Related Experiment Video
Updated: Mar 14, 2026
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
Published on: September 27, 2020
META-GLARE: A meta-system for defining your own computer interpretable guideline system-Architecture and acquisition
Alessio Bottrighi1, Paolo Terenziani1
1Computer Science Institute, Dipartimento di Scienze e Innovazione Tecnologica, Universita' del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy.
Context:
Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities.
Objective:
The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines. Instead of defining "yet another CIG formalism and its manager", we propose META-GLARE (META Guideline Acquisition, Representation, and Execution), a "meta"-system to define new CIG systems.
Method And Materials:
In this paper, META-GLARE, a meta-system to define new CIG systems, is presented. We try to capture the commonalities among current CIG approaches, by providing (i) a general manager for the acquisition, consultation and execution of hierarchical graphs (representing the control flow of actions in CIGs), parameterized over the types of nodes and of arcs constituting it, and (ii) a library of different elementary components of guidelines nodes (actions) and arcs, in which each type definition involves the specification of how objects of this type can be acquired, consulted and executed. We provide generality and flexibility, by allowing free aggregations of such elementary components to define new primitive node and arc types.
Results:
We have drawn several experiments, in which we have used META-GLARE to build a CIG system (Experiment 1 in Section 8), or to extend it (Experiments 2 and 3). Such experiments show that META-GLARE provides a useful and easy-to-use support to such tasks. For instance, re-building the Guideline Acquisition, Representation, and Execution (GLARE) system using META-GLARE required less than one day (Experiment 1).
Conclusions:
META-GLARE is a meta-system for CIGs supporting fast prototyping. Since META-GLARE provides acquisition and execution engines that are parametric over the specific CIG formalism, it supports easy update and construction of CIG systems.
