Title: Mining hierarchical pathology data using inductive logic programming
Authors: Op De Be├ęck, Tim ×
Hommersom, Arjen
Van Haaren, Jan
van der Heijden, Maarten
Davis, Jesse
Lucas, Peter J. F.
Overbeek, Lucy
Nagtegaal, Iris #
Issue Date: Jun-2015
Publisher: Springer
Host Document: Proceedings of the 15th Conference on Artificial Intelligence in Medicine (AIME 2015) vol:9105 pages:76-85
Series Title: Lecture Notes in Computer Science
Conference: Conference of Artificial Intelligence in Medicine location:Pavia, Italy date:17-20 June 2015
Abstract: Considerable amounts of data are continuously generated by pathologists in the form of pathology reports. To date, there has been relatively little work exploring how to apply machine learning and data mining techniques to these data in order to extract novel clinical relationships. From a learning perspective, these pathology data possess a number of challenging properties, in particular, the temporal and hierarchical structure that is present within the data. In this paper, we propose a methodology based on inductive logic programming to extract novel associations from pathology excerpts. We discuss the challenges posed by analyzing these data and discuss how we address them. As a case study, we apply our methodology to Dutch pathology data for discovering possible causes of two rare diseases: cholangitis and breast angiosarcomas.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Computer Science - miscellaneous
× corresponding author
# (joint) last author

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