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The rapidly developing scene of Healthcare 4.0, has turned into a fundamental concern. This paper dives into the multi-layered domain of medical care network safety, with specific emphasis on the persistent phishing assaults. We examine the motivation for cyberattacks and the challenges involved. In this paper we review the phishing attacks in medical care, talking about preventive measures to handle this danger. By embracing this through methodology, Healthcare organizations can sustain their protections, shield patient information, and maintain the trustworthiness of medical care frameworks, all while encouraging a culture of network safety status in the computerized age. [ABSTRACT FROM AUTHOR]

Timely diagnosis is crucial for the successful treatment of a serious medical condition like brain hemorrhage. Deep learning algorithms have shown great promise in applications for medical image analysis, like the identification of brain hemorrhages. The goal of this study is to assess how well various deep learning algorithms can detect brain hemorrhages. Using a suitable dataset, the study evaluates the computational efficiency, accuracy, sensitivity, and specificity of the selected algorithms. The results demonstrate the potential of deep learning models to assist physicians in identifying this potentially fatal condition and demonstrate how well they can identify brain hemorrhages. The study's findings improve automated brain hemorrhage detection technology, improving patient outcomes and the efficiency of healthcare delivery. EfficientNetB3 typically achieves higher accuracy due to its increased model complexity. Despite its increased complexity, EfficientNetB3 is still more parameter-efficient and computationally efficient than many alternative architectures. EfficientNetB3's strong performance and feature extraction capabilities make it a good choice for transfer learning tasks. Out of all the models implemented in this paper, the proposed model with EfficientNetB3 gave the best accuracy for training as well as validation i.e. 99.95% and 93.29% respectively followed by EfficientNetB2, ResNet, SEResNext and ResNext. [ABSTRACT FROM AUTHOR]

For over a decade, mobile applications are frequently used to make people's daily lives more accessible in all areas, such as commerce, tourism, education, medical care, and others. For implementing these applications, several frameworks exist that can accomplish this task, such as Google's Flutter, Microsoft's Xamarin, Facebook's React Native, Apple's Swift UI, Ionic, Django..etc. However, choosing the best framework to develop an application is not a simple enough decision for developers. This paper presents a detailed analysis of the four most widely used frameworks for implementing mobile applications: React Native, Swift UI, Flutter, and Xamarin. Furthermore, this study also aims to assist developers in choosing the most suitable framework by proposing an application that can recommend the best framework according to the needs entered by answering the questions, which are generally related to the relevant criteria resulting from the analysis of these frameworks. [ABSTRACT FROM AUTHOR]

This paper presents a global approach for implementing Digital Predictive Maintenance (DPM), based on the principles of the digital industry. The proposed approach is applicable at both academic and professional scales, across all sectors and all sizes of organisations. This approach consists of four main phases: the identification of the system or work environment, the identification of the specific targets that justify the adoption of digital maintenance, the choice of appropriate digital technologies for predictive maintenance and the implementation of maintenance X.0 at all levels (i.e., strategic, tactical and/or operational). To validate this approach, the healthcare sector was chosen as a field of application. The uninterrupted operation of facilities and equipment in this sector is paramount. The adoption of maintenance X.0, which has already demonstrated significant benefits in other sectors, would be of great value in the healthcare sector. By applying our approach to the Pneumatic Tube System (PTS) at the Ibn Sina University Hospital Center (actually) in Rabat, Morocco, we have succeeded in introducing a structured and streamlined implementation of DPM in the healthcare sector. [ABSTRACT FROM AUTHOR]

The paper presents issues related to methods used in breast histopathological images tumor changes detection. The problem is connected with sustainable health issues which focus on the improvement of health and better delivery of healthcare, rather than late intervention in disease, with resulting benefits to patients and to the environment on which human health depends, thus serving to provide high quality healthcare. The main purpose of the paper is to develop a model based on deep artificial neural networks for the cancer detection in histopathological breast images. The implementation of the proposed model fits in with the concept of sustainable health through the support of the work of doctors in their decisions, diagnosis and in the reduction of the human workload and time, which can be referred to improve the health services. Data set contains 277524 samples from 163 breast histopathological images taken with the WSI scanner. The model is based on a convolutional neural network in the ResNet-18 architecture, which consists of residual blocks. During the final validation on the test set, the network achieved an accuracy of 93.6% and a 87.3% sensitivity in the detection of cancer tissues. The overall performance of the model is characterized by an F1-score of 0.887. The obtained results indicate the possibility of using the system in clinical conditions. [ABSTRACT FROM AUTHOR]

