Frontiers in Health Informatics
Volume:10 Issue: 1, 2021

Among sleep-related disorders, Sleep apneahas been under more attention and it’s the most common respiratory disorder in which respirationceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The present study aimed at analyzing vital signals to diagnose Sleep apneausing machine learning algorithms.

This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(noise elimination and moving average algorithm). Next, using thesingular value decompositionmethod, 12 features were extracted for airflow. Finally, to classify data, SVM with quadratic, polynomialand RBF kernels were trained and tested.

After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance.After 10 fold cross validation method for evaluation, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.

The results show that in online applications or applications where the volume and time of calculations and at the same time the accuracy of the result is very important, The disease can be diagnosed with acceptable accuracy using machine learning algorithms.

The rate of mobile phone use among people, especially young people is increasing. The proper use of mobile phone for utilizing the advantages and stay away from its complications is essential. To obtain a model and how to use mobile phone will facilitate planning for preventing complications. So, in this article, questionnaire development with aimed at examining the pattern of mobile phone use among students of Iranianuniversities.

In this study a self-administered questionnaire was designed based on a literature review in PubMed, EMBASE, Science Direct, and Google Scholar database and using 2 rounds of the Delphi method with the presence of 10 experts from different fields.

In the first Delphi round 6 questions were obtained and in the second round 15 questions were confirmed. The mean of Content Validity Ratio and Content Validity Index for the questionnaire was 93.32 and 92.70, respectively. Aquestionnaire was designed and developed according to the purpose.

Usingthe designed questionnaire, the mobile usage pattern among student universities can be examined and solutions can be considered for them.This can prevent further consequence.

Extracting effective rules from medical data with two indicators of accuracy and high interpretability is essential to increase the accuracy and speed of diagnosis by specialists. As a result, the production of medical assistant systems that are able to detect the rules governing the data plays a vital role in early detection of the disease and thus increase the chances of treatment, disease control and maintaining the quality of life of patients.

In this paper, a system of automatic extraction of rules from medical data by a new hybrid method based on fuzzy logic and genetic algorithm is presented. Genetic algorithms are used to automatically generate these rules. The Parkinson UCI dataset including 195 records and 23 variables was used to evaluate the proposed method based on the criteria of interpretability, accuracy, sensitivity and specificity.

The evaluation of the proposed model on the Parkinson's dataset was the accuracy of 84.62%. This accuracy is supported by 4 fuzzy rules with an average rule length of 2 and using 7 linguistic terms extremely low, very low, low, normal, high, very high and extremely high. All fuzzy membership functions that represent each term have the same width.

The proposed method, based on the three criteria of low number of rules, short rule length and symmetric membership functions with equal width for all variables, is quite suitable for automatic production of accurate and compact rules with high interpretability in medical data. . A 90% dimensionality reduction in the experimental evaluation showed that this model could be used to implement real-time systems.

Electronic medical records play an important role in the management of patients. In order to develop cardiovascular electronic medical record systems, determining minimum data set is necessary. This study aimed to determine the essential data elements for electronic cardiovascular medical record systems.

Medical records of patients with cardiovascular diseases and also the literature were reviewed to develop a questionnaire regarding the data elements. 87 cardiovascular specialists and residents as well as 50 nurses working in cardiovascular departments of hospitals affiliated with Iran University of Medical Sciences participated in the study. The data elements with at least 75% of agreement were considered essential for electronic medical records. Data were analyzed using descriptive statistics in SPSS software.

The essential data elements were classified in 29 classes including admission, death, patients’ main complaints, clinical signs, observations, medications, cardiac surgery, risk factors, laboratory and pathology results, consultation, resuscitation, anesthetic, electrocardiography, blood transfusion or blood products, rehabilitation measures, angiography/venography, exercise testing, endoscopy/colonoscopy, medical imaging, echocardiography, nursing interventions, allergies and side effects, therapeutic implantations, cardiac examinations, physical examinations, angina, referrals, social backgrounds and history. Totally, out of 276 data elements, 245 elements were identified as the essential dataelements for electronic cardiovascular medical record systems.

In this study, essential data elements were defined for electronic cardiovascular medical records. Identifying cardiovascular minimum data set will be an effective step towards integrating and improving the management of these patients' information.

The study purpose is to identify the issues and challenges of the p rosthetic and o rthotic (P&O) rehabilitation services in India .

The online search strategy included electronic search engine databases: Google Scholar, PubMed, Google, and Medline along with websites search of world Health Organization, Government of India relevant Ministries, and Rehabilitation Council of India. All relevant articles were included and included in the present st udy. The articles, reports, web materials included in the study after full article review by authors and the keywords prosthetic, o rthotic, r ehabilitation services, India, c hallenges, p olicy , d isability , issues used for the same.

In the future, th e population growth, older population, increased risk of accidents and other complications may result in more prosthetic and orthotic service demand. To improve their access to the environment and income prosthetic and orthotic rehabilitation needs to be e ndorsed at the grassroots level. H ealth care expenses can be reduced through better Prosthetic and Orthotic rehabilitation s ervices . A nd the users ’ quality of life may enhance through improved movement .

Policymakers and the leaders of health, r ehabilitation, and social care providers should facilitate access to appropriate prosthetic and orthotic technology that provides functional and economic independence. As functioning prosthetic and orthotic device will promote social acceptance to the phys ically challenged and improve their quality of life, satisfaction, education, and job opportunities

SARS-CoV-2 has disseminated globally, and COVID-19 has been labeled as a public health emergency of global concern by the World Health Organization. Since 2019-nCoV (2019 new coronavirus) has a long incubation period and high infectivity, e-Health and its subsets in medical informatics have evolved as a suitable solution to enable the continuity of health services delivery. Also, new health care models are required during the COVID-19 pandemic. The proposed systematic review aims to examine and summarize evidence related to medical informatics applications in COVID-19 crisis, as evidence-based approaches.

A research team consisting of experts in the fields of medical informatics and systematic review methods were guided this review according to the Cochrane Handbook and PRISMA reporting guidelines. PubMed and Scopus databases were searched. Eligibility criteria for including studies reviewed was randomized and non-randomized controlled trials published in English language. Articles performed on medical informatics applications in COVID-19 pandemic during 2019-2020 were identified. Two independent reviewers will assess articles eligibility and extract data into a spreadsheet using a structured pilot-tested form. Collected data and evidence will be synthesized using a thematic synthesis approach. The risk of bias will be assessed in all included studies using appropriate tools.

