Mining Hierarchical Pathology Data Using Inductive Logic Programming

Tim Op De Beéck*, Arjen Hommersom, Jan Van Haaren, Maarten van der Heijden, Jesse Davis, Peter J. F. Lucas, Lucy Overbeek, Iris Nagtegaal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


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.
Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
Subtitle of host publication15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings
EditorsJohn H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek
Place of PublicationCham
PublisherSpringer International Publishing AG
Number of pages10
ISBN (Electronic)9783319195513
ISBN (Print)9783319195506
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine - Pavia, Italy
Duration: 17 Jun 201520 Jun 2015


Conference15th Conference on Artificial Intelligence in Medicine
Abbreviated titleAIME 2015
Internet address


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