TY - GEN
T1 - Predicting COVID-19 Severe Patients and Evaluation Method of 3 Stages Severe Level by Machine Learning
AU - Qu, Jiahao
AU - Sumali, Brian
AU - Mitsukura, Yasue
N1 - Funding Information:
The study was funded by Keio Research Global Institute (grant number: UMIN000041186).
Publisher Copyright:
©2021 IEEE
PY - 2021
Y1 - 2021
N2 - Since the outbreak COVID-19 in Wuhan, China in December 2019, a large number of patients have been seen worldwide, and the number of infections continues to show an increasing trend. The vast majority of COVID-19 patients will have fever, headache, and mild respiratory symptoms, but a small number of severely ill patients will experience respiratory distress and related complications, which seriously endanger their lives. The large number of patients also puts the healthcare system to the test. To maximize the protection of patients' lives and the effective use of medical resources, this study collected blood data from 313 patients by machine learning, used 7 blood test items as the feature quantity, established an effective linear SVM prediction model for severe/non-severe disease (recall: 93.55%, specificity: 93.22%), and for 3 stages evaluation of the degree of severe level in severe patients was developed for patients with critical illness. The abnormal increase in Ferritin values was also found to be closely related to the development of severity.
AB - Since the outbreak COVID-19 in Wuhan, China in December 2019, a large number of patients have been seen worldwide, and the number of infections continues to show an increasing trend. The vast majority of COVID-19 patients will have fever, headache, and mild respiratory symptoms, but a small number of severely ill patients will experience respiratory distress and related complications, which seriously endanger their lives. The large number of patients also puts the healthcare system to the test. To maximize the protection of patients' lives and the effective use of medical resources, this study collected blood data from 313 patients by machine learning, used 7 blood test items as the feature quantity, established an effective linear SVM prediction model for severe/non-severe disease (recall: 93.55%, specificity: 93.22%), and for 3 stages evaluation of the degree of severe level in severe patients was developed for patients with critical illness. The abnormal increase in Ferritin values was also found to be closely related to the development of severity.
KW - Blood data
KW - COVID-19
KW - Machine learning
KW - Prediction
KW - Severity level)
UR - http://www.scopus.com/inward/record.url?scp=85125191666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125191666&partnerID=8YFLogxK
U2 - 10.1109/ICECE54449.2021.9674303
DO - 10.1109/ICECE54449.2021.9674303
M3 - Conference contribution
AN - SCOPUS:85125191666
T3 - 2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021
SP - 277
EP - 281
BT - 2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Electronics and Communication Engineering, ICECE 2021
Y2 - 17 December 2021 through 19 December 2021
ER -