The literature search led to the identification of a total of 1882 and 854 articles retrieved from the PubMed and Scopus databases, respectively. After removing duplicates, 2716 articles remained and underwent title and abstract screening process. The results of this review are expected to be served as a basis for assisting researchers, decision makers, medical informatics specialists, politicians, and others in developing, implementing, and evaluating IT-based tools and interventions to help medical staff in combating and eradicating COVID-19.

This systematic review is the first comprehensive evaluation of MI methods aiming to control and manage covid-19 pandemic. This study highlights applications of medical informatics in pandemic situation and will help future researchers to take the most advantage of using MI in the health system.

Hepatitis C virus is the leading cause of mortality from liver disease. Also, diagnosis systems are usable tools for better disease control and management.The aim of this study was to design an HCV disease prediction system and classify its severity based on data mining methods.

This is an applied research that uses the hepatitis C dataset in the UCI library. The study was conducted in four steps including data preprocessing, data mining, evaluation and system design. In data pre-processing, data balancing techniques were performed. Then, three data mining algorithms (multi-layer perceptron, Bayesian network, and decision tree) were implemented and 10-fold cross-validation method was used to evaluate data mining algorithms. Finally, user interface was designed in MATLAB programming language (version 2016) based on the best algorithm.

The results showed that the over-sampling method improved the performance measures of data mining algorithms in disease prediction, so that in the O-dataset the accuracy of the best method (random forest) was 99.9%. Also, the random forest for the O-dataset had the best performance measures in term of sensitivity, accuracy and f-measure (99.9%) and the 100% specificity amount.

Considering that the presented approach has performed better than all suggested methods in previous studies, the proposed system in this study can be used well in HCV diagnosing and determining its severity.

Different communication services with varying bandwidth are used to send information in the form of telemedicine technology. Bandwidth management, as defined in telemedicine technology, refers to using the desirable communication services according to the type of transaction and the information size to be transferred. Selection of communication services must be in such a way to result in minimum latency in the process of sending information and maintaining maximum cost-effectiveness.

This is an applied research which was conducted in 2019 by questionnaire survey amongst 60 participants, specialized in health information technology and medical informatics, who are working in hospitals and educational institutions of Tehran. Likert rating scale was used to quantify the research questions. Finally, by analyzing each weighted average, this study revealed the desirable communication services that correspond to the required transactions for deployment of telemedicine.

Transfer of multimediainformation, using synchronized teleconferencing via primary low bandwidth technologies,had the lowest number average (0.96) and transmission of hybrid data (combination of picture, text, multimedia templates in synchronized or asynchronized modes) via Asymmetric Digital Subscriber Line (ADSL)technology had the highest average (4.96).

Selection of communication services,with regard to its convergence with theinformation sizeand the type of their application, plays a significant role in controlling network traffic and preventing latency in the process of sending information in the context of telemedicine technology. High bandwidth communication services should be used for those telemedicine systems,which are offeringservices to many users, as well as those in which real-time transmission of information is essential. It needs to be pointed out that with regard to the cost-effectiveness of sending information, it is necessary to use low-cost services with low bandwidth for transfer oflightweightinformationas well as for asynchronous applications in which latency in the process of information transfer is not detrimental.

Data for prevention and tracking of disease should begin prior to the outbreak.The bottleneck for early detecting outbreaks is data. The data are collected from different points of care and aggregated, then analyzed centrally to warn us about what is happening.However, this current pandemic has not utilized data for prevention and tracking in a meaningful way. We believe the prevention problem is the data problem and it should be addressed to prevent the future pandemicsin an effective way.

Growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. In addition, association of various overweight factors and breast cancer has not been extensively explored. The goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set.

Several studies show that a significantly stronger association is obvious between overweight and higherbreast cancerincidence, but the role of some overweight factors such as BMI, insulin-resistance, Homeostasis Model Assessment (HOMA), Leptin, adiponectin, glucose and MCP.1 is still debatable, So for experiment of research work several clinical and biochemical overweight factors, including age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment (HOMA), Leptin, Adiponectin, Resistin and Monocyte chemo attractant protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, Apriori, Hierarchicalclusteringalgorithm (HCM) were applied using orange version 3.22 as an open source data mining tool.

The Apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, HOMA and leptin are two items often simultaneously were seen for BC patients that leads to cancer progression. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of <Adiponectin, MCP.1> has the highest effect on seperation of clusters. In addition HCM was carried out and classifiedBC patients into 1-32 clusters to So this research apply HCM algorithm. We carried out hierarchical clustering with average linkage without purning and classified BC patients into 1–32 clusters in order to identify BC patients with similar charestrictics.

These finding provide the employed algorithms in this study can be helpful to our aim.

Seizure is a transient phenomenon with genesis in excessive abnormal or synchronous neuronal electrical activity in the brain, while epilepsy is defined as a brain dysfunction characterized by persistent predisposition to generate seizures. The identification of epileptogenic electroencephalographic patterns can be performed using machine learning.The present study aimed to develop a transfer learning based classifier able to detect epileptic seizures in images generated from electroencephalographic data graphic representation.

We used the Epileptic Seizure Recognition Data Set,which consists of 500 brain activity records for 23.6 seconds comprising 23 chunks of 178 data points, and transformed the resulting 11500 instances into images by graphically plotting its data points. Those images were then splittedin training and test set and used to build and assess, respectively, a transfer learning-based deep neural network, which classified the images according the presence or absence of epileptic seizures.

The model achieved 100% accuracy, sensitivity and specificity, with AUC-score of 1.0, demonstrating the great potential of transfer learning for the analysis of graphically represented electroencephalographic data.

It is opportune to raise new studies involving transfer learning for the analysis of signal data, with the aim of improving, disseminating and validating its use for daily clinical practice.

As the COVID-19 pandemic spreads around the world, governments are seeking solutions to mitigate contagion. These initiatives use technology to control the movement of infected people, particularly from mobile phone monitoring. This manuscript intends in the first stage to carry out a brief overview of these initiatives at the global level. After that, it aims to identify the main challenges posed by these apps in monitoring the individual's health data and explore good practices that may prove fundamental for the uptake of these solutions on a large-scale.

This study employs a qualitative methodology to perform a review on technological solutions for screening and geolocation of COVID-19 infected people. Five countries have been selected considering the different approaches in the implementation of these technological solutions. Four fundamental principles for the evaluation of these solutions such as consent, proportionality, transparency, and security were considered. Through this approach, it has become feasible to identify and discuss the challenges and best practices in the implementation of these solutions.

Although these applications publicly assume that they guarantee people's fundamental rights this information becomes insufficient. It is necessary to evaluate these solutions specifically considering fundamental principles such as consent, proportionality, transparency, and security. The existence of an independent body authority that can audit these solutions is relevant, besides the voluntary adherence to these applications.

