Advertisement

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)

Abstract

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.

Keywords

Association Rule Degree Distribution Frequent Itemsets Support Threshold Medical Patient Record 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Albert, R., Barabási, A.-L.: Staistical mechanics of complex networks. Reviews of modern physics 74, 47–97 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  2. 2.
    Davis, D.A., Chawla, N.V., Christakis, N.A., Barabási, A.-L.: Time to CARE: a collaborative engine for practical disease prediction. Data Mining and Knowledge Discovery Journal 20, 388–415 (2010)CrossRefGoogle Scholar
  3. 3.
    Hidalgo, C.A., Blumm, N., Barabási, A.-L., Christakis, N.A.: A dynamic network approach for the study of human phenotypes. PLoS Computational Biology 5(4) (2009)Google Scholar
  4. 4.
    Steinhaeuser, K., Chawla, N.V.: A network-based approach to understanding and predicting diseases. In: Social Computing and Behavioral Modeling. Springer, Heidelberg (2009)Google Scholar
  5. 5.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson International Edition, London (2006)Google Scholar
  6. 6.
    Wasserman, S., Faust, K.: Social Network Analysis. Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar

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

Personalised recommendations