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  4. Self-Service Data Preprocessing and Cohort Analysis for Medical Researchers
 
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2019
Conference Paper
Title

Self-Service Data Preprocessing and Cohort Analysis for Medical Researchers

Abstract
Medical researchers are increasingly interested in data-driven approaches to support informed decisions in many medical areas. They collect data about the patients they treat, often creating their own specialized data tables with more characteristics than what is defined in their clinical information system (CIS). Usually, these data tables or sEHR (small electronical health records) are rather small, maybe containing the data of only hundreds of patients. Medical researchers are struggling to find an easy way to first clean and transform these sEHR, and then create cohorts and perform confirmative or exploratory analysis. This paper introduces a methodology and identifies requirements for building systems for self-service data preprocessing and cohort analysis for medical researchers. We also describe a system based on this methodology and the requirements that shows the benefits of our approach. We further highlight these benefits with an example scenario from our projects with clinicians specialized on head&neck cancer treatment.
Author(s)
Burmeister, Jan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Bernard, Jürgen
Univ. of British Columbia
May, Thorsten  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kohlhammer, Jörn  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
IEEE Workshop on Visual Analytics in Healthcare, VAHC 2019  
Conference
Workshop on Visual Analytics in Healthcare (VAHC) 2019  
DOI
10.1109/VAHC47919.2019.8945040
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Individual Health

  • Research Line: Computer graphics (CG)

  • Research Line: Modeling (MOD)

  • Visual analytics

  • electronic patient records

  • Multivariate data

  • data analysis

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