The way these solutions are implemented and imposed in these countries is quite different. The absence of mechanisms to measure how data is stored and processed raise concerns among people. Accordingly, the large-scale adoption of these tools requires that people's fundamental rights be duly considered froma multidimensional perspective

According to global statistics, stroke is known as the main health problem in the world. Many clinical and molecular research, which are stored in the different repository with the various format have been conducted in the area of stroke domain. The heterogeneity of these research data does not make a comprehensive view of the disease. Recently, translational research has been developed to fill the gap between these studies. In this study, we used the integrative disease modeling method to model the underlying mechanism of stroke risk factors.

This study was conducted in three steps: data gathering, model construction, and mechanism discovery. First, using semantic and information retrieval tools, we extracted the cause and effect statement from the literature to create the mechanistic model, and the validated molecular data to evaluate the constructed model. Then, the integrative model was created and evaluated. Finally, we used Gene Set Enrichment Analysis to identify the main biological process and signaling pathways in the mechanism of the disease.

In the evidence-based information retrieval from the literature, 1837 causal statement was extracted. The initial network was created with 648 nodes (molecular, clinical, and environmental factors) and 1837 edges (interactions). Also, 51 genes/proteins and nine single nucleotide polymorphisms were matched with data in the model. The inflammatory response, response to lipid, regulation of body fluid levels, and regulation of response to stress, complement and coagulation cascades, and PPAR signaling pathway were the main biological processes and signaling pathways enriched in GSEA analysis.

This study showed thatwe can identify the underlying mechanism of stroke risk factors and use a proper strategy to prevent it, using Integrative Disease Modeling.

In the crisis of Corona virus epidemic, the education of undergraduate nursing students has faced many challenges. Face-to-face training is closed and academics are forced to adapt to distance learning. As a result, nursing students may encounter unfamiliar technologies. This article provides examples of technologies used to provide nursing education.

This research is an applied study that was conducted in 2021 by systematic review method. Using the keywords of e-learning, nursing students, Corona virus and Covid-19 were found in various databases such as PubMed, ScienceDirect and Google scholar in the last two years (2019-2020) due to the prevalence of coronary heart disease. According to the inclusion and exclusion criteria of the study, which was the time period and English language of the articles, related articles were included in the study, their information was extracted and entered in the checklist, and finally the collected data were analyzed using descriptive statistics. Incoming and outgoing articles are reported using the PRISMA flowchart.

In this study, 275 English articles were found. After reviewing the articles, 12 articles were included in the study. Studies conducted from 2019 to 2020 in the field of e-learning for nursing students. In these articles, eight educational methods for educating nursing students have been introduced, and the use of cloud space for loading course materials and the use of software that can be installed on smartphones were mentioned in 83% of the articles. The use of Zoom, SoundCloud, Microsoft Teams and Blackboard Collaborate software for loading webinars was suggested in 91% ofthe articles.

According to the findings of this article, it was found that most studies have been conducted to introduce applied software to provide practical courses, which due to the practical nature of many nursing courses, the software have a high capability.

Diabetes is a disease associated with high levels of glucose in the blood. Diabetesmake many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The aim of this study is to diagnose Diabetes with machine learning techniques.

The datasets of the article contain several medical predictor variables and one target variable,Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age. The main objective of the machine learning models is to classify of the diabetes disease.

Six classifiers have been also adapted and compared their performance based on accuracy, F1-score, recall, precision and AUC. And Finally, Adaboost has the most accuracy 83%.

In this paper a performance comparison of different classifier models for classifying diagnosis is done. The models considered for comparison are logistic regression, Decision Tree, support vector machine (SVM), xgboost, Random Forest and Adaboost. Finally, in the comparison flow, Adaboost, Logistic Regression, SVMand Random Forest, usually has had a high amount; and their amounts has little differences normally.

Advances in mobile health have led to numerous relevant studies in diagnosis, treatment, and controlling of various diseases. One of the criteria indicating the quality of the previously published studies is the number of citations. Therefore, investigating the features of highly cited articles and identifying the most frequently used mobile technological interventions can affect future research ideas. This study aimed at identifying 100 highly cited interventional studies on mobile health, type of used mobile technologies, and effect of these technologies in various diseases in top-cited articles.

The database employed in this study was the Web of Science, which without limitations wasanalysed in April 2020 to identify 100 highly cited interventional studies in the field of mobile health. The identified studies were classified based on the number of citations, year of publication, country of the first author, type of disease, and use of mobile technology.

A great majority of the studies in the field of interventional mobile health focused on obesity (n=18), addiction (n=15), diabetes (n=13) and mental health disorders (n=12), respectively. Many studies employed mobile technologies to promote lifestyle (weight loss and increased physical activity) (n=20), disease controls (n=20), and treatment adherence (n=18). The mean number of citations per study was 146±97. The most cited study was in the category of viral disease treatment adherence (n=703), and the most cited articles were published in 2012.

Among the reviewed 100 studies, many of the interventional studies regarding mobile health focused on obesity, addiction, diabetes and mental health disorders. Promotion of lifestyle, disease controls, and treatment adherence were effects of mobile technologies in top-cited articles. Text messaging service was used as intervention in most of the studies. Thus, future studies may focus on the use of various mobile applications on different diseases’ prevention, control, and treatment.

The SARS Coronavirus-2 (SARS-CoV-2) pandemic has become a global epidemic that has increased the scientific community's concern about developing and finding a counteraction against this lethal virus. So far, hundreds of thousands of people have been infected by the pandemic due to contamination and spread. This research was therefore carried out to develop potential epitope-based vaccines against the SARS-CoV-2 virus using reverse vaccinology and immunoinformatics approaches.

The material of SARS-COV2 Surface Glycoprotein (S), Membrane Protein (M), and Envelope Protein (E) were downloaded from the NCBI protein database. Each protein has undergone epitopes prediction for MHC class I epitopes, MHC class II epitopes, and Antibody of B-cell epitopes. Selected epitopes according to their antigenicity score was tested for allergenicity and toxicity. Finally, filtered epitopes were used in vaccine construction. Vaccines were constructed, docked against Toll-like receptor 3, and undergone Molecular Dynamic simulation. The vaccine with the best scores, subjected to immune stimulation and cloning design.

Three vaccines were constructed, COVac-1, COVac-2, and COVac-3. Each vaccine was submitted into a deep investigation. The molecular dynamic simulation determines the stability and physical movement of protein atoms and molecules. After Molecular dynamics simulation, COVac-1 was having the best scores. COVac-1 was then subjected to immune simulation analysis to insure the stimulation of innate and adaptive immunity. After passing the immune simulation, COVac-1 was integrated into E.coli pET-30b plasmid using in silico cloning design.

