Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections make the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we were able to successfully implement our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues in EEG denoising processes is removing the ocular artifacts where Electrooculographic (EOG), and EEG signals have an overlap in both frequency and time domains. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals similar to the process used in the image segmentation process. The results confirm that one of our schemes can achieve a reliable and promising accuracy to reduce the Mean square error between the target signal (Pure EEGs) and the predicted signal (Purified EEGs).
Background
In line with the significance of organizational commitment, the question arises "Do spiritual health and psychological well-being optimize teachers' organizational commitment?" The purpose of this study was to determine the relationship between spiritual health, psychological well-being and the organizational commitment of high school teachers.
Methods
This was a cross-sectional study in which, 346 teachers in Tehran high schools participated through multi-stage sampling. The data were collected using Ryff Psychological Well-being Questionnaire (1989), Spiritual Health Questionnaire in Iranian Society (2014) and Organizational Commitment scale of Allen and Meyer (1990), and their relationships were assessed.
Results
Psychological well-being and spiritual health had positive and significant relationship with teachers' organizational commitment. Furthermore, approximately 50% of variations in organizational commitment subscales could be explained by the variables of spiritual health and psychological well-being.
Conclusion
Psychological well-being and spiritual health can predict organizational commitment as the dependent variable among high school teachers.
The COVID-19 pandemic, also known as the coronavirus pandemic, as an emerging disease brought about one of the most critical situations for the health care system. One of the most critical conditions placed facing the health care services' system. The difficulty of managing this crisis, particularly with the special conditions of Iran, the unknown nature of the disease and the lack of sufficient experience, provided the arena for creativity and various innovations. These valuable experiences, if managed and turned into explicit knowledge, will provide valuable reserves for the Iranian and the world and health care system and neglect of explicit knowledge, it will lead to the loss of this enormous capital.
10.30699/jambs.28.127.82 Background & Objective: Hope therapy is an efficient and positive psychology intervention used to treat chronic diseases. The purpose of this study was to investigate the effect of hope therapy on anxiety and depression using an Islamic approach and compare this approach with conventional hope therapy in coronary heart disease (CAD) patients. Materials & Methods: The study was conducted in the form of a randomized trial with pre-post-test, and control groups. A total of 45 patients with CAD were sampled through convenience sampling from a hospital in Qom city, Iran. Data were collected using Snyder's hope questionnaire, and the Hospital Anxiety and Depression Scale (HADS). After collecting pre-test data, the participants were randomly divided into three groups of peers, and the intervention sessions were performed in eight sessions of 90 minutes each. One experimental group received Islamic hope therapy, and the other experimental group was exposed to conventional hope therapy, while the control group received a stress relief package. All three groups undertook a post-test, the data from which were analyzed by SPSS 22, using Levin, Kolmogorov-Smirnov, and covariance tests. Results: Islamic and conventional hope therapy both significantly outperformed the stress relief package in terms of increasing hope and decreasing depression. Also, Islamic hope therapy had an especially significant effect on reducing anxiety. Conclusion: Islamic hope therapy seems similar to conventional hope therapy in its ability to increase hope and reduce depression and is more effective in reducing anxiety. Therefore, it could be helpful in treatment of patients with CAD and other chronic diseases that cause patients a high level of anxiety.
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