Lu Jiayi, Jin Renchao, Song Enmin, Alrashoud Mubarak, Al-Mutib Khaled N, S Al-Rakhami Mabrook
School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.
Department of Software EngineeringCollege of Computer and Information SciencesKing Saud University Riyadh 11543 Saudi Arabia.
IEEE Internet Things J. 2020 Nov 13;8(21):15839-15846. doi: 10.1109/JIOT.2020.3037915. eCollection 2021 Nov.
The outbreak of Coronavirus Disease-2019 (COVID-19) has posed a threat to world health. With the increasing number of people infected, healthcare systems, especially those in developing countries, are bearing tremendous pressure. There is an urgent need for the diagnosis of COVID-19 and the prognosis of inpatients. To alleviate these problems, a data-driven medical assistance system is put forward in this article. Based on two real-world data sets in Wuhan, China, the proposed system integrates data from different sources with tools of machine learning (ML) to predict COVID-19 infected probability of suspected patients in their first visit, and then predict mortality of confirmed cases. Rather than choosing an interpretable algorithm, this system separates the explanations from ML models. It can do help to patient triaging and provide some useful advice for doctors.
2019年冠状病毒病（COVID-19）的爆发对世界卫生构成了威胁。随着感染人数的增加，医疗系统，尤其是发展中国家的医疗系统，承受着巨大压力。对COVID-19的诊断以及住院患者的预后评估迫在眉睫。为缓解这些问题，本文提出了一种数据驱动的医疗辅助系统。基于中国武汉的两个真实数据集，该系统将来自不同来源的数据与机器学习（ML）工具相结合，以预测疑似患者首次就诊时感染COVID-19的概率，进而预测确诊病例的死亡率。该系统并非选择可解释的算法，而是将解释与ML模型分离。它有助于患者分流，并为医生提供一些有用的建议。