Viral pandemics are threatened to face humanity today. The best scenario to fight against any pandemic is utilizing the full power of computational biology, especially immune-informatics, to design and discover in silico new vaccines or molecules that may stimulate the immune system against the invader pathogens or inhibit the pathogen life cycle.

Introduction:Lung cancer is the second most commoncancer for men and women. Using natural language processing to automatically extract information from text, lead to decrease labor of manual extraction from large volume of text material and save time. The aim of this study is to systematically review of studies which reviewed NLP methods in diagnosing and staging lung cancer.Material and Methods:PubMed, Scopus, Web of science, Embase was searched for English language articles that reported diagnosing and staging methods in lung cancer Using NLP until DEC 2019. Two reviewers independently assessed original papers to determine eligibility for inclusion in the review.Results: Of 231 studies, 7 studies were included. Three studies developed a NLP algorithm to scan radiology notes and determine the presence or absence of nodules to identify patients with incident lung nodules for treatment or follow-up. Two studies used NLP to transform the report text, including identification of UMLS terms and detection of negated findings to classifying reports, also one of them used an SVM-based text classification system for staging lung cancer patients. All studies reported various performance measures based on the difference between combinations of methods.Most of studies have reported sensitivity and specificity ofthe NLP algorithm for identifying the presence of lung nodules.Conclusion:Evaluation of studies in diagnosing and staging methods in lung cancer using NLP shows there is a number of studies on diagnosing lung cancer but there are a few works on stagingthat. In some studies, combination of methods was considered and NLP isolated was not sufficient for capturing satisfying results. There are potentials to improve studies by adding other data sources, further refinement and subsequent validation.

There are various applications and health information systems which have been developed to promote the effective retrieval of patient information, statistics, research, and education. Therefore, there is a need to design them in consistency with scientific principles of usability. To this end, the usability of hospital information sub-systems employed at the hospitals of Ahvaz were compared using heuristic evaluation method.The objective of the study was to assess the usability of hospital information system according to heuristic evaluation.

Six trained evaluators, independently determined the ADT subsystem, HIM subsystem, and NIS according to Nielsen’s 10 Heuristic Principles. Since more than half of the hospitals (about 54%) employed Sib application, no specific sampling method was used. After combining the usability problems, the average severity ratings of the problems were calculated, and then the subsystems were compared.

The number of the usability problems of the ADT informationsubsystem, HIM subsystem, and NIS were 40, 39, and 37, respectively. After merging the problems, the features of “user control and freedom” with 20 cases and “flexibility and efficiency of use” with six cases had the highest and the lowest inconsistencies with usability principles. The average severity ratings of the problems also varied between 1.7 and 3.

Heuristic evaluation method is regarded as one of the approaches appropriate to identify usability problems in health information systems. Thus, it is advisable to utilize this method to modify the design of the systems and to improve their efficiency before their implementation in order to increase user satisfaction.

Hepatitis C is a chronic infection caused by hepatitis c virus -a blood borne virus. Therefore, the infection occurs through exposure to small quantities of blood. It has been estimated by World Health Organization (WHO) to have affected 71million people worldwide. This infection costs individual, groups and government a lot because no vaccine has been gotten yet for the treatment. This disease is likely to continue to affect more people because it’s long asymptotic phase which makes its early detection not feasible.

In this study, we have presented machine learning models to automatically classify the diagnosis test of hepatitis and also ranked the test features in order to know how they contribute to the classification which help in decision making process by the health care industry. The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset.

The models were evaluated based on metrics such as Matthews correlation coefficient, F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. We found that using SMOTE techniques helped raise performance of the predictive models. Also, random forest (RF) had the best performance based on Matthews correlation coefficient (0.99), F-measure (0.99), Precision-Recall curve (1.00) and Receiver Operating Characteristic Area Under Curve (0.99).

This discovery has the potential to impact on clinical practice, when health workers aim at classifying diagnosis result of disease at its early stage.

Given, widespread COVID-19 across the world a comprehensive literature review can be used to forecast COVID-19 peak in the countries. The present protocol study aimed to explore epidemic peak prediction models in communicable diseases.

This protocol study was conducted based on Arksey and O'Malley's. This framework encompasses purpose and hypothesis, modeling, model achievements aspects. A systematic search of English in PubMed was conducted to identify relevant studies. In the pilot step, two reviewers independently extracted the variables from 10 eligible studies to develop a primary list of variables and a data extraction form. In the second step, all eligible studies were assessed by researchers. In the third step, two data extraction forms were combined. The data were extracted and categories were created based on frequency. Qualitative and quantitative methods were used to synthesize the extracted data.

The current study were focused on forecasting the epidemic peak time that is a worlds’ concern issue. The results of current scoping review on prediction methods for epidemic disease can provide foundational knowledge, and have important value for the prediction model studies of COVID-19.

Our findings will help researchers by a summary of evidence to present new ideas and further research especially for studies werefocused on COVID-19. Our results can improve the understanding of prediction methods for COVID-19.

Information seeking behavior of the affected populations during a pandemic is believed to significantly influence the way the population manages the epidemic and curb its spread. This study sought to identify and profile reliable sources of information that the residents of Migori and Homa-Bay Counties in Kenya could use to curb the spread of COVID-19 virus and enhance efficient management of risks associated with the pandemic.

A survey method was used in which quantitative data was generated through administration of online questionnaires to 250 participants which were purposively selected. Data was analyzed using SPSS version 20 and results presented in form of tables and graphs. A survey method was used in which quantitative data was generated through administration of online questionnaires to 250 participants which were purposively selected. Data was analyzed using SPSS version 20 and results presented in form of tables and graphs.

The study found out that the top 3 frequently used sources information was television, official government press releases and social media. The study also found out that there was high correlation between the sources that were frequently used and their perceived credibility with a coefficient of R2=0.8426. English was the most preferred language for use in sharing information. Further, the respondents preferred to receive information based on how to protect self and the family.

To counter the spread of misinformation, the study has therefore profiled information sources and recommended that television, official government press releases and properly managed social media should be used to package and share relevant COVID-19 information to reach the target population.

Diabetes is a chronic disease associated with abnormalhigh levels of glucose in the blood. Diabetesmakemany kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The goal of this study is to Predict hospital readmission of Diabetic patients with machine learningtechniques.

The data used in the study are data obtained from the UCI machine learning repository about diabetic patients. The dataset used contains 100,000 instances and it include 55 features from 130 hospitals in the United States for10 years.

This articlegets results from the final stages of evaluation. In this evaluation process, compared the performance of decision tree, random forest, Xgboost, k-neighbors, Adaboost and deep neural network withaccuracy.

