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.

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.

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.

The translational research enterprise requires bi-directional sharing of data, knowledge, and information between researchers in the biosciences and those in clinical disciplines. Informatics efforts in translational research have focused largely on developing automated methods to correlate the results of genomics, proteomics, or mechanistic assay studies with available data on diagnosis, treatment, and outcomes. Often, the latter set of measures involves crude categories, such as ‘cancer’ or ‘no cancer,’ because further details about a patient’s health or performed interventions are lacking. Such limitations create problems of specificity for findings in translational research. There are recent efforts to gather clinical information through standardized Electronic Medical Record representations that include genetics and genomics data [Hoffman 2007]. Such passive observations may yield biological insights into the mechanism of human disease and therapeutics. However, formalized controlled experiments, particularly human clinical trials, are necessary to address potential biases in biomarker analysis [Ransohoff 2005]. As a result, researchers are proposing and undertaking trial designs that include adjunct highthroughput assays or that directly evaluate biological hypotheses. We refer here to such studies as translational clinical trials.

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.

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.

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.

Clinical trials encompass a vast array of studies in treatment, prevention or diagnosis of medical conditions. There is an enormous requirement for knowledge and information management at all stages of the trials – planning, specification, implementation, and analysis. We are building Epoch, a knowledgebased system to manage clinical trials in the Immune Tolerance Network (ITN) in developing new therapeutics in immune-mediated disorders. In the broad spectrum of trial management activities, we are currently targeting two areas that are vital to the successful implementation of a trial – tracking study participants as they advance through the studies, and tracking clinical specimens as they are processed at the trial laboratories. The core of our system is a suite of medical ontologies that conceptualizes the clinical trial domain relevant to our participant and specimen tracking applications. We expect that our ontological solution can provide a stable and consistent platform to support our current and future requirements of clinical trial 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.

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

To characterize further the nature of haemostatic impairment in haemorrhagic fever with renal syndrome, we assessed platelet function in 9 patients in whom the diagnosis was serologically confirmed. Defective platelet aggregation was demonstrated in every patient. An abnormality of the granule release reaction was demonstrated in all of 7 patients tested. Gel-filtered platelets from a normal subject showed normal aggregation in plasma from a patient with impaired aggregation, which is evidence for an intrinsic platelet defect, and against the presence of a circulating inhibitor in this patient.

To characterize the immune response in haemorrhagic fever with renal syndrome, serial changes in immune effector cells were measured in 14 patients. Significant findings included initial elevations of all major leucocyte populations, increases in suppressor T cells and B cells, decreases in helper/suppressor cell ratios, and a dramatic increase in activated T cells. These changes were most marked in severely ill patients. Changes reverted to normal over approximately one week.

Personnel involved with conducting complex clinical research employ a myriad of software applications that meet the demands of managing the research. In the domain of clinical trials that are performed as part of research, software applications vary widely in complexity, are generally autonomous, use very distinct trial knowledge representations, and generate enormous amount of data during the course of the research. Integration of the varied applications to share the domain semantics becomes vital to improve the efficiencies of trial data collection and to ensure the quality of collected data. We have built Epoch, a knowledge-base framework to support the management of clinical trials. We developed a set of ontologies that serves as a central knowledge resource of clinical trial knowledge. We present knowledge transformation methods that we have developed to extract out trial-specific configurations for each of the myriad trial-management and data-analytic applications that a re used in a research enterprise. We have adapted our methods for the Immune Tolerance Network, an international collaboration of scientists and clinicians studying immune- mediated diseases. Our initiative uses semantic technologies to provide a consistent basis for software applications to generate and analyze clinical research data.

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 called 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 three areas that are vital to the successful implementation of a trial: (1) tracking study participants as they advance through the trials, (2) tracking biological specimens as they are processed at the trial laboratories, and (3) visualization of clinical trial data. The core of our software architecture is a suite of ontologies that conceptualizes relevant clinical trial domain. In our presentation, we will discuss the Epoch suite of clinical trial ontologies, and the specification of clinical trials using our ontologies. We will demonstrate how we use our ontologies to configure clinical trial data collection and visualization applications. We will discuss how our current work supports semantic interoperability among clinical trial applications using semantic web technologies.