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There is an increasing interest in the use of stated preference methods to understand individuals' preferences for health and healthcare. There is also a growing interest in understanding heterogeneity in individuals' preferences. Consequently, stated preference studies frequently consider models that capture either or both observed and unobserved preference heterogeneity. A popular preliminary investigation into heterogeneity involves split-sample analysis to compare subgroups' preferences e.g., comparing patients with clinicians, or older patients with younger. In fixed-effects models, the constant variables (the individuals' characteristics) remain stable across choice sets and therefore only enter the choice model when interacted with various attributes and/or levels. However, subgroups of respondents may differ on multiple variables that may not easily be implemented with interaction terms because of complexity and a lack of power thus only one, or a few, variables are typically taken into account in each subgroup model. This paper presents an overview of methods for matching and balancing samples to weight individuals with different characteristics in subgroup analysis and an example of how unweighted comparisons may produce erroneous conclusions regarding the degree of heterogeneity in preferences. We illustrate the issue with synthetic and empirical datasets to explore methods for matching subgroups before and within simple choice models. Our results show that entropy balancing and propensity score matching could be more appropriate than analyses using unmatched preference data when heterogeneity is driven by multiple factors. The paper concludes with a discussion of when matching and weighting may and may not be useful in healthcare decision-making. • Subgroup analysis to investigate preference heterogeneity is commonplace. • Direct subgroup comparisons can be biased if other characteristics also influence preferences. • Matching or weighting techniques can be used to ensure balance of multiple characteristics across subgroups. • Whether these techniques are required depends on the research question of interest. [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]

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]

Medical equipment such as infant incubator, infusion pump, CT scanner, etc. should be maintained properly to meet adequate standards of reliability in healthcare services. This paper proposes a new comprehensive risk-based prioritization framework for selecting the best maintenance strategy. The framework encompasses three steps. In the first step, a fuzzy failure modes and effects analysis (FFMEA) method is applied by considering several risk assessment factors. In the second step, seven miscellaneous dimensions such as use-related hazards, age, and utilization are applied to consider all aspects of hazards and risks in prioritization of medical devices. Finally, a simple method is introduced in the third step in order to find the most suitable maintenance strategy for each device according to the scores produced by the previous steps. A numerical example illustrates the proposed approach and shows that, through the method introduced in this paper, managers can easily classify medical devices for maintenance activities according to their criticality scores. Implementation of this framework could increase the availability of high risk machines in healthcare industries. Moreover, this framework can be applied in other critical industries such as aviation by modifying some criteria and dimensions. [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]

Summary Introduction The monitoring of pre-antiretroviral therapy (pre-ART) is a key indicator of HIV quality of care. This study investigated the association of an electronic medical record system (EMR) with adherence to pre-ART guidelines in rural HIV clinics in Kenya. Methods A retrospective study was carried out to assess the quality of pre-ART care using three indicators: (1) the performance of a baseline CD4 test, (2) time from enrollment in care to first CD4 test, and (3) time from baseline CD4 to second CD4 test. A comparison of these indicators was made pre and post the introduction of an EMR system in 17 rural HIV clinics. Results A total of 18 523 patients were receiving pre-ART care, of whom 38.8% in the paper group had had at least one CD4 test compared to 53.4% in the EMR group ( p < 0.001). The adjusted odds of performing a CD4 test in clinics using an EMR was 1.59 (95% confidence interval 1.49–1.69). The median time from enrolment into HIV care to first CD4 test was 1.40 months (interquartile range (IQR) 0.47–4.87) for paper vs. 0.93 months (IQR 0.43–3.37) for EMR. The median time from baseline to first CD4 follow-up was 7.5 months (IQR 5.97–10.73) for paper and 6.53 months (IQR 5.57–7.87) for EMR. Conclusion The use of the EMR system was associated with better compliance to HIV guidelines for pre-ART care. EMRs have a potential positive impact on quality of care for HIV patients in resource-constrained settings. [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]

