Authors:
Ravi D. Shankar
;
Susana B. Martins
;
Martin J. O’Connor
and
Amar K. Das
Affiliation:
Stanford Medical Informatics, Stanford University, United States
Keyword(s):
Ontology, temporal reasoning, clinical trials, biomedical informatics, Semantic Web, OWL.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Databases and Datawarehousing
;
Decision Support Systems
;
Expert Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Medical and Nursing Informatics
;
Symbolic Systems
Abstract:
Temporal constraints play an important role in the specification and implementation of clinical trial protocols, and subsequently, in the querying of the generated trial data. Protocols specify a temporal schedule of clinical trial activities such as tests, procedures, and medications. The schedule includes temporal constraints on the sequence of these activities, on their duration, and on potential cycles. In this paper, we present our approach to formally represent temporal constraints found in clinical trials. We have identified a representative set of temporal constraints found in protocols to study immune tolerance. Our research group has developed a temporal constraint ontology that allows us to formulate the temporal constraints to the extent required to support clinical trials management. We use this ontology to provide temporal annotation of clinical activities in an encoded clinical trial protocol. We have developed a temporal model that represents time-stamped data and fac
ilitates interval-based temporal operations on the data. Using semantic web technologies, we are building a knowledge-based framework that integrates the temporal constraint ontology with the temporal model to support queries on clinical trial data. Using our approach, we can formally specify temporal constraints, and reason with the temporal knowledge to support management of clinical trials.
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