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.
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Lappenschaar, M., Hommersom, A., Lagro, J., Lucas, P.J.F. (2013). Understanding the Co-occurrence of Diseases Using Structure Learning. In: Peek, N., MarÃn Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_21
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DOI: https://doi.org/10.1007/978-3-642-38326-7_21
Publisher Name: Springer, Berlin, Heidelberg
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