Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance.
J Clin Med
; 11(5)2022 Mar 05.
Article
in English
| MEDLINE | ID: covidwho-1732086
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Language:
English
Year:
2022
Document Type:
Article
Affiliation country:
Jcm11051437
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