Tehran University Medical Journal

Background: Infectious diseases in the pandemic stage have significant life-threatening, psychological and social effects. Identifying the characteristics associated with people's cooperation in self-care leads to greater immunity for themselves and others. Therefore, this study was conducted to predict the self-care of the Iranian people according to their individual and social characteristics in face of the Covid-19 pandemic.
Methods: A survey study was conducted on 1056 adults aged 18 and over in different provinces of Iran through a form of answering online researcher-made questions (n=40, α=0.9) in social networks in four days. This paper studies the level of self-care of people against Covid 19 pandemic which was conducted with the support of Isfahan University of Medical Sciences in April 2016. Data were analyzed using Student t-test and variance. Moreover, "decision tree technique" was used to identify communication patterns.

Background: The pandemic of Covid-19 is spreading around the world. Extensive research is needed to focus on identifying the underlying causes of the disease. This study aimed to investigate the clinical and etiological symptoms of the Covid-19.
Methods: This descriptive-analytical study, conducted on 510 infected patients in the infectious disease clinic of Imam Khomeini Hospital in Tehran from March 2019 to June 2020 for A period of Four months during the first wave of Covid-19 pandemic. The method of selecting patients was continuous and was divided into two groups of 179 inpatients and 331 outpatients based on lung scan and clinical symptoms. Demographic information, clinical signs, and risk factors were collected through a questionnaire and the data were statistically analyzed.

Conclusion: Fever, chills and cough were important symptoms in identifying infected patients with Covid-19. According to the results of the present study and the findings of other studies, the supportive effect of vitamin D3 in reducing the severity of infectious diseases should be considered. Clinical trials with appropriate sample size are recommended to investigate the functional role of this vitamin in Reducing the severity of viral diseases of the respiratory tract.

Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. Several data mining methods exist but finding a suitable one is very important. Today, coronavirus disease (COVID-19) has become one of the causing deadly diseases in the world. The early diagnosis of pandemic coronavirus disease has a significant impact in preventing death. This study aims to extract the key indications of the disease and find the best data mining methods that enhance the accuracy of coronavirus disease diagnosis.
Methods: In this study, to obtain high accuracy in diagnosing COVID-19 disease, a complete and effective workflow over data mining methods was proposed, which includes these steps: data pre-analyzing, indication selection, model creation, the measure of performance, and display of results. Data and related indications of patients with COVID-19 were collected from Kerman Afzalipour Hospital and Rafsanjan, Ali Ebn Abi Taleb Hospital. Prediction structures were made and tested via different combinations of the disease indications and seven data mining methods. To discover the best key indications, three criteria including accuracy, validation and F-value were applied and to discover the best data mining methods, accuracy and validation criteria were considered. For each data mining method, the criteria were measured independently and all results were reported for analysis. Finally, the best key indications and data mining methods that can diagnose COVID-19 disease with high accuracy were extracted.

Background: In the Covid-19 Pandemic, virtual education in universities became essential and came with some challenges, especially for professors who had the role of presenters. In this regard, the study was conducted to predict the performance of professors in providing virtual training in Covid-19 in terms of problem-solving methods and their demographics.
Methods: A descriptive-analytical study was performed on 252 professors of Iranian universities of medical sciences from 2021 April to 2021. Also, demographic characteristics such as gender, field of study, position, job rank and work experience were asked. The faculty members' performance questionnaire in providing virtual training (α=0.89) and the problem-solving methods questionnaire (α=0.75) was administered virtually and the data were analyzed by Random forest, CHAID and ID3 techniques.

Results: Based on used data mining methods findings, factors related to teachers' satisfaction with their performance in providing virtual education were "the possibility of monitoring the performance of homework", "establishing order and regulations", "preparing standard educational content", "using multimedia content", "Mastery of software, educational systems, and multimedia content", and "possibility of examining the quality and quantity of students' learning". Also, interpersonal problem-solving methods, such as "believing in the role of personality traits of people in their behavior", "solving problems with effort and follow-up", "notifying people's mistakes in interpersonal interactions", "giving people the opportunity to check their behavior", "proposing solutions to solve problems for the benefit of both parties", and "dividing big problems into smaller parts" have played a big role in professors' satisfaction about their teaching methods. These characteristics are related to more basic areas such as self-regulation, pursuit and challenge, agreeableness, and realism. Conclusion: The results of the study showed that the performance of teachers in providing virtual education is influenced by some behavioral factors and individual situational abilities. However, despite the virtual training implementation difficulties, it is a productive opportunity that can be used in the days of returning for conditions (after-covid 19 condition) without physical distance along with face-to-face training.