Healthcare offers a rich palette of potential applications of RFID technology. Healthcare provides a rich palette of possible applications of RFID technology. Besides traditional uses such as tracking medical equipment and devices or access control, healthcare can benefit even more significantly from RFID technology. However, using the RFID technology in healthcare raises various problems of scalability, timely identification of tags, security, privacy, and efficient implementation in practice. That is because such systems contain many tags, operate with private personal data, and must respond promptly in concrete, practical situations to avoid malfunctions (errors in the decision process, traffic congestion, and so on). This paper discusses the fundamental requirements of RFID systems raised by healthcare and the limitations of existing schemes. Then, we propose a new RFID scheme that achieves mutual authentication, strong privacy, and constant-time identification in the HPVP model. The scheme employs a secure symmetric-key encryption scheme, making it very efficient in implementation and physically unclonable functions (PUFs) to protect the secret key against adversaries with corruption capabilities. [ABSTRACT FROM AUTHOR]

Remote patient monitoring is a healthcare delivery model that uses technology to collect and transmit patient data from a remote location to healthcare providers for analysis and treatment. Remote patient monitoring systems rely on a network infrastructure to gather and transmit data from patients to healthcare providers through a network. While these systems become more prevalent, they may also become targets for cyberattacks. This paper deals with the development of a domain ontology to facilitate partial automation to improve the security posture of IoT networks used in remote patient monitoring. For this purpose, it captures the semantics of the concepts and properties of the main security aspects of IoT medical devices. This is complemented by a comprehensive ruleset, evaluated by using SPARQL queries, and automated reasoning over the aggregated knowledge. [ABSTRACT FROM AUTHOR]

Mobile companions are a subset of telemedicine and they have gained increasing attention to enhance healthcare services and communication between patients and care providers. These digital tools allow for the delivery of care services through a variety of channels, increasing access to care and convenience for patients. By gathering and analyzing data from patients, mobile companions also provide care providers with valuable insights into patient health, helping to improve care. Additionally, they can be integrated with electronic medical records, making it easier for healthcare providers to access patient information and track progress over time. The use of mobile companions in healthcare services has many advantages and it brings new opportunities to care providers. However, it also presents many challenges to care providers that aim to adopt it. This paper presents a study where is addresses the opportunities and challenges of multichannel mobile companions in healthcare services provided to patients. As a result of this study, a SWOT analysis is presented and discussed regarding the mobile companion in healthcare services. [ABSTRACT FROM AUTHOR]

The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big Data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, and notifying stakeholders as quickly as possible is still in its infancy. [ABSTRACT FROM AUTHOR]

Coronary heart disease patients must reach a hospital within 90 minutes from the start of myocardial infarction to avoid the risk of death. Current hospital locations are optimized for the overall health care system and may not be the best possible for these patients. For this reason, we have constructed a web tool for optimizing the facility locations to study how well they serve for the coronary heart disease patients. In this paper, we explain how the system has been constructed, how it can be used, and show the results using patient data in Finland. Results show that the number of patients at risk could theoretically be reduced to 1% with optimized locations but at the cost of increasing the average travel time. [ABSTRACT FROM AUTHOR]

Health is one of the most important aspects of life. However, people still could not get proper health services. It is caused by limitation to the used technology in hospitals and limitations to get to the hospital. Internet-of-Things (IoT) as one of the most trending topics nowadays, already giving so many solutions in many ways, for example, in healthcare. IoT is implemented in many healthcare ways, including detecting disease as a precaution, treating a disease as a healing solution, and monitoring a disease as a healing process itself. As part of IoT, wearable devices have been developed to help people get the right treatment for themselves. In this paper, a systematic literature review of wearable IoT devices for healthcare is conducted. The review is started by searching the references by pre-defined keywords and filtering the references based on the relevance and the published year. In this review, we discuss the utilization of wearable devices in handling the problem in the healthcare aspects, including disease detection, monitoring, and curing. Other than that, this review paper also explained how the architecture of wearable devices applied. Finally, the future challenge of research in this field is defined. [ABSTRACT FROM AUTHOR]

There are various diseases arising in the world with new unexpected areas. Each disease has its own symptoms and cures. Some people are capable to fight against these diseases but most of them are a part of the attacks done by the diseases. This paper introduces the Parkinson's disease patient with the idea to work their body normally with the help of artificial life to their neurons by adding a chip known as Electroencephalography (EEG) into their brains that work in the form of wearable devices. This is required for those people who are suffering from this disease and could not able to communicate with another people just because they cannot able to send their signals properly to the brain because they do not get the signals on time because of dead neurons. This paper is going to introduce architecture to work on the brain by using several chips, which help to send signals and become a mediator/ transmitter who sends to do work. [ABSTRACT FROM AUTHOR]

The healthcare sector is constantly facing challenges in ensuring security due to the sophisticated attacks by the threat actor for acquiring sensitive patient data. An attack on the system can pose any potential risk to the business continuity. The increased use of information technology in the modern healthcare system makes medical devices and systems more vulnerable to exploitation and possible cyber-security attacks. This paper proposes a flexible and decentralized cyber-security model based on the integration of multi-agent systems and swarm intelligence for tackling the propagation of attacks inside interconnected healthcare organizations and ensuring the whole healthcare ecosystem's security and resilience. The proposed model is based on the collaboration between the agents with different functions and cognitive capabilities, named primary and supervisor agents. Primary agents are lightweight BDI (Belief-Desire-Intention) agents implementing a minimum set of capabilities for monitoring a specific area of the healthcare system; supervisor agents incorporate an extended version of the BDI reasoning to provide advanced capabilities for securing the overall healthcare system by enabling collective intelligence and overall cyber-security awareness. The preliminary experimental results show that the model is robust and responsive for securing the ecosystem. [ABSTRACT FROM AUTHOR]

