Knowledge-based temporal abstraction in diabetes therapy.
We suggest a general framework for solving the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this problem-solving framework as the knowledge-based temporal-abstraction (KBTA) method. The KBTA method emphasizes explicit representation, acquisition, maintenance, reuse, and the sharing of knowledge required for abstraction of time-oriented clinical data. We describe the subtasks into which the KBTA method decomposes its task, the problem-solving mechanisms that solve these subtasks, and the knowledge necessary for instantiating these mechanisms in a particular clinical domain. We have implemented the KBTA method in the RESUME system and have applied it to the task of monitoring the care of insulin-dependent diabetics.
Shahar
Y
Y
Section on Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305-5479 USA.
Das
A K
AK
Tu
S W
SW
Kraemer
F B
FB
Basso
L V
LV
Musen
M A
MA
D001185:Artificial Intelligence; D001786:Blood Glucose / Q000378:metabolism; D002940:Circadian Rhythm / Q000502:physiology; D003663:Decision Trees; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy* / Q000503:physiopathology; D004334:Drug Administration Schedule; D004360:Drug Therapy, Computer-Assisted; D004435:Eating / Q000502:physiology; D006801:Humans; D007328:Insulin / Q000008:administration & dosage; D008991:Monitoring, Physiologic; D013995:Time
D001786:Blood Glucose; D007328:Insulin