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• Opioid abuse disorder is a leading cause of death. • Machine learning has been applied to predict opioid abuse disorder. • A variety of machine-learning methods and practices have been used. • Preliminary results look promising. • Inadequate evaluation metrics and lack of reproducibility hinder use in real life. The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD). The review is split into two parts. The first part summarizes the current research in OUD prediction with ML. The second part evaluates how ML techniques and processes were used to achieve these results and suggests improvements to refine further attempts to use ML for OUD prediction. The review includes peer-reviewed journal papers published on or after 2012 that use healthcare data to predict OUD. We searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore, and Science.gov in September of 2022. Data extracted includes the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models. The review analyzed 16 papers. Three papers created their dataset, five used a publicly available dataset, and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to over half a million. Six papers used one type of ML model, and the remaining ten used up to five different ML models. The reported ROC AUC was higher than 0.8 for all but one of the papers. Five papers used only non-interpretable models, and the other 11 used interpretable models exclusively or in combination with non-interpretable ones. The interpretable models were the highest or second-highest ROC AUC values. Most papers did not sufficiently describe the ML techniques and tools used to produce their results. Only three papers published their source code. We found that while there are indications that ML methods applied to OUD prediction may be valuable, the lack of details and transparency in creating the ML models limits their usefulness. We end the review with recommendations to improve studies on this critical healthcare subject. [ABSTRACT FROM AUTHOR]

Background and Objective Health Information Exchange (HIE) is known as a technology that electronically shares all clinical and administrative data throughout healthcare settings. Despite this technology has a great potential in the healthcare industry, there is a limited and sparse evidence of articles which illustrated the impact of HIE on quality of care and cost-effectiveness. This work presents a systematic review that evaluates the impact of HIE on quality and cost-effectiveness, and the rates of HIE adoption and participation in healthcare organizations. Methods We systematically searched all English papers that were indexed in four major databases (Science Direct, PubMed, IEEE and Web of Science) between 2005 and 2016. Consequently, 32 identified papers appeared in 21 international journals and conferences. Eligible studies independently were critically appraised, collected within data extraction form and then thematically analyzed by two reviewers and if necessary, the third author. The selected papers have been classified based on 11 main categories including publication year, journal and conference names, country and study design, types of data exchanged, healthcare levels, disease or disorder, participants in organizations and individuals, settings characteristics and HIE types, the impact of HIE on quality and cost-effectiveness, and the rates of HIE adoption and participation. Results Of the 32 articles, 25 studies investigated the financial and clinical impact of HIE. Overwhelmingly, HIE studies have reported positive findings for quality and cost-effectiveness of care. 15 of HIE studies (60%) demonstrated positive financial effects and 16 studies (64%) reported positive effects on quality improvement of patient care. However, the overall quality of the evidences was low. In this regard, cohort study (59.38%) was the most common used study design. Nine studies presented the rates of HIE adoption and participation. The lowest and highest participation rates were 15.7% and 79%, respectively. Conclusions HIE can be considered as a superior potential for healthcare information system, resulting to promote patient care quality and reduce costs related to resource utilization. However, further researches are needed in order to provide a better understanding of this domain and accordingly attain new opportunities to increase users’ participation and motivation for successfully adopting this technology. [ABSTRACT FROM AUTHOR]

The 16th World Congress on Medical and Health Informatics (MedInfo 2017) was held August 21–25, 2017, in Hangzhou, China. It provided a valuable platform for sharing the latest medical and health informatics research and related applications to the scientists, medical practitioners, entrepreneurs, and educators as well as students. During this event, on August 23, 2017, an important related topic was presented in a panel discussion entitled “Wearable technologies: Advancing the healthcare in ageing population” by panelists Shabbir Syed-Abdul, Panagiotis Bamidis, Chun-Por Wong, and Xinxin Zhu. Recent advances in health technologies, focusing on the aging population, their benefits and challenges were discussed, and these topics are summarized in this paper. The need for technology to improve of the life of older population, influential and beneficial technologies, for delivering these technologies to patients are described in this paper. [ABSTRACT FROM AUTHOR]

Highlights • Succeeded performing the analysis of a probabilistic model for reducing medication errors with various thresholds. • Picking the optimal threshold is both an art and a science—it should be done with careful reference to both specialties and the purpose of the application. • The AESOP model was observed over 80% accurate (accuracy) for overall departments. Inappropriate prescriptions were determined with a lower rate (i.e. 1%–3%) and the positive predictive value (PPV) ranged from 40%−60%. Abstract Objectives Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. Methods We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness. Results One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80–96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. Conclusion We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system. [ABSTRACT FROM AUTHOR]

