David B. Parrish

Epoch: an ontological framework to support clinical trials management

The increasing complexity of clinical trials has generated an enormous requirement for knowledge and information specification at all stages of the trials, including planning, documentation, implementation, and analysis. We are building a knowledge-based framework (Epoch) to support the management of clinical trials. We are tailoring this approach to the Immune Tolerance Network (ITN), an international research consortium developing new therapeutics in immune-mediated disorders. In the broad spectrum of trial management activities, we currently target two areas that are vital to the successful implementation of a trial: (1) tracking study participants as they advance through the trials, and (2) tracking biological specimens as they are processed at the trial laboratories. The core of our software architecture is a suite of ontologies that conceptualizes relevant clinical trial domain. Our approach can provide ITN and other research organizations a stable and consistent knowledge source for clinical-trial software applications.

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.

Towards semantic interoperability in a clinical trials management system

Clinical trials are studies in human patients to evaluate the safety and effectiveness of new therapies. Managing a clinical trial from its inception to completion typically involves multiple disparate applications facilitating activities such as trial design specification, clinical sites management, participants tracking, and trial data analysis. There remains however a strong impetus to integrate these diverse applications – each supporting different but related functions of clinical trial management – at syntactic and semantic levels so as to improve clarity, consistency and correctness in specifying clinical trials, and in acquiring and analyzing clinical data. The situation becomes especially critical with the need to manage multiple clinical trials at various sites, and to facilitate meta-analyses on trials. This paper introduces a knowledge-based framework that we are building to support a suite of clinical trial management applications. Our initiative uses semantic technologies to provide a consistent basis for the applications to interoperate. We are adapting this approach to the Immune Tolerance Network (ITN), an international research consortium developing new therapeutics in immune-mediated disorders.

Representing and Reasoning with Temporal Constraints in Clinical Trials Using Semantic Technologies

Clinical trial protocols include schedule of clinical trial activities such as clinical tests, procedures, and medications. The schedule specifies temporal constraints on the sequence of these activities, on their start times and duration, and on their potential repetitions. There is an enormous requirement to conform to the constraints found in the protocols during the conduct of the clinical trials. In this paper, we present our approach to formally represent temporal constraints found in clinical trials, and to facilitate reasoning with the constraints. We have identified a representative set of temporal constraints found in clinical trials in the immune tolerance area, and have developed a temporal constraint ontology that allows us to formulate the temporal constraints to the extent required to support clinical trials management. We use the ontology to specify temporal annotation on clinical activities in an encoded clinical trial protocol. We have developed a temporal model to encapsulate time-stamped data, and to facilitate 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.

Knowledge-Based System for Managing Complex Clinical Trials

Clinical trials are formal patient studies that serve as the gold standard method for establishing the efficacy of medical treatments. There is an enormous requirement to specify the knowledge used at all stages of clinical trials, including planning, implementation, and analysis, so that they can be faithfully executed by a clinical research organization. We are building Epoch, a knowledge-based system, to help a large research consortium, the immune tolerance network (ITN), undertake trials to advance new therapeutics in immune-mediated disorders. We are currently targeting two application areas essential to the successful implementation of a trial: (1) tracking study participants as they advance through a study, and (2) tracking biological specimens as they are processed at laboratories. In this paper, we discuss our use of the Web ontology language (OWL) to create a suite of ontologies that conceptualize the clinical trial domain and show how they can inform our system. We show that our ontological framework provides a stable, consistent platform to define, maintain, and distribute the knowledge requirements used in clinical trial management