A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation

Mor Peleg; Alexandra Kogan; Samson W. Tu

doi:10.1007/978-3-030-37446-4_2

A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation

Mor Peleg, Alexandra Kogan, Samson W. Tu

Department of Information Systems

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

Abstract

Goal-based reasoning may be used to support clinical decision making for multimorbidity patients; medical knowledge originating in different computer-interpretable guidelines (CIGs) and from medical ontologies may be matched at the goal level. This matching may be based on CIG metaproperty specifications referring to standard medical ontologies (e.g., as the U.S. Department of Veterans Affairs National Drug File - Reference Terminology) and adhering to standard patient information models (e.g., HL7’s Fast Healthcare Interoperability Resources). To support such knowledge and data integration, we developed a method for specifying metaproperty annotations within PROforma CIGs. We positioned this specification step within an existing method for CIG knowledge elicitation/specification, known as the Consensus method. Because clinicians time is costly, the research question that we evaluated in this study was whether knowledge engineers could successfully use this method to specify clinical practice guideline consensus documents in goal-annotated PROforma terms. The preliminary evaluation with nine information systems students taking an advanced knowledge representation course indicates is encouraging. We discuss the technical and conceptual modeling errors and how they could guide instruction of the goal-oriented CIG modeling.

Original language	English
Title of host publication	Artificial Intelligence in Medicine
Subtitle of host publication	Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers
Editors	Mar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic
Publisher	Springer
Pages	17-28
Number of pages	12
ISBN (Print)	9783030374457
DOIs	https://doi.org/10.1007/978-3-030-37446-4_2
State	Published - 2019
Event	7th Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, KR4HC/ProHealth 2019 and the 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems, TEAAM 2019 held in conjunction with the Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland Duration: 26 Jun 2019 → 29 Jun 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11979 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, KR4HC/ProHealth 2019 and the 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems, TEAAM 2019 held in conjunction with the Artificial Intelligence in Medicine, AIME 2019
Country/Territory	Poland
City	Poznan
Period	26/06/19 → 29/06/19

Bibliographical note

Funding Information:
Acknowledgement. The research was funded by grant 906/16 from Israel Science Foundation. We would like to thank Dr. Irit Hochberg and Dr. Raviv Allon for their advice regarding the gold standard for the clinical guidelines used in the study.

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Computer-interpretable guidelines
FHIR
Goal modeling
Multimorbidity
NDF-RT

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-37446-4_2

Cite this

Peleg, M., Kogan, A., & Tu, S. W. (2019). A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation. In M. Marcos, J. M. Juarez, R. Lenz, G. J. Nalepa, G. J. Nalepa, S. Nowaczyk, M. Peleg, J. Stefanowski, & G. Stiglic (Eds.), Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers (pp. 17-28). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11979 LNAI). Springer. https://doi.org/10.1007/978-3-030-37446-4_2

Peleg, Mor ; Kogan, Alexandra ; Tu, Samson W. / A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers. editor / Mar Marcos ; Jose M. Juarez ; Richard Lenz ; Grzegorz J. Nalepa ; Grzegorz J. Nalepa ; Slawomir Nowaczyk ; Mor Peleg ; Jerzy Stefanowski ; Gregor Stiglic. Springer, 2019. pp. 17-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{dc06398bf23443a8abc77ee9406e48cc,

title = "A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation",

abstract = "Goal-based reasoning may be used to support clinical decision making for multimorbidity patients; medical knowledge originating in different computer-interpretable guidelines (CIGs) and from medical ontologies may be matched at the goal level. This matching may be based on CIG metaproperty specifications referring to standard medical ontologies (e.g., as the U.S. Department of Veterans Affairs National Drug File - Reference Terminology) and adhering to standard patient information models (e.g., HL7{\textquoteright}s Fast Healthcare Interoperability Resources). To support such knowledge and data integration, we developed a method for specifying metaproperty annotations within PROforma CIGs. We positioned this specification step within an existing method for CIG knowledge elicitation/specification, known as the Consensus method. Because clinicians time is costly, the research question that we evaluated in this study was whether knowledge engineers could successfully use this method to specify clinical practice guideline consensus documents in goal-annotated PROforma terms. The preliminary evaluation with nine information systems students taking an advanced knowledge representation course indicates is encouraging. We discuss the technical and conceptual modeling errors and how they could guide instruction of the goal-oriented CIG modeling.",

keywords = "Computer-interpretable guidelines, FHIR, Goal modeling, Multimorbidity, NDF-RT",

author = "Mor Peleg and Alexandra Kogan and Tu, {Samson W.}",

note = "Funding Information: Acknowledgement. The research was funded by grant 906/16 from Israel Science Foundation. We would like to thank Dr. Irit Hochberg and Dr. Raviv Allon for their advice regarding the gold standard for the clinical guidelines used in the study. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 7th Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, KR4HC/ProHealth 2019 and the 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems, TEAAM 2019 held in conjunction with the Artificial Intelligence in Medicine, AIME 2019 ; Conference date: 26-06-2019 Through 29-06-2019",