Highlights • Three simpler, lower workload and thus potentially more clinically desirable alternative nutrition protocols used in conjunction with the STAR insulin Glycaemic Control (GC) protocol are investigated. • Each nutrition protocol is simulated on a 221 patient virtual cohort, and GC performance, safety and workload assessed. • A fixed nutrition rate that varies in steps by day over the first three days of GC can offer similar performance and potentially enhanced safety, while still delivering world leading nutrition delivery to hyperglycemic, insulin resistant patients. • The result is relatively general and could be adapted by STAR or any form of GC to reduce clinical workload without affecting GC safety and quality, and thus make it more clinically acceptable. • The results delineate clear, more general tradeoffs between the safety and performance of glycemic control, workload, and nutrition delivery for hyperglycemic, insulin resistant ICU patients. Abstract Background and Objective Hyperglycaemia is commonplace in the adult intensive care unit (ICU), and has been associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a model-based GC protocol that uniquely maintains normoglycaemia by changing both insulin and nutrition interventions, and has been proven effective in controlling blood glucose (BG) in the ICU. However, most ICU GC protocols only change insulin interventions, making the variable nutrition aspect of STAR less clinically desirable. This paper compares the performance of STAR modulating only insulin, with three simpler alternative nutrition protocols in clinically evaluated virtual trials. Methods Alternative nutrition protocols are fixed nutrition rate (100% caloric goal), CB (Cahill et al. best) stepped nutrition rate (60%, 80% and 100% caloric goal for the first 3 days of GC, and 100% thereafter) and SLQ (STAR lower quartile) stepped nutrition rate (65%, 75% and 85% caloric goal for the first 3 days of GC, and 85% thereafter). Each nutrition protocol is simulated with the STAR insulin protocol on a 221 patient virtual cohort, and GC performance, safety and total intervention workload are assessed. Results All alternative nutrition protocols considerably reduced total intervention workload (14.6–19.8%) due to reduced numbers of nutrition changes. However, only the stepped nutrition protocols achieved similar GC performance to the current variable nutrition protocol. Of the two stepped nutrition protocols, the SLQ nutrition protocol also improved GC safety, almost halving the number of severe hypoglycaemic cases (5 vs. 9, P = 0.42). Conclusions Overall, the SLQ nutrition protocol was the best alternative to the current variable nutrition protocol, but either stepped nutrition protocol could be adapted by STAR to reduce workload and make it more clinically acceptable, while maintaining its proven performance and safety. [ABSTRACT FROM AUTHOR]

Abstract: The eXtensible markup language (XML) is a metalanguage which is useful to represent and exchange data between heterogeneous systems. XML may enable healthcare practitioners to document, monitor, evaluate, and archive medical information and services into distributed computer environments. Therefore, the most recent proposals on electronic health records (EHRs) are usually based on XML documents. Since none of the existing nomenclatures were specifically developed for use in automated clinical information systems, but were adapted to such use, numerous current EHRs are organized as a sequence of events, each represented through codes taken from international classification systems. In nursing, a hierarchically organized problem-solving approach is followed, which hardly couples with the sequential organization of such EHRs. Therefore, the paper presents an XML data model for the Omaha System taxonomy, which is one of the most important international nomenclatures used in the home healthcare nursing context. Such a data model represents the formal definition of EHRs specifically developed for nursing practice. Furthermore, the paper delineates a Java application prototype which is able to manage such documents, shows the possibility to transform such documents into readable web pages, and reports several case studies, one currently managed by the home care service of a Health Center in Central Italy. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to describe three emerging computer-aided diagnosis (CAD) systems induced by Japanese health care needs. CAD has been developing fast in the last two decades. The idea of using a computer to help in medical image diagnosis is not new. Some pioneer studies are dated back to the 1960s. In 1998, the first U.S. FDA (Food and Drug Administration) approved commercial CAD system, a film-digitized mammography system, was launched by R2 Technologies, Inc. The success was quickly repeated by a number of companies. The approval of Medicare CAD reimbursement in the U.S. in 2001 further boosted the industry. Today, CAD has its significance in the economy of the medical industry. FDA approved CAD products in the field of breast imaging (mammography, ultrasonography and breast MRI) and chest imaging (radiography and CT) can be seen. In Japan, as part of the “Knowledge Cluster Initiative” of the government, three computer-aided diagnosis (CAD) projects are hosted at the Gifu University since 2004. These projects are regarding the development of CAD systems for the early detection of (1) cerebrovascular diseases using brain MRI and MRA images by detecting lacunar infarcts, unruptured aneurysms, and arterial occlusions; (2) ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy using retinal fundus images; and (3) breast cancers using ultrasound 3-D volumetric whole breast data by detecting the breast masses. The projects are entering their final development stage. Preliminary results are presented in this paper. Clinical examinations will be started soon, and commercialized CAD systems for the above subjects will appear by the completion of this project. [Copyright &y& Elsevier]

Abstract: This paper provides formal specification of interactions in typical public health surveillance systems involving healthcare agencies at local, state and federal levels. Although few standards exist for exchange of healthcare information, there is a general lack of formal models of the protocols involved in the interactions between the agencies. The quality of medical care provided is an end result of a well designed choreography of diverse services provided by different healthcare entities. One of the major challenges in this field appears to be explicit formal specification of such interactions. Such formal specification work is the first step leading to both design and verification of important properties of public healthcare systems. -calculus is a formal modeling technique for precise specification of semantics in interacting concurrent systems where mobility is involved. Two different configurations of public health surveillance systems are modelled using -calculus in this paper. [Copyright &y& Elsevier]

An essential aspect for the utilization of medical data is their quality, thus a main feature of computer-based medical documentation systems should be to assist the user in complete and plausible data acquisition and maintenance. In this paper we define a grammar for modeling medical documentation systems to increase integrity and completeness of collected data, focusing attention on integrity constraints. An integrity constraint defines requirements that involved entities had to comply with. Furthermore it defines possibly implications in case of failure. The constraints presented in this paper are type constraint, length constraint, domain constraint, key constraint, quantity constraint, reference constraint, search constraint, result constraint, hierarchy constraint, and semantic constraint. Their grammar is declared using a schema in extensible markup language-format (XML-schema). The model introduced here can be used in computer-aided design and implementation of clinical documentation both minimizing effort and ensuring data quality, which was tested by an evaluation based on a specification of a registry for HIV-infected patients. [Copyright &y& Elsevier]

