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)


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.


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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  1. 1.
    Diederichs, C., Berger, K., Bartels, D.: The measurement of multiple chronic diseases - a systematic review on existing multimorbidity indices. J. Gerontol. A Biol. Sci. Med. Sci. 66, 301–311 (2011)CrossRefGoogle Scholar
  2. 2.
    Ritchie, C.S., Kvale, E., Fisch, M.J.: Multimorbidity: An issue of growing importance for oncologists. Journal of Oncology Practice 7, 371–374 (2011)CrossRefGoogle Scholar
  3. 3.
    Mariotto, A.B., Rowland, J.H., Ries, L.A., Scoppa, S., Feuer, E.J.: Multiple cancer prevalence: A growing challenge in long-term survivorship. Cancer Epidemiology, Biomarkers and Prevention 16, 566–571 (2007)CrossRefGoogle Scholar
  4. 4.
    Rosso, S., Angelis, R.D., Ciccolallo, L., Carrani, E., Soerjomataram, I., Grande, E., Zigon, G., Brenner, H.: Multiple tumours in survival estimates. Eur. J. Cancer 45, 1080–1094 (2009)CrossRefGoogle Scholar
  5. 5.
    Vittinghoff, E., Glidden, D., Shiboski, S., McCulloch, C.: Regression Methods in Biostatistics: linear, logistic, survival and repeated measures models. Springer, New York (2005)zbMATHGoogle Scholar
  6. 6.
    Lappenschaar, M., Hommersom, A., Lucas, P.: Probabilistic causal models of multimorbidity concepts. In: AMIA Proceedings of the 2012 Annual Symposium, Chicago, United States, pp. 475–484 (2012)Google Scholar
  7. 7.
    Chen, L., Blumm, N., Christakis, N., Barabasi, A., Deisboeck, T.: Cancer metastasis networks and the prediction of progression patterns. British Journal of Cancer 101, 749–758 (2009)CrossRefGoogle Scholar
  8. 8.
    Sistrom, C.L., Garvan, C.W.: Proportions, odds, and risk. Radiology 230, 12–19 (2004)CrossRefGoogle Scholar
  9. 9.
    Kraemer, H.: Statistical issues in assessing comorbidity. Statistics in Medicine 14, 721–733 (1995)CrossRefGoogle Scholar
  10. 10.
    Valderas, J., Starfield, B., Sibbald, B., Salisbury, C., Roland, M.: Defining comorbidity: Implications for understanding health and health services. Ann. Fam. Med. 7, 357–363 (2009)CrossRefGoogle Scholar
  11. 11.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)Google Scholar
  12. 12.
    Scutari, M.: Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software 35, 122 (2010)Google Scholar
  13. 13.
    Tian, J., Pearl, J., Paz, A.: Finding minimal d-separators. Technical report, Computer Science Department, Cognitive Systems Laboratory, University of California, Los Angeles, USA (1998)Google Scholar
  14. 14.
    Deckx, L., van den Akker, M., Metsemakers, J., Knottnerus, A., Schellevis, F., Buntinx, F.: Chronic diseases among older cancer survivors. Journal of Cancer Epidemiology 2012, Article ID 206414, 7 pages (2012)Google Scholar
  15. 15.
    O’Halloran, J., Miller, G., Britt, H.: Defining chronic conditions for primary care with icpc-2. Familiy Practice 21, 381–386 (2004)CrossRefGoogle Scholar
  16. 16.
    Cooper, G.F., Aliferis, C.F., Ambrosino, R., Aronis, J., Buchanan, B.G., Caruana, R., Fine, M.J., Glymour, C., Gordon, G., Hanusa, B.H., Janosky, J.E., Meek, C., Mitchell, T., Richardson, T., Spirtes, P.: An evaluation of machine-learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine 9, 107–138 (1997)CrossRefGoogle Scholar
  17. 17.
    Alekseyenko, A., Lytkin, N.I., Ai, J., Ding, B., Padyukov, L., Aliferis, C.F., Statnikov, A.: Causal graph-based analysis of genome-wide association data in rheumatoid arthritis. Biology Direct 6, 25–37 (2011)CrossRefGoogle Scholar
  18. 18.
    Wei, E.K., Wolin, K.Y., Colditz, G.A.: Time course of risk factors in cancer etiology and progression. Journal of Clinical Oncology 28, 4052–4057 (2010)CrossRefGoogle Scholar

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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