Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines: A case study on hypertension management

Charalampos Doulaverakis, Vassilis Koutkias, Grigoris Antoniou, Ioannis Kompatsiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Clinical practice guidelines (CPGs) constitute a systematically developed, critical body of medical knowledge which is compiled and maintained in order to assist healthcare professionals in decision making. They are available for diverse diseases/conditions and routinely used in many countries, providing reference material for healthcare delivery in clinical settings. As CPGs are paper-based, i.e. plain documents, there have been various approaches for their computerization and expression in a formal manner so that they can be incorporated in clinical information and decision support systems. Semantic Web technologies and ontologies have been extensively used for CPG formalization. In this paper, we present a novel method for the representation and execution of CPGs using OWL ontologies and SPARQL-based inference rules. The proposed approach is capable of expressing complex CPG constructs and can be used to express formalisms, such as negations, which are hard to express using ontologies alone. The encapsulation of SPARQL rules in the CPG ontology is based on the SPARQL Inference Notation (SPIN). The proposed representation of different aspects of CPGs, such as numerical comparisons, calculations, decision branches and state transitions, and their execution is demonstrated through the respective parts of comprehensive, though complex enough, CPGs for arterial hypertension management. The paper concludes by comparing the proposed approach with other relevant works, indicating its potential and limitations, as well as a future work directions.

LanguageEnglish
Title of host publicationKnowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers
PublisherSpringer Verlag
Pages90-107
Number of pages18
ISBN (Print)9783319550138
DOIs
Publication statusPublished - 1 Jan 2017
EventHEC International Joint Workshop on Knowledge Representation for Health Care - Munich, Germany
Duration: 2 Sep 20162 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10096 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

WorkshopHEC International Joint Workshop on Knowledge Representation for Health Care
Abbreviated titleKR4HC/ProHealth 2016
CountryGermany
City Munich
Period2/09/162/09/16

Fingerprint

SPARQL
Hypertension
Ontology
Modeling
Semantic Web
Decision support systems
Encapsulation
Healthcare
Express
Decision making
Inference Rules
Numerical Comparisons
State Transition
Decision Support Systems
Formalization
Notation
Branch
Decision Making

Cite this

Doulaverakis, C., Koutkias, V., Antoniou, G., & Kompatsiaris, I. (2017). Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines: A case study on hypertension management. In Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers (pp. 90-107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10096 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-55014-5_6
Doulaverakis, Charalampos ; Koutkias, Vassilis ; Antoniou, Grigoris ; Kompatsiaris, Ioannis. / Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines : A case study on hypertension management. Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers. Springer Verlag, 2017. pp. 90-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{a324b29ac61e4b9d97bdcf738452d380,
title = "Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines: A case study on hypertension management",
abstract = "Clinical practice guidelines (CPGs) constitute a systematically developed, critical body of medical knowledge which is compiled and maintained in order to assist healthcare professionals in decision making. They are available for diverse diseases/conditions and routinely used in many countries, providing reference material for healthcare delivery in clinical settings. As CPGs are paper-based, i.e. plain documents, there have been various approaches for their computerization and expression in a formal manner so that they can be incorporated in clinical information and decision support systems. Semantic Web technologies and ontologies have been extensively used for CPG formalization. In this paper, we present a novel method for the representation and execution of CPGs using OWL ontologies and SPARQL-based inference rules. The proposed approach is capable of expressing complex CPG constructs and can be used to express formalisms, such as negations, which are hard to express using ontologies alone. The encapsulation of SPARQL rules in the CPG ontology is based on the SPARQL Inference Notation (SPIN). The proposed representation of different aspects of CPGs, such as numerical comparisons, calculations, decision branches and state transitions, and their execution is demonstrated through the respective parts of comprehensive, though complex enough, CPGs for arterial hypertension management. The paper concludes by comparing the proposed approach with other relevant works, indicating its potential and limitations, as well as a future work directions.",
keywords = "Clinical practice guidelines (CPG), CPG modelling and representation, Hypertension management, Ontologies, Semantic web, SPARQL inference notation (SPIN)",
author = "Charalampos Doulaverakis and Vassilis Koutkias and Grigoris Antoniou and Ioannis Kompatsiaris",
year = "2017",
month = "1",
day = "1",
doi = "10.1007/978-3-319-55014-5_6",
language = "English",
isbn = "9783319550138",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "90--107",
booktitle = "Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers",