A patient's complete medication history is a crucial element for physicians to develop a full understanding of the patient's medical conditions and treatment options. However, due to the fragmented nature of medical data, this process can be very time-consuming and often impossible for physicians to construct a complete medication history for complex patients. In this paper, we describe an accurate, computationally efficient and scalable algorithm to construct a medication history timeline. The algorithm is developed and validated based on 1 million random prescription records from a large national prescription data aggregator. Our evaluation shows that the algorithm can be scaled horizontally on-demand, making it suitable for future delivery in a cloud-computing environment. We also propose that this cloud-based medication history computation algorithm could be integrated into Electronic Medical Records, enabling informed clinical decision-making at the point of care. [ABSTRACT FROM AUTHOR]

Background and objectives Information and communication and technology (ICT) has changed the entire paradigm of society. ICT facilitates people to use medical services over the Internet, thereby reducing the travel cost, hospitalization cost and time to a greater extent. Recent advancements in Telecare Medicine Information System (TMIS) facilitate users/patients to access medical services over the Internet by gaining health monitoring facilities at home. Methods Amin and Biswas recently proposed a RSA-based user authentication and session key agreement protocol usable for TMIS, which is an improvement over Giri et al.'s RSA-based user authentication scheme for TMIS. In this paper, we show that though Amin–Biswas's scheme considerably improves the security drawbacks of Giri et al.'s scheme, their scheme has security weaknesses as it suffers from attacks such as privileged insider attack, user impersonation attack, replay attack and also offline password guessing attack. A new RSA-based user authentication scheme for TMIS is proposed, which overcomes the security pitfalls of Amin–Biswas's scheme and also preserves user anonymity property. Results The careful formal security analysis using the two widely accepted Burrows–Abadi–Needham (BAN) logic and the random oracle models is done. Moreover, the informal security analysis of the scheme is also done. These security analyses show the robustness of our new scheme against the various known attacks as well as attacks found in Amin–Biswas's scheme. The simulation of the proposed scheme using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is also done. Conclusions We present a new user authentication and session key agreement scheme for TMIS, which fixes the mentioned security pitfalls found in Amin–Biswas's scheme, and we also show that the proposed scheme provides better security than other existing schemes through the rigorous security analysis and verification tool. Furthermore, we present the formal security verification of our scheme using the widely accepted AVISPA tool. High security and extra functionality features allow our proposed scheme to be applicable for telecare medicine information systems which is used for e-health care medical applications. [ABSTRACT FROM AUTHOR]

Background and objective In Pervasive Healthcare, novel information and communication technologies are applied to support the provision of health services anywhere, at anytime and to anyone. Since health systems may offer their health records in different electronic formats, the open EHR Foundation prescribes the use of archetypes for describing clinical knowledge in order to achieve semantic interoperability between these systems. Software agents have been applied to simulate human skills in some healthcare procedures. This paper presents a methodology, based on the use of open EHR archetypes and agent technology, which aims to overcome the weaknesses typically found in legacy healthcare systems, thereby adding value to the systems. Methods This methodology was applied in the design of an agent-based system, which was used in a realistic healthcare scenario in which a medical staff meeting to prepare a cardiac surgery has been supported. We conducted experiments with this system in a distributed environment composed by three cardiology clinics and a center of cardiac surgery, all located in the city of Marília (São Paulo, Brazil). We evaluated this system according to the Technology Acceptance Model. Results The case study confirmed the acceptance of our agent-based system by healthcare professionals and patients, who reacted positively with respect to the usefulness of this system in particular, and with respect to task delegation to software agents in general. The case study also showed that a software agent-based interface and a tools-based alternative must be provided to the end users, which should allow them to perform the tasks themselves or to delegate these tasks to other people. Conclusions A Pervasive Healthcare model requires efficient and secure information exchange between healthcare providers. The proposed methodology allows designers to build communication systems for the message exchange among heterogeneous healthcare systems, and to shift from systems that rely on informal communication of actors to a more automated and less error-prone agent-based system. Our methodology preserves significant investment of many years in the legacy systems and allows developers to extend them adding new features to these systems, by providing proactive assistance to the end-users and increasing the user mobility with an appropriate support. [ABSTRACT FROM AUTHOR]

Background and objective In the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. Method This paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. Results The evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162 bps data instead of 3960 bps uncompressed data. Conclusion The proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems. [ABSTRACT FROM AUTHOR]

Background and objectives: The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. Methods: A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. Results: The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. Conclusions: The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach. [ABSTRACT FROM AUTHOR]

Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity. In addition to being a precious support in the procedure of medical decision-making, this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications. Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health. In this paper we enrich the case-based reasoning process by taking into account former evidence-based knowledge. We use the regular four steps approach and implement an additional (iii) step: (i) establish diagnosis, (ii) retrieve treatment, (iii) apply evidence, (iv) adaptation, (v) retain. Each step is performed using tools from knowledge engineering and information processing (natural language processing, ontology, indexation, algorithm, etc.). The case representation is done by the taxonomy component of a medical ontology model. The proposed approach is illustrated with an example from the oncology domain. Medical ontology allows a good and efficient modeling of the patient and his treatment. We are pointing up the role of evidences and specialist's opinions in effectiveness and safety of care. [ABSTRACT FROM AUTHOR]

Abstract: Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA. [Copyright &y& Elsevier]

