Generalizing the detection of clinical guideline interactions enhanced with LOD

Veruska Zamborlini; Rinke Hoekstra; Marcos Da Silveira; Cedric Pruski; Annette Ten Teije; Frank Van Harmelen

doi:10.1007/978-3-319-54717-6_20

Generalizing the detection of clinical guideline interactions enhanced with LOD

Veruska Zamborlini, Rinke Hoekstra, Marcos Da Silveira, Cedric Pruski, Annette Ten Teije, Frank Van Harmelen

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

4 Scopus citations

Abstract

This paper presents a method for formally representing Computer-Interpretable Guidelines. It allows for combining them with knowledge from several sources to better detect potential interactions within multimorbidity cases, coping with possibly conflicting pieces of evidence coming from clinical studies. The originality of our approach is on the capacity to analyse combinations of more than two recommendations, which is useful, for instance, for polypharmacy interactions cases. We defined general models to express evidence as causation beliefs and designed general rules for detecting interactions (e.g., conflicts, alternatives, etc.) enriched with Linked Open Data (e.g. Drugbank, Sider). In particular we show that Linked Open Data sources enable us to detect (suspected) interactions among multiple drugs due to polypharmacy. We evaluate our approach in a scenario where three different clinical guidelines (Osteoarthritis, Diabetes, and Hypertension) are combined. We demonstrate the capability of this approach for detecting several potential conflicts between the recommendations and find alternatives.

Original language	English
Title of host publication	Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers
Editors	Hugo Gamboa, Ana Fred
Publisher	Springer Verlag
Pages	360-386
Number of pages	27
ISBN (Print)	9783319547169
DOIs	https://doi.org/10.1007/978-3-319-54717-6_20
State	Published - Jan 1 2017
Event	Doctoral Consortium - 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016 - Rome, Italy Duration: Feb 21 2016 → Feb 23 2016

Publication series

Name	Communications in Computer and Information Science
Volume	690
ISSN (Print)	1865-0929

Conference

Conference	Doctoral Consortium - 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016
Country/Territory	Italy
City	Rome
Period	02/21/16 → 02/23/16

Keywords

Clinical guidelines
Knowledge representation
Ontologies
Semantic web

Access to Document

10.1007/978-3-319-54717-6_20

Cite this

Zamborlini, V., Hoekstra, R., Silveira, M. D., Pruski, C., Teije, A. T., & Harmelen, F. V. (2017). Generalizing the detection of clinical guideline interactions enhanced with LOD. In H. Gamboa, & A. Fred (Eds.), Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers (pp. 360-386). (Communications in Computer and Information Science; Vol. 690). Springer Verlag. https://doi.org/10.1007/978-3-319-54717-6_20

Zamborlini, Veruska ; Hoekstra, Rinke ; Silveira, Marcos Da et al. / Generalizing the detection of clinical guideline interactions enhanced with LOD. Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers. editor / Hugo Gamboa ; Ana Fred. Springer Verlag, 2017. pp. 360-386 (Communications in Computer and Information Science).

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abstract = "This paper presents a method for formally representing Computer-Interpretable Guidelines. It allows for combining them with knowledge from several sources to better detect potential interactions within multimorbidity cases, coping with possibly conflicting pieces of evidence coming from clinical studies. The originality of our approach is on the capacity to analyse combinations of more than two recommendations, which is useful, for instance, for polypharmacy interactions cases. We defined general models to express evidence as causation beliefs and designed general rules for detecting interactions (e.g., conflicts, alternatives, etc.) enriched with Linked Open Data (e.g. Drugbank, Sider). In particular we show that Linked Open Data sources enable us to detect (suspected) interactions among multiple drugs due to polypharmacy. We evaluate our approach in a scenario where three different clinical guidelines (Osteoarthritis, Diabetes, and Hypertension) are combined. We demonstrate the capability of this approach for detecting several potential conflicts between the recommendations and find alternatives.",

keywords = "Clinical guidelines, Knowledge representation, Ontologies, Semantic web",

author = "Veruska Zamborlini and Rinke Hoekstra and Silveira, {Marcos Da} and Cedric Pruski and Teije, {Annette Ten} and Harmelen, {Frank Van}",

note = "Funding Information: V. Zamborlini—Funded by CNPq (Brazilian National Council for Scientific and Technological Development) within the Science without Borders programme. Funding Information: We would like to thank colleagues from NEMO-UFES/Brazil for fruitful discussions about transitions, causation beliefs and regulations, and also prof. md. Saulo Bortolon for the nice discussions about medical domain; Jan Wielemaker and Wouter Beek (VU Amsterdam) for helping with SWI-Prolog implementation; Wytze Vliestra (Erasmus Rotterdam) for fruitful discussions about the biomedical domain; and Paul Groth (Elsevier) for fruitful discussions about the potential generality of the model and the use of nanopublications. The first author is funded by CNPq (Brazilian National Council for Scientific and Technological Development) within the program Science without Borders. This work was partially funded by the Dutch National Programme COMMIT. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; Doctoral Consortium - 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016 ; Conference date: 21-02-2016 Through 23-02-2016",