Thenumber of selected features by PCA-based feature selection method improve the predictive performance based on accuracy of deep learning and most machine learning models for predicting readmission. The improvement of machine learning models depended on the specific choice of the prediction model, number of selected features, and “k” for k-fold validation.

Cutaneous Leishmaniasis is a neglected tropical disease caused by a parasite. The most common presumptive diagnostic tool for this disease is the visual examination of the associated skin lesions by medical experts. Here, a mobile application was developed to aid this pre - diagno sis using an automatic image recognition software based on a convolutional neural network model.

A total of 2022 images of cutaneous diseases taken from 2012 to 2018 were used for training. Then, in 2019, machine learning techniques w ere tested to develop an automatic classification model. Also, a mobile application was developed and tested against specialized human experts to compare its performance.

Transfer learning using the VGG19 model resulted in a 93% accuracy of the cl assification model. Moreover, on average, the automatic model performance on a randomly selected skin image sample revealed a 99% accuracy while, the ensemble prediction of seven human medical expert’s accuracy was 83%.

Mobile skin monitoring applications are crucial developments for democratizing health access, especially for neglected tropical diseases. Our results revealed that the image recognition software outperforms human medical experts and can alert possible pati ents. Future developments of the mobile application will focus on health monitoring of Cutaneous Leishmaniasis patients via community leaders and aiming at the promotion of treatment adherence

Irrational prescription of antibiotics has become a major global concern, and not only does it have health-related consequences, but it also affects countries’ overall economy. Based on reports and studies, antibiotics are prescribed in approximately 50% of prescriptions in Iran which candemand by patients as a major cause. It is anticipated that increasing the awareness and understanding of both physicians and patients, regarding the antibiotic use and resistance, could play an important role in the rational prescription of antibiotic medications. In this study, we will examine the effect of informing patients via text message right before their appointment on the proportion of prescribed antibiotic medications.

In this study, a randomized control trial (RCT) will be conducted. The setting in which the study will be carry out, consists of 64 physicians (29 general physician and 35 specialist). Unit of randomization will be physicians based on the proportion of their prescriptions that include antibiotic medications (PIA). The first arm of the study is the intervention group, which consists of the patients receiving three text messages in the clinic’s waiting rooms. The second arm is the control group, and consists of the patients who won’t be receiving any text messages. The content of the text messages focuses on the consequences of self-medication with antibiotics, the fact that the use of antibiotics is not an option for curing viral diseases including cold, and it also asks the patients not to demand antibiotics bytrusting their physicians.

The main variable that will be measured is the proportion of prescriptions that include antibiotic medications.

This trial will be the first one to evaluate the patients’ role in the proportion of prescriptions that include antibiotic medications. It is hypothesized that patients’ demand for antibiotic medication is one of the main causes of irrational antibiotic prescription by physicians.

To improve the first step of the hospitalization procedure, appropriate interaction must be established between users and the admission, discharge, and transfer system. The aim of this study was to evaluate the usability of the ADT systemin some of selected Iranian non-teaching hospitals.

This study was cross-sectional research that has evaluated the usability of a selected ADT system using the think-aloud method by 11 medical record administrators. Users were asked to follow the provided scenario, then share and elaborate on what they saw, thought about, did, felt, and decided during their interaction with the system. Users' feedbacks were collected and organized into four main categories for further processing.

To evaluate the usability of an ADT system, four routine scenario tasks were followed by users and only 45.45% of them could implement all tasks. Overall, 36 independent problems were identified. All problems were related to the data entry categories that accounted for the largest share. The most important problems were related to the issues regarding "date of birth" field in this category which deals with the outpatient admission process.

The study of the usability testing method indicatedthat the ADT subsystem of non-teaching hospital has many problems in interact with real users with the system. It showed that more than half of the users could not completely and successfully perform the entire real-world scenario tasks. Furthermore, the most usability problems were found in data entry categories.

The infections with the Nipah virus (NiV ) are highly infectious and may lead to severe febrile encephalitis. High mortality rates in southeastern Asia, including Bengal, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, the Philippines, Thailand, and India, have been reported in NiV outbreaks. Considering the high risk of an epidemic, NiV was declared a priority pathogen b y the World Health Organization . However, for the treatment of this infection, there is no effective therapy or approved FDA medicines. RNA - dependent polymerase RNA (RdRp) plays an important role in viral replication among the nine well - known proteins of NiV.

Fourteen antiviral molecules have been computerized for NiV RNA - dependent RNA polymerase and demonstrated a potential inhibi tion effect against coronavirus (NiV - RdRp). A multi - step molecular docking process, followed by extensive analyzes of molecular binding interactions, binding energy estimates, synthetic accessibility assessments, and toxicity tests.

Molecular doc king analysis reveals that Uprifosbuvir is the most suitable inhibitor for RdRp of Nipah Virus regarding the binding affinity and binding in the target cavity. Although, such studies need clinical confirmation.

The role of anti - viral molecules as a ligand against RNA - dependent RNA polymerase is critical important in the current era. Computational tools such as molecular docking has proven its power in the analysis of molecules interaction. Our analysis reveals the Uprifosbuvir might be a candida te RdRp inhibitor. This study should further investigate the properties of the already identified anti - viral molecules followed by a pharmacological investigation of these in - silico findings in suitable models

Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications. Most of them are not treated because of the complex and costly and time - c onsuming polysomnography (PSG) diagnostic procedure.

This descriptive - analytical study was performed on 50 patients referred to sleep clinic of Imam Khomeini Hospital in Tehran, Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time - frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.

The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.

The proposed system, which has high performance capability in addition t o increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

Diabetes disease is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the ins ulin that is produced. Designing an automated system for regulating blood glucose in patients with diabetes is a solution that researchers have been paying close attention to in recent years. Therefore, safety is the minimum requirement for safety - critical systems such as the artificial pancreas. The present study introduces a safe, robust, performance - guaranteed optimal controller that can safely regulate blood glucose in the disturbance .

In this section, first, regulate blood glucose levels in simulation studies is evaluated. For this purpose, a dynamic model is used. The model includes a virtual patient, an insulin pump, and a continuous blood glucose level sensor. The virtual patient model represents the dynamics of insulin - glucose, carbohydrate - glucose, and exercise - glucose .

The need to not reset the controller parameters for patients in each category is one of the suggested controller's benefits. However, the PID controller needs to reset the parameters for each group of p atients, the predictive control method requires the estimated model of the patient, and its performance is different on different days because the insulin - glucose dynamics for an individual changes day by day.