Abstract: Introduction: A usability test was employed to evaluate two medical software applications at an expert conference setting. One software application is a medical diagnostic tool (electrocardiogram [ECG] viewer) and the other is a medical research tool (electrode misplacement simulator [EMS]). These novel applications have yet to be adopted by the healthcare domain, thus, (1) we wanted to determine the potential user acceptance of these applications and (2) we wanted to determine the feasibility of evaluating medical diagnostic and medical research software at a conference setting as opposed to the conventional laboratory setting. Methods: The medical diagnostic tool (ECG viewer) was evaluated using seven delegates and the medical research tool (EMS) was evaluated using 17 delegates that were recruited at the 2010 International Conference on Computing in Cardiology. Each delegate/participant was required to use the software and undertake a set of predefined tasks during the session breaks at the conference. User interactions with the software were recorded using screen-recording software. The ‘think-aloud’ protocol was also used to elicit verbal feedback from the participants whilst they attempted the pre-defined tasks. Before and after each session, participants completed a pre-test and a post-test questionnaire respectively. Results: The average duration of a usability session at the conference was 34.69min (SD=10.28). However, taking into account that 10min was dedicated to the pre-test and post-test questionnaires, the average time dedication to user interaction of the medical software was 24.69min (SD=10.28). Given we have shown that usability data can be collected at conferences, this paper details the advantages of conference-based usability studies over the laboratory-based approach. For example, given delegates gather at one geographical location, a conference-based usability evaluation facilitates recruitment of a convenient sample of international subject experts. This would otherwise be very expensive to arrange. A conference-based approach also allows for data to be collected over a few days as opposed to months by avoiding administration duties normally involved in laboratory based approach, e.g. mailing invitation letters as part of a recruitment campaign. Following analysis of the user video recordings, 41 (previously unknown) use errors were identified in the advanced ECG viewer and 29 were identified in the EMS application. All use errors were given a consensus severity rating from two independent usability experts. Out of a rating scale of 4 (where 1=cosmetic and 4=critical), the average severity rating for the ECG viewer was 2.24 (SD=1.09) and the average severity rating for the EMS application was 2.34 (SD=0.97). We were also able to extract task completion rates and times from the video recordings to determine the effectiveness of the software applications. For example, six out of seven tasks were completed by all participants when using both applications. This statistic alone suggests both applications already have a high degree of usability. As well as extracting data from the video recordings, we were also able to extract data from the questionnaires. Using a semantic differential scale (where 1=poor and 5=excellent), delegates highly rated the ‘responsiveness’, ‘usefulness’, ‘learnability’ and the ‘look and feel’ of both applications. Conclusion: This study has shown the potential user acceptance and user-friendliness of the novel EMS and the ECG viewer applications within the healthcare domain. It has also shown that both medical diagnostic software and medical research software can be evaluated for their usability at an expert conference setting. The primary advantage of a conference-based usability evaluation over a laboratory-based evaluation is the high concentration of experts at one location, which is convenient, less time consuming and less expensive. [Copyright &y& Elsevier]

Abstract: The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity. [Copyright &y& Elsevier]

Abstract: This paper presents estimates for the parameters included in long-term mixture and non-mixture lifetime models, applied to analyze survival data when some individuals may never experience the event of interest. We consider the case where the lifetime data have a two-parameters exponentiated exponential distribution. The two-parameter exponentiated exponential or the generalized exponential distribution is a particular member of the exponentiated Weibull distribution introduced by [31]. Classical and Bayesian procedures are used to get point and confidence intervals of the unknown parameters. We consider a general survival model where the scale, shape and cured fraction parameters of the exponentiated exponential distribution depends on covariates. [Copyright &y& Elsevier]

Abstract: Radiotherapy is a fast-developing discipline which plays a major role in cancer care. Quantitative analysis of radiotherapy data can improve the success of the treatment and support the prediction of outcome. In this paper, we first identify functional, conceptional and general requirements on a software system for quantitative analysis of radiotherapy. Further we present an overview of existing radiotherapy analysis software tools and check them against the stated requirements. As none of them could meet all of the demands presented herein, we analyzed possible conceptional problems and present software design solutions and recommendations to meet the stated requirements (e.g. algorithmic decoupling via dose iterator pattern; analysis database design). As a proof of concept we developed a software library “RTToolbox” following the presented design principles. The RTToolbox is available as open source library and has already been tested in a larger-scale software system for different use cases. These examples demonstrate the benefit of the presented design principles. [Copyright &y& Elsevier]

Abstract: Patients with an increased risk of thrombosis may require treatment with vitamin K-antagonists such as warfarin. Treatment with warfarin has been reported difficult mainly due to high inter- and intraindividual variability in response to the drug [1]. Using predictive models that can predict International Normalised Ratio (INR) values enables for a higher degree of individualised warfarin dosing regime. This paper reports the outcome of the development of a dynamic prediction model. It takes warfarin intake and INR values as inputs, and uses an individual sensitivity parameter to model response to warfarin intake. The model is set on state-space form and uses Kalman filtering technique to optimise individual parameters. Retrospective test of the model proved robustness to choices of initial parameters, and feasible prediction results of both INR values and suggested warfarin dosage, which may prove beneficial for both patients and healthcare takers. [Copyright &y& Elsevier]