Health Data Bank (HDB) as a type of health data service institution has become a promising third-party for mining the value behind the huge amounts of health data and providing valuable health data service to ordinary people. The customers' continuous contribution behavior is the key element of HDB's success and sustainable development. In this paper, we consider creditworthiness value as a measurement of a customer's contribution behavior. The customers' creditworthiness value is computed based on both the Authentic Behavior and Honest Behavior. Then, we propose a scheme Creditworthiness-based Service Differentiation (CBSD), in which the creditworthiness value is used as the guideline for differential service. The proposed scheme provides the right incentives for the customers to share their valid and authentic personal health data. And the simulation results confirm the ability of the proposed scheme to effectively reduce the health data service level provided to the customers with low creditworthiness value. On the other hand, good customers have a high probability of receiving better service. The shortcomings of the proposed scheme and the future work are concluded at the end of the paper. [ABSTRACT FROM AUTHOR]

The evolution of knowledge and information has changed the practices of health services over the years. The use of Information systems (IS), with the underlying Information Technology (IT), plays a vital role in managing medical information and associated resources, always to provide a better quality of health care. IS/IT is increasingly developing globally, with new features and tasks greatly expanded, leaving paper records and voluminous files behind. The ubiquitous information and computing resources increasingly allow the sharing of all types of data between various entities. New IS/IT will have to be thought to correspond to the new types of monitoring and analysis of biomedical data. This work intends to explore the IS/IT in health and its evolution over the last few years. [ABSTRACT FROM AUTHOR]

There is a growing trend towards data mining applications in medicine. Different algorithms have been explored by medical practitioners in an attempt to assist their work; the diagnosis of breast cancer is one of those applications. Machine learning algorithms are of vital importance to many medical problems, they can help to diagnose a disease, to detect its causes, to predict the outcome of a treatment, etc. K-Nearest Neighbors algorithm (KNN) is one of the simplest algorithms; it is widely used in predictive analysis. To optimize its performance and to accelerate its process, this paper proposes a new solution to speed up KNN algorithm based on clustering and attributes filtering. It also includes another improvement based on reliability coefficients which insures a more accurate classification. Thus, the contributions of this paper are three-fold: (i) the clustering of class instances, (ii) the selection of most significant attributes, and (iii) the ponderation of similarities by reliability coefficients. Results of the proposed approach exceeded most known classification techniques with an average f-measure exceeding 94% on the considered breast-cancer Dataset. [ABSTRACT FROM AUTHOR]

Health Information Systems (HIS) helps in coordinating various activities within hospital through effective and efficient services. Malaysian Government began to introduce HIS in 1999 to cover both clinical and non-clinical information systems. Since then hospitals have been facing challenges to achieve the objectives of its introduction. Thus, it is important to understand determinants for successful implementation in order to ensure sustainable HIS. Various determinants render successful implementation, such as human factors, technology factors as well as organizational factors. However, understanding organizational factors becomes important, since the systems runs within the organization itself. The other two factors; human and technology factors have been discussed elsewhere by the authors. Any issues pertaining to the organization if identified earlier will prevent implementation failure. Hence, this paper aims to provide a review of barriers associated with organization factors hindering the sustainability of HIS, and propose strategies required to minimize the issues discussed. Based on our review, this paper highlighted four main challenges from an organization perspective; high adoption cost, top management support, adequate infrastructure and security and privacy issues. [ABSTRACT FROM AUTHOR]

The virtualization and the digital environments are common learning platforms in several different domains, such as in flying airplanes or controlling nuclear power plants. However, virtual reality is no longer expensive special hardware; the basic installations for virtual and augmented reality can be done within household budgets and with common customer products. In this paper, we study the aspect of usability issues in a scenario, where a new virtual learning environment is built to teach correct prevention mechanics and strategies against common physical and cybersecurity threats in healthcare, namely in a hospital. Our proof-of-concept studies indicate that the concept is functional and that on hardware level components exist. The problems are in the usability and user immersion aspects, which are discussed in this paper and further studied in the proposed research setting. [ABSTRACT FROM AUTHOR]

Although Healthcare Analytics (HA) plays a critical role to uncover the link between data and discover novel and actionable information, the level that each analytics influences clinical decision-making is different. This paper aims to highlight the role of each HA in respect to Precision Medicine (PM). Where, PM is defined as a new approach in medical decision-making that takes into account individual differences in genetic, environmental, and lifestyle indicators. Besides, Simon's model in decision-making assesses the performance of clinical decision-making followed by HA. In addition, SOAP framework is discussed as an example under the influence of HA for adopting PM. [ABSTRACT FROM AUTHOR]