Taking into account different sens itivities of body tissue to insulin, the results of evaluating the controller for two different groups of patients have shown that the controller is resistant to day - to - day changes in patients who may experience changes in insulin sensitivity, even with st ress or medication and will not lose its optimal function. Based on the simulation results, the proposed controller can reduce the external disturbances' effect, whose amplitude is to a good extent within the body's physiological range

Earthquakes, one of the most important natural disasters of the earth, have always caused irreparable damage to human settlements in short time. One of the most important issues that we face after an earthquake is the transfer of earthquake victims and traumatized civilians to safe places and medical centers. The city of Mashhad with different geographical faults and the presence of enormous religious, cultural, historical and industrial assets make Mashhad the most dangerous city in terms of earthquake hazards. In the 9th district of this city, the existence of worn - out structures along the narrow passages and the importance to save time in providing relief proves the need to locate temporary accommodation centers and allocate the injured to safe places.

The process of optimizing the accommodation of people includes 2 main steps 1) Determining candidate locations for temporary accommodation 2) Optimal allocation of population blocks (origin). The weight of criteria was calculated using the pairwise comparison method. Then suitable places for deployment are identified. Criterion in the form of giving a specific wei ght to each, in order to prepare the final map, is of importance. Accordingly, the opinions of experts in the field of urban crisis management have been utilized. Subsequently, using GAMS software and 7 super - innovative algorithms such as SA, PSO, ICA, ACO , ABC, FA, LAFA.

The average process time and cost of 7 algorithms out of ten random problems with 1000 repetitions, and an average of 10 execution times show, that the 3 algorithms ACO, ABC and LAFA have lower cost and process time than the other meta - innovative algorithms. Therefore, we use the above three algorithms to solve the case study

Finally, the LAFA optimization algorithm had obtained a better and more appropriate result due to its execution time and cost being less than the other two algorithms.

Information dashboards were one of the best ways to manage Covid disease. The concept of information dashboards and their important benefits are explained in the present study.

Early detection breast cancer Causes it most c urable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. D ata mining techniques have a growing reputation in the medical field because of high p redictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.

The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) a nd the remaining 3617 patients (47.6%) without breast cancers (benign). Support v ector m achine, m ulti - l ayer p erceptron , d ecision t ree, K nearest neighbor, r andom f orest, n aïve Bayes ian models were developed using 20 fields (risk factor) of the database because d atabase feature was restrictions. Used 10 - fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.

N aïve Bayes ian and artificial neural network are better models for the prediction of b reast cancer risks. N aïve Bayes ian had accuracy of 93%, s pecificity of 93.32%, s ensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, s pecificity of 91.98%, s ensitivity of 92.69%, and ROC of 0.8 .

Strangely the different a rtificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy r isk considers. The significant prognostic components affecting r isk pace of bosom disease distinguished in this investigation, which were approved by r isk, are helpful and could be converted into choice help devices in the clinical area.

The epidemic of viral respiratory diseases in the last 20 years has affected many people around the world. In these situations, telemedicine may reduce unnecessary contacts and the risk of exposure to infection. This study aimed to review the papers performed to manage viral respiratory disease epidemics using telemedicine.

The PubMed and Scopus databases were searched in May 2020 for this systematic review study. Data were extracted from the final included papers based o n the author, country, type of epidemic, telemedicine modality, telecommunication method, objective, participants, clinical outcome, cost, and satisfaction. Descriptive statistics were used to analyze data.

From 365 retrieved papers, 18 papers wer e included. Most of the papers were done in the US and China (67%). Half of the papers were done during the COVID - 19 pandemic. Real - time modality was used in 78% of the papers. The telecommunication method in half of the papers was internet - based. Patients ’ management and treatment was the main objective of the six papers. In 81% of the teleconsultation papers, the consultation was performed between patients and healthcare providers. The clinical outcome of all papers showed that telemedicine was successful in the management of viral respiratory disease epidemics. Cost and satisfaction outcomes were considered in a few papers.

There is considerable evidence to show that telemedicine is a useful and convenient method to manage and control viral r espiratory disease epidemics. Therefore, countries should pay special attention to telemedicine to control the current pandemic and future epidemics and use it extensively.

COVID - 19 virus variants are rapidly spreading across the world. Successful tracing of contacts and early isolation after the onset of symptoms are vital, because, in this period, patients can infect other people having contact with them before isolation. One method for identifying, tracing, screening, and monitoring the potential patients can be self - reporting of information by these individuals. The present letter suggested importance of recording self - reported information in the management of COVID - 19 virus variants using technolog y - based devices

Integrati on of rapidly expanding high - throughput omics technologies and electronic health record (EHR) has created an unprecedented advantage in terms of acquiring routine healthcare data to accelerate genetic discovery. In this regard, EHR can also provide several important advantages to omics research if the integration challenges are well handled. The main purpose of the present study was to review available and published knowledge in the related literature and then to classify and discuss stakeholders’ requireme nts in this domain.

At first, a broad electronic search of all available literature in English was conducted on the topic through a search in the databases of Medline, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), Scopus, and Cochrane. Then, stakeholders’ requirements were tabulated, and finally, a word cloud was generated and analyzed to achieve functional and non - functional cases.

A total of 81 articles were included in the given analysis. Integra tion requirements also consisted of nine functional cases including a uniform approach to the interpretation of genetic tests, standardized terminologies and ontologies, structured data entry as much as possible, an integrated online patient portal, multip le data source handling, machine - readable storing and reporting, research - oriented requirements, pharmacogenomics decision support capabilities, and phenotyping algorithms and knowledge base. Besides, there were three non - functional cases comprised of inte roperability of multiple systems, ethical, legal, security factor, and big data computations.

The main challenges in this way could also have semantic and technical themes. Therefore, system developers could guarantee the success of systems by overcoming the given challenges.

Advances and increasing technology adoption in the field of health have made it possible to implement tools such as clinical dashboards to assist nursing staff in providing better, more effec tive and safer care. The aim of this study was to investigate the role of clinical dashboards in providing nursing care.

This was a review study. For this purpose, the keywords Nursing, Nursing care, Clinical Dashboard, Health Dashboa rd, Evaluation were searched in the database of PubMed, Google Scholar, science direct. Criteria for inclusion in this study were studies that examined the role of clinical dashboards in the field of nursing and were published between 1990 and 2020. The ne cessary information was extracted using a researcher - made checklist and analyzed and reported in a descriptive manner.

A total of 2749 articles were retrieved. After reviewing by title, abstract and keywords, 7 studies that had appropriate content validity were selected for the present study. The intensive care unit had the highest frequency of dashboard use in nursing processes (n=3, 42%). The findings of this study showed that improving the quality of care, reducing medical errors and increasing patient safety are the most important benefits of using clinical dashboards in the field of nursing. Improving nurses' awareness of important patient issues and supporting clinical decisions were next in line.