Abstract: Background: Medical Informatics Systems (MIS) have been suggested as having great potential to improve health care delivery in low resource settings. One of the major barriers for adopting MIS in this context is a lack of adequate network/communication infrastructure. Delay Tolerant Networking (DTN) is an approach for establishing network connectivity in situations where it is possible to support physical transport of the digital information. To date most DTN research has been technically oriented, and very few services have been implemented to support healthcare systems using the technology. It is thus unclear about the potential that DTN may have for supporting MIS systems in low resource settings. The goals of the paper are twofold, first, to gain an initial estimate of interest in different services that can be supported by DTN. Second, to find out the necessary frequency associated with each service for supporting health work in low resource settings. Method: Fifty questionnaires were distributed to attendants at the International Conference on Global Health that had acknowledged having health work experience in a poor connectivity context. The respondents were using a 5-point Likert scale regarding if 9 different potential DTN services “would be useful”. They also were asked how often data delivery would be necessary for these services to be useful. The Chi square was calculated to measure acceptance. Results: 37 responses were received, aggregating the response rate of 74%. The respondents represented having work experience from 8 months to 15 years from 35 resource poor countries. The Chi square test showed very high statistical significance for “strongly agree and agree” for the potential usefulness of the proposed DTN services, with a p-value less than 0.001. The frequency of data delivery that would be necessary for services to be useful varied considerably. Conclusion: This study provides evidence of potential for DTN to support useful services that support health work in low resource settings, and that services like access to email, notification of lab results, backup of EHR and teleconsultation are seem to be most important services that can be supported by DTN. The necessary frequency of data delivery for each service, will be highly dependent on context. In a low resource setting with limited mobility, the physical transport of digital data at a frequency of less than once per week should still be sufficient for useful services like notification of lab results and ordering of medical supplies. Research comparing different methods for delivery of DTN data should thus be useful. Further research and collaboration between MIS and Computer Science research communities is recommended in order to help develop DTN services that can be evaluated. Efforts to enhance awareness among stakeholders about how DTN can be used to support health services should be worthwhile. [Copyright &y& Elsevier]

Abstract: Personalized health (p-health) systems can contribute significantly to the sustainability of healthcare systems, though their feasibility is yet to be proven. One of the problems related to their development is the lack of well-established development tools for this domain. As the p-health paradigm is focused on patient self-management, big challenges arise around the design and implementation of patient systems. This paper presents a reference platform created for the development of these applications, and shows the advantages of its adoption in a complex project dealing with cardio-vascular diseases. [Copyright &y& Elsevier]

Abstract: The use of sophisticated information and communication technologies (ICTs) in the health care domain is a way to improve the quality of services. However, there are also hazards associated with the introduction of ICTs in this domain and a great number of projects have failed due to the lack of systematic consideration of human and other non-technology issues throughout the design or implementation process, particularly in the requirements engineering process. This paper presents the methodological approach followed in the design process of a web-based information system (WbIS) for managing the clinical information in hemophilia care, which integrates the values and practices of user-centered design (UCD) activities into the principles of software engineering, particularly in the phase of requirements engineering (RE). This process followed a paradigm that combines a grounded theory for data collection with an evolutionary design based on constant development and refinement of the generic domain model using three well-known methodological approaches: (a) object-oriented system analysis; (b) task analysis; and, (c) prototyping, in a triangulation work. This approach seems to be a good solution for the requirements engineering process in this particular case of the health care domain, since the inherent weaknesses of individual methods are reduced, and emergent requirements are easier to elicit. Moreover, the requirements triangulation matrix gives the opportunity to look across the results of all used methods and decide what requirements are critical for the system success. [Copyright &y& Elsevier]

Abstract: Patient empowerment might be one key to reduce the pressure on health care systems challenged by the expected demographic changes. Knowledge based systems can, in combination with automated sensor measurements, improve the patients’ ability to review their state of health and make informed decisions. The Arden Syntax as a standardized language to represent medical knowledge can be used to express the corresponding decision rules. In this paper we introduce Arden2ByteCode, a newly developed open source compiler for the Arden Syntax. Arden2ByteCode runs on Java Virtual Machines (JVM) and translates Arden Syntax directly to Java Bytecode (JBC) executable on JVMs. Arden2ByteCode easily integrates into service oriented architectures, like the Open Services Gateway Initiative (OSGi) platform. Apart from an evaluation of compilation performance and execution times, Arden2ByteCode was integrated into an existing knowledge supported exercise training system and recorded training sessions have been used to check the implementation. [Copyright &y& Elsevier]

Abstract: Prevention and control of Healthcare Associated Infections (HAIs) has become a high priority for most healthcare organizations. Mathematical models can provide insights into the dynamics of nosocomial infections and help to evaluate the effect of infection control measures. The model presented in this paper adopts an individual-based and stochastic approach to investigate MRSA outbreaks in a hospital ward. A computer simulation was implemented to analyze the dynamics of the system associated with the spread of the infection and to carry out studies on space and personnel management. This study suggests that a strict spatial cohorting might be ineffective, if it is not combined with personnel cohorting. [Copyright &y& Elsevier]

Abstract: This paper presents a patient specific deformable heart model that involves the known electrical and mechanical properties of the cardiac cells and tissue. The whole heart model comprises ten Tusscher''s ventricular and Nygren''s atrial cell models, the anatomical and electrophysiological model descriptions of the atria (introduced by Harrild et al.) and ventricle (given by Winslow et al.), and the mechanical model of the periodical cardiac contraction and resting phenomena proposed by Moireau et al. During the propagation of the depolarization wave, the kinetic, compositional and rotational anisotropy is handled by the tissue, organ and torso model. The applied patient specific parameters were determined by an evolutionary computation method. An intensive parameter reduction was performed using the abstract formulation of the searching space. This patient specific parameter representation enables the adjustment of deformable model parameters in real-time. The validation process was performed using simultaneously measured ECG and ultrasound image records that were compared with simulated signals and shapes using an abstract, parameterized evaluation criterion. [Copyright &y& Elsevier]

