145,983 research outputs found
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Enhancing reuse of data and biological material in medical research : from FAIR to FAIR-Health
The known challenge of underutilization of data and biological material from biorepositories as potential resources
formedical research has been the focus of discussion for over a decade. Recently developed guidelines for improved
data availability and reusabilityâentitled FAIR Principles (Findability, Accessibility, Interoperability, and
Reusability)âare likely to address only parts of the problem. In this article,we argue that biologicalmaterial and data
should be viewed as a unified resource. This approach would facilitate access to complete provenance information,
which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for
optimization of long-term storage strategies, as demonstrated in the case of biobanks.Wepropose an extension of the
FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility
and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological
material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human
material and data. These FAIR-Health principles should then be applied to both the biological material and data. We
also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of
volume and breadth of medical data generation, as well as the associated need to process the data efficiently.peer-reviewe
Health informatics domain knowledge analysis: An information technology perspective
Health Informatics is an intersection of information technology, several disciplines of medicine and health care. It sits at the common frontiers of health care services including patient centric, processes driven and procedural centric care. From the information technology perspective it can be viewed as computer application in medical and/or health processes for delivering better health care solutions. In spite of the exaggerated hype, this field is having a major impact in health care solutions, in particular health care deliveries, decision making, medical devices and allied health care industries. It also affords enormous research opportunities for new methodological development. Despite the obvious connections between Medical Informatics, Nursing Informatics and Health Informatics, most of the methodologies and approaches used in Health Informatics have so far originated from health system management, care aspects and medical diagnostic. This paper explores reasoning for domain knowledge analysis that would establish Health Informatics as a domain and recognised as an intellectual discipline in its own right
A New Statistic: The US Census Bureauâs Supplemental Poverty Measure
This article examines the dynamic relationship between macroeconomic performance and measures of poverty in the United States. The article is organized as follows. Section 2 presents insights on the relationship between poverty and macroeconomic performance that emerge from the literature. The emphasis is on empirical studies from 1986 to 2011. Section 3 provides a snapshot of the change in poverty over National Bureau of Economic Research-dated recessions for a variety of poverty measures. Section 4 uses vector autoregressions (VARs) to characterize the response of poverty to innovations in various social indicators and measures of macroeconomic performance. Section 5 expands the empirical analysis to include alternative measures of povertyâa consumption-based poverty rate constructed by Meyer and Sullivan (2010) and an income-based poverty rate constructed by Broda and colleagues (2009) by using a consumer price index that has been adjusted for substitution and quality bias. Section 6 conducts a forecasting exercise for income poverty and consumption poverty. Section 7 concludes and offers suggestions for future research
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Privacy-preserving model learning on a blockchain network-of-networks.
ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction
The Use of Marketing Knowledge in Formulating and Enforcing Consumer Protection Policy
The purpose of this first chapter of the handbook is to discuss how the findings and approaches offered by the marketing discipline are used in consumer protection policy
China Employment Law Update - February 2017
In This Issue: China Makes Two Major Moves in Its Social Insurance System Government Announces Policy to Fingerprint Foreigners Entering China Government Signals Intent to Change Laws to Improve Workplace Safety Government Working on Various Measures to Further Develop Data Privacy Protections Guangdong, Tianjin and Jiangsu enhance protections for female employees Zhejiang Province high court provides guidance on numerous labor dispute issues Former Employee Ordered to Publicly Apologize and Pay Legal Costs for Defaming Employer Shanghai Court Upholds Termination of Senior Executive with Large Personal Debt Chongqing Court Holds Two Employees Partly Liable for Their Employer\u27s Losses from Sca
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