Mining hierarchical pathology data using inductive logic programming

Tim Op De Beéck; Arjen Hommersom; Jan van Haaren; Maarten van der Heijden; Jesse Davis; Peter Lucas; Lucy Overbeek; Iris Nagtegaal

doi:10.1007/978-3-319-19551-3_9

Mining hierarchical pathology data using inductive logic programming

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
Editors	Riccardo Bellazzi, Lucia Sacchi, John H. Holmes, Niels Peek
Publisher	Springer
Pages	76-85
Number of pages	10
ISBN (Print)	9783319195506
DOIs	https://doi.org/10.1007/978-3-319-19551-3_9
Publication status	Published - 2015
Externally published	Yes
Event	15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy Duration: 17 Jun 2015 → 20 Jun 2015 Conference number: 15

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9105
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th Conference on Artificial Intelligence in Medicine, AIME 2015
Abbreviated title	AIME 2015
Country/Territory	Italy
City	Pavia
Period	17/06/15 → 20/06/15

Keywords

n/a OA procedure

Access to Document

10.1007/978-3-319-19551-3_9

Cite this

Op De Beéck, T., Hommersom, A., van Haaren, J., van der Heijden, M., Davis, J., Lucas, P., Overbeek, L., & Nagtegaal, I. (2015). Mining hierarchical pathology data using inductive logic programming. In R. Bellazzi, L. Sacchi, J. H. Holmes, & N. Peek (Eds.), Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings (pp. 76-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105). Springer. https://doi.org/10.1007/978-3-319-19551-3_9

Op De Beéck, Tim ; Hommersom, Arjen ; van Haaren, Jan et al. / Mining hierarchical pathology data using inductive logic programming. Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. editor / Riccardo Bellazzi ; Lucia Sacchi ; John H. Holmes ; Niels Peek. Springer, 2015. pp. 76-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Op De Beéck, T, Hommersom, A, van Haaren, J, van der Heijden, M, Davis, J, Lucas, P, Overbeek, L & Nagtegaal, I 2015, Mining hierarchical pathology data using inductive logic programming. in R Bellazzi, L Sacchi, JH Holmes & N Peek (eds), Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9105, Springer, pp. 76-85, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, 17/06/15. https://doi.org/10.1007/978-3-319-19551-3_9

Mining hierarchical pathology data using inductive logic programming. / Op De Beéck, Tim; Hommersom, Arjen; van Haaren, Jan et al.
Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. ed. / Riccardo Bellazzi; Lucia Sacchi; John H. Holmes; Niels Peek. Springer, 2015. p. 76-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Mining hierarchical pathology data using inductive logic programming

AU - Op De Beéck, Tim

AU - Hommersom, Arjen

AU - van Haaren, Jan

AU - van der Heijden, Maarten

AU - Davis, Jesse

AU - Lucas, Peter

AU - Overbeek, Lucy

AU - Nagtegaal, Iris

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N2 - 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.

AB - 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.

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A2 - Bellazzi, Riccardo

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ER -

Op De Beéck T, Hommersom A, van Haaren J, van der Heijden M, Davis J, Lucas P et al. Mining hierarchical pathology data using inductive logic programming. In Bellazzi R, Sacchi L, Holmes JH, Peek N, editors, Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. Springer. 2015. p. 76-85. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-19551-3_9

Mining hierarchical pathology data using inductive logic programming

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