Data visualization is key in the Big Data context, enabling different cognitive perspectives over large datasets. These visual perspectives can prompt relevant advantages with regard to healthcare records, because they may contribute to a faster, more understandable, and adequate way to capture patients' health history and overall condition, thus improving healthcare quality. Timelines are well-known visual artifacts that help healthcare professionals to visualize patients' electronic health records (EHR) over a time period. As data stored in EHR tend to quickly grow with each interaction between patient and healthcare system both number and size, traditional linear timelines are can increasingly become more difficult to manage visually, as they often span over different screens. Considering that a holistic analysis is desirable to provide proper and quality health services, data visualization should enable a seamless understanding of patients' health history and overall condition. When dealing with critical episodes - such as an emergency - where time is an important factor, this is even more decisive. Furthermore, traditional timelines do not support multidimensional data representation. This paper presents a new visual model of time-dependent EHR based on radial models. It is capable of simultaneously displaying several data categories that characterize patients' medical history, enabling medical professionals to be aware of different data categories over time in a single display, without the need to scroll between screens. [ABSTRACT FROM AUTHOR]

Discovering workflow patterns in event-logs is important for many organizations to understand and optimize organizational processes. Although numerous algorithms have been proposed in the literature to discover patterns in sequences of symbols, most of them are inadequate to discover patterns in rich event-log data. In this paper, motivated by the analysis of patient pathways in the health domain, a rich type of event logs, called activity-cost event logs, is considered where each event is associated with a cost. The paper formalizes the problem of mining interesting low-cost patterns in these logs by combining novel concepts of penalties (activity costs) and consistency of patterns, with traditional measures of confidence, length, and time. Furthermore, to extract these patterns efficiently from event logs, an algorithm named TWINCLE (Time-WINdow, Cost and LEngth constrained sequential rule mining) is proposed. Experiments carried out on benchmark datasets and real-life healthcare event logs show that proposed algorithm is efficient and can discover interesting patterns. [ABSTRACT FROM AUTHOR]

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the role of the networking choices on health care applications. The variation in the implementation of networks that evolved to Medical Grade Networks are explored for five different medical network applications in terms of the network parameters, performance and success results of these health care applications. The analysis covers basic components such as network design, Cisco IPS 4260 sensor, firewalls, PACS, and Cisco catalyst switch. The discussion about the functions of the components individually and the integration of components is also included. This comparison specifies the features of these five health care applications and helps in deciding the best fit to a heath care facility. This paper contributes to help investors in elevating needs such as network scalability, security management, and day to day services and advantages. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak posed several challenges to healthcare professionals in treating patients: limiting the spread of infection; develop appropriate short-term strategies; and formulating long-term plans. Each of the affected countries had specific capacities before the outbreak began. This potential may have helped reduce the spread of the disease and should help reduce its impact. This paper uses the VMCM method to determine the capacity of health services in European countries. It allowed for a ranking of countries in terms of this potential. This allows the capacity of the health service to be related to the number of infected and deceased people. The purpose of this article is to examine the relationship of infections and mortality on COVID-19 to health care capacity. [ABSTRACT FROM AUTHOR]

Healthcare is a vital human need and always had an important role in our lives, especially in the last year when the pandemic context raised a call for innovative and safe delivery methods. Together with the enlargement in the use of wearable sensors and smartphones, remote healthcare monitoring applications have unfolded at a fast pace, helping with the prevention of spreading diseases as well as to strengthen, help, and assist the health of a patient when a doctor is not physically available. This paper proposes a remote patient monitoring system within an application whose purpose is to bring patients closer to vital medical care. Based on voice interaction and wearable sensors, our application congregates the health data through the Google Fit app from the patient's phone, and then it conducts analysis and sends automatic data alerts for detected anomalies, providing an appealing interface and capabilities to make it engaging to all age groups. Moreover, every patient is under the supervision of a doctor who is constantly monitoring the patient's profile, ensuring assistance through presumptive diagnosis, treatments, or pieces of advice for boosting the quality of lifestyle. [ABSTRACT FROM AUTHOR]

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS Explorer to select an optimal architecture that provide the necessary capabilities to meet key performance attributes (KPAs) in a dynamic, complex healthcare business environment. The SoS Explorer produced an optimal meta-architecture where all but two systems (disease and facility processes) participated with many of the systems having at least four interfaces. The healthcare meta-architecture produced in this study is not a solution to address the challenges of the healthcare enterprise architecture but provides insight on the areas – systems, capabilities, characteristics, and interfaces – to pay attention to where agility is an important attribute and not to be severely compromised. [ABSTRACT FROM AUTHOR]

