Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach [chapter]

Paolo Terenziani, Stefania Montani, Alessio Bottrighi, Mauro Torchio, Gianpaolo Molino, Luca Anselma, Gianluca Correndo
2003 Lecture Notes in Computer Science  
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,
more » ... 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. The overall challenge of designing and implementing such tools is very complex. In this paper we show how in the GLARE system the adoption of AI techniques provides relevant advantages, especially from the point of view of the user-friendliness of the approach (a more detailed description of GLARE's basic features can be found in [20] ). GLARE's architecture is sketched in section 2. In section 3, we highlight GLARE's representation formalism. Section 4 and section 5 describe the acquisition tool and the execution tool functionalities respectively, with specific attention to the treatment of temporal constraints. Section 6 sketches some testing results. Finally, section 7 presents comparisons and conclusions.
doi:10.1007/978-3-540-39853-0_44 fatcat:rezrosjtoradvivzbf3r5gtqoi