ABSTRACT
Long-term sequelae refer to persistent symptoms or signs for >6 months after SARS-CoV-2 infection. The most common symptoms of sequelae are fatigue and neuropsychiatric symptoms (concentration difficulty, amnesia, cognitive dysfunction, anxiety, and depression). However, approved treatments have not been fully established. Herbal medicines are administered for 12 weeks to patients who continuously complain of fatigue or cognitive dysfunction for >4 weeks that only occurred after COVID-19 diagnoses. Based on the Korean Medicine syndrome differentiation diagnosis, patients with fatigue will be administered Bojungikgi-tang or Kyungok-go, whereas those with cognitive dysfunction will be administered Cheonwangbosim-dan. Results could support evidence that herbal medicines may mitigate fatigue and cognitive dysfunction caused by COVID-19. Furthermore, by investigating the effects of herbal medicines on changes in metabolite and immune response due to COVID-19, which may be responsible for sequelae, the potential of herbal medicines as one of the therapeutic interventions for post-acute sequelae of SARS-CoV-2 infection can be evaluated. Therefore, the effects of herbal medicine on fatigue and cognitive dysfunction sequelae due to COVID-19 will be elucidated in this study to provide an insight into the preparation of medical management for the post-acute sequelae of SARS-CoV-2 infection.
ABSTRACT
Several radiologists have paid attention to computer‐aided detection (CAD) systems which assist in classifying diseases on chest x‐ray (CXR). Recently, with the outbreak of COVID‐19, CAD based on deep learning has an important role in screening COVID‐19 on CXR. However, imbalanced training datasets such as COVID‐19 datasets, COVID‐19 (473), pneumonia (5458), and normal (7966) cause difficulty in classification. In this paper, we suggest a new evaluation approach, OVASO, that selectively combines one‐versus‐all (OVA) classifier and one‐versus‐one (OVO) to overcome class imbalance caused by the lower number of COVID‐19 training datasets. In addition, as part of efforts to properly apply transfer learning, we initialized batch normalization (BN) values including γ and β from the viewpoint of transfer learning and found that appropriate initialization at all binary models, OVASO's components, usually increased the binary models' performance. As a result, the proposed OVASO model achieved improved accuracy and F1‐score of 95.33% and 95.88%, respectively. Furthermore, the suggested OVASO performed similarly to COVID‐Net, which is the current state‐of‐the‐art model for classifying COVID‐19 on CXR. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)