Accuracy in data classification depends on the dataset used for learning. Now-a-days the most important cause of death for both men and women is due to the Liver Problem. The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data of Liver disorder to develop intelligent medical decision support systems to help the physicians. In this paper, we propose the use decision trees J48, Naive Bayes, ANN, ZeroR, 1BK and VFI algorithm to classify these diseases and compare the effectiveness, correction rate among them. Detection of Liver disease in its early stage is the key of its cure. It leads to better performance of the classification models in terms of their predictive or descriptive accuracy, diminishing of computing time needed to build models as they learn faster, and better understanding of the models. In this paper, a comparative analysis of data classification accuracy using Liver disorder data in different scenarios is presented. The predictive performances of popular classifiers are compared quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improvement of our previous work in this field by developing a heart rate, body temperature and blood pressure monitor system based on new Arduino Mega micro-system device. It offers the advantage of portability over old embedded system (tape-based recording systems). The paper focus on: how we implemented algorithms to analyze heart beat rate signals in real-time, how to fusion data of different sensors mainly here temperature and blood pressure and to transmit the data via radio frequency (Xbee module). Then explain a web server application for healthcare givers to access the data. In addition, it allows doctors to get the heart beat rate file of the patient by email every twenty four hours. It can also be used to control patients or athletic person over a long period. The system reads, stores and analyses the heart beat rate signals, body temperature repetitively in real-time. The hardware and software design are oriented towards a single-chip microcontroller-based system, hence minimizing the size. The important features of this paper are the implementation of extremums and energy in the algorithm to compute patient heart rate and detect any anomaly within the P wav in ECG signal. The first tests were encouraging. [ABSTRACT FROM AUTHOR]

Increasingly, healthcare is provided by a team of care providers from different organizations. Cross-organizational healthcare data sharing is a major issue in interoperable healthcare organizations. Studies have shown that quality of care can be put at risk when patients are transferred from one organization to another, while the need for protecting patient privacy is sometimes an inhibitor to providing information computing technology (ICT) solutions. This paper presents a systematic literature review of cross-organizational healthcare data sharing. The review includes research related to laws and regulations as well as proposed methodological and ICT solutions. Our methodology for querying, filtering and selecting relevant papers from scientific, academic and general repositories is explained and the selected papers are categorized and compared in terms of scope, contributions, and future directions. Based on this analysis, we outline a possible research direction for developing ICT solutions that healthcare providers and regulators would be willing to adopt. Based on our review, we concluded that inspite of the liberal regulations around data sharing among authorized healthcare providers, these organizations are utterly reluctant to collaborate on patient information. Fear of a breech of personal health information, and the shortage of technological facilitators that are compatible with the existing health information systems, are the main causes of the cross-organizational interoperability problems in the healthcare sector. The existing collaborative technologies require considerable initial investments that the current healthcare system is not willing to spend funds on. [ABSTRACT FROM AUTHOR]

Adequate health care facilities are not available for people of low resource settings in India. It is even worse when it comes to the monitoring of the health of pregnant women in villages. In this paper we discuss about the integrated healthcare system which has both vital parameter monitoring and ultrasound scanning, particularly for pregnant women in rural areas. We present in depth about the significance of vital parameter measurements in pregnant women and the system we designed for this purpose and the experimental setup. The measured parameters are stored in SD card by the healthcare workers who carry the portable system to villages. This SD card is taken to the nearest primary health care center for diagnosis of any pre-natal disorders by medical experts. The same is reported back to the pregnant women in case of any treatment is required. The measured parameter values are also provided in this paper. Such a system would help in global effort for reduction in child and mother mortality rates in low resource settings, identification of prenatal disorders and treatment at right time. [ABSTRACT FROM AUTHOR]

Chronic diseases are becoming one of the world's leading causes of death and disability. By 2020, it is predicted to account for almost three quarters of all deaths. A potential approach to monitor patients with chronic diseases is the implementation of home healthcare systems, using new technologies. The Internet of Things (IoT) and the Radio Frequency Identification (RFID) technologies are evolving rapidly with innovative applications, particularly in the healthcare sector. The aim of this paper is to develop a Tele-health system, based on IoT technology, for monitoring elderly individuals suffering from Alzheimer's. This paper describes a working prototype that is able to capture the vital signs and deliver the desired data care remotely for elderly patients staying at home, using wearable ECG wireless sensor. A UHF passive wearable RFID wristband is used to monitor the whereabouts of the elderly. This prototype is successfully tested on a number of patients at the King Fahd University of Petroleum and Minerals (KFUPM) Medical Centre in Saudi Arabia. [ABSTRACT FROM AUTHOR]

The paper aims to present the C3HIS Ontology project, a web based solution for Covid-19 Crisis Health Care Information System. In the health care services, employee skills are a major resource and an essential part of everyday practice and a requirement for all health professions. We aim to prove how using individual profiles based on competencies can make a difference between life and death in times. As the performance assessment is driven by actors competencies we have to put human actors in the core of quality processes of health care services management in COVID-19 crisis. [ABSTRACT FROM AUTHOR]