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Zamborlini, V, Hoekstra, R, Silveira, MD, Pruski, C, Teije, AT & Harmelen, FV 2017, Generalizing the detection of clinical guideline interactions enhanced with LOD. in H Gamboa & A Fred (eds), Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers. Communications in Computer and Information Science, vol. 690, Springer Verlag, pp. 360-386, Doctoral Consortium - 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016, Rome, Italy, 02/21/16. https://doi.org/10.1007/978-3-319-54717-6_20

Generalizing the detection of clinical guideline interactions enhanced with LOD. / Zamborlini, Veruska; Hoekstra, Rinke; Silveira, Marcos Da et al.
Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers. ed. / Hugo Gamboa; Ana Fred. Springer Verlag, 2017. p. 360-386 (Communications in Computer and Information Science; Vol. 690).

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

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AU - Teije, Annette Ten

AU - Harmelen, Frank Van

N1 - Funding Information: V. Zamborlini—Funded by CNPq (Brazilian National Council for Scientific and Technological Development) within the Science without Borders programme. Funding Information: We would like to thank colleagues from NEMO-UFES/Brazil for fruitful discussions about transitions, causation beliefs and regulations, and also prof. md. Saulo Bortolon for the nice discussions about medical domain; Jan Wielemaker and Wouter Beek (VU Amsterdam) for helping with SWI-Prolog implementation; Wytze Vliestra (Erasmus Rotterdam) for fruitful discussions about the biomedical domain; and Paul Groth (Elsevier) for fruitful discussions about the potential generality of the model and the use of nanopublications. The first author is funded by CNPq (Brazilian National Council for Scientific and Technological Development) within the program Science without Borders. This work was partially funded by the Dutch National Programme COMMIT. Publisher Copyright: © Springer International Publishing AG 2017.

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N2 - This paper presents a method for formally representing Computer-Interpretable Guidelines. It allows for combining them with knowledge from several sources to better detect potential interactions within multimorbidity cases, coping with possibly conflicting pieces of evidence coming from clinical studies. The originality of our approach is on the capacity to analyse combinations of more than two recommendations, which is useful, for instance, for polypharmacy interactions cases. We defined general models to express evidence as causation beliefs and designed general rules for detecting interactions (e.g., conflicts, alternatives, etc.) enriched with Linked Open Data (e.g. Drugbank, Sider). In particular we show that Linked Open Data sources enable us to detect (suspected) interactions among multiple drugs due to polypharmacy. We evaluate our approach in a scenario where three different clinical guidelines (Osteoarthritis, Diabetes, and Hypertension) are combined. We demonstrate the capability of this approach for detecting several potential conflicts between the recommendations and find alternatives.

AB - This paper presents a method for formally representing Computer-Interpretable Guidelines. It allows for combining them with knowledge from several sources to better detect potential interactions within multimorbidity cases, coping with possibly conflicting pieces of evidence coming from clinical studies. The originality of our approach is on the capacity to analyse combinations of more than two recommendations, which is useful, for instance, for polypharmacy interactions cases. We defined general models to express evidence as causation beliefs and designed general rules for detecting interactions (e.g., conflicts, alternatives, etc.) enriched with Linked Open Data (e.g. Drugbank, Sider). In particular we show that Linked Open Data sources enable us to detect (suspected) interactions among multiple drugs due to polypharmacy. We evaluate our approach in a scenario where three different clinical guidelines (Osteoarthritis, Diabetes, and Hypertension) are combined. We demonstrate the capability of this approach for detecting several potential conflicts between the recommendations and find alternatives.

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KW - Knowledge representation

KW - Ontologies

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Zamborlini V, Hoekstra R, Silveira MD, Pruski C, Teije AT, Harmelen FV. Generalizing the detection of clinical guideline interactions enhanced with LOD. In Gamboa H, Fred A, editors, Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers. Springer Verlag. 2017. p. 360-386. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-54717-6_20

Generalizing the detection of clinical guideline interactions enhanced with LOD

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Keywords

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