Towards a Conceptual Model for Enhancing Reasoning About Clinical Guidelines

Zamborlini, Veruska; Silveira, Marcos Da; Pruski, Cédric; Teije, Annette ten; Harmelen, Frank van

doi:10.1007/978-3-319-13281-5_3

Cited by 15 publications

(21 citation statements)

References 11 publications

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“…We start by addressing realistic but simplified case studies, and add more complexity according to the lessons learned in each iteration. Therefore, this paper is the continuation of earlier work reported in (Zamborlini et al, 2014a;Zamborlini et al, 2014b;Zamborlini et al, 2015b;Zamborlini et al, 2015a). In this series of work, we investigated (i) what knowledge is required to represent and reason about CGs (rather than how to acquire such knowledge), particularly for supporting the multimorbidity issue; (ii) how it can be formalized; (iii) how it can be implemented using Semantic Web technologies, so that (iv) we can exploit the medical knowledge available as Linked Open Data (LOD).…”

Section: Introductionsupporting

confidence: 77%

“…Formal languages proposed for representing clinical guidelines as "computer interpretable" ones (Annette ten Teije, 2008;Peleg, 2013) were not designed to handle the combination of multiple CIGs (Zamborlini et al, 2014a). An alternative solution is the development of alert systems that are independent of the CGs.…”

Section: Related Workmentioning

confidence: 99%

“…In (Zamborlini et al, 2014a) we analyzed related work that addresses recommendation interactions in different levels. Our research focuses on what we called the CIG level, i.e.…”

Section: Related Workmentioning

confidence: 99%

“…Explicit description of local constraints and impact of recommendations is considered an important source of information for increased reasoning capabilities and improved explanation of conflicts in (Bonacin et al, 2013). The model described in (Zamborlini et al, 2014a) introduced clinical recommendations as governing care actions that cause state transitions; an extended version of this model presented in (Zamborlini et al, 2014b) defines different ways in which recommendation can interact according to the referred actions and transitions. The implementation and evaluation of the model using Semantic Web languages was proposed by us in (Zamborlini et al, 2015b).…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

Zamborlini

Hoekstra

Silveira

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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Zamborlini, V.; Hoekstra, R.J.; da Silveira, M.; Pruski, C.; ten Teije-Koppen, A.C.M.; van Harmelen, F.A.H. Link to publication Citation for published version (APA):Zamborlini, V., Hoekstra, R., da Silveira, M., Pruski, C., ten Teije, A., & van Harmelen, F. (2016 General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract: This paper presents a method for formally representing Computer-Interpretable Guidelines to deal with multimorbidity. Although some approaches for merging guidelines exist, improvements are still required for combining several sources of information and coping with possibly conflicting pieces of evidence coming from clinical studies. Our main contribution is twofold: (i) we provide general models and rules for representing guidelines that expresses evidence as causation beliefs; (ii) we introduce a mechanism to exploit external medical knowledge acquired from Linked Open Data (Drugbank, Sider, DIKB) to detect potential interactions between recommendations. We apply this framework to merge three guidelines (Osteoarthritis, Diabetes, and Hypertension) in order to illustrate the capability of this approach for detecting potential conflicts between guidelines and eventually propose alternatives.

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Section: Introductionsupporting

confidence: 77%

Section: Related Workmentioning

confidence: 99%

“…In (Zamborlini et al, 2014a) we analyzed related work that addresses recommendation interactions in different levels. Our research focuses on what we called the CIG level, i.e.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

Zamborlini

Hoekstra

Silveira

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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“…Few approaches focused on interaction detection. The most relevant approach in such an area is the one in [16]. It provides a CIG and domain-independent conceptual model for medical actions and reasoning forms operating on it.…”

Section: Related Work and Comparisonsmentioning

confidence: 99%

GLARE-SSCPM: an Intelligent Systemto Support the Treatment of Comorbid Patients

2024

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The development of software tools supporting physicians in the treatment of comorbid patients is a challenging goal and a "hot topic" in Medical Informatics and Artificial Intelligence. Computer Interpretable Guidelines (CIGs) are consolidated tools to support physicians with evidencebased recommendations in the treatment of patients affected by a specific disease. However, the applications of two or more CIGs on comorbid patients is critical, since dangerous interactions between actions from different CIGs may arise. GLARE-SSCPM is the first tool supporting, in an integrated way, (i) the knowledge-based detection of interactions, (ii) the management of the interactions, and (iii) the final "merge" of (part of) the CIGs operating on the patient. GLARE-SSCPM is characterized by being very supportive to physicians, providing them support for focusing, interaction detection, and for an "hypothesize and test" approach to manage the detected interactions. To achieve such goals, it provides advanced Artificial Intelligence techniques. Preliminary tests in the educational context, within the RoPHS project, have provided encouraging results.

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Querying Probabilistic Temporal Constraints for Guideline Interaction Analysis: GLARE’s Approach

Andolina¹,

Anselma

Piovesan

et al. 2018

Advances in Artificial Intelligence - IBERAMIA 2018

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The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. However, practically speaking, such interactions occur over time. The GLARE project explicitly provides knowledge representation, temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, the effects of actions have a probabilistic distribution in time, and being able to reason (through constraint propagation) and to query probabilistic temporal constraints further enhances the support for interaction detection.

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Towards a Conceptual Model for Enhancing Reasoning About Clinical Guidelines

Cited by 15 publications

References 11 publications

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

GLARE-SSCPM: an Intelligent Systemto Support the Treatment of Comorbid Patients

Querying Probabilistic Temporal Constraints for Guideline Interaction Analysis: GLARE’s Approach

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