Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

Journal: Studies in health technology and informatics
PMID: 39234730

Abstract

INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exception-tolerant hierarchical knowledge bases (i.e., knowledge bases, where rule-based knowledge is represented on several levels of abstraction), privacy concerns have not been addressed extensively in this context yet. However, privacy plays an important role, especially for medical applications.

Authors

  • Xenia Heilmann
    Institute of Computer Science, Johannes Gutenberg University, Mainz, Germany.
  • Valentin Henkys
    Institute of Computer Science, Johannes Gutenberg University, Mainz, Germany.
  • Daan Apeldoorn
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Germany.
  • Konstantin Strauch
    Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany and Institute of Medical Informatics, Biometry, and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany.
  • Bertil Schmidt
  • Timm Lilienthal
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.
  • Torsten Panholzer
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Germany.

Keywords

External Resources

Popular Topics
Recent Journals