Abstract
Evidence-based clinical guidelines require frequent updates due to research and technology advances. The quality of guideline updates can be improved if the knowledge underlying the guideline text is explicitly modelled using the so-called guideline patterns (GPs), mappings between a text fragment and a formal representation of its corresponding medical knowledge.
Ontology-driven extraction of linguistic patterns is a method to automatically reconstruct the control knowledge captured in guidelines, which facilitates a more effective modelling and authoring of clinical guidelines. We illustrate by examples the use of a method for generating and searching for linguistic guideline patterns in the text of a guideline for treatment of breast cancer, and provide a general evaluation of usefulness of these patterns in the modelling of the guideline analyzed.
This work has been supported by the European Commission’s IST program, under contract number IST-FP6-508794 Protocure-II.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mesh (Medical Subject Headings), http://www.nlm.nih.gov/mesh/meshhome.html
Natl. Cancer Institute Ontology, http://www.mindswap.org/2003/CancerOntology
Protocure 2 Project, http://www.protocure.org
Unified Medical Language System, http://www.nlm.nih.gov/research/umls/
CBO. Guideline for the Treatment of Breast Carcinoma. PMID: 12474555 (2002)
Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.: Domain-Specific Keyphrase Extraction. In: Procs. Int. Joint Conf. on AI, pp. 668–673. Morgan Kaufmann Publishers, San Francisco (1999)
Friedman, C., Hripcsak, G.: Evaluating Natural Language Processors in the Clinical Domain. In: Procs. Conf. on Natural Language and Medical Concept Representation, pp. 41–52. IMIA WG6 (1997)
Huffman, S.B.: Learning Information Extraction Patterns from Examples. In: Learning for Natural Language Processing, pp. 246–260 (1995)
Miksch, S., Shahar, Y., Johnson, P.: Asbru: A Task-Specific, Intention-Based, and Time-Oriented Language for Representing Skeletal Plans. In: Procs. 7th W-shop on Knowledge Engineering: Methods and Languages (KEML 1997) (1997)
Moreno, A., Perez, C.: From Text to Ontology: Extraction and Representation of Conceptual Information. In: Procs. Conference on TIA (May 2001)
Riloff, E.: Automatically Generating Extraction Patterns from Untagged Text. In: Procs. 13th Nat. Conf. on AI (AAAI 1996), pp. 1044–1049 (1996)
Riloff, E., Shoen, J.: Automatically Acquiring Conceptual Patterns without an Annotated Corpus. In: Procs. 3rd W-shop on Very Large Corpora, New Jersey, pp. 148–161. Assoc. for Computational Linguistics (1995)
Svatek, V., Ruzicka, M.: Mark-up Based Analysis of Narrative Guidelines with the Stepper Tool. In: Procs. Symposium on Computerized Guidelines and Protocols (CGP 2004). IOS Press, Amsterdam (2004)
Seyfang, A., Miksch, S., Votruba, P.: Specification of Formats of Intermediate, Asbru and Kiv Representations. TR-D2.2a, Protocure-II (June 2004)
Witten, I.: Adaptive Text Mining: Inferring Structure from Sequences. J. of Discrete Algorithms (2000)
Zhong, J., Zhu, H., Li, J., Yu, Y.: Conceptual Graph Matching for Semantic Search. In: ICCS, pp. 92–196 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Serban, R., ten Teije, A., van Harmelen, F., Marcos, M., Polo-Conde, C. (2005). Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical Guidelines. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_28
Download citation
DOI: https://doi.org/10.1007/11527770_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
eBook Packages: Computer ScienceComputer Science (R0)