Clinical Data Analysis Using IoT Data Analytics Platforms

2020 ◽  
pp. 271-293
Author(s):  
R. Sujatha ◽  
S. Nathiya ◽  
Jyotir Moy Chatterjee
Keyword(s):  
Data Analysis ◽  
Clinical Data ◽  
Data Analytics ◽  
Clinical Data Analysis

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.

User Evaluation of an Integrated Medical Workstation for Clinical Data Analysis

1993 ◽  
Vol 32 (05) ◽  
pp. 365-372 ◽  
Author(s):  
T. Timmeis ◽  
J. H. van Bemmel ◽  
E. M. van Mulligen
Keyword(s):  
Data Analysis ◽  
Clinical Research ◽  
Clinical Data ◽  
University Hospital ◽  
User Evaluation ◽  
Scientific Staff ◽  
Medical Institutions ◽  
Clinical Data Analysis ◽  
Research Problems ◽  
The University

AbstractResults are presented of the user evaluation of an integrated medical workstation for support of clinical research. Twenty-seven users were recruited from medical and scientific staff of the University Hospital Dijkzigt, the Faculty of Medicine of the Erasmus University Rotterdam, and from other Dutch medical institutions; and all were given a written, self-contained tutorial. Subsequently, an experiment was done in which six clinical data analysis problems had to be solved and an evaluation form was filled out. The aim of this user evaluation was to obtain insight in the benefits of integration for support of clinical data analysis for clinicians and biomedical researchers. The problems were divided into two sets, with gradually more complex problems. In the first set users were guided in a stepwise fashion to solve the problems. In the second set each stepwise problem had an open counterpart. During the evaluation, the workstation continuously recorded the user’s actions. From these results significant differences became apparent between clinicians and non-clinicians for the correctness (means 54% and 81%, respectively, p = 0.04), completeness (means 64% and 88%, respectively, p = 0.01), and number of problems solved (means 67% and 90%, respectively, p = 0.02). These differences were absent for the stepwise problems. Physicians tend to skip more problems than biomedical researchers. No statistically significant differences were found between users with and without clinical data analysis experience, for correctness (means 74% and 72%, respectively, p = 0.95), and completeness (means 82% and 79%, respectively, p = 0.40). It appeared that various clinical research problems can be solved easily with support of the workstation; the results of this experiment can be used as guidance for the development of the successor of this prototype workstation and serve as a reference for the assessment of next versions.

scholarly journals Clinical data analysis reveals the role of OGR1 (GPR68) in head and neck squamous cancer

2020 ◽  
Vol 3 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Wenlong Zhang ◽  
Yong Han ◽  
Weisha Li ◽  
Lin Cao ◽  
Libo Yan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Head And Neck ◽  
Clinical Data ◽  
Clinical Data Analysis ◽  
Squamous Cancer

scholarly journals Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study

2020 ◽  
Vol 1 (6) ◽  
pp. e254-e262 ◽  
Author(s):  
Anchi Wu ◽  
Valia T Mihaylova ◽  
Marie L Landry ◽  
Ellen F Foxman
Keyword(s):  
Data Analysis ◽  
Influenza A Virus ◽  
Clinical Data ◽  
Experimental Infection ◽  
Influenza A ◽  
Clinical Data Analysis ◽  
Infection Study
Sign in / Sign up

Export Citation Format

Share Document