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Purpose This retrospective, observational cohort study investigated the association of blood eosinophil counts within 1 week of hospitalization for acute exacerbation of COPD (AECOPD) with subsequent risk of all-cause and COPD-related readmission from a large integrated health system. Patients and Methods Electronic medical records were extracted for index hospitalization for AECOPD at all Intermountain Healthcare hospitals. The primary outcome was the relationship of blood eosinophil count to 30-day all-cause readmission; secondary outcomes were 60-day, 90-day, and 12-month all-cause readmission, COPD-related readmission, and empiric derivation of the eosinophil count with the highest area under the curve (AUC) for predicting 30-day all-cause readmission. Results Of 2445 included patients, 1935 (79%) had a blood eosinophil count

Introduction Tobacco use and other cardiovascular risk factors often accompany chronic obstructive pulmonary disease (COPD). This study derived and validated the Summit Score to predict mortality in people with COPD and cardiovascular risks. Methods SUMMIT trial subjects (N=16,485) ages 40-80 years with COPD were randomly assigned 50%/50% to derivation (N=8181) and internal validation (N=8304). Three external COPD validations from Intermountain Healthcare included outpatients with cardiovascular risks (N=9251), outpatients without cardiovascular risks (N=8551), and inpatients (N=26,170). Cox regression evaluated 40 predictors of all-cause mortality. SUMMIT treatments including combined fluticasone furoate (FF) 100μg/vilanterol 25μg (VI) were not included in the score. Results Mortality predictors were FEV1, heart rate, systolic blood pressure, body mass index, age, smoking pack-years, prior COPD hospitalizations, myocardial infarction, heart failure, diabetes, anti-thrombotics, anti-arrhythmics, and xanthines. Combined in the Summit Score (derivation: c=0.668), quartile 4 vs 1 had HR=4.43 in SUMMIT validation (p 19. Conclusion In this post hoc analysis of SUMMIT trial data, the Summit Score was derived and validated in multiple Intermountain COPD populations. The score was used to identify a subpopulation in which mortality risk was lower for FF 100μg/VI 25μg treatment. Trial registration The SUMMIT trial is registered at ClinicalTrials.gov as number NCT01313676.

ObjectivesThe Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive.DesignProspective observational cohort study.SettingPrimary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020.ParticipantsPatients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020.Primary and secondary outcome measuresThe primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately.ResultsAmong 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged ConclusionsIMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.

The demand for personalised products from consumers is a growing trend and as such requires a paradigm shift from mass-production to mass-customization. The realisation of Industry 4.0 provides opportunities for the industrial sector to create modular and flexible production lines that can be rapidly adapted to cater for on-demand manufacturing needs. This modular approach demands a flexible communications infrastructure that can provide reliable connectivity across these manufacturing cells. The use of static wired networks limits adaptability and as such wireless communications are receiving far more attention. Wireless offers the advantages of low cost, seamless deployment, higher flexibility and scalability. However, before wireless communications can be leveraged they need to meet the tight constraints of industrial automation. As per Ultra Reliable Low Latency Communication (URLLC) for industrial communications, sub-millisecond-latency and 1 packet loss in 100K packets is essential. Industrial channel specific optimized approach using Polar codes is proposed as a solution.

Background: As the COVID-19 pandemic evolves, stratifying risk is increasingly important. The Intermountain Risk Score (IMRS) uses the complete blood count (CBC) and basic metabolic profile (BMP) as a first-line predictor of mortality and is widely validated. We hypothesized that IMRS predicts COVID-19 outcomes. Methods: Intermountain Healthcare patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020. Viral RNA testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was conducted March 3 through November 2, 2020. IMRS used published sex-specific weightings and associations were evaluated for the composite of COVID-19 hospitalization or mortality. Findings: Among 3,883 patients, 8.2% were hospitalized, 1.6% died. Subjects with low, mild, moderate, and high-risk IMRS had the composite endpoint in 3.5% (52/1,502), 8.6% (108/1,256), 15.5% (152/979), and 28.1% (41/146), respectively. Versus low-risk, subjects in mild, moderate, and high-risk groups had HR=2.33 (95% CI: 1.67, 3.24), HR=4.01 (CI: 2.93, 5.50), and HR=8.34 (CI: 5.54, 12.57), respectively. Subjects aged

