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  5. Digitalisierung im klinischen Umfeld mittels Graphdatenbanken

Digitalisierung im klinischen Umfeld mittels Graphdatenbanken

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  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users

Digitalisierung im klinischen Umfeld mittels Graphdatenbanken

Digitalization in clinical settings using graph databases

(Non-FAU Project)

Overall project:
Project leader: Oliver Haas
Project members: Andreas Maier, Oliver Haas
Start date: October 1, 2018
End date:
Acronym:
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
URL:

Abstract

In clinical settings, different data is stored in different systems. These data are very heterogeneous, but still highly interconnected. Graph databases are a good fit for this kind of data: they contain heterogeneous "data nodes" which can be connected to each other. The basic question is now if and how clinical data can be used in a graph database, most importantly how clinical staff can profit from this approach. Possible scenarios are a graphical user interface for clinical staff for easier access to required information or an interface for evaluation and analysis to answer more complex questions. (e.g., "Were there similar patients to this patient? How were they treated?")

Publications

  • Haas O., Maier A., Rothgang E.:
    Using Associative Classification and Odds Ratios for In-Hospital Mortality Risk Estimation
    Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML). (Online, July 23, 2021 - July 23, 2021)
    Open Access: https://www.cse.cuhk.edu.hk/~qdou/public/IMLH2021_files/15_CameraReady_OddsRatios.pdf
    URL: https://sites.google.com/view/imlh2021/program
    BibTeX: Download

Friedrich-Alexander-Universität Erlangen-Nürnberg
Lehrstuhl für Mustererkennung (Informatik 5)

Martensstr. 3
91058 Erlangen
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