Frontiers in Health Informatics
Volume:9 Issue: 1, 2020

Providing care for patients and preventing complications is one of the major subjects in medical sciences. Computerized Physician Order Entry (CPOE) with a decision support system is expected to deliver many benefits. A system with decision support system may help clinicians, patients, and others to suggest patient-appropriate evidence-based treatment options. The present study was conducted to prepare a conceptual model for a CPOE system of diabetic patients (Type 2) using Unified Modeling Language (UML). Then, a software program was designed accordingly.

This cross-sectional study was conducted in 2017. A minimum data set of patient records was used as the patient profile in the system, and a list of drugs and functional requirements of the CPOE system for diabetic patients was provided. Following the confirmation of the minimum data set by diabetes specialists, UML figures were drawn and the software was designed.

The minimum data set of patient records included demographic and clinical information as well as laboratory tests. Functional requirements of the CPOE system for type 2 diabetic patients consisted of the possibility of recording simple and complicated orders, connecting the system to the pharmacy or other auxiliary information systems, controlling drug side effects, etc.

A CPOE system should have minimum errors in documentations and provide information on allergies, drug interactions, and side effects in a timely manner to reduce medical errors, especially drug errors, increase physician efficiency and patient satisfaction, andfinally promote the quality of healthcare services

Data exchange across healthcare facilities is a major issue in healthcare information systems. Standards play an important role in the context of communication. In this paper, we surveyed the usage of standards in the hospital information systems (HISs) in the affiliated hospitals of Tehran University of Medical Sciences.

This survey was performed in 2014-2015. Seventeen hospitals that had an HIS were surveyed. The data were collected using a structured questionnaire. The design of the questionnaire was based on a literature review and consisted of three parts. Descriptive statistics were used to analyze the data.

XML, HL7 and DICOM are commonly used international interchange standards. In the case of security standards, 76.5% of HISs do not support the HIPPA and CEN TC 251 security standards. ICD was the most commonly used terminology standard in the HISs. Several studies have indicated that HISs should cover data exchange, security and terminology standards to provide integration of heterogeneous systems.

In the current study, the role of standards in the architecture of the HISs was inconspicuous. To make the HIS effective, it is necessary to consider the standards when developing the system. In this matter, legislation could help.

Today, the introduction of smartphones into the healthcare field has led to rapid growth in application development; provided many important tasks such as maintaining health and access to information, communications and counseling, referral and data collection, management and supervision of patients; and facilitated patient self-care and participation.

In order to design a mobile-based self-care application, the designing purpose was first defined and then related literature was reviewed and different kinds of software designed in this field were identified. Using the specified information elements, UML diagrams (Use-Case, Sequence, and Activity) were plotted by Visual Paradigm Enterprise 8 software. In fact, a feasibility assessment was carried out, based on which the application prototype was developed and provided to 10 patients to achieve their opinions. In the next step, the final application components were determined based on user feedback and experts approval. In the final step, Quiz Satisfaction Survey Questionnaire v.7.0 was used to assess users' satisfaction.

Based on user questions and answers, and answers available in the database, the application offers appropriate therapies that emphasize the presence of patients in health centers.

The presented application helps with providing rapid information and awareness about different aspects of the disease.

Studying trends in observed rates provides valuable information in terms of need assessment, planning of programs and development indicators of each country. The purpose of the present study was to apply the regression model and the Fourier series in terms of predicting the trends in growth and mortality rate of coronavirus disease.

In this study, two linear analysis methods were used to predict the incidence and mortality rate of coronavirus disease in Iran and China. The methods used are linear regression and Fourier transform. The data used were collected by referring to the official media of the mentioned countries, the general form of which is a time series of the incidence and mortality rate in recent days and the model implemented to estimate the incidence and mortality rate for the coming days. Python programming language version 3.7 is used to implement models.

The results of this study show that the rates of coronavirus disease incidence and mortality are still increasing. Meanwhile, the Fourier transform-based analytical method is more accurate than the linear regression method and on the other hand, the accuracy of both algorithms for predicting mortality wasmuch higher than the prediction rate. This indicates that the mortality rate is higher than that of its linearity over time. The other point is that based on the results of this study, however, linear methods are very suitable for future prediction, due to the nature of epidemic diseases whose growth chart is nonlinear, linear methods cannot be used to predict the rate and mortality used in distant times.

