Report Number: CS-TR-94-1529
Institution: Stanford University, Department of Computer Science
Title: A knowledge-based method for temporal abstraction of clinical data
Author: Shahar, Yuval
Date: October 1994
Abstract: This dissertation describes a domain-independent method
specific to the task of abstracting higher-level concepts
from time-stamped data. The framework includes a model of
time, parameters, events and contexts. I applied my framework
to several domains of medicine. My goal is to create, from
time-stamped patient data, interval-based temporal
abstractions such as "severe anemia for 3 weeks in the
context of administering AZ T."
The knowledge-based temporal-abstraction method decomposes
the task of abstracting higher-level abstractions from input
data into five subtasks. These subtasks are solved by five
domain-independent temporal-abstraction mechanisms. The
temporal-abstraction mechanisms depend on four
domain-specific knowledge types.
I implemented the knowledge-based temporal-abstraction method
in the RESUME system. RESUME accepts input and returns output
at all levels of abstraction; accepts input out of temporal
order, modifying a view of the past or of the present, as
necessary; generates context-sensitive, controlled output;
and maintains several possible concurrent interpretations of
I evaluated RESUME in the domains of protocol-based care,
monitoring of children's growth, and therapy of diabetes.
A formal specification of a domain's temporal-abstraction
knowledge supports acquisition, maintenance, reuse, and
sharing of that knowledge.