Biomedical subjects

Cynthia A Brandt

Publications and source records attributed to Cynthia A Brandt.

4 recordsLinked to original sources

PubMed2006

Informatics tools to improve clinical research study implementation.

BACKGROUND: There are numerous potential sources of problems when performing complex clinical research trials. These issues are compounded when studies are multi-site and multiple personnel from different sites are responsible for varying actions from case report form design to primary data collection and data entry. METHODS: We describe an approach that emphasizes the use of a variety of informatics tools that can facilitate study coordination, training, data checks and early identification and correction of faulty procedures and data problems. The paper focuses on informatics tools that can help in case report form design, procedures and training and data management. CONCLUSION: Informatics tools can be used to facilitate study coordination and implementation of clinical research trials.

Clinical Trials as Topic↗

PubMed2004

Managing complex change in clinical study metadata.

In highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system.

Artificial Intelligence↗

PubMed2004

Exploring the portability of informatics capabilities from a clinical application to a bioscience application.

This report describes XDesc (eXperiment Description), a pilot project that serves as a case study exploring the degree to which an informatics capability developed in a clinical application can be ported for use in the biosciences. In particular, XDesc uses the Entity-Attribute-Value database implementation (including a great deal of metadata-based functionality) developed in TrialDB, a clinical research database, for use in describing the samples used in microarray experiments stored in the Yale Microarray Database (YMD). XDesc was linked successfully to both TrialDB and YMD, and was used to describe the data in three different microarray research projects involving Drosophila. In the process, a number of new desirable capabilities were identified in the bioscience domain. These were implemented on a pilot basis in XDesc, and subsequently "folded back" into TrialDB itself, enhancing its capabilities for dealing with clinical data. This case study provides a concrete example of how informatics research and development in clinical and bioscience domains has the potential for synergy and for cross-fertilization.

Clinical Medicine↗

PubMed2002

Metadata-driven creation of data marts from an EAV-modeled clinical research database.

Generic clinical study data management systems can record data on an arbitrary number of parameters in an arbitrary number of clinical studies without requiring modification of the database schema. They achieve this by using an Entity-Attribute-Value (EAV) model for clinical data. While very flexible for creating transaction-oriented systems for data entry and browsing of individual forms, EAV-modeled data is unsuitable for direct analytical processing, which is the focus of data marts. For this purpose, such data must be extracted and restructured appropriately. This paper describes how such a process, which is non-trivial and highly error prone if performed using non-systematic approaches, can be automated by judicious use of the study metadata-the descriptions of measured parameters and their higher-level grouping. The metadata, in addition to driving the process, is exported along with the data, in order to facilitate its human interpretation.

Breast Neoplasms↗