The accuracy of the mathematics-based methods for predicting the trajectory of COVID-19 was really high. We predicted that the epidemics of COVID-19will be high during 10 days. If the data are reliable and there are no second transmissions, we can accurately predict the transmission dynamics of the COVID-19across the cities in China and Iran. The mathematics-inspired methods are a powerful tool for helping public health planning and policymaking.

Heart disease is known as one of the most important causes of death in today's society and sofar no definitive method has been found to predict it and several factors are effective in contracting this disease. Therefore, the aim of this study was to provide a data mining model for predicting heart disease.

This study used standard data from UCI. These data include four Cleveland, Hungarian, Swiss and Long Beach VA databases. These data include 13 independent variables and one dependent variable. The data are missing, and the EM algorithm was used to control this loss, and atthe end of the data, a suggestion algorithm was implemented that combined the two random forest algorithms and the artificial neural network.

In this study, data was divided into two training sets and 10-Fold method was used. To evaluate the algorithms, three indicators of sensitivity, specificity, accuracy were used and the accuracy of the prediction algorithm for four data Cleveland, Hungarian, Switzerland and Long Beach VA reached 87.65%, 94.37%, 93.45% and 85%, respectively. Then, the proposed algorithm was compared with similar articles in this field, and it was found that this algorithm is more accurate than similar methods.

The results of this study showed that by combining the two algorithms of random forest and artificial neural network, a suitable model for predicting heart attacks can be provided.

The skilled IT staff about big data analytics can motivate organizations to adopt the big data analytics. The aim of the current study is to present the knowledge, attitude, and challenges of the big data analytics based on IT staffs’ viewpoints in a developing country.

A self-administered semi-structured questionnaire was developed based on a literature review. Content validity and face validity were measured using Delphi technique. The questionnaire comprised of three parts including knowledge, attitude, and challenges. Descriptive statistics were used to summarize the results. The chi-square test was applied to identify associations between knowledge and attitude of participants with the demographic characteristics.

Out of a total of 250 IT staffs, 120 participated in the study. Knowledge levels were low, moderate, and high in 35.0%, 33.3%, and 31.7 % of the participants, respectively. The two most affecting factors on the knowledge level of participants were age groups and sex. IT staffs hold a positive attitude toward big data analytics. The most of IT staffs believed that big data management is necessary for the country and they agreed that big data analyzes can provide many advantages to organization managers. As well, 35 challenges of the big data analytics were identified.

Theresults showedthat the big data analytics face with many problemsin following issues: awareness and education, recruiting skilled specialists, presentation big data analytics benefits to IT managers and policy-makers, conducting research projects, developing a strategic plan at national and local levels.

The minimum data set is a standard method for collecting key data elements, which will finally improve healthcare and quality of treatment services. Electronic documentation in the intensive care unit (ICU) has a significant effect on the quality of data. In addition, using structured data and standard formats can facilitate documentation of progress note data. Therefore, the aimof this study was to create a minimum data set for an effective design and implementation of electronic documentation of progress note in the ICU.

This is an applied qualitative study conducted in the general intensive care unit of Namazi hospital in Shiraz, which is the largest education and treatment center in Shiraz and the only referral hospital in Southern Iran. In this study, four stages were used for designing the minimum data sets of electronic progress note: 1. Using Englishliterature, 2. Local expert review, 3. Designing prototypes, and 4. Conducting group sessions. Finally, data were analyzed using descriptive statistics through SPSS 21 software.

The minimum data set for electronic documentation of progress notein the ICU included the two demographic and clinical sections. In addition, the clinical data were classified into 11 major groups, each consisting of other items. Meanwhile, 46.8% (66 out of 141) of information items were obtained from reviewing the literature and 53.2% (76 out of 141) from interviews. In group sessions, 99.29% of information items were finalized by experts.

It is essential to create a standard and structured minimum data set for the electronic design and implementation of progress note data. In such a case, accurate, thorough and timely electronic documentation in presenting instantaneous reports on the status of patients is effective in management and clinical decision-makings.