Abstract: Chronic asthmatic sufferers need to be constantly observed to prevent sudden attacks. In order to improve the efficiency and effectiveness of patient monitoring, we proposed in this paper a novel data mining mechanism for predicting attacks of chronic diseases by considering of both bio-signals of patients and environmental factors. We proposed two data mining methods, namely Pattern Based Decision Tree (PBDT) and Pattern Based Class-Association Rule (PBCAR). Both methods integrate the concepts of sequential pattern mining to extract features of asthma attacks, and then build classifiers with the concepts of decision tree mining and rule-based method respectively. Besides the general clinical data of patients, we considered environmental factors, which are related to many chronic diseases. For experimental evaluations, we adopted the children asthma allergic dataset collated from a hospital in Taiwan as well as the environmental factors like weather and air pollutant data. The experimental results show that PBCAR delivers 86.89% of accuracy and 84.12% of recall, and PBDT shows 87.52% accuracy and 85.59 of recall. These results also indicate that our methods can perform high accuracy and recall on predictions of chronic disease attacks. The readable rules of both classifiers can provide patients and healthcare workers with insights on essential illness related information. At the same time, additional environmental factors of input data are also proven to be valuable in predicting attacks. [ABSTRACT FROM AUTHOR]

Abstract: Secondary use of health data has a vital role in improving and advancing medical knowledge. While digital health records offer scope for facilitating the flow of data to secondary uses, it remains essential that steps are taken to respect wishes of the patient regarding secondary usage, and to ensure the privacy of the patient during secondary use scenarios. Consent, together with depersonalisation and its related concepts of anonymisation, pseudonymisation, and data minimisation are key methods used to provide this protection. This paper gives an overview of technical, practical, legal, and ethical aspects of secondary data use and discusses their implementation in the multi-institutional @neurIST research project. [ABSTRACT FROM AUTHOR]

In Intensive Care Units doctors have to manage several alarm situations in patients. When a doctor analyzes the state of the patient, (s)he has to decide if there is an alarm situation and make decisions about what actions to perform. It is desirable to detect these situations before they occur, because the solution could be easier and the doctor has more time to react. An intelligent system could analyze the information, extract conclusions, format and order the causes leading to the severe condition. This would be helpful for a doctor, and would make the decision-making process easier. A system capable of performing such operations is presented here. This is not a diagnosis application but a tool to detect alarm situations for patient safety. A prototype capable of making retrospective evaluation of the condition of the patients has been developed. This system is based on the MCRDR technology, which has been extended to deal with the requirements of this domain. The evaluation of the system is also reported in this paper. [Copyright &y& Elsevier]

Abstract: Consistent tight blood sugar control in critically ill patients has proven elusive. Properly accounting for the saturation of insulin action and reducing the need for frequent measurements are important aspects in intensive insulin therapy. This paper presents a composite metabolic model, ‘Glucosafe’, that integrates models and parameters from normal physiology and accounts for the reduced rate of glucose gut absorption and saturation of insulin action in patients with reduced insulin sensitivity. Particularly, two different sites of reduced insulin sensitivity, before and after the non-linearity of insulin action, are explored with this model. These approaches are assessed based on the model’s accuracy in retrospectively predicting blood glucose measurements of 10 randomly chosen, hyperglycemic intensive care patients. For each patient, median absolute percent error is <25% for prediction times min and modelling reduced insulin sensitivity after the non-linearity, compared to <29% for modelling reduced insulin sensitivity before the non-linearity. Scaling the insulin effect (after the non-linearity) is a suitable assumption in this model structure. These results are preliminary and subject to further and more extensive validation of the model’s capability to predict the longer term (>2h) blood glucose excursion in critically ill patients. [Copyright &y& Elsevier]

Abstract: In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. In this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a mobile u-health service system called THE-MUSS. THE-MUSS supports the development and running of u-health services with functions, modules, and facilities that are commonly required for various mobile u-health services. Aiming to achieve reusability and evolvability design goals, basic modules to support bio-signal capturing, processing, analysis, diagnosis, and feedback are developed and stacked in the layered architecture of THE-MUSS. A U-health service platform, design tool, portal, and matrix-based disease group identification method are the major components constituting the THE-MUSS architecture. We confirmed that THE-MUSS is practically useful for mobile u-health services by developing mobile stress and weight management services on THE-MUSS. The more u-health services are developed in THE-MUSS, the better services it can provide in the future. [Copyright &y& Elsevier]

Abstract: This paper shows the application of a type of formal software verification technique known as lightweight model checking to a domain model in healthcare informatics in general and public health surveillance systems in particular. One of the most complex use cases of such a system is checked using assertions to verify one important system property. This use case is one of the major justifications for the complexity of the domain model. Alloy Analyzer verification tool is utilized for this purpose. Such verification work is very effective in either uncovering design flaws or in providing guarantees on certain desirable system properties in the earlier phases of the development lifecycle of any critical project. [Copyright &y& Elsevier]

Abstract: Healthcare institutions are increasingly turning to digital medical imaging systems to promote better diagnosis and treatment of their patients. The implementation of the Picture Archiving and Communication System (PACS) clearly contributes to an increase in the productivity of health professionals. However, despite the amount of research that has been done in the past two decades, there are still several technological hurdles that hinder the wide adoption of PACS in the Web environment. In this paper, we present a Web-enabled PACS that through the inclusion of several DICOM services and compression methods promotes medical image availability and greater accessibility to users. [Copyright &y& Elsevier]

