jang Tsai[SUP] 2 3 [/SUP], Li-Chen Yen[SUP] 4 [/SUP], Ya-Hsin Yao[SUP] 5 [/SUP], Tsung-Ta Chiang[SUP] 6 [/SUP], Chun-Hsiang Chiu[SUP] 6 [/SUP], Te-Yu Lin[SUP] 6 [/SUP], Kuo-Ming Yeh[SUP] 6 [/SUP], Feng-Yee Chang[SUP] 6 [/SUP]
Affiliations
Abstract
During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients suspected of COVID-19, of which 27 had confirmed COVID-19. The clinical characteristics of these patients were used to train artificial intelligence (AI) models such as support vector machine (SVM), decision tree, random forest, and artificial neural network for diagnosing COVID-19. When analyzing the performance of the models, SVM showed the highest sensitivity (SVM vs. decision tree vs. random forest vs. artificial neural network: 100% vs. 42.86% vs. 28.57% vs. 71.43%), while decision tree and random forest had the highest specificity (SVM vs. decision tree vs. random forest vs. artificial neural network: 88.37% vs. 100% vs. 100% vs. 94.74%) in the diagnosis of COVID-19. With the aid of AI models, physicians may identify COVID-19 patients earlier, even with few baseline data available, and segregate infected patients earlier to avoid hospital cluster infections and to ensure the safety of medical professionals and ordinary patients in the hospital.
Keywords: COVID-19; artificial intelligence; artificial neural network; decision tree; random forest; support vector machine (SVM).
. 2022 Mar 5;11(5):1437.
doi: 10.3390/jcm11051437.
Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance
Ying-Chuan Wang[SUP] 1 [/SUP], Dung-Jang Tsai[SUP] 2 3 [/SUP], Li-Chen Yen[SUP] 4 [/SUP], Ya-Hsin Yao[SUP] 5 [/SUP], Tsung-Ta Chiang[SUP] 6 [/SUP], Chun-Hsiang Chiu[SUP] 6 [/SUP], Te-Yu Lin[SUP] 6 [/SUP], Kuo-Ming Yeh[SUP] 6 [/SUP], Feng-Yee Chang[SUP] 6 [/SUP]
Affiliations
- PMID: 35268531
- DOI: 10.3390/jcm11051437
Abstract
During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients suspected of COVID-19, of which 27 had confirmed COVID-19. The clinical characteristics of these patients were used to train artificial intelligence (AI) models such as support vector machine (SVM), decision tree, random forest, and artificial neural network for diagnosing COVID-19. When analyzing the performance of the models, SVM showed the highest sensitivity (SVM vs. decision tree vs. random forest vs. artificial neural network: 100% vs. 42.86% vs. 28.57% vs. 71.43%), while decision tree and random forest had the highest specificity (SVM vs. decision tree vs. random forest vs. artificial neural network: 88.37% vs. 100% vs. 100% vs. 94.74%) in the diagnosis of COVID-19. With the aid of AI models, physicians may identify COVID-19 patients earlier, even with few baseline data available, and segregate infected patients earlier to avoid hospital cluster infections and to ensure the safety of medical professionals and ordinary patients in the hospital.
Keywords: COVID-19; artificial intelligence; artificial neural network; decision tree; random forest; support vector machine (SVM).