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Understanding the Co-occurrence of Diseases Using Structure Learning

  • Martijn Lappenschaar
  • Arjen Hommersom
  • Joep Lagro
  • Peter J. F. Lucas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)

Abstract

Multimorbidity, i.e., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases can be complex due to the various interactions between diseases. To better understand the co-occurrence of diseases, we propose Bayesian network structure learning methods for deriving the interactions between risk factors. In particular, we propose novel measures for structural relationships in the co-occurrence of diseases and identify the critical factors in this interaction. We illustrate these measures in the oncological area for better understanding co-occurrences of malignant tumours.

Keywords

Chronic Obstructive Pulmonary Disease Pancreatic Cancer Irritable Bowel Syndrome Bayesian Network Chronic Liver Disease 
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 2013

Authors and Affiliations

  • Martijn Lappenschaar
    • 1
  • Arjen Hommersom
    • 1
  • Joep Lagro
    • 1
  • Peter J. F. Lucas
    • 1
  1. 1.Radboud University NijmegenThe Netherlands

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