Antibiotic resistance information exchanges : interim guidance

Antibiotic resistance (AR) is a major clinical and public health threat with potential to unravel more than half a century of human health advances offered by modern medical care. Unfortunately, modern healthcare delivery is notably contributory to the spread of antibiotic-resistant organisms, as patients who have become colonized with resistant organisms often receive care across multiple healthcare settings (e.g., ambulatory care, acute care hospitals (ACHs), and various long-term care (LTC) settings, including long-term acute care hospitals (LTACHs) and skilled nursing facilities (SNFs)).Although the threat of antibiotic-resistant organism transmission from a colonized patient to physically proximate patients remains for the duration of colonization, the lack of information sharing between healthcare facilities often results in the colonized status of a patient being unknown to a receiving or admitting facility. When this occurs, the appropriate infection control precautions are less likely to be used from the start of patient care, which increases the likelihood that resistant organisms will spread to other patients.The need for improved AR situational awareness is a major challenge to the U.S. Centers for Disease Control and Prevention\u2019s (CDC\u2019s) strategy to contain the most threatening forms of resistance and the genes responsible for such phenotypes. To fulfill their central role in implementing the CDC\u2019s containment strategy, some state health departments have developed systems (Multidrug-Resistant Organism (MDRO) Registries or MDRO Alert Systems, referred to herein as AR Information Exchanges (ARIEs)) that track patients previously colonized or infected with specific MDROs and then alert healthcare providers when these patients are admitted to a facility. The term AR Information Exchange emphasizes the importance of multidirectional information flow amongst healthcare facilities and public health authorities, as opposed to unidirectional data collection and storage.This interim guidance is intended for operational use by individuals and organizations responsible for developing or enhancing an ARIE; however, it does not constitute legal advice. Public health agencies should follow applicable laws, statues, and/or regulations when developing ARIEs with questions about directed to the entity\u2019s legal counsel.CS 324851-AARIE-Interim-Guidance-508.pdf20211158

Retrofitting Autonomic Capabilities onto Legacy Systems

Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is a promising solution to ever-increasing system complexity and the spiraling costs of human management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build 'systems of systems' that draw from a gamut of disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand, modify or even recompile the target system's code. We present an autonomic infrastructure that operates similarly to active middleware, to explicitly add autonomic services to pre-existing systems via continual monitoring and a feedback loop that performs, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, independent of the rest of the full infrastructure, for use with a large variety of target systems. This work has been validated by several case studies spanning multiple application domains

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

An Open Framework for Integrating Widely Distributed Hypermedia Resources

The success of the WWW has served as an illustration of how hypermedia functionality can enhance access to large amounts of distributed information. However, the WWW and many other distributed hypermedia systems offer very simple forms of hypermedia functionality which are not easily applied to existing applications and data formats, and cannot easily incorporate alternative functions which would aid hypermedia navigation to and from existing documents that have not been developed with hypermedia access in mind. This paper describes the extension to a distributed environment of the open hypermedia functionality of the Microcosm system, which is designed to support the provision of hypermedia access to a wide range of source material and application, and to offer straightforward extension of the system to incorporate new forms of information access

Configuração automática de plataforma de gestão de desempenho em ambientes NFV e SDN

