Medicine and Health Sciences Commons^™

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues

VMASC Publications

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …

A Roadmap For Building Data Science Capacity For Health Discovery And Innovation In Africa, Joseph Beyene, Solomon W. Harrar, Mekibib Altaye, Tessema Astatkie, Tadesse Awoke, Ziv Shkedy, Tesfaye B. Mersha

Statistics Faculty Publications

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In …

Big Data Analytics Applied To Healthcare, Xuejuan Zhang, Boris Vishnevsky

School of Continuing and Professional Studies Student Papers

In this paper, we review the recent literature related to Big Data Analytics (BDA). We also discuss ways of applying BDA in Healthcare. In Section 1, we discuss the definition of Big Data Analytics and its characteristics. In Section 2, we discuss the healthcare ecosystem's main stakeholders and the data of each main stakeholder. Section 3 discusses the challenges and opportunities of leveraging Big Data Analytics by healthcare stakeholders.

Prospects And Challenges Of Population Health With Online And Other Big Data In Africa; Understanding The Link To Improving Healthcare Service Delivery, Rowland Edet, Bolarinwa Afolabi

Department of Sociology: Faculty Publications

Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by …

Ai Techniques For Covid-19, Adedoyin Ahmed Hussain, Ouns Bouachir, Fadi Al-Turjman, Moayad Aloqaily

All Works

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the …

Using Big Data Analytics To Improve Hiv Medical Care Utilisation In South Carolina: A Study Protocol, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Mohammad Rifat Haider, Xiaoming Li

Faculty Publications

Introduction Linkage and retention in HIV medical care remains problematic in the USA. Extensive health utilisation data collection through electronic health records (EHR) and claims data represent new opportunities for scientific discovery. Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). The data warehouse includes EHR, claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services. The …

The Security Of Big Data In Fog-Enabled Iot Applications Including Blockchain: A Survey, Noshina Tariq, Muhammad Asim, Feras Al-Obeidat, Muhammad Zubair Farooqi, Thar Baker, Mohammad Hammoudeh, Ibrahim Ghafir

All Works

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the …

Machine Learning For Ecosystem Services, Simon Willcock, Javier Martínez-López, Danny A.P. Hooftman, Kenneth J. Bagstad, Stefano Balbi, Alessia Marzo, Carlo Prato, Saverio Sciandrello, Giovanni Signorello

Rubenstein School of Environment and Natural Resources Faculty Publications

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where …

Vetcompass Australia: A National Big Data Collection System For Veterinary Science, Paul Mcgreevy, Peter Thomson, Navneet K. Dhand, David Raubenheimer, Sophie Masters, Caroline S. Mansfield, Timothy Baldwin, Ricardo J. Soares Magalhaes, Jacquie Rand, Peter Hill, Anne Peaston, James Gilkerson, Martin Combs, Shane Raidal, Peter Irwin, Peter Irons, Richard Squires, David Brodbelt, Jeremy Hammond

Paul McGreevy, PhD

Is The Force Awakened? Publication Trends In Oncology Big Data As Phase Ii Cancerlinq Is Launched, Hind Rafei, Benjamin Viernes, Angelike P. Liappis, Dalia Abdelaziz Mobarek

GW Research Days 2016 - 2020

Background: The American Society of Clinical Oncology launched CancerLinQ project in 2010 to provide real-time data collection, mining and visualization, clinical decision support, and quality feedback. Creation of a big data software platform is currently underway to power the CancerLinQ in the phase II of the project. This would allow for evidence driven practice and rapid learning for cancer care providers. Additionally, adequate knowledge about the utility of Big Data to encourage provider utilization in high Impact Factor (IF) journals is needed. We aimed to assess trends and quality of Big Data published in Oncology.

Methods: Peer-reviewed English …

The Potentials And Challenges Of Big Data In Public Health, Rena N. Vithiatharan

Australian eHealth Informatics and Security Conference

The potential to use big data sources for public health increases with the broadening availability of data and improved methods of analysis. Whilst there are some well-known examples of the opportunistic use of big data, such as GoogleFlu, public health has not yet realised the full potential of such data sources. A literature review was undertaken to identify the potential of such data collections to impact public health, and to identify what challenges are currently limiting this potential. The potential include improved real-time analysis, research and development and genome studies. However, challenges listed are poor universal standardisation and classification, privacy …

Big Data In Healthcare: What Is It Used For?, Rebecca Hermon, Patricia A H Williams

Australian eHealth Informatics and Security Conference

Big data analytics is a growth area with the potential to provide useful insight in healthcare. Whilst many dimensions of big data still present issues in its use and adoption, such as managing the volume, variety, velocity, veracity, and value, the accuracy, integrity, and semantic interpretation are of greater concern in clinical application. However, such challenges have not deterred the use and exploration of big data as an evidence source in healthcare. This drives the need to investigate healthcare information to control and reduce the burgeoning cost of healthcare, as well as to seek evidence to improve patient outcomes. Whilst …

Inferences & Connections, Tamara Kneese

Media Studies

Data-oriented systems are inferring relationships between people based on genetic material, behavioral patterns (e.g., shared geography imputed by phone carriers), and performed associations (e.g., "friends" online or shared photographs). What responsibilities do entities who collect data that imputes connections have to those who are implicated by association? For example, as DNA and other biological materials are collected outside of medicine (e.g., at point of arrest, by informatics services like 23andme, for scientific inquiry), what rights do relatives (living, dead, and not-yet-born) have? In what contexts is it acceptable to act based on inferred associations and in which contexts is it …