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. 2021 May 4;9(1):247-257.
doi: 10.1109/TNSE.2021.3077280. eCollection 2022 Jan.

Adjuvant Therapy System of COVID-19 Patient: Integrating Warning, Therapy, Post-Therapy Psychological Intervention

Miao Li  1 , Yixue Hao  1 , Yaxiong Ma  1 , Jincai Chen  1 , Long Hu  1 , Min Chen  1 , Kai Hwang  2   3 , Zhongchun Liu  4
Affiliations

Adjuvant Therapy System of COVID-19 Patient: Integrating Warning, Therapy, Post-Therapy Psychological Intervention

Miao Li et al. IEEE Trans Netw Sci Eng. .

Abstract

The 2019 novel coronavirus(COVID-19) spreads rapidly, and the large-scale infection leads to the lack of medical resources. For the purpose of providing more reasonable medical service to COVID-19 patients, we designed an novel adjuvant therapy system integrating warning, therapy, and post-therapy psychological intervention. The system combines data analysis, communication networks and artificial intelligence(AI) to design a guidance framework for the treatment of COVID-19 patients. Specifically, in this system, we first can use blood characteristic data to help make a definite diagnosis and classify the patients. Then, the classification results, together with the blood characteristics and underlying diseases disease characteristics of the patient, can be used to assist the doctor in treat treating the patient according to AI algorithms. Moreover, after the patient is discharged from the hospital, the system can monitor the psychological and physiological state at the data collection layer. And in the data feedback layer, this system can analyze the data and report the abnormalities of the patient to the doctor through communication network. Experiments show the effectiveness of our proposed system.

Keywords: 5G communication; COVID-19; adjuvant therapy system; blood characteristic.

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Figures

Fig. 1.
Fig. 1.
The framework of the integrated adjuvant therapy system.
Fig. 2.
Fig. 2.
Patient pre-treatment scheme.
Fig. 3.
Fig. 3.
Architecture of the therapeutic scheme for patients.
Fig. 4.
Fig. 4.
The post-therapy patients rehabilitation scheme.
Fig. 5.
Fig. 5.
SVM,RF and XGBoost algorithms were used to diagnose patients, and the performance of the three algorithms was compared: (a) Confusion matrix; (b) P-R curve.
Fig. 6.
Fig. 6.
SVM,RF and XGBoost algorithms were used to compare the severity of disease for patients, and the performance of the three algorithms was compared: (a) Confusion matrix; (b) P-R curve.
Fig. 7.
Fig. 7.
SVM,RF and XGBoost algorithms were used to compare numbers of days in hospital, and the performance of the three algorithms was compared: (a) Confusion matrix; (b) P-R curve.
See this image and copyright information in PMC

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