Introduction: In 2019, the USA experienced an outbreak of E-cigarette or Vaping-Associated Lung Injury (EVALI). Clinicians, government, and media publicized the EVALI outbreak. We hypothesized that such publicity might yield a trend of earlier presentation with less severe disease as the outbreak continued. Methods: We studied all patients who presented with EVALI at an Intermountain hospital or clinic between the first case (Jun 27, 2019) and Jan 9, 2020. We compared patients who presented early in the outbreak (prior to date of median case) to those who presented later. Our primary outcome was hospital length-of-stay (LOS), with secondary outcomes of ICU admission and respiratory failure (receipt of mechanical ventilation, non-invasive positive pressure ventilation, or high-flow nasal cannula). We used Wilcoxon rank-sum for comparison of central tendencies and Fisher’s exact test for proportions. We modeled association between date and hospital LOS and respiratory failure using regression. Results: We identified 114 patients from 13 centers. Median age was 28, 67% were male. Patients presenting later in the outbreak had shorter LOS compared to those who presented early (median 3 days, IQR 1-5 vs. 5, IQR 2-8, p Conclusions: Hospital LOS and respiratory failure decreased during the EVALI outbreak, suggesting milder presentation as the outbreak progressed.

Purpose This retrospective, observational cohort study investigated the association of blood eosinophil counts within 1 week of hospitalization for acute exacerbation of COPD (AECOPD) with subsequent risk of all-cause and COPD-related readmission from a large integrated health system. Patients and Methods Electronic medical records were extracted for index hospitalization for AECOPD at all Intermountain Healthcare hospitals. The primary outcome was the relationship of blood eosinophil count to 30-day all-cause readmission; secondary outcomes were 60-day, 90-day, and 12-month all-cause readmission, COPD-related readmission, and empiric derivation of the eosinophil count with the highest area under the curve (AUC) for predicting 30-day all-cause readmission. Results Of 2445 included patients, 1935 (79%) had a blood eosinophil count

Artificial Intelligence (AI) is an enabling technology that when integrated into healthcare applications and smart wearable devices such as Fitbits etc. can predict the occurrence of health conditions in users by capturing and analysing their health data. The integration of AI and smart wearable devices has a range of potential applications in the area of smart healthcare but there is a challenge in the black box operation of decisions made by AI models which have resulted in a lack of accountability and trust in the decisions made. Explainable AI (XAI) is a domain in which techniques are developed to explain predictions made by AI systems. In this paper, XAI is discussed as a technique that can used in the analysis and diagnosis of health data by AI-based systems and a proposed approach presented with the aim of achieving accountability. transparency, result tracing, and model improvement in the domain of healthcare.

An IoT eco-system includes IoT network components, network services and network participants such as organizations, consumers, governments, and businesses. Due to its diversity and scale, trustworthiness is a critical concern to be considered during architectural design and the operational phase of these eco-systems. To do this, security, privacy, reliability, resilience and safety must be assured. However, existing solutions partially address these requirements using centralized approaches that come with challenges such as a single point of failure, scalability, and dependence on a third party. In this context, Distributed Ledger Technology (DLT) and Smart Contracts, due to its intrinsic properties of transparency, immutability, and underlying secure-by-design architecture, allows distributed, decentralized, automated workflows, which can be incorporated to automate the management of the next generation IoT networks. In this paper, we propose a framework for IoT eco-systems providing seamless integration between IoT and DLT to create a decentralized trusted architecture, which ensures trustworthiness of IoT eco-systems at design time and a trust reputation model based on the architecture to protect it during the run-time. Furthermore, we have presented the initial steps towards the implementation of this framework.