year = "2019",

doi = "10.1007/978-3-030-37446-4_2",

language = "English",

isbn = "9783030374457",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "17--28",

editor = "Mar Marcos and Juarez, {Jose M.} and Richard Lenz and Nalepa, {Grzegorz J.} and Nalepa, {Grzegorz J.} and Slawomir Nowaczyk and Mor Peleg and Jerzy Stefanowski and Gregor Stiglic",

booktitle = "Artificial Intelligence in Medicine",

address = "Germany",

}

Peleg, M , Kogan, A & Tu, SW 2019, A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation. in M Marcos, JM Juarez, R Lenz, GJ Nalepa, GJ Nalepa, S Nowaczyk, M Peleg, J Stefanowski & G Stiglic (eds), Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11979 LNAI, Springer, pp. 17-28, 7th Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, KR4HC/ProHealth 2019 and the 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems, TEAAM 2019 held in conjunction with the Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, 26/06/19. https://doi.org/10.1007/978-3-030-37446-4_2

A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation. / Peleg, Mor ; Kogan, Alexandra; Tu, Samson W.
Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers. ed. / Mar Marcos; Jose M. Juarez; Richard Lenz; Grzegorz J. Nalepa; Grzegorz J. Nalepa; Slawomir Nowaczyk; Mor Peleg; Jerzy Stefanowski; Gregor Stiglic. Springer, 2019. p. 17-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11979 LNAI).

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

TY - GEN

T1 - A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation

AU - Peleg, Mor

AU - Kogan, Alexandra

AU - Tu, Samson W.

N1 - Funding Information: Acknowledgement. The research was funded by grant 906/16 from Israel Science Foundation. We would like to thank Dr. Irit Hochberg and Dr. Raviv Allon for their advice regarding the gold standard for the clinical guidelines used in the study. Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Goal-based reasoning may be used to support clinical decision making for multimorbidity patients; medical knowledge originating in different computer-interpretable guidelines (CIGs) and from medical ontologies may be matched at the goal level. This matching may be based on CIG metaproperty specifications referring to standard medical ontologies (e.g., as the U.S. Department of Veterans Affairs National Drug File - Reference Terminology) and adhering to standard patient information models (e.g., HL7’s Fast Healthcare Interoperability Resources). To support such knowledge and data integration, we developed a method for specifying metaproperty annotations within PROforma CIGs. We positioned this specification step within an existing method for CIG knowledge elicitation/specification, known as the Consensus method. Because clinicians time is costly, the research question that we evaluated in this study was whether knowledge engineers could successfully use this method to specify clinical practice guideline consensus documents in goal-annotated PROforma terms. The preliminary evaluation with nine information systems students taking an advanced knowledge representation course indicates is encouraging. We discuss the technical and conceptual modeling errors and how they could guide instruction of the goal-oriented CIG modeling.

AB - Goal-based reasoning may be used to support clinical decision making for multimorbidity patients; medical knowledge originating in different computer-interpretable guidelines (CIGs) and from medical ontologies may be matched at the goal level. This matching may be based on CIG metaproperty specifications referring to standard medical ontologies (e.g., as the U.S. Department of Veterans Affairs National Drug File - Reference Terminology) and adhering to standard patient information models (e.g., HL7’s Fast Healthcare Interoperability Resources). To support such knowledge and data integration, we developed a method for specifying metaproperty annotations within PROforma CIGs. We positioned this specification step within an existing method for CIG knowledge elicitation/specification, known as the Consensus method. Because clinicians time is costly, the research question that we evaluated in this study was whether knowledge engineers could successfully use this method to specify clinical practice guideline consensus documents in goal-annotated PROforma terms. The preliminary evaluation with nine information systems students taking an advanced knowledge representation course indicates is encouraging. We discuss the technical and conceptual modeling errors and how they could guide instruction of the goal-oriented CIG modeling.

KW - Computer-interpretable guidelines

KW - FHIR

KW - Goal modeling

KW - Multimorbidity

KW - NDF-RT

UR - http://www.scopus.com/inward/record.url?scp=85078416757&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-37446-4_2

DO - 10.1007/978-3-030-37446-4_2

M3 - Conference contribution

AN - SCOPUS:85078416757

SN - 9783030374457

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

SP - 17

EP - 28

BT - Artificial Intelligence in Medicine

A2 - Marcos, Mar

A2 - Juarez, Jose M.

A2 - Lenz, Richard

A2 - Nalepa, Grzegorz J.

A2 - Nowaczyk, Slawomir

A2 - Peleg, Mor

A2 - Stefanowski, Jerzy

A2 - Stiglic, Gregor

PB - Springer

T2 - 7th Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, KR4HC/ProHealth 2019 and the 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems, TEAAM 2019 held in conjunction with the Artificial Intelligence in Medicine, AIME 2019

Y2 - 26 June 2019 through 29 June 2019

ER -

Peleg M , Kogan A, Tu SW. A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation. In Marcos M, Juarez JM, Lenz R, Nalepa GJ, Nalepa GJ, Nowaczyk S, Peleg M, Stefanowski J, Stiglic G, editors, Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Revised Selected Papers. Springer. 2019. p. 17-28. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-37446-4_2

A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this