With the advancement of technology and the limitations of the conventional healthcare system, an improvised framework for healthcare system is needed. This paper presents another cloud based skeleton which relates key segments of any healthcare framework which are patient, doctor, symptom and disease. The paper fundamentally concentrates on how these parts are inter-related and how we can infer suitable data from them. As an implementation, it shows the basic healthcare analyser interface which takes data as input and mines the data by using some of the data mining techniques like clustering. It is convenient for government associations which point at investigating restorative issues and to enhance health conditions of India. [ABSTRACT FROM AUTHOR]

Asthma care services help avoiding frequent attacks to the asthmatics and save time of the expert physicians. In case base reasoning (CBR) previous experience is being used instead of getting lost. This paper presents a knowledge support system for asthma care services with main emphasis on CBR. The framework of the knowledge support system (KSS) has been discussed in the paper. The KSS has three main components user interface, CBR process which calculates similarity index for new cases based on a threshold value and Ontological Process which generates the care plans for the matched cases and saves the care plans in CBR container. This developed system will help in improving the quality of life of asthmatic patients. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has penetrated its roots in almost every domain of life. Smart healthcare is one of the major domains that extensively uses IoT infrastructures and solutions. IoT-based smart healthcare systems have immensely added value to the healthcare domain with the use of wearable and mobile devices. This leads to a substantial use of health data sharing for the improved, accurate, and timely diagnosis. However, smart healthcare systems are highly vulnerable to several security breaches and various malignant attacks, such as privacy leakage, tempering, forgery, etc. Recently, the blockchain technology emerged as a propitious solution against such breaches and challenges. This paper presents an up-to-date survey on different challenges and open issues faced in smart healthcare due to the traditional security measures along with the security requirements of such domains. It also amalgamates the potentials of blockchain technology as a promising security measure, highlights potential challenges in the healthcare domain, and provides an analysis of different blockchain-based security solutions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects huge number of devices using the internet to collect data. The IoT has a variety of application domains, including health care. In this scenario, the patients can use wearable or implanted medical sensors for measuring medical parameters anywhere and anytime. The medical sensors are typically equipped with batteries and have limited energy. In order to prolong the network lifetime, a powerful routing protocols are required. In this paper, we propose PEERP: An Priority-based Energy-Efficient Routing Protocol for Reliable Data Transmission in Healthcare using the IoT. We classify health information into two categories assigned corresponding priorities: Emergency Situation and Vital Health Data. The Emergency Situation is the highest priority data and should be successfully delivered as soon as required. The Vital Health data is the data requested by doctors for continuous monitoring of the patients. The direct communication is used for critical data while Multi-hop communication is used for vital health data delivery. The simulation results show that the routing protocol can improve the energy utilization of the sensor nodes and prolong the network lifetime while guaranteeing the delivery ratio and reliability. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems, enabling continuous health and well-being data collection. As PPG monitoring is relatively simple, non-invasive, and convenient, it is widely used in a variety of wearable devices (e.g., smart bands, smart rings, smartphones) to acquire different vital signs such as heart rate and pulse rate variability. However, the accuracy of such vital signs highly depends on the quality of the signal and the presence of artifacts generated by other resources such as motion. This unreliable performance is unacceptable in health monitoring systems. To tackle this issue, different studies have proposed motion artifacts reduction and signal quality assessment methods. However, they merely focus on improvements in the results and signal quality. Therefore, they are unable to alleviate erroneous decision making due to invalid vital signs extracted from the unreliable PPG signals. In this paper, we propose a novel PPG quality assessment approach for IoT-based health monitoring systems, by which the reliability of the vital signs extracted from PPG quality is determined. Therefore, unreliable data can be discarded to prevent inaccurate decision making and false alarms. Exploiting a Convolutional Neural Networks (CNN) approach, a hypothesis function is created by comparing heart rate in the PPG with corresponding heart rate values extracted from ECG signal. We implement a proof-of-concept IoT-based system to evaluate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

The healthcare industry is progressively involved in adopting new technologies to provide improved quality of care given to patients. The implementation of RFID technology has globally impacted several industries and this revolution has improved the aspects of service delivery in the healthcare industry as well. The RFID technology has the potential to track medical assets and interact with almost any of the medical devices, pharmaceutical materials, IT equipment, or individual patients, deployed in hospitals all over the world. The motivation behind this paper is to investigate the advantages and obstacles to implement RFID technology in the healthcare sector highlighted in the literature. Further, we highlight the most possible methods or technologies to be adapted to overcome the limitations of implementation. [ABSTRACT FROM AUTHOR]

Forecasting risk of diseases helps for preparedness for increased epidemic in health care. Also it helps in planning and monitoring resource needs at all levels of public health. Diseases mapping technique is used to visualization of the geographical distribution which give a general idea about the coverage of disease in Kazakhstan. The official statistical data was used in order to identify the most risky diseases which cover in our country. Developing the disease risk map relies on the method that was used for relative risk estimation. This paper proposes the Holt method that was chosen in order to analyze data and forecasting disease risks. The analysis involved using the number of cases that obtained from the Ministry of Health in Kazakhstan. The outcomes of analysis are displayed through graph and map, and the distribution of high and low risk areas of disease occurrences for all states in Kazakhstan can be identified in the risk map. [ABSTRACT FROM AUTHOR]

