Oliver Haas

• Integration of heterogeneous clinical data
• Interpretable machine learning in healthcare
• Epidemiological association rule mining and associative classification

• 2010 to 2015:
  B. Sc. and M.Sc. in Mathematics at FAU Erlangen-Nürnberg
• 2015 to 2018:
  Software Engineer (Data Mining, Business Intelligence) at infoteam Software AG
• Since 10/2018:
  Researcher at Pattern Recognition Lab and OTH Amberg-Weiden

2018

• Digitalization in clinical settings using graph databases

  (Non-FAU Project)

  Term: since October 1, 2018
  Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
  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?")

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