Energy consumption scheduling algorithms allow energy harvesting Internet of Things (IoT) devices to maximize the amount of harvested energy that they consume, while maintaining uninterrupted, indefinite, operation. The existing works in the area show a tradeoff between solution quality and computational complexity. At one end fast but suboptimal algorithms can lead to energy waste and power outages. At the other, optimal algorithms are computationally prohibitive for the constrained hardware of the IoT. This paper argues that the tradeoff can be avoided, and presents the MAllEC energy consumption scheduler that maximizes the allowed energy consumption while minimizing energy waste and power outages, with linear time complexity. MAllEC is compared against the state of the art through simulations using long term (14 years) traces of solar irradiance, and shown to consistently achieve the minimum energy waste and power outage. The linear time complexity of MAllEC is measured on constrained IoT hardware (8-bit Tmote Sky) to be low enough so that MAllEC can be executed unintrusively. This paper provides proof of MAllEC’s optimality and shows that, in an application with dynamic, adjustable packet rate, MAllEC can maintain indefinite, uninterruptible, operation at an average rate of almost 100 packets per minute, where a 3-Ah battery powered device, at the same rate, would deplete after less than 200 days.

Background The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown. Methods All inpatient admissions except obstetric patients at Intermountain Healthcare’s 21 adult care hospitals from 2010–2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient’s index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality). Results Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p

Rationale: Although chronic obstructive pulmonary disease (COPD) exacerbation frequency is stable in research cohorts, whether severe COPD exacerbation frequency can be used to identify patients at high risk for future severe COPD exacerbations and/or mortality is unknown. Methods: Severe COPD exacerbation frequency stability was determined in 3 distinct clinical cohorts. A total of 17,450 patients with COPD in Intermountain Healthcare were categorized based on the number of severe COPD exacerbations per year. We determined whether exacerbation frequency was stable and whether it predicted mortality. These findings were validated in 83,134 patients from the U.S. Veterans Affairs (VA) nationwide health care system and 3326 patients from the University of Chicago Medicine health system. Results: In the Intermountain Healthcare cohort, the majority (84%, 14,706 patients) had no exacerbations in 2009 and were likely to remain non-exacerbators with a significantly lower 6-year mortality compared with frequent exacerbators (2 or more exacerbations per year) (25% versus 57%, p

Background A better understanding of the epidemiology and clinical features of invasive fungal infection (IFI) is integral to improving outcomes. We describe a novel case-finding methodology, reporting incidence, clinical features, and outcomes of IFI in a large US health care network. Methods All available records in the Intermountain Healthcare Enterprise Data Warehouse from 2006 to 2015 were queried for clinical data associated with IFI. The resulting data were overlaid in 124 different combinations to identify high-probability IFI cases. The cohort was manually reviewed, and exclusions were applied. European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group Consensus Group definitions were adapted to categorize IFI in a broad patient population. Linear regression was used to model variation in incidence over time. Results A total of 3374 IFI episodes occurred in 3154 patients. The mean incidence was 27.2 cases/100 000 patients per year, and there was a mean annual increase of 0.24 cases/100 000 patients (P = .21). Candidiasis was the most common (55%). Dimorphic fungi, primarily Coccidioides spp., comprised 25.1% of cases, followed by Aspergillus spp. (8.9%). The median age was 55 years, and pediatric cases accounted for 13%; 26.1% of patients were on immunosuppression, 14.9% had autoimmunity or immunodeficiency, 13.3% had active malignancy, and 5.9% were transplant recipients. Lymphopenia preceded IFI in 22.1% of patients. Hospital admission occurred in 76.2%. The median length of stay was 16 days. All-cause mortality was 17.0% at 42 days and 28.8% at 1 year. Forty-two-day mortality was highest in Aspergillus spp. (27.5%), 20.5% for Candida, and lowest for dimorphic fungi (7.5%). Conclusions In this population, IFI was not uncommon, affected a broad spectrum of patients, and was associated with high crude mortality.

