scholarly journals Patient Bayesian Inference: Cloud-Based Healthcare Data Analysis Using Constraint-Based Adaptive Boost Algorithm

2020 ◽  
Author(s):  
Shahid Naseem
Keyword(s):  
Data Analysis ◽  
Bayesian Inference ◽  
Healthcare Data

Big Data Analysis for Cardiovascular Diseases

2018 ◽  
pp. 102-119
Author(s):  
Miguel A. Sánchez-Acevedo ◽  
Zaydi A. Acosta-Chí ◽  
Beatriz A. Sabino-Moxo ◽  
José A. Márquez-Domínguez ◽  
Rosa M. Canton-Croda
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Medical Devices ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Health Issues ◽  
Research Papers ◽  
Healthcare Data ◽  
Common Risk Factors ◽  
The Common

In the healthcare field, plenty of clinical data is generated every day from patient records, surveys, research papers, medical devices, among others sources. These data can be exploited to discover new insights about health issues. For helping decision makers and healthcare data managers, a survey of research works and tools covering the process of handling big data in the healthcare field is included. A methodology for CVD prevention, detection and management through the use of tools for big data analysis is proposed. Also, it is important to maintain privacy of patients when handling healthcare data; therefore, a list of recommendations for maintaining privacy when handling healthcare data is presented. Specific clinical analysis are recommended on those regions where the incidence rate of CVD is high, but a weak relation with the common risk factors is observed according to historical data. Finally, challenges which need to be addressed are presented.

Design and Application of a Containerized Hybrid Transaction Processing and Data Analysis Framework

2018 ◽  
Vol 10 (3) ◽  
pp. 76-90
Author(s):  
Ye Tao ◽  
Xiaodong Wang ◽  
Xiaowei Xu
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
User Interfaces ◽  
Relational Databases ◽  
Transaction Processing ◽  
Data Sets ◽  
Healthcare Data ◽  
Development Framework ◽  
Hybrid Development ◽  
Transaction Processing Systems

This article describes how rapidly growing data volumes require systems that have the ability to handle massive heterogeneous unstructured data sets. However, most existing mature transaction processing systems are built upon relational databases with structured data. In this article, the authors design a hybrid development framework, to offer greater scalability and flexibility of data analysis and reporting, while keeping maximum compatibility and links to the legacy platforms on which transaction business logics run. Data, service and user interfaces are implemented as a toolset stack, for developing applications with functionalities of information retrieval, data processing, analyzing and visualizing. A use case of healthcare data integration is presented as an example, where information is collected and aggregated from diverse sources. The workflow and simulation of data processing and visualization are also discussed, to validate the effectiveness of the proposed framework.

Healthcare data analysis system for regional medical union in smart city

2018 ◽  
Vol 5 (4) ◽  
pp. 334-349 ◽  
Author(s):  
Boyi Xu ◽  
Ling Li ◽  
Daiping Hu ◽  
Bin Wu ◽  
Congcong Ye ◽  
...  
Keyword(s):  
Data Analysis ◽  
Smart City ◽  
Healthcare Data ◽  
Regional Medical ◽  
Analysis System ◽  
Data Analysis System

scholarly journals Automatic Bayesian inference for LISA data analysis strategies

2006 ◽  
Author(s):  
Alexander Stroeer ◽  
Jonathan Gair ◽  
Alberto Vecchio
Keyword(s):  
Data Analysis ◽  
Bayesian Inference ◽  
Analysis Strategies

scholarly journals Challenges and Trends in Clinical Data Analytics

2020 ◽  
pp. 348-360
Keyword(s):  
Health Care ◽  
Data Analysis ◽  
Clinical Data ◽  
Data Analytics ◽  
Health Care Systems ◽  
Data Sets ◽  
Handwritten Documents ◽  
Healthcare Data ◽  
Clinical Data Analysis ◽  
Care Systems