• To present a very recent systematic survey about online heart monitoring systems. • To highlight the number of remote technologies enabled to aid cardiac patient and family. • The IoT-based techniques found are identified and its advances and open issues are discussed. • This survey is interesting both for researchers and health professionals. The Internet of Health Things promotes personalized and higher standards of care. Its application is diverse and attracts the attention of a substantial section of the scientific community. This approach has also been applied by people looking to enhance quality of life by using this technology. In this paper, we perform a survey that aims to present and analyze the advances of the latest studies based on medical care and assisted environment. We focus on articles for online monitoring, detection, and support of the diagnosis of cardiovascular diseases. Our research covers published manuscripts in scientific journals and recognized conferences since the year 2015. Also, we present a reference model based on the evaluation of the resources used from the selected studies. Finally, our proposal aims to help future enthusiasts to discover and enumerate the required factors for the development of a prototype for online heart monitoring purposes. [ABSTRACT FROM AUTHOR]

Medical waste management (MWM) is a challenging issue for medical facility managers owing to its potential to prevent environmental and health risks. Many treatment technologies (TTs) have been used for MWM, and selecting appropriate TTs is a complex multi-criteria decision-making (MCDM) problem. In this paper, we propose a novel integrated MCDM method based on the best-worst method (BWM) and the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method under the Fermatean fuzzy environment to evaluate, rank, and select treatment technologies for medical waste management. First, novel distance measure and entropy measure for Fermatean fuzzy sets are developed, and their properties are examined. Second, different treatment technologies are evaluated by experts using Fermatean fuzzy sets. Third, the experts' weights are determined using a novel method based on the entropy measure, and the criteria weights are calculated by a novel hybrid criteria weight calculation method. Finally, the ranking of different treatment technologies is determined by the proposed method. To validate the proposed method, a real case study in Jinan, China is presented, where the proposed method is applied to determine the optimal health care waste (HCW) treatment technologies from five TTs with eight criteria. The results are compared to other MCDM methods, which shows the effectiveness and reliability of the proposed method. Moreover, sensitivity analysis is also carried out to show the robustness of the proposed method. From the results, it can be found that the proposed method could provide reliable and robust results for obtaining the optimal health care waste treatment technology. • Proposes a novel decision-making method under Fermatean fuzzy set called FF-BWM-VIKOR. • The FF-BWM-VIKOR method is proposed to select health care waste treatment technology. • Novel distance measure and entropy measure for Fermatean fuzzy sets are developed. • A hybrid criteria weight calculation method is proposed to determine weights of criteria. • A real case of health care waste treatment technology selection is presented. [ABSTRACT FROM AUTHOR]

The efficient distribution and timely vaccine delivery to the masses during any pandemic situation require reliable and efficient vaccine cold chain management (VCCM). Therefore, the present study is an effort to explore the factors of VCCM to develop a sustainable and resilient healthcare delivery system in any health outbreak. The factors of VCCM are scrutinized using Fuzzy-Delphi method and then prioritized adopting Fuzzy-Analytical Hierarchy Process (F-AHP) approach while considering the stakeholders' inputs. Finally, factors are categorized into clusters by implementing a machine learning tool ' k -means clustering' algorithm. The results of the study are further validated by conducting extended robustness tests. The findings of the study highlight the factors, namely, cold chain infrastructure, vaccine storage guidelines and availability of skilled professionals as the most important for VCCM. The ′ k -means clustering' algorithm analysis classifies all the 16 factors into three clusters: continuous process improvement (including eight factors), stakeholders' coordination (five factors) and maximum resource utility (three factors). The study can be significantly useful to VCCM decision-makers to implement the prioritized factors by ensuring the advanced cold chain infrastructure and issuing special storage and distribution guidelines to fight against health outbreaks like COVID-19. This is one of the few papers to discuss the factors of VCCM for developing a sustainable and resilient healthcare delivery system in the context of pandemics and stakeholders can address the factors in different clusters at a time. [Display omitted] • Vaccine cold chain management (VCCM) to develop a resilient healthcare delivery system during health outbreaks. • Prioritization of factors of VCCM using Fuzzy-Analytic Hierarchy Process approach. • Categorization of factors of VCCM by implementing a machine learning tool of K − m e a n s clustering algorithm. • Storage and distribution guidelines to fight against the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The smart healthcare system plays a vital role in modern healthcare, facilitating the exchange of Electronic Patient Records (EPR), and improving medical care. Nevertheless, safeguarding the inherent security and confidentiality of EPR data persists as a formidable challenge. This issue demanding rigorous attention and innovative solutions. Digital watermarking safeguards the genuineness and integrity of digital images, and is widely employed. In medical imaging, it is vital to guarantee the confidentiality and security of patient data. In this paper, we propose an Encryption-Then-Embedding-Based data hiding scheme for medical images that combines cryptography and watermarking techniques. The proposed technique first encrypts the patient information using Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) before embedding. The intensity-based image segmentation method is then used to select the Region of Non-interest (RONI) for embedding the encrypted patient information and the watermark using a Fused Transform-Based Method (FTBM). The experiments conducted in this study utilize a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The findings of this study demonstrate the efficacy of the proposed technique in safeguarding patient data while simultaneously preserving the quality of medical images. In comparison to existing techniques, the proposed approach demonstrates superior performance in terms of security, authenticity, and integrity. [ABSTRACT FROM AUTHOR]

