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 the data. 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.