International audience; There are strong policy drivers for the promotion of energy efficiency in buildings. In the literature, Model Predictive Control (MPC) is seen as a promising solution to deal with the energy management problem in buildings. Model identification is the primary task involved in the design of MPC control and defining the good level of complexity for the thermal dynamic model is a critical question. This paper focuses on the development of reliable models that can be used to support the deployment of (Distributive (Di)) MPC application.

The collaborative nature of industrial wireless sensor networks (IWSNs) brings several advantages over traditional wired industrial monitoring and control systems, including self-organization, rapid deployment, flexibility, and inherent intelligent processing. In this regard, IWSNs play a vital role in creating more reliable, efficient, and productive industrial systems, thus improving companies competitiveness in the marketplace. Industrial Wireless Sensor Networks: Applications, Protocols, and Standards examines the current state of the art in industrial wireless sensor networks and outlines future directions for research. What Are the Main Challenges in Developing IWSN Systems? Featuring contributions by researchers around the world, this book explores the software and hardware platforms, protocols, and standards that are needed to address the unique challenges posed by IWSN systems. It offers an in-depth review of emerging and already deployed IWSN applications and technologies, and outlines technical issues and design objectives. In particular, the book covers radio technologies, energy harvesting techniques, and network and resource management. It also discusses issues critical to industrial applications, such as latency, fault tolerance, synchronization, real-time constraints, network security, and cross-layer design. A chapter on standards highlights the need for specific wireless communication standards for industrial applications. A Starting Point for Further Research Delving into wireless sensor networks from an industrial perspective, this comprehensive work provides readers with a better understanding of the potential advantages and research challenges of IWSN applications. A contemporary reference for anyone working at the cutting edge of industrial automation, communication systems, and networks, it will inspire further exploration in this promising research area.

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR.Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems.Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates.Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.

An important aspect of the management and control of modern data centers is cooling and energy optimization. Airflow and temperature measurements are key components for modeling and predicting environmental changes and cooling demands. For this, a wireless sensor network (WSN) can facilitate the sensor deployment and data collection in a changing environment. However, the challenging characteristics of these scenarios, e.g., temperature fluctuations, noise, and large amounts of metal surfaces and wiring, make it difficult to predict network behavior and therefore network planning and deployment. In this paper we report a 17-month long deployment of 30 wireless sensor nodes in a small data center room, where temperature, humidity and airflow were collected, along with RSSI, LQI, and battery voltage. After an initial unreliable period, a connectivity assessment performed on the network revealed a high noise floor in some of the nodes, which together with a default low CCA threshold triggered no packet transmissions, yielding a low PDR for those nodes. Increasing the CCA setting and relocating the sink allowed the network to achieve a reliability of 99.2% for the last eight months of the deployment, therefore complying with the project requirements. This highlights the necessity of using proper tools and dependable protocols, and defining design methodologies for managing and deploying WSNs in real-world environments.

Building energy consumption in Ireland makes accounts for 40% of total energy usage, so reducing building energy usage is of great importance. Building integration with microgrids offers a transition to a more energy and cost efficient infrastructure with more fine grained control being possible. To guarantee microgrid stability and efficient operation, this paper formulates a constrained model predictive control problem by using a simplified hybrid system model with logic constraints; and building load and energy price predictions for the dynamic energy management of the microgrid. In terms of a use case we focus on the microgrid installed at our research building comprising of a combined heat and power unit/boilers/storage tank/battery bank. The effectiveness of the proposed control algorithm is illustrated through numerical simulations.