Digital images are being frequently used for diagnosis in clinics today. Diagnostic images with identifying patient data are stored and transmitted across open networks. Security measures are therefore becoming more and more crucial to safeguard the confidentiality of sensitive patient data. In medical imaging, adjacent pixels show a strong degree of connection. This paper presents an efficient intra-inter bit-level pixel operation-based image encryption method that eliminates pixel correlation and provides strong security. In this method, after gathering medical image data from IoT sensors, the intra bit-level pixel permutation and diffusion operations are executed separately and in parallel in each of the bit planes, and the inter bit-level pixel permutation and diffusion operations are then performed in the concatenated bit-planes. The bit-level operation not only permutes but also diffuses the pixels at the same time. As a result, the security of the algorithm is increased by executing several pixel permutations and diffusions. In addition to this, the approach is effective due to the parallel execution of bit-plane permutation and diffusion. The simulation results and the security analysis indicate the superior performance of the proposed algorithm. [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]

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]

In recent years, there has been an exponential increase in the usage of Healthcare 4.0-based diagnostics systems across the globe. In healthcare 4.0, the patient's records are stored in electronic health record (EHR) repository which may be loacted either at a centralized or distributed locations to help the Doctors to easily access the patient's healthcare data from anywhere at any time. As this data is accessed from the database repository using an open channel, i.e., the Internet, so, security and privacy are major concerns while accessing it from any location. Motivated from these facts, in this paper, we propose a biometric-based authentication scheme to ensure secure access of the patients EHR from any location. In the proposal, we first identified various security threats and challenges in accessing EHR from the database repository. Then, the secure biometric-based scheme is designed which is validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The results obtained demonstrated that the proposed scheme is superior (in terms of computation and communication costs) in comparison to the traditional state-of-the-art existing schemes. [ABSTRACT FROM AUTHOR]

Abstract Unlike micro-local studies on turn allocation, this study combines macro- and micro-local (Traverso, 2004) perspectives to explore turn allocation of the multi-participant medical-service-seeking 1 (MSS) party composed of the patient and his/her companion in accompanied medical consultations in China. Through both qualitative and quantitative analyses of the transcription of 20 audio recorded consultations in China, the talk distribution within the MSS party is examined in terms of the structural organization of a medical visit. Features of the talk distribution in different phases of the medical consultation and factors influencing turn allocation are explored. It is found that talk is not equally distributed within the MSS party, and all phases other than the opening and the closing witness a significant or marginally significant difference in talk contribution by the patient and the companion. Factors influencing turn allocation within the MSS party vary from phase to phase: epistemic gradient, sick-role-triggered activity transfer and coalition to offset power asymmetry are among the most important. The paper sheds light on the study of polylogue, enables cross-cultural comparisons of medical consultations, and has methodological implications for further studies of medical conversations. Highlights 1. Turn allocation in accompanied medical consultations operates at two levels: between parties and within a party. 2. The talk distribution within the treatment-seeking party between the patient and the companion varies from phase to phase. 3. The intra-party turn allocation is tacitly or overtly negotiated between the patient and the companion influenced by a range of factors, such as epistemic gradient, sick role-triggered activity transfer, and coalition to offset power asymmetry. [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 An improved Gaussian process regression (GPR) is presented to predict the remaining useful life (RUL) of a gyroscope being integral to its prognostics and health management, despite uncertainties in the mean and variance values. In our approach, the degradation model of the gyroscope innovatively serves as a global model in the GPR methodology to capture the actual trends of the RUL. Moreover, the ball bearing whose rolling contact wear is responsible for the drifting in the gyroscope is considered as an essential component for the gyroscope RUL estimation. Employing the GPR method and the physical degradation model, the prognosis for the ball bearing can successfully predicts the defect before it occurs. Compared to other data-driven algorithms, results obtained for a gyroscope in an inertial navigation system confirm that the proposed method can be applied for drift prognostics with significant efficiency. Highlights • The aim of the paper is to develop a new global model to improve the prediction performance of the GP. • An improved GPR is presented to predict the RUL of a gyroscope. • The proposed approach offers some advantages over predicting long-term behaviors of damage. [ABSTRACT FROM AUTHOR]