The coronavirus outbreak has become a serious issue of the entire world. In some ways, the ability to provide outbreak rate prediction is helpful. Therefore the main purpose of this study is to investigate the incidence pattern ofConfirmed COVID-19 Cases in Iran, and comparison between countries with high infected person such as USA, Brazil and others.

A total of 7801401infected cases with COVID 19 related countries with highest infection, USA, Brazil, India, Russia, Peru, Chile, Mexico, Spain, UK, South Africa, Iran and Pakistanin 17 weeks timespan was extracted from the Daily New Cases chart athttps://www.worldmeters. Info/coronavirus/. Also, the incidence rate pattern was presented.The frequency distribution charts usedto compare countries.

In Iran, from the interval of first week to the end of fifth week after observing the 100th case of infection, the trend of identifying patients was upward, and after that, it showed a decreasing tendency until the end of the 10th week. However, it seems that from the 10th to the 12th week, the trend has been increasing and after that it has been almost constant. In countries such as South Africa, India, and Brazil, however, this trend has roughly always been ascending during this period, and in other countries it has been fluctuated.

The Covid-19has become pandemic disease. Finding similarincidence rate with other countries aimed for applyingappropriate intervention is helpful.

The rapid global dissemination of COVID-19 culminated in the mobilization of great technological efforts aimed at its better understanding and control. In this paper, Machine Learning gains notoriety, and its application has been widely documented for pathophysiological, diagnostic, therapeutic, prognostic and monitoring of COVID-19 purposes.The present paper aimed to build a model for the prediction of the diagnosis of COVID-19 based on blood count results and age of patients and to identify the main characteristics taken into account by the algorithm for the predictive decision.

Anonymous data from 1157 patients made available by the COVID-19 Data Sharing / BR repository were used. The work took place in two distinct stages: description and analysis of the data; and construction of the predictive model.

With the exception of hemoglobin measurement, mean corpuscular volume, red cell distribution width, mean platelet volume and neutrophil-lymphocyte ratio, there was a statistically significant association of all other hematological parameters assessed with COVID-19. The predictive model developed from the XGBoost classifier reached an accuracy of 80.0% with a sensitivity of 75.6% and specificity of 82.0%. The variables that had the greatest influence on the predictive decision were basophil, eosinophil and leukocyte measurements.

The present paper confirms the potential of using blood count results, a widely available and accessible test, in the context of the diagnostic evaluation and pathophysiological investigation of COVID-19.

This work highlights the relevance of the systematizationand dissemination of data related to COVID-19 for use in new research.

Cardiac electrophysiology (EP) studies the electrical heart conduction system which is used for diagnosis and treatment of cardiac arrhythmias. In this context, a huge amount of data is generated, requiring efficient and effective access, interpretation, and data analysis from multiple sources in aunified view. To resolve this challenge, this paper presents an ontology to reconcile data heterogeneity problems in this domain.

The cardiac EP ontology was constructed according to the life cycle of ontology building. Structural, functional, and expert evaluation was performed to ensure its quality and usability.

Cardiac EP ontology was developed using protégé environment and implemented in OWL editing tool.It presented a detailed hierarchical structure of the cardiac EP domain with around 324 instances describing cardiac EP-related concepts.

Cardiac EP ontology provides an explicit formal description of the concepts, relationships, and properties associated with cardiac electrophysiology making seamlessdata integration between multiple heterogeneous databases. It also is a useful framework for knowledge representation in knowledge-based systems, as well as for explicit communication between experts in the EP domain.

In this study, Persian Android mobile health (mhealth) applications were studied to describe usage of dangerous permissions in health related mobile applications. So the most frequently normal and dangerous permissions used in mHealth applications were reviewed.

We wrote a PHP script to crawl information of Android apps in “health” and “medicine” categories from Cafebazaar app store. Thenpermission information of these applicationwere extracted.

11627 permissions from 3331 studied apps were obtained. There was at least onedangerous permission in 48% of reviewed apps. 41% of free applications, 53% of paid applications and 71% of in-purchase applications contained dangerous permissions. 1321 applications had writing permission to external storage of phone (40%), 1288 applications had access to read from external storage (39%), 422 applications could read contact list and ongoing calls (13%) and 188 applications were allowed to access phone location (5%).

