Clinical Data Analysis Using IoT Data Analytics Platforms

Author(s):  
R. Sujatha ◽  
S. Nathiya ◽  
Jyotir Moy Chatterjee

: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.


1993 ◽  
Vol 32 (05) ◽  
pp. 365-372 ◽  
Author(s):  
T. Timmeis ◽  
J. H. van Bemmel ◽  
E. M. van Mulligen

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.


2020 ◽  
Vol 3 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Wenlong Zhang ◽  
Yong Han ◽  
Weisha Li ◽  
Lin Cao ◽  
Libo Yan ◽  
...  

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