Background: This paper identifies the best instruments for service providers to measure the quality of life (QoL) of children with a disability, with a focus on their alignment with the Convention on the Rights of Persons with a Disability (CRPD).Methods: This study reviewed systematic reviews to identify generic QoL instruments for children and adolescents, followed by an appraisal process using newly developed criteria. QoL instruments with a health status, functioning, and condition-specific focus were excluded.Results: Twenty generic QoL instruments for children were identified from existing systematic reviews to undergo further review. Only 2 of the 20 instruments were recommended for service providers to measure the QoL of children with a disability (KIDSCREEN and KINDL). Many pediatric QoL instruments (N = 9) focus on functioning and are not consistent with the CRPD, confounding a child's functioning with their feelings about their life. KIDSCREEN and KINDL have self-report and parent report versions, are applicable for childhood and adolescence, demonstrate adequate reliability and validity, involved children in their development, focus on wellbeing, are likely to be able to be completed by a child with a disability, and are low in cost.Conclusions: Many instruments focus on functioning rather than wellbeing and thus may not capture the QoL of children with a disability. A child's functional limitations may not be consistent with their feelings about life. Two instruments that assess wellbeing and meet the criteria important for service providers now require further testing to explore their usefulness and validity for children with varying abilities. [ABSTRACT FROM AUTHOR]

Abstract Chronic Disease Prediction plays a pivotal role in healthcare informatics. It is crucial to diagnose the disease at an early stage. This paper presents a survey on the utilization of feature selection and classification techniques for the diagnosis and prediction of chronic diseases. Adequate selection of features plays a significant role for enhancing accuracy of classification systems. Dimensionality reduction helps in improving overall performance of machine learning algorithm. The application of classification algorithms on disease datasets yields promising results by developing adaptive, automated and intelligent diagnostic systems for chronic diseases. Parallel classification systems can be used to expedite the process and to enhance the computational efficiency of results. This work presents a comprehensive overview of various feature selection methods and their inherent pros and cons. We then analyze adaptive classification systems and parallel classification systems for chronic disease prediction. [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]

Introduction: Administrative hospital databases represent an important tool for hospital financing in many national health systems and are also an important data source for clinical, epidemiological and health services research. Therefore, the data quality of such databases is of utmost importance. This paper aims to present a systematic review of root causes of data quality problems affecting administrative hospital data, creating a catalogue of potential issues for data quality analysts to explore.Methods: The MEDLINE and Scopus databases were searched using inclusion criteria based on two following concept blocks: (1) administrative hospital databases and (2) data quality. Studies' titles and abstracts were screened by two reviewers independently. Three researchers independently selected the screened studies based on their full texts and then extracted the potential root causes inferred from them. These were subsequently classified according to the Ishikawa model based on 6 categories: "Personnel", "Material", "Method", "Machine", "Mission" and "Management".Results: The result of our investigation and the contribution of this paper is a classification of the potential (105) root causes found through a systematic review of the 77 relevant studies we have identified and analyzed. The result was represented by an Ishikawa diagram. Most of the root causes (25.7%) were associated with the category "Personnel" - people's knowledge, preferences, education and culture, mostly related to clinical coders and health care providers activities. The quality of hospital documentation, within category "Material", and aspects related to financial incentives or disincentives, within category "Mission", were also frequently cited in the literature as relevant root causes for data quality issues.Conclusions: The resultant catalogue of root causes, systematized using the Ishikawa framework, provides a compilation of potential root causes of data quality issues to be considered prior to reusing these data and that can point to actions aimed at improving data quality. [ABSTRACT FROM AUTHOR]