Most of Android permissions are harmless but significant numberof the apps have at least one dangerous permission which increase the security risk. So paying attention to the permissions requested in the installation step is the best way to ensure that the application installed on your phone can only access what you want

One of the challenges of multidisciplinary disciplines such as Medical Informatics, is the lack of familiarity with research fields. Due to the specializations and clinical facilities concentrated in each university, research is being done differently and with variety. Therefore, in this study, in order to identify the most researched fields and the neglected fields of research, the dissertations done in the field of medical informatics in Iranian universities were studied based on the health informatics framework.

Defended dissertations available to master and doctoral students of medical informatics during 2011 to 2019 in the universities of Tehran, Iran, Tarbiat Modares, Shahid Beheshti, Shiraz, Tabriz and Mashhad were collected. Three medical informatics expertsassigned dissertation titles to a competency and an area of skill based on health informatics competencies framework. The second stage of the study was performed by two other experts (different from the previous three experts). Each dissertation title was assigned to a specific competency anda specific area of skill by the majority opinion method.

The results showed that the most of master and doctoral dissertations were in the field of information science and methods, in which area of skill of data analysis and visualization, whichdecision support systems and informatics for participatory healthwere more than others. AmongPhD students, the area of skillof decision support system and architecture of health information systems weremore popular. PhD students at the universities of Mashhad, Tehran and Shahid Beheshti worked in the field of methods and basic principles of activities more than other areas, information and communication technology, biomedical science and health were not considered.

Results of this research could be helpful forfield researchers in terms of conducting new research in the field and can help to design useful, scientific and effective research projects.

The outbreak of COVID19 has led to a global health and economic crisis. Although no approved treatment exists to date, vaccine prototypes, antiviral medication, preventive measures, andtreatment strategies are studied by scientists and pharmaceutical companies worldwide. The objective of this paper is to examine the COVID19 death cases in South Korea in order to identify the distinct features of the deceased, such as sex, age, underlying medical conditions, which can be targeted when searching for a COVID19 treatment strategy.

Data regarding sex, age, and underlying conditions of the deceased and current cases was obtained from South Korea’s Centers for Disease Control and the Korean Statistical Information Service (data retrieved on May 21, 2020). The data were examined to identify any trends between the parameters using direct statistical analysis. Personal variables of COVID19 patients were studied, such as their sex, age, and preexisting health conditions. The data were analyzed in termsof possible factors leading to COVID19 complications and resulting in patients' deaths.

As of May 21, 2020, 11142 confirmed cases and 264 deaths were reported in South Korea. Sex has not had an impact on the death rate, but it directly correlates with age. No deaths were reported for cases of individuals under 30 years old, and only five deaths were reported between the ages of 30 and 50. Additionally, 98.5 % of victims suffered from an underlying condition. The primary underlying condition in deceased cases was related to circulatory system disorders. The results of the statistical analysis were further used to devise a classification of COVID19 risk factors. It consists of three categories ranging from low to high-risk levels.

Treatment targeted at patients over 60 years old and with circulatory system disorders can reduce the death rate of COVID19 infected patients in South Korea.

The global prevalence of hearing loss is around 5 percent in low to middle-income countries. The main purpose of this study is validating a mobile-based Pure Tone Audiometry (PTA), Dichotic Digit Test (DDT) and Speech in Noise (SIN) hearing tests for hearing loss screening purposes in Persian people comparing with routine audiometry exam results.

This is a single blind randomized controlled trial for comparing a mobile application for hearing screening exams. We designed and standardized PTA, DDT and SIN tests for Persian people and settled them into a specific developed mobile application called “Shenava®”. In the audiology clinic, we will recruit at least 100 healthy adult participants, 50 for the case and 50 for the control group. The first group will pass “Shenava®” and standard test respectively and the other group will pass the tests vice versa to prevent order bias.

The results of the tests performed by “Shenava®” and audiometry exam will be analyzed to ensure the accuracy and validity of the” Shenava®” in comparison with standard audiometric exam results.

Hearing tests are costly even for time and money and need a lot of efforts for audiologists and the patients. By designing a mobile app for hearing tests, we hope to be able to make diagnostic screening easier for hearing loss, and relying on the diagnostic value of this tool, it may encourage the patients to have a better follow up and effective treatment plan.