Abstract: The focus on the use of existing and new technologies to facilitate advances in medical imaging and medical informatics (MIMI) is often directed to the technical capabilities and possibilities that these technologies bring. The technologies, though, in acting as a mediating agent alter the dynamics and context of information delivery in subtle ways. While these changes bring benefits in more efficient information transfer and offer the potential of better healthcare, they also disrupt traditional processes and practices which have been formulated for a different setting. The governance processes that underpin core ethical principles, such as patient confidentiality and informed consent, may no longer be appropriate in a new technological context. Therefore, in addition to discussing new methodologies, techniques and applications, there is need for a discussion of ethical, legal and socio-economic (ELSE) issues surrounding the use and application of technologies in MIMI. Consideration of these issues is especially important for the area of medical informatics which after all exists to support patients, healthcare practitioners and inform science. This paper brings to light some important ethical, legal and socio-economic issues related to MIMI with the aim of furthering an interdisciplinary approach to the increasing use of Information and Communication Technologies (ICT) in healthcare. [Copyright &y& Elsevier]

Abstract: The population of health care costs is typically skewed, heteroscedastic, and may include zero costs. Without proper accounting for these special distributional features, resulting prediction may be biased, and wrong inferences about the distribution of patients’ health care costs may be made. Welsh and Zhou [A.H. Welsh, X.H. Zhou, Estimating the retransformed mean in a heteroscedastic two-part model, J. Stat. Plan. Inference 136 (2006) 860–881] proposed a semi-parametric regression model, which addressed these special features. In this paper we developed a software program to implement this statistical method, which would provide better prediction of health care costs for clinical researchers. Our program computed two mean estimators, their asymptotical standard deviation, confidence interval, and optional bootstrap confidence interval. Our program included user-friendly interactive mode and more efficient and flexible batch mode. It was written in free statistical computing language R and could be run on a wide variety of platforms. [Copyright &y& Elsevier]

Abstract: We show how multinomial logistic models with correlated responses can be estimated within SAS software. To achieve this, random effects and marginal models are introduced and the respective SAS code is given. An example data set on physicians’ recommendations and preferences in traumatic brain injury rehabilitation is used for illustration. The main motivation for this work are two recent papers that recommend estimating multinomial logistic models with correlated responses by using a Poisson likelihood which is statistically correct but computationally inefficient. [Copyright &y& Elsevier]

Abstract: Computer simulation on medical sciences has gain increasing popularity as computational technology advances. Successful thermal modeling of the human eye will assist in enabling early detections of eye abnormalities such as inflammatory. However, validity of every computer simulated results must be benchmarked with experimental measurement and this can be a daunting task especially in biomedical fields where experimental data is not in abundance. This paper presents a 2D finite element (FE) human eye model developed to simulate its thermal steady state conditions based on the properties and parameters reported in the open literatures. The results are verified with experimental and computational results obtained by previous studies on human as well as animal eyes. Results show discrepancy of only 0.33% when compared to images from infrared (IR) screening and a difference of only 0.127% compared to another finite element model. The sensitivity analysis also provides good agreement with results by previous studies. This promising simulation allows new possibility in computational methods for eye health care. [Copyright &y& Elsevier]

Abstract: Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20–30%. In contrast, typical bedside testing kits have errors of approximately 7–10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the continuous glucose monitoring system (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R =0.81 (range: 0.68–0.88) as compared to a reported value of R =0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute 1-h glucose prediction error of 6.20% (range: 4.97–8.06%) with an average standard deviation per patient of 5.22% (range: 3.26–8.55%). Note that all the methods and results are generalisable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies. [Copyright &y& Elsevier]

Abstract: In this paper we present storage structures for efficiently processing XML path queries on healthcare data stored in smart cards using XML format. In the design of the storage structures that we present, properties of healthcare data and fundamental types of queries on the healthcare data are taken into account as well as the constraints of the smart cards. We compare the space and time efficiency of the proposed storage structures for various types of queries. [Copyright &y& Elsevier]

Summary: Health level 7 (HL7) is a standard for medical information exchange. It defines data transfers for the application systems in the healthcare environment. Alternatively, the extensible markup language (XML) is a standard for data exchange using the Internet. If exchange messages follow the content and the sequence defined by HL7 and are expressed in the XML format, the system may benefit from the advantages of both standards. In creating the XML schema, we found ambiguities in HL7 message structures that cause the XML schema to be non-deterministic. These ambiguous expressions are summarized within 12 structures and can be replaced with equivalent or similar unambiguous structures. The finite state automata are used to verify expression equivalence. Applying this schema, an XML document may eliminate redundant segment group definitions and make the structure simple and easy to reproduce. In this paper, we discuss the methods and our experience in resolving ambiguous problems in HL7 messages to generate a deterministic XML schema. [Copyright &y& Elsevier]

In many developing countries including Turkey, telemedicine systems are not in wide use due to the high cost and complexity of the required technology. Lack of these systems however has serious implications on patients who live in rural areas. The objective of this paper is to present a simple and economically affordable alternative to the current systems that would allow experts to easily access the medical data of their remote patients over the Internet. The system is developed in client-server architecture with a user-friendly graphical interface and various services are implemented as dynamic web pages based on PHP. The other key features of the system are its powerful security features and platform independency. An academic prototype is implemented and presented to the evaluation of a group of physicians. The results reveal that the system could find acceptance from the medical community and it could be an effective means of providing quality health care in developing countries. [Copyright &y& Elsevier]

Given that clinicians presented with identical clinical information will act in different ways, there is a need to introduce into routine clinical practice methods and tools to support the scientific homogeneity and accountability of healthcare decisions and actions. The benefits expected from such action include an overall reduction in cost, improved quality of care, patient and public opinion satisfaction. Computer-based medical data processing has yielded methods and tools for managing the task away from the hospital management level and closer to the desired disease and patient management level. To this end, advanced applications of information and disease process modelling technologies have already demonstrated an ability to significantly augment clinical decision making as a by-product. The wide-spread acceptance of evidence-based medicine as the basis of cost-conscious and concurrently quality-wise accountable clinical practice suffices as evidence supporting this claim. Electronic libraries are one-step towards an online status of this key health-care delivery quality control environment. Nonetheless, to date, the underlying information and knowledge management technologies have failed to be integrated into any form of pragmatic or marketable online and real-time clinical decision making tool. One of the main obstacles that needs to be overcome is the development of systems that treat both information and knowledge as clinical objects with same modelling requirements. This paper describes the development of such a system in the form of an intelligent clinical information management system: a system which at the most fundamental level of clinical decision support facilitates both the organised acquisition of clinical information and knowledge and provides a test-bed for the development and evaluation of knowledge-based decision support functions. [Copyright &y& Elsevier]