The latest MIMC III (Medical Information Mart for Intensive Care III) database has rich information on over 58k patient's medical histories for over 11 years. Based on MIMC III database, this paper presents a study of those patient's primary disease risk prediction modeling, which focuses on assessing future disease risks for an individual who is ready for discharge. We explore a framework that combines regression modeling and deep learning techniques to substantially improve the performance of developed models. Firstly, a regression model will be applied to predicting the length of stay for a patient's future ICU visit. Secondly, deep learning approach will be adopted to assess individual's future visit in terms of time and the primary disease. If the modeling gets adopted in a hospital, the predicted results can be promisingly utilized as a reference for medical professionals and experts to offer effective health care guidance for patients. The proposed framework can also be utilized for developing an innovative tool that helps individuals create and maintain a better healthcare plan over time. [ABSTRACT FROM AUTHOR]

This study was initiated for the benefit of the Tunisian Ministry of Health and the Informatics Centre of the Ministry of Health (CIMSP). The scope of our study is restricted to two university hospitals: HUC Habib Thameur considered as pilot institution and the University Hospital Charles Nicolle. We have identified the "hospitalized patient pathway with additional acts" process as a typical process for conducting this study. The objective of this paper is to propose a quality assurance approach for automating and managing hospital business processes. This is to show how to build a framework for an existing process in order to evaluate it and analyse it. In particular, we are looking into identifying the process's operational limits, its malfunctioning and shortcoming, its degree of ICT integration and finally we are interested in evaluating the process's quality when deployed on different health care sites. It is in this sense that we propose, in this work, to put in place an approach for managing hospital business processes. This approach contributes first to ensuring reliable monitoring of the processes by obtaining an overview of each one. Second, it helps to precisely measure the minimum and maximum duration of execution of the process. Third, we also aim to identify the formal and informal actors involved and validate their commitment and expertise with relation to the implementation of the process. We chose to use Business Process Modelling and Notation (BPMN) [13] and UML for our business processes modelling. Both technologies are recognized in the industrial world and are recommended by a wide audience for formal definition, documentation and understanding of the hospital business process. Finally, the proposed approach can also be exploited for the development of new processes as extension or enhancement to the existing ones. The resulting models will be used as a framework for actors centred process management and evaluation. [ABSTRACT FROM AUTHOR]

Process mining is applied in literature to many case studies in various domains. It represents an explorative procedure, very helpful to the domain experts for analyzing data from multiple perspectives. In this paper, we concentrate on extracting knowledge from event logs related to a health care process that is recorded by a Business Process Management System and mining Key Performance Indicators content associated to process instances data. Thus, this work contributes to the evaluation and the enhancement of the healthcare process using Process mining tool. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the Personal Health Service Framework (PHSF) is proposed which is an open architecture for developing patient-centric health applications and monitoring systems. PHSF uses new advancements of service-oriented architec- ture to provide a baseline for developing health-related systems. PHSF services are used to make self-monitoring and remote monitoring possible, and to link healthcare providers and patients. PHSF is implemented as a combination of web services provided in 7 components in 4 layers. A heart monitoring application built on PHSF is described in this paper. [Copyright &y& Elsevier]

Abstract: Current healthcare systems are no longer sustainable; governments around the world have come to realise that the rapidly growing demand for healthcare cannot be matched by sufficient financial resources, and that improving efficiency and cutting costs is not enough. System-wide changes that include people, products, processes, and services are required. All the relevant stakeholders need to get involved in developing alternative healthcare system models and be able to understand and interact with models of such systems in order to accelerate the adoption and diffusion of changes. This paper presents a systems approach to developing affordable business models based on the specific case example of novel portable ultrasound scanning device – the GE Vscan – for use in primary care. We investigated whole system changes required for adopting Vscan. Systems engineering principles are applied to represent alternative business models around this technology. Object Process Methodology (OPM) is utilized to visualise the conceptual models in order for subject matter experts to successfully participate in the development and validation of alternative healthcare systems. The paper concludes with a discussion on wider implications of adopting systems engineering methods and techniques. [Copyright &y& Elsevier]

Interoperability is accepted as a fundamental necessity for the successful realization of Healthcare Information Systems. It can be achieved by utilizing consistent standards defining syntactic and semantic meaning of the information being exchanged. HL7 FHIR is one of such open standards for Health Information Exchange (HIE). While HL7 FHIR supports Representational State Transfer (REST) architecture and Service-oriented Architecture (SOA) for seamless information exchange, it inherits the inflexibility and complexity associated with the RESTful approach. GraphQL is a query language developed by Facebook that provides promising techniques to overcome these issues. In this paper, we exploit the use of GraphQL and HL7 FHIR for HIE; present an algorithm to map HL7 FHIR resources to a GraphQL schema, and created a prototype implementation of the approach and compare it with a RESTful approach. Our experimental results indicate that the combination of GraphQL and HL7 FHIR-based web APIs for HIE is performant, cost-effective, scalable and flexible to meet web and mobile clients requirements. [ABSTRACT FROM AUTHOR]

