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Knowledge-based temporal abstraction for diabetic monitoring
1Section on Medical Informatics, Stanford University School of Medicine, CA 94305-5479.
Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1994
Summary
We created a knowledge-based temporal-abstraction (KBTA) method to generate abstract concepts from clinical data. This approach focuses on managing the knowledge required for temporal abstraction in healthcare applications.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Clinical data is often time-stamped and requires interpretation to form meaningful concepts.
- Existing methods may lack a systematic approach for creating abstract, interval-based clinical concepts.
Purpose of the Study:
- To present a general method for creating abstract, interval-based concepts from time-stamped clinical data.
- To detail the knowledge representation, acquisition, maintenance, reuse, and sharing aspects of this method.
Main Methods:
- Developed the knowledge-based temporal-abstraction (KBTA) method.
- Described five problem-solving mechanisms for KBTA subtasks.
- Identified four types of knowledge for KBTA instantiation.
Main Results:
- A general method for knowledge-based temporal-abstraction has been established.
- The KBTA method's knowledge management facets are detailed.
- Five problem-solving mechanisms and four knowledge types are defined for KBTA.
Conclusions:
- The KBTA method provides a structured approach for deriving abstract concepts from clinical data.
- This method facilitates knowledge management crucial for temporal abstraction in healthcare.
- KBTA has potential applications, such as in monitoring patients with diabetes.
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