Knowledge-data integration for temporal reasoning in a clinical trial system.

Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing and integrating temporal data and domain knowledge is difficult with the database technologies used in most clinical research systems. There is often a disconnect between the database representation of research data and corresponding domain knowledge of clinical research concepts. In this paper, we present a set of methodologies for undertaking ontology-based specification of temporal information, and discuss their application to the verification of protocol-specific temporal constraints among clinical trial activities. Our approach allows knowledge-level temporal constraints to be evaluated against operational trial data stored in relational databases. We show how the Semantic Web ontology and rule languages OWL and SWRL, respectively, can support tools for research data management that automatically integrate low-level representations of relational data with high-level domain concepts used in study design.

TrialWiz: an ontology-driven tool for authoring clinical trial protocols.

There has long been great interest in the clinical research community for automated support of clinical trials management. At the core of such efforts is formal specification of protocol knowledge. Building a clinical-trial knowledge base is a complex task involving software engineers and domain experts. As part of our Epoch ontological framework for clinical trials management, we have developed TrialWiz, an authoring tool for encoding a clinical-trial knowledge base. The main goals of TrialWiz are to manage the complexity of the protocol-encoding process and to improve efficiency in knowledge acquisition. TrialWiz provides intelligent guidance through the process of acquiring clinical-trial knowledge; graphical user interfaces intuitive to clinical trialists; a repository of reusable knowledge; and facilities to export the knowledge in different formats. We have successfully used TrialWiz to encode example clinical trials at the Immune Tolerance Network (ITN). In this presentation, we will demonstrate the intuitive authoring of clinical trial protocols using TrialWiz and how the protocol knowledge can be used by different clinical trial management applications at run time.

Knowledge-level querying of temporal patterns in clinical research systems.

Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing this domain knowledge is difficult in relational database systems. As a result, there is a need for principled methods to overcome the disconnect between the database representation of time-oriented research data and corresponding knowledge of domain-relevant concepts. In this paper, we present a set of methodologies for undertaking knowledge level querying of temporal patterns, and discuss its application to the verification of temporal constraints in clinical-trial applications. Our approach allows knowledge generated from query results to be tied to the data and, if necessary, used for further inference. We show how the Semantic Web ontology and rule languages, OWL and SWRL, respectively, can support the temporal knowledge model needed to integrate low-level representations of relational data with high-level domain concepts used in research data management. We present a scalable bridge-based software architecture that uses this knowledge model to enable dynamic querying of time-oriented research data.

An ontology-based architecture for integration of clinical trials management applications.

Management of complex clinical trials involves coordinated-use of a myriad of software applications by trial personnel. The applications typically use distinct knowledge representations and generate enormous amount of information during the course of a trial. It becomes vital that the applications exchange trial semantics in order for efficient management of the trials and subsequent analysis of clinical trial data. Existing model-based frameworks do not address the requirements of semantic integration of heterogeneous applications. We have built an ontology-based architecture to support interoperation of clinical trial software applications. Central to our approach is a suite of clinical trial ontologies, which we call Epoch, that define the vocabulary and semantics necessary to represent information on clinical trials. We are continuing to demonstrate and validate our approach with different clinical trials management applications and with growing number of clinical trials.