COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led to
a treacherous and devastating catastrophe for humanity. At the time of writing,
no specific antivirus drugs or vaccines are recommended to control infection
transmission and spread. The current diagnosis of COVID-19 is done by
Reverse-Transcription Polymer Chain Reaction (RT-PCR) testing. However, this
method is expensive, time-consuming, and not easily available in straitened
regions. An interpretable and COVID-19 diagnosis AI framework is devised and
developed based on the cough sounds features and symptoms metadata to overcome
these limitations. The proposed framework's performance was evaluated using a
medical dataset containing Symptoms and Demographic data of 30000 audio
segments, 328 cough sounds from 150 patients with four cough classes (
COVID-19, Asthma, Bronchitis, and Healthy). Experiments' results show that the
model captures the better and robust feature embedding to distinguish between
COVID-19 patient coughs and several types of non-COVID-19 coughs with higher
specificity and accuracy of 95.04 $\pm$ 0.18% and 96.83$\pm$ 0.18%
respectively, all the while maintaining interpretability.

通过咳嗽声音特征和症状元数据，设计并开发了可解释的 COVID-19 诊断 AI 框架，能够以 95.04% 至 96.83% 的高特异性和准确性区分 COVID-19 患者的咳嗽和非 COVID-19 患者的咳嗽。