The capture and integration of structured data from point of care into clinical registries has been a challenge. However, this effort is very important toward a qualitative patient care and research. Collection, organization and interpretation of clinical data can help to improve evidence-based medicine practices. Worksheets data capture are prevalent, but, not flexible, protected, workflow pleasant, and user friendly and do not support the creation of standardized and interoperable data. The aim of this study was to design and implement anelectronic data capture (EDC) instrument to be use in context of cardiovascularelectrophysiologyinvasive procedures.

This descriptive and developmental study conducted in three phases as follows. 1) data standardization according national and international data element templates published by specialized societies; 2) developing of an initial data collection and clinical research workflow 3), establishing of electronic case reports using Research Electronic Data Capture(REDCap) in accordance with the Health Insurance Portability and Accountability Act (HIPAA) privacy rule.

Three case report forms was developed that included demographics, medical history, physical examination, laboratory tests, imaging procedures, electrophysiology (EP) procedures, as well as medications and follow-up information. Data-entry validation criteria have been implementing in electronic data collection instrument to assure validity and precision when data enter in electronic form.

This paper describes the process used to create an EDC application. Data collection applications were successfully develop as an a priori step in a clinical research for assisting data collection and management in a case of cardiovascularEP invasive procedures.

One of the most common types of cancer is breast cancer, which is considered as the second leading cause of death in women in Iran. Due to the fatality of this type of cancer, it is very important to diagnose the disease in the early stages and starting the treatment process. One of the methods to diagnose breast cancer is using mechanical arms (robot manipulator)to touch and measure the force in terms of displacement at the site of the breast touch by the robot. The hardness of the cancer tissue can affect the force diagram in terms of displacement, which can be used as a diagnostic method. The present study was performed to prepare a simulation model of breast soft tissue behavior considering subsurface masses. Then, a proposed classificationsystem was designed to fit it.

In this section, first, the soft tissue behavior of the breast is simulated by considering sub-surface masses. The simulations are performed for a piece of tissue that is in the shape of a rectangular cube, as well as different dimensions of a spherical mass that is located at different depths and coordinates. Using simulation, various force-displacement diagrams have been obtained, based on which a data network.

The displacement force diagram for different modes is obtained using simulation. By giving the resulting diagrams to the trained system, the size and depth of the mass is determined. By comparing the obtained results with the initial model and the actual size and depth of the mass, a very good conformityis observed, which indicates the correct operation of the designed system and the performed simulation process.

The proposed design system was used to diagnose the presence of tumors in tissue with sub-surface mass. The results show a high percentage of this method in diagnosis. However, the accuracy of this method can be greatly increased by increasing the amount of data given to the XCS system for training. On the other hand, instead of simulation data, test data on healthy and unhealthy people can be used for training.

Big data analysis has raised controversies today and attracted many students and academics for its dramatic advantages. The present research aims to investigate the extent to which students in different universities of Mashhad are familiar with this type of analysis.

The present cross-sectional research was conducted on university students of different fields of study in Mashhad, Iran. A questionnaire wasdeveloped based on a review of the related literature in PubMed, Google Scholar, Science Direct and EMBAS. The target questionnaire explored students' knowledge of big data analysis. To this aim, 142 students participated in this research and completed the target questionnaire. Their responses were analyzed descriptively.

The majority age of participants ranged between 21 and 28 years. 59% of these participants were female; 27% had less than a year of work experience; the academic grade of the majority of participants was Master's or Ph.D. 42% enjoyed a desirable knowledge of big data analysis. The largest number of hours of scientific and non-scientific studies belonged to basic science students and more specifically that of pharmacology.

Despite the significance and benefits of big data analysis, students' unfamiliarity with the essentiality of these analyses in industries and research is considerable. It seems that the field or grade of studies has no effect on one's knowledge of big data analysis. Probably, the design of specialized educational courses with this concern can help to promote individuals' knowledge of big data analysis.

Colorectal cancer (CRC) is a development of abnormal cells eitherin the colon or rectum. CRC is the 3rd leading cause of death in 2018. It first arises during pre-cancerous stages called polyps. The detection and removal of a polyp are important to increase the survival rate of the patient. Although the various method of polyp detection is available, colonoscopy remains the standard in detection and removal of polyps. Several studies showed how Artificial Intelligence (AI) used in colonoscopy such as in detecting polyps, assessing physicians and predicting patients witha high risk of CRC. This study will describe the involvement of AI in colonoscopy and its role in improving the survival rates of patients with CRC.

