Traumatic Brain Injury (TBI) is a problem of major medical and socioeconomic significance, although the pathogenesis
of its sequelae is not completely understood. As part of a large, multi-center project to study mild and moderate TBI, a
database and informatics system to integrate a wide-range of clinical, biological, and imaging data is being developed.
This database constitutes a systems-based approach to TBI with the goals of developing and validating biomarker panels that might be used to diagnose brain injury, predict clinical outcome, and eventually develop improved therapeutics. This paper presents the architecture for an informatics system that stores the disparate data types and permits easy access to the data for analysis.
The creation of an integrated biomedical information database requires diverse and flexible schemas. Although relational
database systems seem to be an obvious choice for storage, traditional designs of relational schemas cannot support integrated biomedical information in the most effective ways. Therefore, new models for managing diverse and flexible schemas in relational databases are required for such systems. This paper proposes several schema models for integrated biomedical information using relational tables, and presents an experimental evaluation of their efficiency.
The current trend towards systems medicine will rely heavily on computational and bioinformatics capabilities to collect,
integrate, and analyze massive amounts of data from disparate sources. The objective is to use this information to make
medical decisions that improve patient care. At Georgetown University Medical Center, we are developing an
informatics capability to integrate several research and clinical databases. Our long term goal is to provide researchers at
Georgetown's Lombardi Comprehensive Cancer Center better access to aggregated molecular and clinical information
facilitating the investigation of new hypotheses that impact patient care. We also recognize the need for data mining
tools and intelligent agents to help researchers in these efforts.
This paper describes our initial work to create a flexible platform for researchers and physicians that provides access to
information sources including clinical records, medical images, genomic, epigenomic, proteomic and metabolomic data.
This paper describes the data sources selected for this pilot project and possible approaches to integrating these databases.
We present the different database integration models that we considered. We conclude by outlining the proposed
Information Model for the project.