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Indirect Association Rules Mining in Clinical Texts

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11089))

Abstract

This paper presents a method for structured information extraction from patient status description. The proposed approach is based on indirect association rules mining (IARM) in clinical text. This method is language independent and unsupervised, that makes it suitable for applications in low resource languages. For experiments are used data from Bulgarian Diabetes Register. The Register is automatically generated from pseudonymized reimbursement requests (outpatient records) submitted to the Bulgarian National Health Insurance Fund in 2010–2016 for more than 5 million citizens yearly. Experiments were run on data collections with patient status data only. The great variety of possible values (conditions) makes this task challenging. The classical frequent itemsets mining algorithms identify just few frequent pairs only even for small minimal support. The results of the proposed IARM method show that attribute-value pairs of anatomical organs/systems and their condition can be identified automatically. IARM approach allows extraction of indirect relations between item pairs with support below the minimal support.

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Notes

  1. 1.

    SNOMED, https://www.snomed.org/.

  2. 2.

    Medical Subject Headings—MESH, https://www.nlm.nih.gov/mesh/.

  3. 3.

    UMLS, https://www.nlm.nih.gov/research/umls/.

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    http://icd9.chrisendres.com/.

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    http://apps.who.int/classifications/icd10/browse/2016/en#/.

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    Laurence, A. AntConc (Version 3.4. 4w)(Computer software). Tokyo, Japan:Waseda University. http://www.laurenceanthony.net/ (2014).

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    http://www.philippe-fournier-viger.com/spmf/index.php.

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Acknowledgments

This research is supported by the grant SpecialIZed Data MIning MethoDs Based on Semantic Attributes (IZIDA), funded by the Bulgarian National Science Fund in 2017–2019. The team acknowledges the support of Medical University - Sofia, the Bulgarian Ministry of Health and the Bulgarian National Health Insurance Fund.

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Authors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetla Boytcheva

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  1. Svetla Boytcheva
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Correspondence to Svetla Boytcheva .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

  2. Universität des Saarlandes, Saarbrücken, Germany

    Josef van Genabith

  3. DFKI GmbH, Saarbrücken, Germany

    Thierry Declerck

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Boytcheva, S. (2018). Indirect Association Rules Mining in Clinical Texts. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-99344-7_4

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  • Online ISBN: 978-3-319-99344-7

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