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

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

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.

Grants and funding

This work was supported by the National Key R&D Program of China under Grant 2018YFC1314600. This work was supported by the National Natural Science Foundation of China under Grant No. 61672246. Prof. Kai Hwang's work was supported by Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). This work was supported by Technology Innovation Fund of Huazhong University of Science and Technology under Grant No. 2020JYCXJJ066. Prof. Long Hu's work was supported by the China National Natural Science Foundation under Grant No. 61802139. This work was also supported by the Technology Innovation Project of Hubei Province of China under Grant No. 2019AHB061.