Search for research articles conducted from various resources including PubMed and Google Scholar. The keywords of ‘Artificial Intelligence’ and ‘Colonoscopy’ were used. 6 research articles about the use of AI in colonoscopy and were published in the interval time of 2017 –2019 were selected. Such interval time was chosen due to the recent emergence of AI in colonoscopy.

Studies of AI in colonoscopy showed how it improves medical diagnostic of CRC in several ways, including in improving adenoma detection rate (ADR), finding physicians with a high Adenoma Detection Rate (ADR) and predicting patients with high risk of CRC. However, the use of AI also associated with limitations derived either from the model, datasets or study design.

A Combination of AI and colonoscopy has the potential to improve the diagnostic accuracy and survival rate of patients with CRC. Further study would be required to find the best possible cases for model, datasets and study design in order to overcome the limitations and eventually achieve the best possible results.

Breast cancer is one of the most common cancers and a serious concern for women's health. Providing sufficient information to these patients increases the level of their participation and improves the quality of their care. Therefore, given the high survival rate of this cancer, it is necessary to understand their information needs. The purpose of this study was to evaluate the information needs of women with breast cancer.

The study is a systematic review of the literature. A search of the databases of PubMed, Scopus, Science Direct and ProQuest has been conducted on studies published in English over the period 2010-2017. 2881 articles were retrieved and evaluated for title, abstract and full text and after eliminating duplicate and unrelated cases, 18 articles related to the purpose of the study were selected. The articles were then analyzed using content analysis.

Of the 2881 retrieved articles, 18 studies on the information needs of patients with breast cancer were finally reviewed. According to these studies, most information needs were in the areas of diagnosis and treatment (first rank), daily activities (second rank), disease acceptance and self-image (third rank), personal and family life (fourth rank) and sexual health (fifth rank). The most important information needs in the field of diagnosis and treatment was outcomes and side effects of treatment, in the area of daily activities on the impact of disease on social activity, in the area of disease acceptance and self-image was breast reconstruction, body appearance and need for consultation, in the area of personal life, cancer risk for the family and in the area of sexual health was the effect of cancer on sexual attraction were the most cited needs.

Providing information to patients is one of the most important factors in supporting cancer care and understanding the information needs is the first step in seeking information. Patients with breast cancer are interested in receiving information that will help them understand cancer, make decisions about it, and manage their treatment.

Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems.

The literature search was performed in peer reviewed journals indexed in PubMed by keywords such as medical decision making, clinical decision support systems, Reminder systems, diabetes, interface, interaction, information to 2019. This article review the diabetes decision support systems based on system design metrics (interface, interaction, and information), type and purpose of decision support system.

32 of the 35 articles were decision support systems that provided specific warnings, reminders, a set of physician guidelines, or other recommendations for direct action. The most important decisions of the systems were support for blood glucose control and insulin dose adjustment, as well as 13 warning and reminder articles. Of the 35 articles, there were 21 user interface items (such as simplicity, readability, font sizes and so on), 23 interaction items (such as Fit, use selection tools, facilitate ease of use and so on) and 31 item information items (such as Content guidance, diagnostic support and concise and so on).

This study identified important aspects of designing decision support system,It can be applied not only to diabetic patients but also to other decision support systems. Most decision support systems take into account a number of design criteria; system designers can look at design aspects to improve the efficiency of these systems. Decision support system evaluation models can also be added to the factors under consideration.

Ultrasound images are one of the main contributorsfor evaluating of thyroid nodules. However, reading ultrasound imaging is not easy and strongly depends to doctors’ experiences. Therefore, a CAD system could assist doctors in evaluating thyroid ultrasound images to reduce the impact of subjective experience on the diagnostic results.With the best of our knowledgethere is not anyarticles that actually provide a systematic review of deep learning application in analyzing ultrasound images of thyroid nodules and Hence, a comprehensive review of studies in this field can be useful, therefore the protocol of this systematic Review will be presented to reach this goal.