• Experimental assessment of Personal Protective Equipment (PPE) for medical care. • The PPE uses a face shield combined with an air-sealed curtain. • The maximum efficiency was 82% when exposed to a uniform polluted environment. • Human thermal plume reduces the velocity and affects the device efficiency by 2%. • Human breathing process improves the PPE efficiency by 32%. This paper presents the performance assessment of a novel air-sealed visor for medical care based on experiments. This Personal Protective Equipment (PPE) provides effective aerodynamic sealing of the breathing zone, thus reducing the risk of inhaling droplets and aerosols carrying a potential viral load. The air curtain supply system was designed after computational fluid dynamics exploratory simulations to determine suitable airflow conditions. The main parameters under study are the air curtain velocities and the fraction of contaminated air in the breathing zone. The impact of different airflow rates, human breathing, thermal plume and lateral barriers to reduce the risk of leakage, was investigated. For the designed airflow rate and considering the worst-case scenario with a contaminated environment, results indicate that this PPE can reduce the contaminated air in the breathing zone by 31% and 82%, without and with lateral physical barriers, respectively. Although the human body thermal plume has a high impact on the air curtain velocity profile, it just affects the device efficiency by 2%. The breathing process increases the air curtain velocities improving the PPE efficiency by 32%. This study shows the potential of using a visor with aerodynamic sealing as an infection control barrier for healthcare workers. [ABSTRACT FROM AUTHOR]

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems. [ABSTRACT FROM AUTHOR]

Objective: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care.Material and Method: In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency.Results: We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment.Conclusions: The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect. [ABSTRACT FROM AUTHOR]

Advanced manufacturing techniques such as 3-dimensional (3D) printing, while mature in other industries, are starting to become more commonplace in clinical care. Clinicians are producing physical objects based on patient clinical data for use in planning care and educating patients, all of which should be managed like any other healthcare system data, except it exists in the "real" world. There are currently no provisions in the Health Insurance Portability and Accountability Act (HIPAA) either in its original 1996 form or in more recent updates that address the nature of physical representations of clinical data. We submit that if we define the source data as protected health information (PHI), then the objects 3D printed from that data need to be treated as both (PHI), and if used clinically, part of the clinical record, and propose some basic guidelines for quality and privacy like all documentation until regulatory frameworks can catch up to this technology. Many of the mechanisms designed in the paper and film chart era will work well with 3D printed patient data. [ABSTRACT FROM AUTHOR]

Purpose: The progress of the Millennium Development Goals (MDGs) shows that sustained global action can achieve success. Despite the unprecedented achievements in health and education, more than one billion people, many of them in conflict-affected areas, were unable to reap the benefits of the MDG gains. The recently developed Sustainable Development Goals (SDGs) are even more ambitious then their predecessor. SDG 3 prioritizes health and well-being for all ages in specific areas such as maternal mortality, communicable diseases, mental health, and healthcare workforce. However, without a shift in the approach used for conflict-affected areas, the world's most vulnerable people risk being left behind in global development yet again. We must engage in meaningful discussions about employing innovative strategies to address health challenges fragile, low-resource, and often remote settings. In this paper, we will argue that to meet the ambitious health goals of SDG 3, digital health can help to bridge healthcare gaps in conflict-affected areas.Methods: First, we describe the health needs of populations in conflict-affected environments, and how they overlap with the SDG 3 targets. Secondly, we discuss how digital health can address the unique needs of conflict-affected areas. Finally, we evaluate the various challenges in deploying digital technologies in fragile environments, and discuss potential policy solutions.Discussion: Persons in conflict-affected areas may benefit from the diffusive nature of digital health tools. Innovations using cellular technology or cloud-based solutions overcome physical barriers. Additionally, many of the targets of SDG 3 could see significant progress if efficacious education and outreach efforts were supported, and digital health in the form of mHealth and telehealth offers a relatively low-resource platform for these initiatives. Lastly, lack of data collection, especially in conflict-affected or otherwise fragile states, was one of the primary limitations of the MDGs. Greater investment in data collection efforts, supported by digital health technologies, is necessary if SDG 3 targets are to be measured and progress assessed. Standardized EMR systems as well as context-specific data warehousing efforts will assist in collecting and managing accurate data. Stakeholders such as patients, providers, and NGOs, must be proactive and collaborative in their efforts for continuous progress toward SDG 3. Digital health can assist in these inter-organizational communication efforts.Conclusion: The SDGS are complex, ambitious, and comprehensive; even in the most stable environments, achieving full completion towards every goal will be difficult, and in conflict-affected environments, this challenge is much greater. By engaging in a collaborative framework and using the appropriate digital health tools, we can support humanitarian efforts to realize sustained progress in SDG 3 outcomes. [ABSTRACT FROM AUTHOR]

