Ravi D. Shankar

Modeling guidelines for integration into clinical workflow.

The success of clinical decision-support systems requires that they are seamlessly integrated into clinical workflow. In the SAGE project, which aims to create the technological infra-structure for implementing computable clinical practice guide-lines in enterprise settings, we created a deployment-driven methodology for developing guideline knowledge bases. It involves (1) identification of usage scenarios of guideline-based care in clinical workflow, (2) distillation and disambiguation of guideline knowledge relevant to these usage scenarios, (3) formalization of data elements and vocabulary used in the guideline, and (4) encoding of usage scenarios and guideline knowledge using an executable guideline model. This methodology makes explicit the points in the care process where guideline-based decision aids are appropriate and the roles of clinicians for whom the guideline-based assistance is intended. We have evaluated the methodology by simulating the deployment of an immunization guideline in a real clinical information system and by reconstructing the workflow context of a deployed decision-support system for guideline-based care. We discuss the implication of deployment-driven guideline encoding for sharability of executable guidelines.

Using Semantic Web Technologies for Knowledge-Driven Querying of Biomedical Data

Software applications that work with biomedical data have significant knowledge-management requirements. Formal knowledge models and knowledge-based methods can be very useful in meeting these requirements. However, most biomedical data are stored in relational databases, a practice that will continue for the foreseeable future. Using these data in knowledge-driven applications requires approaches that can form a bridge between relational models and knowledge models. Accomplishing this task efficiently is a research challenge. To address this problem, we have developed an end-to-end knowledge-based system based on Semantic Web technologies. It permits formal design-time specification of the data requirements of a system and uses those requirements to drive knowledge-driven queries on operational relational data in a deployed system. We have implemented a dynamic OWL-to-relational mapping method and used SWRL, the Semantic Web Rule Language, as a high-level query language that uses these mappings. We have used these methods to support the development of a participant tracking application for clinical trials and in the development of a test bed for evaluating biosurveillance methods.

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.

An Ontology-Driven Mediator for Querying Time-Oriented Biomedical Data

Most biomedical research databases contain considerable amounts of time-oriented data. However, temporal knowledge about the contextual meaning of such data is not usually represented in a principled fashion. As a result, investigators often develop custom techniques for temporal data analysis that are difficult to reuse. We addressed this problem by developing a set of knowledge-driven methods and tools for temporally representing and querying biomedical data, and have integrated them using a mediator approach. A central issue driving our work is a need to integrate temporal representations of data in relational databases with the domain-specific semantics of temporal patterns used in querying. This paper presents a formal temporal knowledge model using the semantic Web ontology and rule languages, OWL and SWRL, respectively. The model informs the mediator of the temporal semantics used for data analysis. We show that our approach provides the computational foundation for much-needed software to make sense of complex temporal patterns in two biomedical research domains

Efficiently querying relational databases using OWL and SWRL

For the foreseeable future, most data will continue to be stored in relational databases. To work with these data in ontology-based applications, tools and techniques that bridge the two models are required. Mapping all relational data to ontology instances is often not practical so dynamic data access approaches are typically employed, though these approaches can still suffer from scalability problems. The use of rules with these systems presents an opportunity to employ optimization techniques that can significantly reduce the amount of data transferred from databases. To illustrate this premise, we have developed tools that allow direct access to relational data from OWL applications. We express these data requirements by using extensions to OWLs rule language SWRL. A variety of optimization techniques ensure that this process is efficient and scales to large data sets.

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.

ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the Shared Data portal (www.immport.org/immport-open), which allows research data to be repurposed to accelerate the translation of new insights into discoveries.

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.

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data

Summary: Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data‐driven science. We have developed RImmPort that prepares NIAID‐funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology. Availability and Implementation: RImmPort is available as part of Bioconductor (bioconductor.org/packages/RImmPort). Contact: rshankar@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.