:Today’s technological advancements facilitated the researcher in collecting and organizing various forms of healthcare data. Data is an integral part of health care analytics. Drug discovery for clinical data analytics forms an important breakthrough work in terms of computational approaches in health care systems. On the other hand, healthcare analysis provides better value for money. The health care data management is very challenging as 80% of the data is unstructured as it includes handwritten documents, images; computer-generated clinical reports such as MRI, ECG, city scan, etc. The paper aims at providing a summary of work carried out by scientists and researchers who worked in health care domains. More precisely the work focuses on clinical data analysis for the period 2013 to 2019. The organization of the work carried out is specifically with concerned to data sets, Techniques, and Methods used, Tools adopted, Key Findings in clinical data analysis. The overall objective is to identify the current challenges, trends, and gaps in clinical data analysis. The pathway of the work is focused on carrying out on the bibliometric survey and summarization of the key findings in a novel way.

scholarly journals MiCADO-Edge: Towards an Application-level Orchestrator for the Cloud-to-Edge Computing Continuum

2021 ◽  
Vol 19 (4) ◽  
Author(s):  
Amjad Ullah ◽  
Huseyin Dagdeviren ◽  
Resmi C. Ariyattu ◽  
James DesLauriers ◽  
Tamas Kiss ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Video Processing ◽  
Time Management ◽  
Realistic Case ◽  
Healthcare Data ◽  
Computing Environments ◽  
Management Policies ◽  
Run Time ◽  
Monitoring Information

AbstractAutomated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

scholarly journals VALUE AND APPLICABILITY OF LARGE ADMINISTRATIVE HEALTHCARE DATABASES IN PROSTHETICS AND ORTHOTICS OUTCOMES RESEARCH

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Taavy Miller ◽  
Shane Wurdeman
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Health Economics ◽  
Clinical Data ◽  
Outcomes Research ◽  
Healthcare Decision ◽  
Healthcare Data ◽  
Healthcare Decision Making

The goal of health economics and outcomes research is to improve healthcare decision making. In the absence of high-value clinical data, the availability and quality of administrative healthcare data could be vital in the generation of evidence for orthotics and prosthetics services. The purpose of this article is to provide a stronger understanding of administrative healthcare data analysis, an area that has been scarcely examined within prosthetics and orthotics despite the wealth of information available within such data. Examples of common datasets in this arena currently available are provided, as well as an overview of the limitations and advantages of studies utilizing such datasets. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35958/28315 How To Cite: Miller TA, Wurdeman S. Value and applicability of large administrative healthcare databases in prosthetics and orthotics outcomes research. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.4. https://doi.org/10.33137/cpoj.v4i2.35958 Corresponding Author: Taavy A Miller, PhD, CPODepartment of Clinical and Scientific Affairs, Hanger Clinic, Austin, Texas, USA.E-Mail: [email protected] ID: https://orcid.org/0000-0001-7117-6124

Big Data Analysis for Cardiovascular Diseases

2019 ◽  
pp. 60-77
Author(s):  
Miguel A. Sánchez-Acevedo ◽  
Zaydi A. Acosta-Chí ◽  
Beatriz A. Sabino-Moxo ◽  
José A. Márquez-Domínguez ◽  
Rosa M. Canton-Croda
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Medical Devices ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Health Issues ◽  
Research Papers ◽  
Healthcare Data ◽  
Common Risk Factors ◽  
The Common

In the healthcare field, plenty of clinical data is generated every day from patient records, surveys, research papers, medical devices, among others sources. These data can be exploited to discover new insights about health issues. For helping decision makers and healthcare data managers, a survey of research works and tools covering the process of handling big data in the healthcare field is included. A methodology for CVD prevention, detection and management through the use of tools for big data analysis is proposed. Also, it is important to maintain privacy of patients when handling healthcare data; therefore, a list of recommendations for maintaining privacy when handling healthcare data is presented. Specific clinical analysis are recommended on those regions where the incidence rate of CVD is high, but a weak relation with the common risk factors is observed according to historical data. Finally, challenges which need to be addressed are presented.

Sign in / Sign up

Export Citation Format

Share Document