Mestrado em Engenharia de Computadores e TelemáticaWith 5G set to arrive within the next three years, this next-generation of mobile networks will transform the mobile industry with a profound impact both on its customers as well as on the existing technologies and network architectures. Software-Defined Networking (SDN), together with Network Functions Virtualization (NFV), are going to play key roles for the operators as they prepare the migration from 4G to 5G allowing them to quickly scale their networks. This dissertation will present a research work done on this new paradigm of virtualized and programmable networks focusing on the performance management, supervision and monitoring domains, aiming to address Self-Organizing Networks (SON) scenarios in a NFV/SDN context, with one of the scenarios being the detection and prediction of potential network and service anomalies. The research work itself was done while participating in a R&D project designated SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) funded by the European Commission under the H2020 5G-PPP programme, with Altice Labs being one of the participating partners of this project. Performance management system advancements in a 5G scenario require aggregation, correlation and analysis of data gathered from these virtualized and programmable network elements. Both opensource monitoring tools and customized catalog-driven tools were either integrated on or developed with this purpose, and the results show that they were able to successfully address these requirements of the SELFNET project. Current performance management platforms of the network operators in production are designed for non virtualized (non- NFV) and non programmable (non-SDN) networks, and the knowledge gathered while doing this research work allowed Altice Labs to understand how its Altaia performance management platform must evolve in order to be prepared for the upcoming 5G next generation mobile networks.Com o 5G prestes a chegar nos próximos três anos, esta próxima geração de redes móveis irá transformar a indústria de telecomunicações móveis com um impacto profundo nos seus clientes assim como nas tecnologias e arquiteturas de redes. As redes programáveis (SDN), em conjunto com a virtualização de funções de rede (NFV), irão desempenhar papéis vitais para as operadoras na sua migração do 4G para o 5G, permitindo-as escalar as suas redes rapidamente. Esta dissertação irá apresentar um trabalho de investigação realizado sobre este novo paradigma de virtualização e programação de redes, concentrando-se no domínio da gestão de desempenho, supervisionamento e monitoria, abordando cenários de redes auto-organizadas (SON) num contexto NFV/SDN, sendo um destes cenários a deteção e predição de potenciais anomalias de redes e serviços. O trabalho de investigação foi enquadrado num projeto de I&D designado SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) financiado pela Comissão Europeia no âmbito do programa H2020 5G-PPP, sendo a Altice Labs um dos parceiros participantes deste projeto. Avanços em sistemas de gestão de desempenho em cenários 5G requerem agregação, correlação e análise de dados recolhidos destes elementos de rede programáveis e virtualizados. Ferramentas de monitoria open-source e ferramentas catalog-driven foram integradas ou desenvolvidas com este propósito, e os resultados mostram que estas preencheram os requisitos do projeto SELFNET com sucesso. As plataformas de gestão de desempenho das operadoras de rede atualmente em produção estão concebidas para redes não virtualizadas (non-NFV) e não programáveis (non- SDN), e o conhecimento adquirido durante este trabalho de investigação permitiu à Altice Labs compreender como a sua plataforma de gestão de desempenho (Altaia) terá que evoluir por forma a preparar-se para a próxima geração de redes móveis 5G

Evaluation of Attribute-Based Access Control (ABAC) for EHR in Fog Computing Environment

Fog computing - a connection of billions of devices nearest to the network edge- was recently proposed to support latency-sensitive and real time applications. Electronic Medical Record (EMR) systems are latency-sensitive in nature therefore fog computing considered as appropriate choice for it. This paper proposes a fog environment for E-health system that contains highly confidential information of patients Electronic Health Records (EHR). The proposed E-health system has two main goals: (1) Manage and share EHRs between multiple fog nodes and the cloud,(2) Secure access into EHR on Fog computing without effecting the performance of fog nodes. This system will serve different users based on their attributes and thus providing Attribute Based Access Control ABAC into the EHR in fog to prevent unauthorized access. We focus on reducing the storing and processes in fog nodes to support low capabilities of storage and computing of fog nodes and improve its performance. There are three major contributions in this paper first; a simulator of an E-health system is implemented using both iFogSim and our iFogSimEhealthSystem simulator. Second, the ABAC was applied at the fog to secure the access to patients EHR. Third, the performance of the proposed securing access in E-health system in fog computing was evaluated. The results showed that the performance of fog computing in the secure E-health system is higher than the performance of cloud computing

ID-based user-centric data usage auditing scheme for distributed environments

Recent years have witnessed the trend of increasingly relying on remote and distributed infrastructures, mainly owned and managed by third parties. This increased the number of reported incidents of security breaches compromising users' personal data, where involved entities may massively collect and process massive amounts of such data. Toward these challenges, this paper combines hierarchical Identity Based Cryptographic (IBC) mechanisms with emerging blockchain technologies and introduces a blockchain-based data usage auditing architecture ensuring availability and accountability in a personal data-preserving fashion. The proposed approach relies on smart auditable contracts deployed in blockchain infrastructures. Thus, it offers transparent and controlled data access, sharing and processing, so that unauthorized entities cannot process data without data subjects' consent. Moreover, thanks to the usage of hierarchical ID-based encryption and signature schemes, the proposed solution protects and ensures the confidentiality of users' personal data shared with multiple data controllers and processors. It also provides auditing capacities with tamper-proof evidences for data usage compliance, supported by the intrinsic properties of the blockchain technology