Data centres are part of today's critical information and communication infrastructure, and the majority of business transactions as well as much of our digital life now depend on them. At the same time, data centres are large primary energy consumers, with energy consumed by IT and server room air conditioning equipment and also by general building facilities. In many data centres, IT equipment energy and cooling energy requirements are not always coordinated, so energy consumption is not optimised. Most data centres lack an integrated energy management system that jointly optimises and controls all its energy consuming equipments in order to reduce energy consumption and increase the usage of local renewable energy sources. In this chapter, the authors discuss the challenges of coordinated energy management in data centres and present a novel scalable, integrated energy management system architecture for data centre wide optimisation. A prototype of the system has been implemented, including joint workload and thermal management algorithms. The control algorithms are evaluated in an accurate simulation‐based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.

Environment perception is crucial for the safe navigation of vehicles and robots to detect obstacles in their surroundings. It is also of paramount interest for navigation of human beings in reduced visibility conditions. Obstacle avoidance systems typically combine multiple sensing technologies (i.e., LiDAR, radar, ultrasound and visual) to detect various types of obstacles under different lighting and weather conditions, with the drawbacks of a given technology being offset by others. These systems require powerful computational capability to fuse the mass of data, which limits their use to high-end vehicles and robots. INSPEX delivers a low-power, small-size and lightweight environment perception system that is compatible with portable and/or wearable applications. This requires miniaturizing and optimizing existing range sensors of different technologies to meet the user&rsquo, s requirements in terms of obstacle detection capabilities. These sensors consist of a LiDAR, a time-of-flight sensor, an ultrasound and an ultra-wideband radar with measurement ranges respectively of 10 m, 4 m, 2 m and 10 m. Integration of a data fusion technique is also required to build a model of the user&rsquo, s surroundings and provide feedback about the localization of harmful obstacles. As primary demonstrator, the INSPEX device will be fixed on a white cane.

This poster presents the R4Platform which provides an integrated set of tools and services that can be used as an enabler for continuous performance auditing in the built environment. The objective is to provide a modular set of components that when composed can provide an end-to-end auditing system for building managers, ESCOs and energy providers. The poster will present the platform architecture and its application in a real world environment, this will include a visual demonstration of the platform., The authors wish to acknowledge the support of Enterprise Ireland under commercialisation fund programme CF2014-4623A part of the ERDF2014-2020

The energy consumption of buildings lies often far above the performance objectives of the design phase. This is due to several factors among other serious deficits in the energy operation of building services. TOPAs adopts the principle of continuous performance auditing by not only considering energy use but also knowledge and understanding of the buildings use and their climatic state. Thus it provides a holistic performance auditing process through supporting tools and methodologies that try to minimise the gap between predicted and actual energy use. TOPAs offers an open BMS (Building Management System) IoT driven framework. This framework is composed of core services to connect to any BMS and aggregate all the information in an open platform. Add-on services are also available to improve the understanding of buildings and reduce further the gap.

Traditionally, Wireless Sensor and Actuator Networks or ( WSANs ) have been used as a standalone technology for a specific application purpose such as heating control. The current growth in embedded ICT infrastructure is leading to the deployment of a wide range of embedded systems in our environment, which motivates System of Systems [5] architectures and ultimately, with deployment of IP technologies into this space, the Internet of Things [10] paradigm. However, to simplify system operation and maintenance as well as to reduce costs, WSANs must become an infrastructure that is capable of providing services to multiple end users concurrently rather than requiring a new infrastructure for a new purpose. Here, we present the concept of a WSAN infrastructure as a WSAN Cloud , which provides services to multiple application and data collection systems following the cloud computing paradigm. Each instance of the WSAN cloud (i.e. a specific set of services configured by a particular end user/system) utilises the WSAN infrastructure as if it was a unique network provisioned for specific requirements. A realisation of the WSAN Cloud in the form of Network as a Service or NaaS requires a WSAN to support a service orientated software architecture allowing other systems to provision the WSAN infrastructure for their specific needs and allowing multiple systems to use the WSAN uniquely and concurrently. The WSAN-Service Orchestration Architecture " WSAN-SOrA " presented here, is a novel approach to service provisioning of embedded networked systems and enables WSANs to act as cloud ready infrastructures that facilitate on-demand provisioning for potentially multiple individual backend systems.

