Guide to Low Cost Electronic Data Capture Systems for Clinical Trials

There are many ways to use computer systems to record the information generated during the course of clinical trials. The Electronic Data Capture, or EDC, systems have a range of features from a simple basic functionality to sophisticated and complex specialty systems. The costs of these systems also vary from very expensive proprietary products to a recent trend of Open Source software that is distributed without a license fee. Traditionally, academic projects have made use of existing software resources like spreadsheets and Microsoft Access databases. This guide will present information on the low cost options using existing software or Open Source systems that are supported by the Institute for Translational Health Sciences. The description of the features of each option is intended to help an investigator select appropriate software. Additional proprietary and free software options will be described on in future reports

Ontology View Query Management

Like views in relational databases, ontology views are expressed as queries, but over source ontologies rather than tables. To enhance the reusability of such views, we are constructing a view Query Manager application. The Query Manager allows queries to be edited, executed, and stored for reuse. View queries are discoverable by searching the Query Manager's metadata catalog. The Query Manager also supports the storage of materialized view results upon which further queries may be issued

Enabling RadLex with the Foundational Model of Anatomy Ontology to Organize and Integrate Neuro-imaging Data

In this study we focused on empowering RadLex with an ontological framework and additional content derived from the Foundational Model of Anatomy Ontology1 thereby providing RadLex the facility to correlate the different standards used in annotating neuroradiological image data. The objective of this work is to promote data sharing, data harmonization and interoperability between disparate neuroradiological labeling systems

Integrating and Ranking Uncertain Scientific Data

Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates

Composite annotations: requirements for mapping multiscale data and models to biomedical ontologies

Abstract—Current methods for annotating biomedical data resources rely on simple mappings between data elements and the contents of a variety of biomedical ontologies and controlled vocabularies. Here we point out that such simple mappings are inadequate for large-scale multiscale, multidomain integrative “virtual human” projects. For such integrative challenges, we describe a “composite annotation” schema that is simple yet sufficiently extensible for mapping the biomedical content of a variety of data sources and biosimulation models to available biomedical ontologies

Using multiple reference ontologies: Managing composite annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models

Relationship auditing of the FMA ontology

The Foundational Model of Anatomy (FMA) ontology is a domain reference ontology based on a disciplined modeling approach. Due to its large size, semantic complexity and manual data entry process, errors and inconsistencies are unavoidable and might remain within the FMA structure without detection. In this paper, we present computable methods to highlight candidate concepts for various relation- ship assignment errors. The process starts with locating structures formed by transitive structural relationships (part_of, tributary_of, branch_of) and examine their assignments in the context of the IS-A hierarchy. The algorithms were designed to detect five major categories of possible incorrect relationship assignments: circular, mutually exclusive, redundant, inconsistent, and missed entries. A domain expert reviewed samples of these presumptive errors to confirm the findings. Seven thousand and fifty-two presumptive errors were detected, the largest proportion related to part_of relationship assignments. The results highlight the fact that errors are unavoidable in complex ontologies and that well designed algorithms can help domain experts to focus on concepts with high likelihood of errors and maximize their effort to ensure consistency and reliability. In the future similar methods might be integrated with data entry processes to offer real-time error detection

Content-specific auditing of a large scale anatomy ontology

Biomedical ontologies are envisioned to be usable in a range of research and clinical applications. The requirements for such uses include formal consistency, adequacy of coverage, and possibly other domain specific constraints. In this report we describe a case study that illustrates how application specific requirements may be used to identify modeling problems as well as data entry errors in ontology building and evolution. We have begun a project to use the UW Foundational Model of Anatomy (FMA) in a clinical application in radiation therapy planning. This application focuses mainly (but not exclusively) on the representation of the lymphatic system in the FMA, in order to predict the spread of tumor cells to regional metastatic sites. This application requires that the downstream relations associated with lymphatic system components must only be to other lymphatic chains or vessels, must be at the appropriate level of granularity, and that every path through the lymphatic system must terminate at one of the two well known trunks of the lymphatic system. It is possible through a programmable query interface to the FMA to write small programs that systematically audit the FMA for compliance with these constraints. We report on the design of some of these programs, and the results we obtained by applying them to the lymphatic system. The algorithms and approach are generalizable to other network organ systems in the FMA such as arteries and veins. In addition to illustrating exact constraint checking methods, this work illustrates how the details of an application may reflect back a requirement to revise the design of the ontology itself

Lightweight distributed XML-based integration of translational data

A distributed XQuery engine sends sub queries to separate XML data sources, and then combines the results into a single XML composite result. The system is lightweight in that it is very simple to add a new data source. An illustrative example is given for integrating data from an electronic data capture (EDC) system and a separate specimen management system

Distributed XQuery-based integration and visualization of multimodality data: Application to brain mapping.

This paper addresses the need for relatively small groups of collaborating investigators to integrate distributed and heterogeneous data about the brain. Although various national efforts facilitate large-scale data sharing, these approaches are generally too “heavyweight” for individual or small groups of investigators, with the result that most data sharing among collaborators continues to be ad hoc. Our approach to this problem is to create a “lightweight” distributed query architecture, in which data sources are accessible via web services that accept arbitrary query languages but return XML results. A Distributed XQuery Processor (DXQP) accepts distributed XQueries in which subqueries are shipped to the remote data sources to be executed, with the resulting XML integrated by DXQP. A web-based application called DXBrain accesses DXQP, allowing a user to create, save and execute distributed XQueries, and to view the results in various formats including a 3-D brain visualization. Example results are presented using distributed brain mapping data sources obtained in studies of language organization in the brain, but any other XML source could be included. The advantage of this approach is that it is very easy to add and query a new source, the tradeoff being that the user needs to understand XQuery and the schemata of the underlying sources. For small numbers of known sources this burden is not onerous for a knowledgeable user, leading to the conclusion that the system helps to fill the gap between ad hoc local methods and large scale but complex national data sharing efforts