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Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach.

Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach.
Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach.
In this paper, we present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines. GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques at different levels in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed, providing a set of representation primitives. Second, a user-friendly acquisition tool has been designed and implemented, on the basis of the knowledge representation formalism. The acquisition tool provides various forms of help for the expert physicians, including different levels of syntactic and semantic tests in order to check the well-formedness of the guidelines being acquired. Third, a tool for executing guidelines on a specific patient has been made available. The execution module provides a hypothetical reasoning facility, to support physicians in the comparison of alternative diagnostic and/or therapeutic strategies. Moreover, advanced and extended AI techniques for temporal reasoning and temporal consistency checking are used both in the acquisition and in the execution phase. The GLARE approach has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure.
978-3-540-20119-9
Springer
Terenziani, Paolo
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Montani, Stefania
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Bottrighi, Alessio
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Torchio, Mauro
c3ab56bc-ea58-4811-89f2-722c45093645
Molino, Gianpaolo
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Anselma, Luca
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Correndo, Gianluca
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Cappelli, Amedeo
b97656a4-ddad-4d73-bf20-4802cc7203c6
Turini, Franco
fbc2e582-8d89-4d06-9dcf-069263ab1bf3
Terenziani, Paolo
b0ffa980-2092-4f6d-bfa5-5d8ad4ee4599
Montani, Stefania
0f02665f-230c-4b0e-8129-2043f8ae70e1
Bottrighi, Alessio
5bb9b2a8-5087-46a3-a04a-b3a0a13c85e7
Torchio, Mauro
c3ab56bc-ea58-4811-89f2-722c45093645
Molino, Gianpaolo
070e8f3d-d1cb-40db-a6b8-a111bae05752
Anselma, Luca
ce769333-c941-4e6e-a83a-b43185fb3773
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Cappelli, Amedeo
b97656a4-ddad-4d73-bf20-4802cc7203c6
Turini, Franco
fbc2e582-8d89-4d06-9dcf-069263ab1bf3

Terenziani, Paolo, Montani, Stefania, Bottrighi, Alessio, Torchio, Mauro, Molino, Gianpaolo, Anselma, Luca and Correndo, Gianluca , Cappelli, Amedeo and Turini, Franco (eds.) (2003) Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach. (Lecture Notes in Computer Science, 2829), vol. 2829, Springer

Record type: Book

Abstract

In this paper, we present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines. GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques at different levels in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed, providing a set of representation primitives. Second, a user-friendly acquisition tool has been designed and implemented, on the basis of the knowledge representation formalism. The acquisition tool provides various forms of help for the expert physicians, including different levels of syntactic and semantic tests in order to check the well-formedness of the guidelines being acquired. Third, a tool for executing guidelines on a specific patient has been made available. The execution module provides a hypothetical reasoning facility, to support physicians in the comparison of alternative diagnostic and/or therapeutic strategies. Moreover, advanced and extended AI techniques for temporal reasoning and temporal consistency checking are used both in the acquisition and in the execution phase. The GLARE approach has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure.

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Published date: 9 October 2003
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 267927
URI: http://eprints.soton.ac.uk/id/eprint/267927
ISBN: 978-3-540-20119-9
PURE UUID: a5ac50fb-77f9-458f-b845-ae3b459c970b
ORCID for Gianluca Correndo: ORCID iD orcid.org/0000-0003-3335-5759

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Date deposited: 22 Sep 2009 15:15
Last modified: 14 Mar 2024 09:00

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Contributors

Author: Paolo Terenziani
Author: Stefania Montani
Author: Alessio Bottrighi
Author: Mauro Torchio
Author: Gianpaolo Molino
Author: Luca Anselma
Author: Gianluca Correndo ORCID iD
Editor: Amedeo Cappelli
Editor: Franco Turini

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