Lightweight Data Integration Frameworks for Clinical Research

Research data from a single clinical study is often spread across multiple applications and systems. We present a reusable, lightweight, secure framework for automatically integrating and querying study data from heterogeneous sources in order to answer routine, operational questions for researchers

A lightweight freezer management system for small laboratories

In clinical studies, researchers must often maintain a freezer inventory of biosamples. Existing software packages, designed to track and manage freezer inventory, are not always suitable for small laboratories. We present a lightweight, low cost, alternative that's more appropriate for small studies with limited resources

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

The Value of Value Sets

A common definition of value set will be provided and fully characterized relative to its proposed uses. We will describe, compare, and contrast several approaches to specifying and referencing value sets in a stable manner over time. The term “value set”, although ubiquitous within biomedical informatics has no common definition and has yet to be fully described in a formal manner. It is essential for the design and launch of new ontologies, biomedical informatics applications and data sharing environments that a common and well-­‐ understood definition of “value set” is provided. It is also essential that options and trade-­‐offs be understood for what type of technology is appropriate for the implementation and usage of particular types of value set for particular use cases

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages

Distributed Queries for Quality Control Checks in Clinical Trials

Operational Quality Control (QC) checks are standard practice in clinical trials and ensure ongoing compliance with the study protocol, standard operating procedures (SOPs) and Good Clinical Practice (GCP). We present a method for defining QC checks as distributed queries over case report forms (CRF) and clinical imaging data- sources. Our distributed query system can integrate time-sensitive information in order to populate QC checks that can facilitate discrepancy resolution workflow in clinical trials

A Partnership Approach for Electronic Data Capture in Small-Scale Clinical Trials

The data collection process for clinical trials can be a tedious and error-prone process, and even a barrier to initiating small-scale studies. Electronic Data Capture (EDC) software can meet the need for faster and more reliable collection of data, but these informatics solutions can also be difficult to for researchers to set up. Establishing a full-featured commercial Clinical Trials Management System (CTMS) ecosystem is not realistic due to current institutional resource constraints. As an alternative solution, our Biomedical Informatics core (BMI) provided the technical expertise to pilot each EDC system in partnership with research teams and performed a qualitative evaluation using criteria we had established with prior research.1 When we began our pilot process, we assumed that each system’s EDC functionality would be the most important aspect and we produced a whitepaper focused on functionality.2 However, as we worked with various study teams it became clear they were willing to work around limitations since any web-based EDC software was a step up from paper forms. In our evaluation we found that the design of the Catalyst Web Tools3 made it difficult to use for clinical trials. OpenClinica4 has the most advanced functionality, for example in site management and complex CRF design, but what documentation is available is written in less user-friendly technical language. REDCap5 had a very clear advantage due to its ease of use extensive tutorials, and online training materials. In early 2010, BMI decided on REDCap as the preferred EDC software to support for small-scale studies. Since then usage has steadily increased. As of August 2010 there were 98 active REDCap users and 16 production studies at the University of Washington, Seattle Children’s, Fred Hutchinson Cancer Research Center, and Bastyr University, with collaborators from many other institutions. Post-evaluation, in addition to maintaining our installation of REDCap we are concentrating on future work in two areas: partnerships with investigators to enhance the local usage of REDCap, and informatics research to solve problems in data integration and interoperability. BMI members have contributed to the Ontology of Clinical Research.7 Additionally through our i2b2 Cross-Institutional Clinical Translational Research (CICTR) project we have identified use cases for moving data between REDCap and i2b2.8 Lastly, in keeping with our “bottom up” philosophy we are applying lightweight data integration techniques to query across REDCap and other systems, such as freezer inventory

Value Sets via Ontology Views

We present a method for defining value sets as queries over ontologies (ontology views), and a mechanism for evaluating such queries. In particular we demonstrate an approach utilizing reusable template queries and parameterized URLs. We illustrate this method using an example from the Ontology of Clinical Research (OCRe)

Application of neuroanatomical ontologies for neuroimaging data annotation

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are “part of” which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website at http://bioportal.bioontology.org/ontologies/10005 . The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining. Keywords: ontology, neuroanatomy, data minin

Intelligent Queries over BIRN Data using the Foundational Model of Anatomy and a Distributed Query-Based Data Integration System

We demonstrate the usefulness of the Foundational Model of Anatomy (FMA) ontology in reconciling different neuroanatomical parcellation schemes in order to facilitate automatic annotation and “intelligent” querying and visualization over a large multisite fMRI study of schizophrenic versus normal controls