Security and Privacy are increasing concerns in nearly all industries. Recently, the use of Electronic Health Records (EHR) has been expanded to reduce the cost and provide efficient healthcare services. Therefore, it is compulsory to ensure that EHR provides the desired levels of security and privacy. In this paper, we introduce a robust hybrid-security protocol in order to increase the level of privacy and reduce the communication cost between parties. It is a secure multifactor remote user protocol that allows the patient to securely exchange her medical information with her doctor over an insecure communication channel. It makes use of three-factor authentication technique in order to protect users' identities (i.e. password, smart device, biometrics.) Biometrics are difficult to forge, steal, or forget. A simulation of the proposed protocol is carried out by the automated formal tool, AVISPA. [ABSTRACT FROM AUTHOR]

Blockchain technology is one of the most important and disruptive technologies in the world. Multiple industries are adopting the blockchain technology to innovate the way they work. One of the industries that are looking to adopt the blockchain is the healthcare industry. In fact, the protection of the private information stored in hospital database is a critical issue. In this paper we propose a method aimed to protect information exchanged in hospital networks, with particular regard to magnetic resonance images. As required from blockchain technology, each host network must validate the transiting data network: we exploit formal equivalence checking to perform this validation, by modeling magnetic resonance images in terms of automata by exploiting radiomic features. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems, enabling continuous health and well-being data collection. As PPG monitoring is relatively simple, non-invasive, and convenient, it is widely used in a variety of wearable devices (e.g., smart bands, smart rings, smartphones) to acquire different vital signs such as heart rate and pulse rate variability. However, the accuracy of such vital signs highly depends on the quality of the signal and the presence of artifacts generated by other resources such as motion. This unreliable performance is unacceptable in health monitoring systems. To tackle this issue, different studies have proposed motion artifacts reduction and signal quality assessment methods. However, they merely focus on improvements in the results and signal quality. Therefore, they are unable to alleviate erroneous decision making due to invalid vital signs extracted from the unreliable PPG signals. In this paper, we propose a novel PPG quality assessment approach for IoT-based health monitoring systems, by which the reliability of the vital signs extracted from PPG quality is determined. Therefore, unreliable data can be discarded to prevent inaccurate decision making and false alarms. Exploiting a Convolutional Neural Networks (CNN) approach, a hypothesis function is created by comparing heart rate in the PPG with corresponding heart rate values extracted from ECG signal. We implement a proof-of-concept IoT-based system to evaluate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract This paper describes the creation of a knowledge base for an intelligent healthcare management system dealing with cases of stroke, myocardial infarction and depression. The main purpose of the knowledge base is to discover and evaluate risk factors and situations which can lead to such diseases, and to enable the formation and support of a preventative measures plan. The present version of the knowledge base is implemented using a heterogeneous semantic network approach and utilizes expert opinions about risk factors and events influencing an individual's health. Data includes genetic predisposition, lifestyle, and external environment. Data is compiled with the aid of questionnaires, mobile devices, case histories and information from social media. Information from social media is analyzed using data and text mining methods with the goal of evaluating the user's condition. All of the data obtained is accumulated in a single database. The knowledge base establishes risk factors, including changes in those factors over the course of time, and circumstances or events which might precipitate the emergence of pathology. Hypotheses are generated about the current state of the user's health, the active risk factors which created conditions for the onset of disease, and circumstances which might produce an increase or decrease in risk factors. Prophylactic measures to reduce those risks are suggested through analysis of the hypotheses generated. Recommendations regarding prophylactic measures are formed with the aid of the knowledge base, the user case-library, and collaborative filtering methods. Recommendations are based on 4P medicine, which re-quires mandatory participation of system users in maintaining their health. [ABSTRACT FROM AUTHOR]

Smartphones have become an integral part in life of users, mainly because over the course of recent years, they have become extremely mainstream, cheap, flexible, and they pack high-end hardware that offers high computational capabilities. Many, if not all of today’s smartphones are equipped with sophisticated sensors which enable smart mobile sensing. The programmable nature of these sensors in the smartphones enable a wide array of possibilities to achieve user-centric or environmental sensing. Even though there have been different approaches proposed to develop a smartphone app, platform, design frameworks, APIs, and even application-specific architectures, there is a lack of generalised referenceable architecture in the literature. In this paper, we propose a generic reference architecture, which can be derived to create more concrete mobile sensing or mobile app architectures. Furthermore, we realise the proposed reference architecture in a healthcare use case, specifically in the context of applying smart mobile sensing to support tinnitus research. [ABSTRACT FROM AUTHOR]