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1.
Ieee Transactions on Molecular Biological and Multi-Scale Communications ; 8(4):239-248, 2022.
Article in English | Web of Science | ID: covidwho-2308181

ABSTRACT

The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.

2.
IEEE Sensors Journal ; 23(2):889-897, 2023.
Article in English | Scopus | ID: covidwho-2246807

ABSTRACT

Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of 'heavy centre' to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small. © 2001-2012 IEEE.

3.
Middle East Journal of Cancer ; 13(4):648-656, 2022.
Article in English | EMBASE | ID: covidwho-2067589

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) emerged in December 2019 in China and exhibited as a highly contagious viral infection which led to a high level of mortality and morbidity. It is followed by a great deal of complications, such as serious psychological disorders. There are a few studies evaluating the psychological status of COVID-19 on the patients with cancer in Iran. Method: This was a cross-sectional study carried out on 94 patients with cancer who referred to Haft-e-Tir hospital for radiotherapy and chemotherapy from 20 April to 15 may, 2020. The data collection tool was the impact of events scale-revised (IES-R). Results: The prevalence of anxiety disorders and obsessive compulsive disorder based on past psychiatric history in the patients was 11.7% and 2.1%, respectively. The results revealed that age was significantly related to avoidance dimension score (B =-0. 209, 95% CI:-0.084 to-0.335). Regarding hyper arousal dimension score, the results were as follows: rural residency (B = 5.091, 95% CI: 0.610 to 9.573), past psychiatric history (PPH) (B = 8.312, 95% CI: 4.314 to 12.310), and radiotherapy (B =-2.976, 95% CI:-5.878 to-0.074) had a statistically significant relationship with the hyper arousal dimension score. Conclusion: The patients with cancer had a severe form of COVID-19. Individuals with cancer who had a previous psychiatric history are more vulnerable to post-traumatic stress disorder symptoms after trauma.

4.
IEEE Sensors Journal ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1992662

ABSTRACT

Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of ’heavy centre’to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small. IEEE

5.
IEEE Transactions on Molecular, Biological, and Multi-Scale Communications ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1901509

ABSTRACT

The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure. IEEE

6.
Iranian Journal of Psychiatry and Behavioral Sciences ; 15(3), 2021.
Article in English | Scopus | ID: covidwho-1485404

ABSTRACT

Background: The COVID-19 pandemic as a stressor can harm the community's mental health. Iran is one of the first countries to be severely affected by COVID-19 since February 2020. Objectives: This study aimed to assess the rates of COVID-19-related Posttraumatic Stress Symptoms (PTSS) and the general mental health burden among the Iranian population during the pandemic and to explore the potential influencing factors. Methods: Through a web-based cross-sectional survey, based on social media, data were collected from self-selected volunteers using a demographic information form, General Health Questionnaire-28 (GHQ-28), and Impact of Event Scale-Revised (IES-R). Results: Among 1,910 analyzed respondents, the overall prevalence of COVID-19-related PTSS and general mental health burden was 62.4 and 43.6%, respectively. Regarding mental health, the burden was greater in the social and anxiety dimensions than in the physical and depression dimensions. The prevalence of PTSS was higher in women, younger age groups, divorced/widowed individuals, people with a history of psychiatric disorders, and those who had experienced other stressful events in the last year (P-values < 0.05). Multivariable logistic regression showed that a positive history of other stressful events and the GHQ-28 score were the potential influencing factors associated with PTSS (AOR = 2.468 and 6.007, respectively;P-values < 0.001). Conclusions: The study identified a significant mental health burden and PTSS among Iranians during the COVID-19 pandemic. Continuous assessment and monitoring of the psychological consequences of pandemics should be considered in Iran and other countries. © 2021, Author(s).

7.
IEEE Sensors Journal ; 2021.
Article in English | Scopus | ID: covidwho-1035509

ABSTRACT

Social distancing and remote work are becoming more prevalent in the post-covid world. At the same time, there is a huge demand for remote healthcare sessions as well. Although a growing number of such sessions are now utilizing online platforms as a medium of communication, other critical parameters such as the affective state and other feedback opportunities are lost during the transmission of this digital information. This paper presents a solution that leverages a brain-computer interface system for this affective feedback and a humanoid robot for teaching effectively during remote sessions. The solution uses Kinect as a sensing mechanism for the trainer. It utilizes state-of-the-art deep learning algorithms at the back-end to understand the emotional state of the trainee. The training poses (from humanoid’s camera feed and kinect) are calculated using AlphaPose compared using inverse kinematics. To ascertain the trainees’state (high valence and arousal vs. low valence and arousal), a Capsule Network was used that gives an average accuracy of 90.4% for this classification with a low average inference time of 14.3ms on the publicly available DREAMER and AMIGOS datasets. The system also allows real-time communication through the humanoid, making this experience even more distinct for the trainee. IEEE

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