Personal health care has obtained increasing importance in the field of health care as the populations’ age in the industrialised countries and resources available for health care remain limited. Personal health care through digital television is an exiting possibility in the realisation of new types of services answering to this demand for increased personal action and responsibility in health care. The possibilities of digital television in health care are studied in the Health Care Television (HCTV) research project of the Digital Media Institute at Tampere University of Technology. In this paper personal health care services are studied mainly from the perspective of the interactive service infrastructure of digital television. Firstly we present the general infrastructure of digital television and the different interactive service types of digital television. The usage of these service types in personal health care applications is also discussed. Finally, a web-based application based on chronic atrial fibrillation and its test use is presented. The application is used as a research platform for personal health care applications in digital television. [Copyright &y& Elsevier]

• We investigate the territory alignment problem related to health care services. • Which mathematical model would be optimal for solving the territory alignment problem related to health care services? • We use a methodology that combines three methods of analysis in a novel manner: social network analysis, longitudinal analysis, and mapping change analysis The aim of this study is to present a new methodology to explore a field of research and exercise this technique to find good mathematical models to solve the problem of territorial alignment applied to health services. For this purpose we show a methodology that combines three methods of analysis: social network analysis, longitudinal analysis, and mapping change analysis. In this paper, we applied the mapping change method, originally used in large networks, to small and medium ones, and used the Tabu search scheme instead of simulated annealing. Finally, to highlight the significant changes over time of keywords networks, an alluvial diagram is used to show the significance clusterings through the subperiods studied. The work reports on the most relevant authors on the subject and the most widely used mathematical models applied to solve the problem. [ABSTRACT FROM AUTHOR]

• The Internet of Things will help physicians and hospital staff perform their duties comfortably and intelligently. • Health-care is one of the most useful areas for IoT use. • A comprehensive overview of the main uses of IoT in health-care has been done. • A comprehensive taxonomy of IoT in health-care is presented. • The most important challenges the future use of IoT in health-care are highlighted. With the recent advances in the Internet of Things (IoT), the field has become more and more developed in healthcare. The Internet of things will help physicians and hospital staff perform their duties comfortably and intelligently. With the latest advanced technologies, most of the challenges of using IoT have been resolved, and this technology can be a great revolution and has many benefits in the future of digital. Healthcare is one of the most useful areas for IoT use. The most important application of IoT is to monitor and make quick decisions in critical situations. Thanks to this technology-based treatment approach, there is an unprecedented opportunity to better the quality and productivity of treatments and better the patient's well-being and better government funding. In this paper, we provide a comprehensive overview of the primary uses of IoT in healthcare. We used the Systematic Literature Review (SLR) method to analyze and comparison articles published in this field between 2015 and March 2020. A comprehensive taxonomy is presented based on the contents of the articles under study. In this article, a brief overview of selected articles based on research questions is given and highlights the most critical challenges and case studies for the future use of IoT in healthcare. According to a detailed study of the 89 articles and a glimpse into about 208 articles, challenges and future trends in healthcare have been identified. [ABSTRACT FROM AUTHOR]

Abstract: Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML. [Copyright &y& Elsevier]

Abstract: This paper describes a probabilistic causal model for the caring procedure to be followed on wheelchair users with spinal injury. Due to loss of sensation and movement caused by spinal cord injuries, the information extracted about patient findings (i.e. the signs and symptoms) can often be incomplete. This, in turn, introduces uncertainty in assessing the existence and severity of a given condition—and thus, employment of the appropriate caring procedure. Bayesian networks are a framework that enables probabilistic inference; therefore, they are useful for diagnostic reasoning and selection of the appropriate caring procedure in the face of uncertainty. The network structure and numerical parameters are based on data elicited from the qualified staff nurses and available literature of the National Spinal Injury Centre, Stoke Mandeville Hospital, Aylesbury, UK, as well as the compiled knowledge base within the DIMITRA rule-based expert system [M. Athanasiou, J.Y. Clark, DIMITRA: an online expert system for carers of paraplegics and quadriplegics, International Journal of Healthcare Technology and Management 7(5) (2006) 44–451]. We also present the model and report the results of the diagnostic performance tests using the AgenaRiskn [Agena Limited, AgenaRisk Software Package, http://www.agena.co.uk] Bayesian network package. [Copyright &y& Elsevier]

Abstract: One of the main items on the eHealth agenda of the European Community is the design and promotion of electronic patient summaries as an instrument to guarantee the continuity of healthcare across national boundaries and to facilitate the pervasive delivery of healthcare, thus ensuring the right to patient mobility and increasing the productivity and quality of health service delivery. From a technical point of view, this objective requires middleware technology which is able to cope with the stringent interoperability, multi-lingualism, distribution, and privacy requirements arising in eHealth settings. This paper presents triplespace computing, a coordination middleware for the Semantic Web,and demonstrates its relevance to the realization of the European Patient Summary, an infrastructure for sharing and coordinating access to patient summaries at the European level. [Copyright &y& Elsevier]