Classical Chemometrics Methods Applied for Clinical Data Analysis

2017 ◽  
pp. 39-42
Author(s):  
R. Bleiziffer ◽  
M. Culea ◽  
C. Sarbu ◽  
P. Podea ◽  
S. Suvar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clinical Data ◽  
Clinical Data Analysis

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

Using an integrated software package for clinical data analysis on a microcomputer

1993 ◽  
Vol 23 (5) ◽  
pp. 349-357
Author(s):  
Nanda Kumar Ganesan ◽  
David Liu ◽  
Edward H. Sims
Keyword(s):  
Data Analysis ◽  
Clinical Data ◽  
Software Package ◽  
Clinical Data Analysis ◽  
Integrated Software

scholarly journals Clinical Data Analysis: An Opportunity to Compare Machine Learning Methods

2016 ◽  
Vol 100 ◽  
pp. 731-738 ◽  
Author(s):  
A. Salcedo-Bernal ◽  
M.P. Villamil-Giraldo ◽  
A.D. Moreno-Barbosa
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Clinical Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Data Analysis
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