Graphical abstractDisplay Omitted Highlights? Innovative technologies to accelerate meaningful use of health care IT are described. ? The SHARPn team is developing an open-source framework for EHR data interoperability. ? Parallel development of tools for patient phenotyping uses standardized EHR data. ? Disparate EHR data were normalized and accessed by a phenotyping rules engine. ? A data throughput test informed design of the framework. Challenges are discussed. The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation's health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation's many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or 'liquidity' of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.

Wireless sensor networks are a key enabling technology for industrial monitoring applications where the use of wireless infrastructure allows high adaptivity and low cost in terms of installation and retrofitting. To facilitate the move from the current wired designs to wireless designs, concerns regarding reliability must be satisfied. Current standardization efforts for industrial wireless systems lack specification on efficient routing protocols that mitigate reliability concerns. Consequently, this work presents the InRout route selection algorithm, where local information is shared among neighbouring nodes to enable efficient, distributed route selection while satisfying industrial application requirements and considering sensor node resource limitations. Route selection is described as a multi-armed bandit task and uses Q-learning techniques to obtain the best available solution with low overhead. A performance comparison with existing approaches demonstrates the benefits of the InRout algorithm, which satisfies typical quality of service requirements for industrial monitoring applications while considering sensor node resources. Simulation results show that InRout can provide gains ranging from 4% to 60% in the number of successfully delivered packets when compared to current approaches with much lower control overhead.

In this paper we present an adaptive distributed cross-layer routing algorithm (ADCR) for hybrid wireless network with dedicated relay stations (HWN*). To support versatile multimedia communication for Mobile Terminals (MTs), the HWN* integrates a cellular network, an ad hoc network and fixed relay nodes (RNs). A MT may borrow cellular data channels that are available thousands mile away via secure multi-hop RNs, where RNs are placed at flexible locations in the network. The MT can also communicate with each other or access internet ubiquitously. We discuss cross media access and network layers routing issues. The ADCR establishes routing paths across RNs or cellular network while providing appropriate QoS (quality of service). Through simulation, we verify the routing performance benefits of HWN* over conventional cellular systems and other hybrid network frameworks. It is anticipated that the simulation results reported in this paper will serve as a guideline for research based on distributed source routing involving heterogeneous wireless technologies.

This paper describes the creation of an IoT driven architecture to support the realization of an OpenBMS approach to managing blocks of buildings. The objective is to overcome the complexities of integration, operation and management of heterogeneous building systems by leveraging existing IoT approaches. The goal is to eliminate vertical data silos and enable the holistic management of energy across existing and new building blocks.

Within the context of Internet of Things there is an expectation that devices will always be connected and an assumption that data will always be available, however there is little concern for the physical devices producing these data streams. There is a need to balance the appetite for data with the constraints and capabilities of the supporting physical infrastructure. This paper presents a management framework for wireless sensor networks within IoT ecosystems. This framework through cooperation and negotiation can lead to the creation of multiple virtual networks deployed over the same physical infrastructure to share resources, context, insight etc., in order to meet dynamic service requirements. This necessitates a shift from traditional management approaches focused on centralized management for bespoke solutions to the development of novel approaches for autonomous management via distributed intelligent gateways that proactively monitor and manage IoT WSN infrastructures to support multiple application verticals.

As the field of medicine grows more complicated and doctors become more specialized in a particular field, the number of healthcare providers involved in healing an individual patient increases. This is particularly true of patients with multiple chronic conditions. Establishing effective communications among the care providers becomes critical to facilitate care coordination and more efficient resource use, which will ultimately result in health outcome improvement. The first step for care coordination is to understand who have been involved in a patient's care and their relationships with the patient. The widespread adoption of Electronic Health Records provides us an opportunity to explore solutions to well-coordinated care. This paper presents the concept of a patient's care team and demonstrates the feasibility of identifying relevant healthcare providers for an individual patient by leveraging electronic patient encounter data. Combined with network analysis techniques, we further visualize the care team structure with quantified strength of patient-provider relationships. Our work is foundational to the larger goal of patient-centered, coordinated care in the digital age of healthcare.

