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The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical
Y Shahar1, S Miksch, P Johnson
1Section on Medical Informatics, Stanford University, CA 94305-5479, USA. shahar@smi.stanford.edu
Artificial Intelligence in Medicine
|October 21, 1998
Summary
This study introduces Asbru, a machine-readable language for clinical guidelines, enabling care providers to apply and critique them using patient records. It supports intention recognition and action critique for better clinical decision-making.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Clinical Decision Support
Background:
- Clinical guidelines are dynamic knowledge schemata refined by healthcare providers.
- Current systems lack robust support for non-designer care providers applying guidelines.
- Dynamic instantiation and refinement of guidelines pose challenges for automated systems.
Purpose of the Study:
- To develop methods supporting care providers in applying clinical guidelines.
- To enable recognition of provider intentions and critique of actions against guidelines and patient records.
- To create a machine-readable guideline representation and an automated acquisition tool.
Main Methods:
- Investigating tasks for guideline application by non-designers.
- Developing methods for intention recognition and action critique.
- Representing guidelines using a task-specific ontology and the Asbru language.
- Utilizing the PROTEGE-II framework for automated guideline acquisition.
Main Results:
- Identified domain-specific knowledge requirements for guideline application methods.
- Presented Asbru, a machine-readable language for guideline representation and annotation.
- Introduced an automated tool for clinical guideline acquisition based on a shared ontology.
Conclusions:
- Asbru facilitates dynamic application and critique of clinical guidelines.
- Automated tools can aid in the acquisition and representation of clinical guidelines.
- The Asgaard project provides methods for supporting care providers in complex clinical scenarios.