Knowledge-based temporal abstraction for diabetic monitoring.
We have developed a general method that solves the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this method as knowledge-based temporal-abstraction (KBTA). In this paper, we focus on the knowledge representation, acquisition, maintenance, reuse and sharing aspects of the KBTA method. We describe five problem-solving mechanisms that solve the five subtasks into which the KBTA method decomposes its task, and four types of knowledge necessary for instantiating these mechanisms in a particular domain. We present an example of instantiating the KBTA method in the clinical area of monitoring insulin-dependent-diabetes patients.
Shahar
Y
Y
Section on Medical Informatics, Stanford University School of Medicine, CA 94305-5479.
Das
A K
AK
Tu
S W
SW
Kraemer
F B
FB
Musen
M A
MA
D001185:Artificial Intelligence; D001786:Blood Glucose / Q000032:analysis; D003922:Diabetes Mellitus, Type 1 / Q000097:blood; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D012984:Software; D013997:Time Factors
D001786:Blood Glucose