A Comorbidity Network Approach to Predict Disease Risk

  • Francesco Folino
  • Clara Pizzuti
  • Maria Ventura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6266)


A prediction model that exploits the past medical patient history to determine the risk of individuals to develop future diseases is proposed. The model is generated by using the set of frequent diseases that contemporarily appear in the same patient. The illnesses a patient could likely be affected in the future are obtained by considering the items induced by high confidence rules generated by the frequent diseases. Furthermore, a phenotypic comorbidity network is built and its structural properties are studied in order to better understand the connections between illnesses. Experimental results show that the proposed approach is a promising way for assessing disease risk.


Association Rule Degree Distribution Frequent Itemsets Support Threshold Medical Patient Record 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Francesco Folino
    • 1
  • Clara Pizzuti
    • 1
  • Maria Ventura
    • 1
  1. 1.Institute for High Performance Computing and Networking (ICAR)Italian National Research Council (CNR)RendeItaly

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