Clinical dashboards in the field o f nursing care can reduce errors and possible negligence in the treatment by integration patient information and providing a comprehensive visual view of important patient information and as a suitable tool for evidence - based clinical and nursing decision support.

Breast cancer rates have been increasing worldwide, particularly among young women, suggesting important interactions between genes and health behaviors. At t he same time, mobile technology, including smartphones applications (apps), has emerged as a new tool for delivering healthcare and health - related services. In 2019, there were nearly 670 publicly available breast cancer apps designed to provide disease an d treatment information, to manage disease, and raise overall awareness.

In order to conduct a review, the Medline, Scopus and PubMed database s w ere searched with the keywords "mobile health", "mobile health in electronics health", "b reast cancer and electronic health"," mobile health and breast cancer"," mobile health and breast cancer qualify life" and their equivalent. O ut o f the 60 articles found, after the depth of the criteria Inclusion in the study, 16 articles remained, which w ere reviewed and given using PRISMA 2020 checklist . SPSS software v. 22 was used for description analysis.

A total of 16 articles met the inclusion criteria and were included in this review. All studies have determined the positive impact of applic ations on cancer detection and clinical health outcomes. In addition, more than half of mobile applications have multiple functions, such as providing information, planning and education. Furthermore, most studies examining patient satisfaction and quality improvement have shown that users of healthcare applications are significantly more satisfied with life, leading to better quality.

The evidence of the studies which are included in this systematic review is currently limited , but it suggests that the mobile apps might be an acceptable information source for women with breast cancer and lead to improved patient well - being.

Inadequate anesthetic, including under or over dosage, may lead to intraoperative awareness or prolonged recovery. Fuzzy expert systems can assist anesthesiologist to manage drug dosage in a right manner. Designing a fuzzy rule - based expert system to deter mine the Propofol anesthetic drug dosage was the main objective of this study.

This is a retrospective study. Fuzzy IF - THEN rules were defined based on evidences and experts’ linguistic rules for Propofol dose determination. Fuzzy too lbox in MATLAB software was used to design the system. Validation of system conducted with calculation of mean absolute error (MAE) and root mean squared error (RMSE). Also, difference mean between actual and predicted doses was tested with paired t - test i n SPSS V.26 software. Data from 50 ENT (ears, nose, and throat) surgeries were used to validate the fuzzy system.

MAE for induction and maintenance doses was 0.128 and 1.95 respectively. RMSE for induction and maintenance doses was 0.228 and 3.383 respectively. Based on paired t - test result, there was no significant correlation between actual and predicted values (P>0.05).

Obtained value from test and validation of system demonstrated a high performance and satisfying accuracy of the sy stem. Therefore, this expert system can be used as a decision support system to determine initial dosage of anesthetic drugs. It can also be used for anesthesia students to learn drug administration.

Diagnostic point - of - care (POC) tests are considered as an approach to ease the diagnosis of diseases, deliver quicker patient care, and improve patient safety. The aim of this stu dy was to review the diagnostic POC tests with an approach to data management.

In this review study, PubMed, Science Direct, Google Scholar, Scopus, and Wolters Kluwer databases were searched from 2000 to 2020 using a combination of r elated keywords. A total of 96 articles were retrieved of which 48 articles considered as relevant. The content of these articles were then analyzed with respect to the aim of the study. The inclusion criteria for the articles were: 1) they focused the PO C test; 2) addressed data management aspects; 3) written in English. Articles that only addressed the POC tests from a clinical or technical perspective and with no indication of data management were excluded.

Rapid and timely collection and proc essing of test results, the capability of exchanging test results, and capabilities such as documentation and data quality control play a significant role in reducing the average length of stay in hospital, planning, decision - making, organizing, controllin g clinical and managerial activities, and achieving the efficiency of services provided.

In addition to applying diagnostic POC tests technologies, medical settings should have necessary approaches for managing data generated by these technolog ies to improve better use of data in service delivery.

Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.

In this paper, a clinical decision support system for the diagnosis of gestational diabetes is pres ented by combining artificial neural network and meta - heuristic algorithm. In this study, four meta - innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Th en these four algorithms were compared with each other. The data set contains 768 records and 8 dependent variables. This data set has 200 missing records, so the number of study records was reduced to 568 records.

The data were divided into two sets of training and testing by 10 - Fold method. Then, all four algorithms of neural - genetic network, ant - neural colony network, neural network - particle Swarm optimization and neural network - cuckoo search on the data The trainings were performed and then ev aluated by the test set. And the accuracy of 95.02 was obtained. Also, the final output of the algorithm was examined with two similar tasks and it was shown that the proposed model worked better.

In this study showed that the combination of t wo neural network and genetic algorithms can provide a suitable predictive model for disease diagnosis.

Introduct ion: Cancer is an incurable disease that affects people regardless of age, sex, race and social, economic and cultural status. Most cancer patients are treated with a combination of treatments based on the type of tumor, the extent of the disease, and thei r physical condition. Self - management programs empower people to deal with illness and improve their quality of life. Telemedicine in the form of mobile applications, websites and social networks is one of the effective tools to achieve this goal. The aim of this study was to investigate the impact of telemedicine and social media on self - care of cancer patients .

English related articles were searched based on keywords in the title and abstract using PubMed and Scopus databases (from 1 963 to December 2020). Keywords included telemedicine, social networking, self - care and m - health. Inclusion criteria included all studies published in English that examined the impact of telemedicine and social media on cancer patients' self - care. Review a rticles and non - intervention articles were excluded from the study .

A total of 516 articles were selected by title. After reviewing the abstract, 80 articles remained to be reviewed. After evaluating the full text of these articles, 9 eligible art icles were selected for final review. In terms of the type of cancer among these studies, prostate cancer had the largest share (33%). In line with the main purpose of this study, in 7 articles (77.8%) telemedicine had a significant positive effect on self - care of cancer patients and increased self - care. In one article (11.1%) this effect was negative and reduced self - care. In 1 article (11.1%) no effect was observed .

According to the results of the present study, it seems that web - based interve ntions and mobile health in most articles have been effective in increasing patients' self - care. However, due to the increasing number of cancers as well as the increasing use of telemedicine in the field of chronic diseases and cancer, the need for furthe r studies is felt in this field .

Our aim in this study was to summarize information on the use of intelligent models for predicti ng and diagnosing the Coronavirus disease 2019 (COVID - 19) to help early and timely diagnosis of the disease.

A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Sco pus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies wer e critically reviewed for risk of bias using p rediction model r isk of b ias a ssessment t ool.