The classification of subjects with different stages of knee OsteoArthritis (OA) using bone texture analysis is a challenging task in medical imaging. This paper presents a new approach for texture analysis of radiographic OA in knee X-ray images. First, a preprocessing step based on a 2D finite impulse response filter is applied on the X-ray images. Then, a set of image content descriptors is extracted from the complex wavelet decomposition using different statistics of a new concept of the relative phases of complex coefficients. The Von Mises and wrapped Cauchy probability density functions are used to model the distribution of relative phase coefficients. Finally, the estimated parameters for each image are used in the classification task, to verify the effectiveness and robustness of the proposed texture analysis on knee X-ray images from the OsteoArthritis Initiative (OAI). Results show that the proposed approach leads to improved texture analysis with a classification rate of 80.38%. [ABSTRACT FROM AUTHOR]

Cloud computing platform is used to improve the operational efficiency of business processes and to provide services to users. The fast growth of healthcare industry is shifting its traditional business model to cloud-enabled business model, which can fulfill the resource demand of different applications in healthcare industries. The job of healthcare system can vary from a simple patient record retrieval to a complex biomedical image analysis. On the other hand, the shared configurable computing, storage and other resources are provided to the users as a service over the Internet on rented basis. Although huge amount of resources are provided by the cloud to the healthcare systems to carry out complex time-consuming and data-intensive operations, scheduling of diverse healthcare applications onto large numbers of physical servers is an evolving issue, which needs to be addressed. In this paper, a scheduling framework is designed for the intelligent distribution of healthcare related jobs based on their types by taking advantage of existing heterogeneous distributed data center management solutions. This loosely coupled architecture works atop the existing solutions whose components can run in parallel on different nodes. The proposed framework for cloud computing can not only handle a variety of jobs but also can recover itself from any accidental crash. [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]

With the growing concern about health issues and the emergence of online communities based on user-generated content (UGC), more and more people are participating in online health communities (OHCs) to exchange opinions and health information. This paper aims to examine whether and how male and female users behave differently in OHCs. Using data from a leading diabetes community in China (Tianmijiayuan), we incorporate three different techniques: topic modeling analysis, sentiment analysis and friendship network analysis to investigate gender differences in chronic online health communities. The results indicated that (1) Male users' posting content was usually more professional and included more medical terms. Comparatively speaking, female users were more inclined to seek emotional support in the health communities. (2) Female users expressed more negative emotions than male users did, especially anxiety and sadness. (3) In addition, male users were more centered and influential in the friendship network than were women. Through these analyses, our research revealed the behavioral characteristics and needs for different gender users in online health communities. Gaining a deeper understanding of gender differences in OHCs can serve as guidance to better meet the information needs, emotional needs and relationship needs of male and female patients. [ABSTRACT FROM AUTHOR]

Background: Research has begun to focus on whether Advance Care Planning (ACP) has the capacity to influence care, and to examine whether ACP can be effective in meeting patients' wishes at the end of their lives. Little attention has been paid, however, to the validity and clinical relevance of existing measures.Methods: A search of Medline and CINHAL identified ACP studies measuring concordance between end-of-life (EoL) preferences and the care received. Databases were searched from 2000 to August 2016. We developed a checklist to evaluate the quality of included studies. Data were collected on the proportion of patients who received concordant care, extracted from manuscript tables or calculated from the text.Outcomes: Of 2941 papers initially identified, nine eligible studies were included. Proportions of patients who received concordant care varied from 14% to 98%. Studies were heterogeneous and methodologically poor, with limited attention paid to bias/external validity. Studies varied with regards to design of measures, the meaning of relevant terms like "preference" "EoL care" and "concordance," and the completeness of reported data.Conclusion: Methodological variations and weaknesses compromise the validity of study results, and prevent meaningful comparisons between studies or synthesis of the results. Effectively evaluating whether ACP interventions enhance a patient's capacity to receive the care they want requires harmonization of research. This demands standardization of methods across studies, validating of instruments, and consensus based on a consistent conceptual framework regarding what constitutes a meaningful outcome measure. [ABSTRACT FROM AUTHOR]

