Biomedical Diagnosis Based on Ion Mobility Spectrometry – A Case Study Using Probabilistic Relational Modelling and Learning

  • Marc Finthammer
  • Ryszard Masternak
  • Christoph Beierle
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 300)


Aiming at providing a non-invasive and easy-to-use method for the early detection of bronchial carcinoma, it has been proposed to apply ion mobility spectrometry (IMS) to the breath a person exhales. Extending previous work using such IMS data, we report on a case study using methods of probabilistic relational modelling and learning. By taking additional features of an IMS measurement into account and using refined clustering and modelling methods, inference accuracy is increased.


Drift Time Inductive Logic Programming Brier Score Peak Cluster Markov Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marc Finthammer
    • 1
  • Ryszard Masternak
    • 1
  • Christoph Beierle
    • 1
  1. 1.Dept. of Computer ScienceFernUniversität in HagenHagenGermany

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