Due to the high costs of conventional mental health care, there has been a rise in the application of web-based technologies in recent years, i.e., telemental health care. We conducted this systematic review in 2017, using high quality research articles on the applications, technologies, advantages and challenges associated with telemental health care published since year 2000. We used a combination of relevant key words to search four major databases, such as "Web of Sciences, Embase, PubMed and Science Direct". From among 156 articles, which had been published since 2000, twenty five articles met all of the inclusion criteria and were selected for the final review. The information extracted from these articles were used to construct Tables 1 and 2. Also, the materials derived from 55 credible articles were used as further support and complementary facts to substantiate the information presented in the Discussion section. The findings revealed that telemental health care is an extended domain supportive of conventional mental health services. Currently, telemental health care has multiple capabilities and technologies for providing effective interventions to patients with various mental illnesses. It provides clinicians with a wide variety of innovative choices and strategies for mental interventions, in addition to significant future potentials. Telemental health care can provide effective and adaptable solutions to the care of mental illnesses universally. While being comparable to in-person services, telemental health care is particularly advantageous and inexpensive through the use of current technologies and adaptable designs, especially in isolated communities.

Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.

Advanced technology has increased the use of telemedicine and Information Technology (IT) in treating or rehabilitating diseases. An increased use of technology increases the importance of the ethical issues involved. The need for keeping patients' information confidential and secure, controlling a number of therapists' inefficiency as well as raising the quality of healthcare services necessitates adequate heed to ethical issues in telemedicine provision. The goal of this review is gathering all articles that are published through 5 years until now (2012-2017) for detecting ethical issues for providing telemedicine services and Information technology. The reason of this time is improvement of telemedicine and technology through these years. This article is important for clinical practice and also to world, because of knowing ethical issues in telemedicine and technology are always important factors for physician and health providers. the required data in this research were derived from published electronic sources and credible academic articles published in such databases as PubMed, Scopus and Science Direct. The following key words were searched for in separation and combination: tele-health, telemedicine, ethical issues in telemedicine. A total of 503 articles were found. After excluding the duplicates (n= 93), the titles and abstracts of 410 articles were skimmed according to the inclusion criteria. Finally, 64 articles remained. They were reviewed in full text and 36 articles were excluded. At the end, 28 articles were chosen which met our eligibility criteria and were included in this study. Ethics has been of a great significance in IT and telemedicine especially the Internet since there are more chances provided for accessing information. It is, however, accompanied by a threat to patients' personal information. Therefore, suggestions are made to investigate ethics in technology, to offer standards and guidelines to therapists. Due to the advancement in technology, access to information has become simpler than the past. This has prompted hackers to seize the opportunity. This research shows that the ethical issues in telemedicine can be investigated from several aspects like technology, doctor-patient relationship, data confidentiality and security, informed consent, patient's and family's satisfaction with telemedicine services. Following ethical issues in telemedicine is a primary aspect of high quality services. In other words, if therapists abide by ethical rules, they can provide better services for patients. Attention to ethical issues in telemedicine guarantees a safer use of the services.

Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis. The proposed model can diagnose CAD based on clinical data and without the use of an invasive procedure. Methods: In this paper, machine-learning (ML) techniques were used for the early detection of CAD, which were applied to a CAD dataset known as Z-Alizadeh Sani. Since this dataset has 54 features, the Pearson correlation feature selection method was conducted to identify the most effective features. Then, six machine learning techniques including decision tree, deep learning, logistic regression, random forest, support vector machine (SVM), and Xgboost were employed based on a semi-random-partitioning framework. Result: Applying Pearson feature selection to the dataset demonstrated that only eight features were the most effective for CAD diagnosis. The results of running the six machine-learning models on the selected features showed that logistic regression and SVM had the same performance with 95.45% accuracy, 95.91% sensitivity, 91.66% specificity, and a 96.90% F1 score. In addition, the ROC curve indicates a similar result regarding the AUC (0.98). Conclusions: Prediction is an important component of medical decision support systems. The results of the present study showed that feature selection has a high impact on machine-learning performance and, regardless of the evaluation metrics of the machine-learning models, determining the effective features is very important. However, SVM and Logistic Regression were designated as the best models according to our selected features.

To review published evidences about using information technology interventions in diabetes care and determine their effects on managing diabetes. Systemati