Development of an agile knowledge engineering framework in support of multi-disciplinary translational research.

In October 2006, the National Institutes of Health launched a new national consortium, funded through Clinical and Translational Science Awards (CTSA), with the primary objective of improving the conduct and efficiency of the inherently multi-disciplinary field of translational research. To help meet this goal, the Ohio State University Center for Clinical and Translational Science has launched a knowledge management initiative that is focused on facilitating widespread semantic interoperability among administrative, basic science, clinical and research computing systems, both internally and among the translational research community at-large, through the integration of domain-specific standard terminologies and ontologies with local annotations. This manuscript describes an agile framework that builds upon prevailing knowledge engineering and semantic interoperability methods, and will be implemented as part this initiative.

Conceptual dissonance: evaluating the efficacy of natural language processing techniques for validating translational knowledge constructs.

The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.

Ontology-anchored Approaches to Conceptual Knowledge Discovery in a Multi-dimensional Research Data Repository.

Chronic Lymphocytic Leukemia (CLL) is the most common adult leukemia in the U.S., and is currently incurable. Though a small number of biomarkers that may correlate to risk of disease progression or treatment outcome in CLL have been discovered, few have been validated in prospective studies or adopted in clinical practice. In order to address this gap in knowledge, it is desirable to discover and test hypotheses that are concerned with translational biomarker-to-phenotype correlations. We report upon a study in which commonly available ontologies were utilized to support the discovery of such translational correlations. We have specifically applied a technique known as constructive induction to reason over the contents of a research data repository utilized by the NCI-funded CLL Research Consortium. Our findings indicate that such an approach can produce semantically meaningful results that can inform hypotheses about higher-level relationships between the types of data contained in such a repository.

Implementation of a metadata architecture and knowledge collection to support semantic interoperability in an enterprise data warehouse.

In order to enhance interoperability between enterprise systems, and improve data validity and reliability throughout The Ohio State University Medical Center (OSUMC), we have initiated the development of an ontology-anchored metadata architecture and knowledge collection for our enterprise data warehouse. The metadata and corresponding semantic relationships stored in the OSUMC knowledge collection are intended to promote consistency and interoperability across the heterogeneous clinical, research, business and education information managed within the data warehouse.

Innovative applications of an enterprise-wide information warehouse.

The Information Warehouse at The Ohio State University Medical Center is a comprehensive effort integrating data from over 70 sources throughout the enterprise. The IW serves a broad diversity of customers in all mission areas of the medical center, from clinical operations and administration to education, to research. This comprehensiveness has facilitated an innovative application of cross-disciplinary technologies and methodologies to problem domains beyond the roles traditionally envisioned for data warehousing.

Augmenting Oracle Text with the UMLS for enhanced searching of free-text medical reports.

The intrinsic complexity of free-text medical reports imposes great challenges for information retrieval systems. We have developed a prototype search engine for retrieving clinical reports that leverages the powerful indexing and querying capabilities of Oracle Text, and the rich biomedical domain knowledge and semantic structures that are captured in the UMLS Metathesaurus.

Leveraging an information warehouse to create translational research environment for wound care center.

In order to discover new biomarkers and therapeutic agents for personalized wound care, a vast amount of clinical information is collected and stored at The Ohio State University Medical Center (OSUMC) Comprehensive Wound Center (CWC). The Information Warehouse (IW) group at OSUMC has developed and implemented a comprehensive data collection network and analysis pipeline to support clinical, translational and outcomes research, and cost analyses that can be converted into clinical best practices for wound care.

Integrative specimen information service - a campus-wide resource for tissue banking, experimental data annotation, and analysis services.

We present the architecture and approach of an evolving campus-wide information service for tissues with clinical and data annotations to be used and contributed to by clinical researchers across the campus. The services provided include specimen tracking, long term data storage, and computational analysis services. The project is conceived and sustained by collaboration among researchers on the campus as well as participation in standards organizations and national collaboratives.