Activation of the MDR1 (ABCB1) gene is a common event conferring multidrug resistance (MDR) in human cancers. We investigated MDR1 activation in MDR variants of a human sarcoma line, some of which express a mutant MDR1, which facilitated the study of allelic gene expression. Structural alterations of MDR1, gene copy numbers, and allelic expression were analyzed by cytogenetic karyotyping, oligonucleotide hybridization, Southern blotting, polymerase chain reaction, and DNA heteroduplex assays. Both chromosome 7 alterations and several cytogenetic changes involving the 7q21 locus are associated with the development of MDR in these sarcoma cells. Multistep-selected cells and their revertants contain three- to six-fold MDR1 gene amplification compared with that of the drug-sensitive parental cell line MES-SA and single-step doxorubicin-selected mutants. MDR1 gene amplification precedes the emergence of a mutant allele in cells that were coselected with doxorubicin and a cyclosporin inhibitor of P-glycoprotein (P-gp). Allele-specific oligonucleotide hybridization showed that the endogenous mutant allele was present as a single copy, with multiple copies of the normal allele. Reselection of revertant cells with doxorubicin in either the presence or the absence of the P-gp inhibitor resulted in exclusive reexpression of the mutant MDR1 allele, regardless of the presence of multiple wild-type MDR1 alleles. These data provide new insights into how multiple alleles are regulated in the amplicon of drug-resistant cancer cells and indicate that increased expression of an amplified gene can result from selective transcription of a single mutant allele of the gene.

To support rapid prototype development and integration of emerging smart home applications, we discuss a flexible and modular middleware framework implementation for home gateway system. The suggested implementation allows for uniting all heterogeneous networked resources with the gateway and publishes semantic enhancement of the cues being triggered by network resources to be processed in a meaningful way by applications responsible for autonomic management and control of home ecosystem. The fundamental building blocks of middleware framework includes (i) a communication middleware that aims for disguising the networked infrastructure heterogeneity, and for facilitating auto-commissioning or auto-registration of disparate resources (sensors, actuators, appliances, meters etc.) (ii) a semantic middleware built on unified ontological model for semantic uplifting of networked resource descriptions and measurements with metadata (i.e., resource capabilities, observed phenomena, spatial properties, etc.) with reasoning ability to deduce temporal situations in home environment at high-level abstraction. These inferred situations are subsequently exposed to an array of applications agents. The integrated middleware framework is implemented using OSGi (open system gateway infrastructure) based service-oriented architecture and its functionality has been validated for number of scenarios realized on real test bed made-up of heterogeneous networked resources from different manufactures.

Research objective To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. Materials and methods Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems—Mayo Clinic and Intermountain Healthcare—were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. Results Using CEMs and open-source natural language processing and terminology services engines—namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)—we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was

With the push for smart environments and the advent of concepts like Systems of Systems and Internet of Things, the need for underlying large-scale wireless sensor networks (WSNs) infrastructures is evident, however it is likely that no single private user could justify the costs to be incurred for a large-scale WSN deployment and the subsequent management and maintenance costs. In order to drive down costs and maximize the WSN utility a shared infrastructure approach makes large-scale multipurpose WSNs viable. The shared infrastructure paradigm where multiple applications and end users act on a single physical WSN infrastructure in parallel requires a fundamental change in the way WSN resources are managed are, specifically at the WSN device level. This paper draws on the cloud computing infrastructure as a service (IaaS) model and presents Serviceware - a middleware approach for infrastructure virtualization in next generation WSNs, where the WSN resources are exposed as services. Virtualization enables the slicing of the physical infrastructure into unique segments or virtual sensor networks that can be configured for individual end users according to their specific requirements.