Abstract
Sarcoidosis is a multisystem disorder that is characterized by the formation of granulomas in certain organs of the body. The exact cause of sarcoidosis is unknown but evidence exists that sarcoidosis results from exposure of genetically susceptible hosts to specific environmental agents. The wide degree of clinical heterogeneity might indicate that sarcoidosis is not a single polymorphic disease but a collection of genetically complex diseases. As a first step to identify the hypothesized subcategories, large amounts of multidimensional data are collected that are divided into distinct levels. We investigated how clustering techniques can be applied to support the interpretation of sarcoidosis and subsequently to reveal categories of sarcoidosis data. An attempt is made to relate multiple clusters between the different data levels based on validation criteria.
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Karthaus, V.L.J., Donkers, H.H.L.M., Grutters, J.C., van den Herik, H.J., van den Bosch, J.M.M. (2007). Multi-level Clustering in Sarcoidosis: A Preliminary Study. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_27
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DOI: https://doi.org/10.1007/978-3-540-73599-1_27
Publisher Name: Springer, Berlin, Heidelberg
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