We gathered 1650 articles through database searches. After the full - text assessment 31 articles were included. Neural n etworks and d eep n eural n etwork vari ants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area u nder the c urve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0 .84 to 0 .99, and AUC in external validation of diagnostic models varied from 0 .73 to 0 .94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of partic ipants and lack of external validation.

Diagnostic and prognostic models for COVID - 19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID - 19 related prediction models is needed

Health companies need investment for development. Due to the high risk of their activities, it is very difficult to attract investment for this field, but this lack of financial resources leads to the failure of these companies, so providing a model for predicting profits and losses in companies is very important and functional.

In this study, a combination of two logistic regression al gorithms and differential analysis were used to design a profit and loss forecasting model. Also, the information of 20 companies in the field of health was used to evaluate the proposed model. 10 profitable companies and 10 loss - making companies were sele cted and for each company, nine variables independent of the financial information of these companies were collected.

The designed prediction model was implemented on the data in this study. To do this, the data were divided into two sets: trainin g and testing. The prediction model was implemented on training data and evaluated by test data and reached 99.65% sensitivity, 94.75% specificity and 96.28% accuracy. The proposed model was then compared with the methods of decision tree C4.5, Bayesian, s upport vector machine, nearest neighborhood and multilayer neural network and it was found to have a better output.

In this study, it was found that the risk in the field of health investment can be reduced, so the profit and loss situation of health companies can be predicted with appropriate accuracy. It was also found that the combination of logistic regression and differential analysis algorithms can increase the accuracy of the prediction model.

One way to reduce medication errors in the cardiovascular settings is to electronically prescribe medication through the computerized physician order entry system (CPOE). Improper design and non - compliance with users' needs are obstacles to implementing this system. Therefore, it is necessary to consider the standard mini mum data set (MDS) of this system in order to meet the basic needs of its users. The aim of this study was to introduce MDS in the cardiovascular CPOE drug system to standardize data items as well as to facilitate data sharing and integration with other sy stems.

This study was a survey study conducted in 1399 in Iran. The study population was all cardiologists in Iran. The data collection tool was a researcher - made questionnaire consisting of 33 questions. Data were analyzed in SPSS - 24 using descriptive statistics.

A total of 31 cardiologists participated in this study. The participants identified 19 of the 25 drug data items as essential for drug MDS. Five data items (Medication name, Medication dosage, Medication frequency, Medication start date and Patient medication history) were considered essential by more than 90% of the participants.

The results of this study identified drug MDS for the cardiovascular CPOE system. The results of this study can be a model for CPOE system designers to develop new systems or upgrade existing systems.

On March 20, 2020, the World Health Organization (WHO) announced the spread of SARS - CoV - 2 infection in most countries worldwide as a pandemic. COVID - 19 is mainly disseminated through hu man - to - human transmission route via direct contact and respiratory droplets. Telehealth and/or telemedicine technologies are beneficial methods that could be employed to deal with pandemic situation of communicable infections. The purpose of this proposed systematic review study is to sum up the functionalities, applications, and technologies of telemedicine during COVID - 19 outbreak.

This review will be carried out in accordance with the Cochrane Handbook and PRISMA (Preferred Reportin g Items for Systematic Reviews and Meta - Analyses) reporting guidelines. PubMed and Scopus databases were searched for related articles. Randomized and non - randomized controlled trials published in English in scientific journals were identified to be evalua ted for eligibility. Articles conducted on telemedicine services (TMS) during COVID - 19 outbreak (2019 - 2020) were identified to be evaluated.

The literature search for related articles in PubMed and Scopus databases led to the identification and re trieval of a total of 1118 and 485 articles, respectively. After eliminating duplicate articles, title and abstract screening process was performed for the remaining 1440 articles. The current study findings are anticipated to be used as a guide by researc hers, decision makers, and managers to design, implement, and assess TMS during COVID - 19 crisis.

As far as we know, this systematic review is conducted to comprehensively evaluate TM methods and technologies developed with the aim of controllin g and managing COVID - 19 pandemic. This study highlights important applications of telemedicine in pandemic conditions, which could be employed by future health systems in controlling and managing communicable infections when an outbreak occurs.

One of the well - known problems related to the information quality is the information incompleteness in health information systems. The purpose of this study was to investigate the completeness rate of patients’ information recorded in the hospital information system, sending information from which to Iranian electronic health record system (SEPAS) seemed to be unsuccessful.

This study was conducted in six hospitals affiliated with Kerman University of Medical Sciences (KUMS) in Iran. In this study, 882 records which had failed to be sent from three hospital information systems to SEPAS were reviewed and data were collected using a checklist. Data were analyzed using descriptive and inferential statistics with S PSS 18.

A total of 18758 demographic and clinical information elements were examined. The rate of completeness was 55%. The highest completeness rate of demographic information was related to name, surname, gender, nationality, date of birth, fath er's name, marital status, place of residence, telephone number (79 - 100%), and in clinical information it was related to the final diagnosis (74%). The completeness rate of some information elements was significantly different among the hospitals (p <0.05) . The completeness rate of information communicated to the Iranian national electronic health record was at a moderate level.

This study showed that completeness rate is different among hospitals using the same hospital information system. The results of this study can help the health policymakers and developers of the national electronic health record in developing countries to improve completeness rate and also information quality in health information systems

Telenursing during the COVID - 19 pandemic with an emphasis on self - care is an effective approach to help patients, hospitals, as well as community. Despite the many challenges and benefits, tele - nu rsing can be used to help COVID 19 patients with new technologies. This study aimed to explore the challenges and opportunities of using tele - nursing in the COVID 19 Pandemic for helping patients with COVID 19 to gain better care.

An integrative review was conducted from December, 2019 to January, 2021. Databases of PubMed, MEDLINE, Web of Science, Scopus, CINHAL, and google scholar were searched on the concept of tele - nursing by using the following keywords, of COVID - 19, Coronavirus, Telenursing, nurse roles, technology, Pandemics and Internet. DaA ta were analyzed according to Broome method.

The main results of tele - nursing in COVID 19 includes: implementation problems, insurance coverage, prevention of nurses, the problem of continuing care, and changing the roles of nurses’ infections, development of nursing knowledge, the emergence of technological care providing, emphasis on patient independence and transmission cycle control.

Tele - nursing, this, despite the ch allenges, has many benefits that are effective in the current situation and effective, and reliable measure, through effective planning and implementation, help control COVID - 19.

Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually . Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning m odels for disease prediction.

Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.

The algorithm perf ormance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.

Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying alg orithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classifi cation . As a result of evaluation, better performance was observed in both Decision Tree and Logistic Re gression models.