Accurate and quick treatment of patients is the most important aim of the health care systems, especially at emergency departments (ED), as such departments are dealing with life and death situations on a daily basis. Nevertheless, the patients scheduling problem (PSP) is often very difficult to solve in practice particularly by applying manual approaches. To this end, the target of this paper is to study the PSP in ED. We propose a hybrid ILS/VND algorithm that aims to minimize the total waiting time of patient’s. After generating the initial solution using the "Triage - First in First out" (TFF) heuristic, the solution is further improved by employing a VND algorithm. The proposed VND involves four neighborhood structures in order to disrupt the actual solution and provide a better exploration of the search space. The obtained results show that the hybrid ILS/VND algorithm is computationally effective and provides high-quality solutions. [ABSTRACT FROM AUTHOR]

Quality is much sought after in, and a basic foundation for, good impact assessment (IA). However, the term is rarely defined, has an uncertain relationship with IA effectiveness, and it means different things to different stakeholders, which can lead to debates over the legitimacy associated with an IA process. Thus, IA quality needs conceptualising to position research and practice within broader understandings. This paper contributes to this conceptualisation by identifying nine dimensions of quality through a process of literature review drawing on three fields of study in which quality and quality management have already been debated and conceptualised: education; health care; and business. This approach sidesteps the plural views on quality existing within the field of IA itself which might otherwise bias the identification of quality dimensions. We therefore propose that the dimensions of IA quality are: Efficiency; Optimacy; Conformance; Legitimacy; Equity; Capacity Maintenance; Transformative Capacity; and Quality Management. A literature review of IA research and practice confirms the relevance of the identified quality dimensions to IA. We identify, to an extent, the relationship between quality and effectiveness. Quality aligns with procedural and transactive effectiveness, partly aligns with normative effectiveness and is distinct from, but helps to deliver, substantive effectiveness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has challenged organizations to adapt under uncertainty and time pressure, with no pre-existing protocols or guidelines available. For organizations to learn to adapt effectively, there is a need to understand the perspectives of the frontline workforce involved in everyday operations. This study implemented a survey-tool to elicit narratives of successful adaptation based on the lived experiences frontline radiology staff at a large multispecialty pediatric hospital. Fifty-eight members of the radiology frontline staff responded to the tool between July and October of 2020. Qualitative analysis of the free-text data revealed five categories of themes that underpinned adaptive capacity of the radiology department during the pandemic: information flow, attitudes and initiative, new and adjusted workflows, availability and utilization of resources, and collaboration and teamwork. Enablers of adaptive capacity included timely and clear communication about procedures and policies from the leadership to frontline staff, and revised workflows with flexible work arrangements, such as remote patient screening. Responses to multiple choice questions in the tool helped identify the main categories of challenges faced by staff, factors that enabled successful adaptation, and resources used. The study demonstrates the use of a survey-tool to proactively identify frontline adaptations. The paper also reports a system-wide intervention resulting directly from a discovery enabled by the findings based on the use of RETIPS in the radiology department. In general, the tool could be used in concert with existing learning mechanisms, such as safety event reporting systems, to inform leadership-level decisions to support adaptive capacity. • A proactive learning tool was implemented in a pediatric radiology deparment for workers to share narratives of adaptation at the start of the COVID-19 pandemic. • Fifty eight responses were received between July and October of 2020 that provided information on how staff at various organizational levels were coping with an unprecedented situation. • Analysis of the responses revealed exemplars and patterns relating to cross-level information flows, worker initiatives, adapted workflows, utilization of resources, and collaboration and coordination of work. • Organization Learning: based on findings relating to the efficacy of timely communications to staff, the radiology leadership expanded the scope of their regular information dissemination to include key operational groups. • The tool is presented as part of a framework for proactive learning to support organizational adaptive capacity. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) is a specialized field with applications both in medical and non-medical domains. In WBAN, nodes' (both wearable and implanted) may vary in capabilities, hence, carrying data at various rates. Propagating such data reliably and efficiently is challenging and results in increased delay and poor service. In sensitive healthcare monitoring applications, communicating sensitive data reliably with less delay is highly desirable. In WBANs, unwanted traffic is often generated, which wastes network bandwidth and causes delay. To address this issue, in this paper, we have proposed an efficient QoS-based Multi-Path Routing (MPR) scheme for WBAN. In MPR, the incoming traffic is categorized into normal and emergency. Emergency traffic has been given high priority and routed on the best path within the network that improves the reliability and throughput of a WBAN. The proposed scheme outperforms state-of-the-art approaches in terms of energy efficiency, network throughput, packet drop ratio, packet delivery ratio, and end-to-end delay. For 1000 data packets, collecting eight bio-sensor nodes yields a maximum throughput of 11,000 packets transmitted, and successively, in 10000 data collecting, with less packet drop ratio to the MS (Medical Server). Lastly, a fuzzy logic-based evaluation of the proposed scheme further testifies that the proposed work outperforms state-of-the-art approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

