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Background: A medical device is any instrument, apparatus, implement, machine, appliance, software, material, which is intended material, to be utilized, either alone or in combination, for medical purpose. These devices should work precisely and the maintenance program of them has also a key role to achieve this goal. Many of the maintenance programs have not considered important functional parameters such as equipment type, risk factors, and expert opinion. Objective: The purpose of this study is to present a novel fuzzy method for medical device risk assessment. The obtained values for risk could be used to prioritize maintenance operations by considering allocation budget.Material and Methods: This experimental study aims to make a new application of Ordered-Weighted Average operator in aggregation of different parameters for calculating Risk Priority Number. This model is a fuzzy multi-criteria decision making approach based on risk maintenance framework for medical device prioritization. Results: A limited budget is one of the barrier in medical centers. The suggested framework presents a simple and reliable method to choose the best maintenance strategy for each kind of medical device by considering budget limitation. Based on obtained results from numerical model, defibrillators and surgical suction have respectively the highest and the lowest priority in mentioned example. Conclusion: Risk prioritization of medical devices is valuable because the medical centers can prioritize maintenance operations and thereby to establish preference of maintenance strategy. Implementation of our proposed maintenance program has many effective results in medical center budgets.

Today, due to the massive amount of information, NoSQL databases are used to compute complex data. These databases are used to store semi-structured and unstructured data for big data management. In this paper, fuzzy queries are executed by users on the information stored in the Neo4j graph database, which show results in defuzzy manner. To evaluate the efficiency of the proposed method, we considered a database of manufactured company, two price and quality fuzzy variable were considered and used to define and execute the fuzzy queries that the results can verify the performance of the proposed method. Additionally, a counselor (who has information about the data) defines fuzzy terms, membership functions, and fuzzy rules table. The most important process in this method is to find the center of gravity in order to defuzzify the final result. So, an algorithm has been implemented for this purpose by C # programming language. The greatest query time is due to find center of gravity. The evaluation results show that the query time increasing by using the proposed approach than the query time by using Cypher language, is acceptable due to the complexity of fuzzy concepts; therefore, the proposed solution will be suitable for using ambiguous and fuzzy queries in large databases.

One of the modern paradigms to develop a system is object oriented analysis and design. In this paradigm, there are several objects and each object plays some specific roles. There is a sequence of activities to develop an analysis model. In the first step, we work in developing an initial use case model. Then in the second step, they identify a number of concepts and build a glossary of participating objects. Identifying attributes of objects (and classes) is one of the most important steps in the object-oriented paradigm. This paper proposes a method to identify attributes of objects and verify them. The method is also concerned itself with classifying and eliminating the incorrect attributes of objects. Then the method is evaluated in a large application, a Control Command Police System. After that, several guidelines on attributes of objects, based on the practical experience obtained from the evaluation, are provided.

Nowadays, medical implants are widely used around the world. Medical implant device safety is closely related to public health and national security. With all the governmental considerations, issues such as selling or distributing an unapproved, defective, or expired implant and record fraud are still widespread in medical device supply chains. Blockchain, the next generation of information technology, is designed to provide better trust and transparency in information management systems. This paper presents the development of a “Medical Implant blockchain” system based on blockchain technology. This system can provide good traceability and transparency throughout the value chains of implants in a national supply chain. This study provides evidence for the necessity of a blockchain system development for the medical implants supply chain to increase trust and equity between various stakeholders. It shows that legal organizations can create a safe and reliable trading environment by using an information management system based on blockchain technology. Implementing a blockchain medical implants management system enables verified information transmission and immutably data storage on the network. This system could provide valuable information with higher transparency by minimizing the risk of data corruption for all the beneficiaries, such as legal authorities, medical centers, specialists, and patients.

Nowadays, medical devices are widely used around the world. In every country, the safety of these devices is inextricably related to public health and security. Despite all these sensitivities and legal oversight, issues such as lack of transparency in the medical device supply chain, lack of real feedback from incidents and clinical reports related to these devices, and consequently the failure to update risk management systems and improve the quality of these devices, resulting in increased safety risks for users/patients. Therefore, the development and implementation of an effective post-market surveillance and management system for medical devices is essential.In this article, in addition to a review of some applications of blockchain technology in the health sector, the requirements and structural framework for an information and management system for post-market surveillance of medical devices based on this technology were also presented. This system was developed with the approach of tracking medical devices to enhance transparency, provide rapid information dissemination, and reduce the rate of adverse incidents, and clinical reports related to these devices throughout their supply chain. A blockchain-based post-market surveillance system for medical devices could provide all stakeholders with valuable and real recommendations and information in the form of smart contracts by ensuring complete informational transparency throughout the supply chain. Blockchain technology could be used by regulatory and oversight organizations to effectively monitor and manage the performance of medical devices, ensuring greater transparency, and thereby guaranteeing enhanced safety and quality for stakeholders.

Mobile social networks facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With increasing the number of users on social networks, the large amount of shared information and the dissemination of created information cause some problems such as rumor propagation and access to incorrect information. The most powerful tools for validity of received information is based upon the trust value which is assigned by the others. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. In addition, there may be various communities on social networks and all users will not be directly connected to each other, which leads to a more complex process of calculating trust. In this research, using user characteristics in social networks, a fuzzy classification approach is proposed, and the trust is computed between users in a class using a computational model. Also trust is gained between users who are not directly connected, using the combination, transition and aggregation processes. By comparing the results, it can be seen that the proposed method recognizes trustworthy people with high precision.

Health and health services are two inseparable parts of one's life. Each person has had different needs for health services at least several times during their life cycle and would resolve them with available facilities. Regarding the high popularity of social networks in the last two decades, one of the tools that can provide many opportunities for people in the health field is social networking. In this research, we introduce a health social network which focuses on users or patients’ association with doctors and a variety of health services. In order to improve this network’s performance, we suggest a recommender system that can offer users a doctor, a special expertise in order to ask medical consultation, or an article, based on their needs. We have used heterogeneous information networks for modeling the health social network. These networks cover several types of objects, such as physicians, patients and consultation, and also several types of relationships, such as requesting or answering a consultation. For the recommender model, we use each user’s implicit feedback which they register on the network, according to the methods provided by the heterogeneous information networks. Bayesian Personalized Ranking is used in recommender model’s learning algorithm. This algorithm is a combination of ranking scores method and the foresaid learning algorithm. In the end, we will show how to use this social network and the recommender system, by applying the suggested method on our dataset.