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Background: According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms. Methods: The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14.2) software on the prepared data; and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software. Results: This study was able to predict MI % 75.28 and 77.77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively. Conclusions: Predicting studies aim to support rather than replace clinical judgment. Our prediction models are not sufficiently accurate to supplant decision-making by physicians but have considerable tips about MI risk factors. [ABSTRACT FROM AUTHOR]

Background and Objective. Kinect-based rehabilitation is an effective solution for creating motivation and promoting adherence to rehabilitation programs in stroke patients. The current study was aimed at examining the effects of Kinect-based rehabilitation systems on performance improvement, domains of use, and its limitations for stroke patients. Method. This study was conducted according to Arksey and O'Malley's framework. To investigate the evidence on the effects of Kinect-based rehabilitation, a search was executed in five databases (Web of Science, PubMed, Cochrane Library, Scopus, and IEEE) from 2010 to 2020. Results. Thirty-three articles were finally selected by the inclusion criteria. Most of the studies had been conducted in the US (22%). In terms of the application of Kinect-based rehabilitation for stroke patients, most studies had focused on the rehabilitation of upper extremities (55%), followed by balance (27%). The majority of the studies had developed customized rehabilitation programs (36%) for the rehabilitation of stroke patients. Most of these studies had noted that the simultaneous use of Kinect-based rehabilitation and other physiotherapy methods has a more noticeable effect on performance improvement in patients. Conclusion. The simultaneous application of Kinect-based rehabilitation and other physiotherapy methods has a stronger effect on the performance improvement of stroke patients. Better effects can be achieved by designing Kinect-based rehabilitation programs tailored to the characteristics and abilities of stroke patients. [ABSTRACT FROM AUTHOR]

The present study aimed to systematically search in app stores and intended to carry out content analysis of free Persian mobile health apps in the management of COVID-19 and, ultimately determine the relationship between the popularity and quality of these apps. According to a researcher-made checklist including five axes of ease of use, privacy, data sharing, education, and monitoring, four app markets such as Myket, Bazzar, Google Play and App Store were searched from May 2021 up to now. The findings showed that all selected apps performed well in terms of ease of use and privacy but they needed to be improved in terms of education, monitoring, and data sharing. Also, there was no significant relationship between the popularity and quality of these apps. Owing to the high penetration rate of smartphones in Iran and the low popularity of COVID-19 apps, government, developers, and investors are required to improve the quality of apps and their marketing. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an ecosystem that integrates physical objects, software and hardware to interact with each other. Aging of population, shortage of healthcare resources, and rising medical costs make IoT-based technologies necessary to be tailored to address these challenges in healthcare. This systematic literature review has been conducted to determine the main application area of IoT in healthcare, components of IoT architecture in healthcare, most important technologies in IoT, characteristics of cloud-based architecture, security and interoperability issues in IoT architecture and effects, and challenges of IoT in healthcare. Sixty relevant papers, published between 2000 and 2016, were reviewed and analyzed. This analysis revealed that home healthcare service was one of the main application areas of IoT in healthcare. Cloud-based architecture, by providing great flexibility and scalability, has been deployed in most of the reviewed studies. Communication technologies including wireless fidelity (Wi-Fi), Bluetooth, radio-frequency identification (RFID), ZigBee, and Low-Power Wireless Personal Area Networks (LoWPAN) were frequently used in different IoT models. The studies regarding the security and interoperability issues in IoT architecture in health are still low in number. With respect to the most important effects of IoT in healthcare, these included ability of information exchange, decreasing stay of hospitalization and healthcare costs. The main challenges of IoT in healthcare were security and privacy issues. [ABSTRACT FROM AUTHOR]

Acute appendicitis is considered as one of the most prevalent diseases needing urgent action. Diagnosis of appendicitis is often complicated, and more precision in diagnosis is essential. The aim of this paper was to construct a model to predict acute appendicitis based on pathology reports. The analysis included 181 patients with an early diagnosis of acute appendicitis who had admitted to Shahid Modarres hospital. Two well-known neural network models (Radial Basis Function Network (RBFNs) and Multi-Layer Perceptron (MLP)) and logistic regression model were developed based on 16 attributes related to acute appendicitis diagnosis respectively. Statistical indicators were applied to evaluate the value of the prediction in three models. The predicted sensitivity, specificity, positive predicted value, negative predictive values, and accuracy by using MLP for acute appendicitis were 80%, 97.5%, 92.3%, 93%, and 92.9%, respectively. Maine variables for correct diagnosis of acute appendicitis were leukocytosis, sex and tenderness, and right iliac fossa pain. According to the findings, the MLP model is more likely to predict acute appendicitis than RBFN and logistic regression. Accurate diagnosis of acute appendicitis is considered an essential factor for decreasing mortality rate. MLP based neural network algorithm revealed more sensitivity, specificity, and accuracy in timely diagnosis of acute appendicitis. [ABSTRACT FROM AUTHOR]

Background: Due to the widespread use of mobile technology and the low cost of this technology, implementing a mobile-based self-management system can lead to adherence to the medication regimens and promotion of the health of people living with HIV (PLWH). We aimed to identify requirements of a mobile-based self-management system, and validate them from the perspective of infectious diseases specialists. Method: This is a mixed-methods study that carried out in two main phases. In the first phase, we identified requirements of a mobile-based self-management system for PLWH. In the second phase, identified requirements were validated using a researcher made questionnaire. The statistical population was infectious diseases specialists affiliated to Tehran University of Medical Sciences. The collected data were analyzed using SPSS statistical software (version 19), and descriptive statistics. Results: By full-text review of selected studies, we determined requirements of a mobile-based self-management system in four categories: demographic, clinical, strategically and technical capabilities. According to the findings, 6 data elements for demographic category, 11 data elements for clinical category, 10 items for self-management strategies, and 11 features for technical capabilities were selected. Conclusion: Using the identified preferences, it is possible to design and implement a mobile-based self-management system for HIV-positive people. Developing a mobile-based self-management system is expected to progress the skills of self-management PLWH, improve of medication regimen adherence, and facilitate communication with healthcare providers. [ABSTRACT FROM AUTHOR]

Electronic Health Record (EHR) is one of the most important achievements of information technology in healthcare domain, and if deployed effectively, it can yield predominant results. The aim of this study was a SWOT (strengths, weaknesses, opportunities, and threats) analysis in electronic health record implementation. This is a descriptive, analytical study conducted with the participation of a 90-member work force from Hospitals affiliated to Tehran University of Medical Sciences (TUMS). The data were collected by using a self-structured questionnaire and analyzed by SPSS software. Based on the results, the highest priority in strength analysis was related to timely and quick access to information. However, lack of hardware and infrastructures was the most important weakness. Having the potential to share information between different sectors and access to a variety of health statistics was the significant opportunity of EHR. Finally, the most substantial threats were the lack of strategic planning in the field of electronic health records together with physicians' and other clinical staff's resistance in the use of electronic health records. To facilitate successful adoption of electronic health record, some organizational, technical and resource elements contribute; moreover, the consideration of these factors is essential for HER implementation. [ABSTRACT FROM AUTHOR]

Heart diseases are among the nation's leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including C5.0, Neural Network, Support Vector Machine (SVM), K-Nearest Neighborhood (KNN) and Logistic Regression, developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural Network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients' medical record and developed predicting models using data mining algorithm. The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Based on the association rules, five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. However, since we were not able to determine the rational of this model, and considering the fact that our major goal was to present a model with the highest precision which is also applicable for clinical purposes, the CHAID model with an overall precision of 93.4%was selected as the final model. Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk. [ABSTRACT FROM AUTHOR]