This protocol includes five stages: research questions definition, search strategy design, study selection, quality assessment and data extraction. We developed search for relevant English languagearticles using the PubMed, Scopus and Science Direct. Inclusion and exclusion criteria were defined and flow diagram is conducted, from 623 studies retrieved, 27 studies were included, after quality assessment data was extracted based on defined categories.

The result of this systematic review can help researchers with comprehensive view and the summary of evidence to present new ideas and further research andrepresent a state of the art in this field.

In this study a protocol was used for doing a systematic review on various deep learning applications in thyroid ultrasound such as feature selection, classification, localization, detection and segmentation. Articles were screened based on the following items: study and patient information, dataset, method, results and comparison method.

Epidemic diseases have always caused considerable physical and financial casualties for governments. By the end of the year 2019, the Covid19 pandemic emerged for the first time in China and rapidly infected the globe.

As information technology plays a significant role in the current healthcare system, the aim of the present study was to conduct a systematic review to determine the role of electronic health in the Covid19crisis.

This review was carried out on articles published from December 2019 until March 17th 2020 by searching keywords and their equivalents in "MeSH" in PubMed, Web of Science, and Scopus databases and Google search engine.

In total, from 72 found articles, 28 were recognized based on their research topic. After imposing inclusion and exclusion criteria, eventually 6 original articles and 8 reports were selected for further analysis. Results showed that reviewed articles had mentioned the effective role of IT in: diagnosing Corona patients, addressing the spread of the disease, providing sufficient education for the public to prevent the disease, and recognizing high-risk areas. Telemedicine, machine learning algorithms,deep learning, Augmented intelligence, neural networks, Global positioning system, and geographical information system have been the most widely used technologies.

It was shown that defeating the Covid19 is impossible without the help of technology. Experiences with the effectivity of using electronic health in controlling and monitoring the prevalence of Covid19 can be used to deal with other pandemic diseases in the future as well; and to avoid possible casualties and economic regressions while rapidly providing solutions for similar critical situations

The significant usage of health websites and their roles as diagnostic and therapeutic tools have increased the importance of evaluating their credibility. Health websites are evaluated using the criteria introduced in the health guidelines; therefore, this study aimed to evaluate the adequacy of these criteria.

In this critical review study, the guidelinesfor "Health Websites Evaluation" and "Website Evaluation in Other Subject Areas" were extracted using sensitive keywords from valid databases, classification, comparison and content analyses were performed using scientific methods designed in this study.

The results indicate that in terms of various components of health websites, the evaluation criteria are not adequate. Note that health website evaluation criteria are designed based on the evaluation criteria of other subject areas. Therefore, the criteria share problems similar to those of the guidelines for other subject areas, and they ignore the evaluation of the specific features of health websites. It is necessary to have reliable and accurate guidelines to evaluate health websites.

One of the most significant advantages of these guidelines is that using software provides an infrastructure for the automatic evaluation of health websites. Thus, the evaluation results will be available to the general public in the form of awebsite ranking.

The use of mobile applications (apps) become widespread and Provide many benefits especially in healthcare. According to theWorld Health Organization, osteoporosis is one of the most common diseases of elderly in the world. Like other chronic conditions, disease self-management can prove fruitful. Using a mobile application for Osteoporosis can improve patient care and self-management by encouraging patients to take a more active role in their health.

This study presents a systematic review of mHealth applications, available on Google Play Store, Bazaar market (as a local market) and also Apple App Store,for both the English and Persian speakers. The assessment criteria, including content, visual aids, reminders, health warnings, social and design of selected apps, were tested during July 2019.

Reviewing the 19 included applications showed thatthe most and least focus of apps content was on exercise with 84% repetition and the osteoporosis fracture that no program addressed this issue separately. Findings on reminders, health warnings, and visual aids were not very encouraging (available in 11%apps). Reminders were more common in English-speaking apps than Persian-speaking ones, and Visual aids, one of the benefits of mobile apps over paper logbooks, were provided only in2 apps. The opportunity to share data in social networks was available in 26% of apps, and in the design section, most of the apps have nosignificantflaws, but 74% of cases did not provide any clear instructions required for the elderly.

The review shows that there are rather few products on offer and the ones that are available display low quality, poor performance, and evidence-based information is also insufficiently used. Further efforts are required to collect data that will support the design of validated evidence-based educational functions for mHealth apps.