}

Doulaverakis, C, Koutkias, V, Antoniou, G & Kompatsiaris, I 2017, Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines: A case study on hypertension management. in Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10096 LNAI, Springer Verlag, pp. 90-107, HEC International Joint Workshop on Knowledge Representation for Health Care, Munich, Germany, 2/09/16. https://doi.org/10.1007/978-3-319-55014-5_6

Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines : A case study on hypertension management. / Doulaverakis, Charalampos; Koutkias, Vassilis; Antoniou, Grigoris; Kompatsiaris, Ioannis.

Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers. Springer Verlag, 2017. p. 90-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10096 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines

T2 - A case study on hypertension management

AU - Doulaverakis, Charalampos

AU - Koutkias, Vassilis

AU - Antoniou, Grigoris

AU - Kompatsiaris, Ioannis

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Clinical practice guidelines (CPGs) constitute a systematically developed, critical body of medical knowledge which is compiled and maintained in order to assist healthcare professionals in decision making. They are available for diverse diseases/conditions and routinely used in many countries, providing reference material for healthcare delivery in clinical settings. As CPGs are paper-based, i.e. plain documents, there have been various approaches for their computerization and expression in a formal manner so that they can be incorporated in clinical information and decision support systems. Semantic Web technologies and ontologies have been extensively used for CPG formalization. In this paper, we present a novel method for the representation and execution of CPGs using OWL ontologies and SPARQL-based inference rules. The proposed approach is capable of expressing complex CPG constructs and can be used to express formalisms, such as negations, which are hard to express using ontologies alone. The encapsulation of SPARQL rules in the CPG ontology is based on the SPARQL Inference Notation (SPIN). The proposed representation of different aspects of CPGs, such as numerical comparisons, calculations, decision branches and state transitions, and their execution is demonstrated through the respective parts of comprehensive, though complex enough, CPGs for arterial hypertension management. The paper concludes by comparing the proposed approach with other relevant works, indicating its potential and limitations, as well as a future work directions.

AB - Clinical practice guidelines (CPGs) constitute a systematically developed, critical body of medical knowledge which is compiled and maintained in order to assist healthcare professionals in decision making. They are available for diverse diseases/conditions and routinely used in many countries, providing reference material for healthcare delivery in clinical settings. As CPGs are paper-based, i.e. plain documents, there have been various approaches for their computerization and expression in a formal manner so that they can be incorporated in clinical information and decision support systems. Semantic Web technologies and ontologies have been extensively used for CPG formalization. In this paper, we present a novel method for the representation and execution of CPGs using OWL ontologies and SPARQL-based inference rules. The proposed approach is capable of expressing complex CPG constructs and can be used to express formalisms, such as negations, which are hard to express using ontologies alone. The encapsulation of SPARQL rules in the CPG ontology is based on the SPARQL Inference Notation (SPIN). The proposed representation of different aspects of CPGs, such as numerical comparisons, calculations, decision branches and state transitions, and their execution is demonstrated through the respective parts of comprehensive, though complex enough, CPGs for arterial hypertension management. The paper concludes by comparing the proposed approach with other relevant works, indicating its potential and limitations, as well as a future work directions.

KW - Clinical practice guidelines (CPG)

KW - CPG modelling and representation

KW - Hypertension management

KW - Ontologies

KW - Semantic web

KW - SPARQL inference notation (SPIN)

UR - http://www.scopus.com/inward/record.url?scp=85014965240&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-55014-5_6

DO - 10.1007/978-3-319-55014-5_6

M3 - Conference contribution

SN - 9783319550138

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 90

EP - 107

BT - Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers

PB - Springer Verlag

ER -

Doulaverakis C, Koutkias V, Antoniou G, Kompatsiaris I. Applying SPARQL-based inference and ontologies for modelling and execution of clinical practice guidelines: A case study on hypertension management. In Knowledge Representation for Health Care - HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Revised Selected Papers. Springer Verlag. 2017. p. 90-107. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-55014-5_6