The incidence of hyperuricemia and gout has been increasing year by year, and it is showing a younger trend. However, the first-line drugs currently used for hyperuricemia and gouty arthritis have serious side effects that limit their clinical application. Amomum villosum Lour. has been widely used in China for thousands of years as a traditional medical and edible plant, and previous screening showed that the ethanol extract of Amomum villosum Lour. could effectively inhibit the activity of xanthine oxidase. Based on this discovery, this paper had achieved in-depth mechanism research. The results showed that the ethanol extract of Amomum villosum Lour. could treat hyperuricemia by reducing the production of uric acid via inhibition of xanthine oxidase and increasing the excretion of uric acid via regulation of urate transporters. Meanwhile, the extract also showed a certain protective effect on hepatic and renal damage caused by hyperuricemia. With the formation of extensive uric acid, gouty arthritis will be induced by the deposition of monosodium urate in the joint. The extract could also relieve the inflammation by reducing the expression of inflammatory cytokines. In conclusion, the extract deserves focused research and development as a potential medicine, health care product or supplemented food for the prevention and treatment of hyperuricemia and gouty arthritis. [Display omitted] • The EA could reduce the production of uric acid via inhibition of xanthine oxidase. • The EA could increase the excretion of uric acid via regulation of urate transporters. • The EA showed a certain protective effect on hepatic and renal damage caused by hyperuricemia. • The EA could relieve the gouty arthritis by reducing the expression of inflammatory cytokines. • The EA deserves focused research as a potential medicine for the treatment of hyperuricemia and gouty arthritis. [ABSTRACT FROM AUTHOR]

Electronic health record systems have the potential to improve the quality of health services primarily through the availability of health information. Implementing EHR systems in healthcare facilities has been met with an alarming rate of failure. This paper reviews the literature identifying barriers to implement an EHR system. Identifying the barriers will be a precursor to assessing readiness for such a system. A structured literature review was done in accord with the PRISMA guidelines. The barriers identified were categorized into the information systems resources. The review suggests that people resource (user resistance and lack of skills) and procedure resource (concern for return on investment and lack of administrative and policy support) are the primary barriers to overcome. Further studies are directed to examine the barriers in detail and recognize how to address said barriers. [ABSTRACT FROM AUTHOR]

Quality attributes play a relevant role in Service-Oriented Computing, as they allow distinguishing similar services. These attributes impact on various activities related to the life-cycle of service-based applications (SBAs). The effect starts from service discovery and permeates other activities such as service level agreement establishment and monitoring. Considering the relevance of quality attributes, they must be precisely defined. Moreover, as these attributes are intrinsically dynamic, they must be permanently monitored. This paper proposes a meta-model that formally defines and connects service, quality and event domains. The connection between service and quality domains enables the design of models that specify applications’ functional and non-functional requirements. The connection between quality and event domains allows engineers to define how quality metrics can be computed based on a set of runtime events. Conformant models can be interpreted at runtime by compatible platforms to dynamically configure their monitoring mechanisms. [ABSTRACT FROM AUTHOR]

Ridge regression is an important approach in many applications, such as healthcare system of smart wearable equipments. Due to the limited resources of wearable devices, outsourcing is a promising computation paradigm. Nevertheless, it also suffers from some privacy challenges, as outsourced computation probably involves some sensitive data. In this paper, we propose a ridge regression outsourcing scheme, which can securely utilize the cloud to analyse large-scale wearable device dataset and dramatically reduce the computation cost of the resource-limited clients. Technically, we use random vectors and dense matrices to perturb input dataset and regression output, such that both input privacy and output privacy can be efficiently protected. Then, we present a highly-efficient verification algorithm to robustly check the correctness of cloud’s answer against a dishonest/lazy cloud server. Finally, we evaluate our scheme through theoretical analysis and extensive experiments. The results show we can achieve input/output privacy, correctness, robust checkability and practical efficiency. [ABSTRACT FROM AUTHOR]