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Prakash M Nadkarni

Publications and source records attributed to Prakash M Nadkarni.

10 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↗
PubMed2005

Metadata-driven Delphi rating on the Internet.

Paper-based data collection and analysis for consensus development is inefficient and error-prone. Computerized techniques that could improve efficiency, however, have been criticized as costly, inconvenient and difficult to use. We designed and implemented a metadata-driven Web-based Delphi rating and analysis tool, employing the flexible entity-attribute-value schema to create generic, reusable software. The software can be applied to various domains by altering the metadata; the programming code remains intact. This approach greatly reduces the marginal cost of re-using the software. We implemented our software to prepare for the Conference on Guidelines Standardization. Twenty-three invited experts completed the first round of the Delphi rating on the Web. For each participant, the software generated individualized reports that described the median rating and the disagreement index (calculated from the Interpercentile Range Adjusted for Symmetry) as defined by the RAND/UCLA Appropriateness Method. We evaluated the software with a satisfaction survey using a five-level Likert scale. The panelists felt that Web data entry was convenient (median 4, interquartile range [IQR] 4.0-5.0), acceptable (median 4.5, IQR 4.0-5.0) and easily accessible (median 5, IQR 4.0-5.0). We conclude that Web-based Delphi rating for consensus development is a convenient and acceptable alternative to the traditional paper-based method.

Delphi Technique↗
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↗
PubMed2003

Achieving evolvable Web-database bioscience applications using the EAV/CR framework: recent advances.

The EAV/CR framework, designed for database support of rapidly evolving scientific domains, utilizes metadata to facilitate schema maintenance and automatic generation of Web-enabled browsing interfaces to the data. EAV/CR is used in SenseLab, a neuroscience database that is part of the national Human Brain Project. This report describes various enhancements to the framework. These include (1) the ability to create "portals" that present different subsets of the schema to users with a particular research focus, (2) a generic XML-based protocol to assist data extraction and population of the database by external agents, (3) a limited form of ad hoc data query, and (4) semantic descriptors for interclass relationships and links to controlled vocabularies such as the UMLS.

Database Management Systems↗
PubMed2003

Temporal query of attribute-value patient data: utilizing the constraints of clinical studies.

We describe an interface and architecture for ad hoc temporal query of TrialDB, a clinical study data management system (CSDMS). A clinical study focuses primarily on the effect of therapy on a group of patients, who have individually enrolled in a study at different times. Relative times (chronological offsets from the time of enrollment) are therefore more useful than absolute times when collectively describing therapeutic or adverse events. For logistic reasons, study parameter values are typically recorded at fixed relative times ('study events'), which serve as time-stamps and can be used by CSDMS temporal query algorithms to simplify temporal computations. The entity-attribute-value model of clinical data storage, used by both CSDMSs and clinical patient record systems, complicates temporal query. To apply temporal operators, data for parameters of interest must first be transiently converted into conventional relational form, with one column per parameter.

Academic Medical Centers↗
PubMed2003

Text mining neuroscience journal articles to populate neuroscience databases.

We have developed a program NeuroText to populate the neuroscience databases in SenseLab (http://senselab.med.yale.edu/senselab) by mining the natural language text of neuroscience articles. NeuroText uses a two-step approach to identify relevant articles. The first step (pre-processing), aimed at 100% sensitivity, identifies abstracts containing database keywords. In the second step, potentially relevant abstracts identified in the first step are processed for specificity dictated by database architecture, and neuroscience, lexical and semantic contexts. NeuroText results were presented to the experts for validation using a dynamically generated interface that also allows expert-validated articles to be automatically deposited into the databases. Of the test set of 912 articles, 735 were rejected at the pre-processing step. For the remaining articles, the accuracy of predicting database-relevant articles was 85%. Twenty-two articles were erroneously identified. NeuroText deferred decisions on 29 articles to the expert. A comparison of NeuroText results versus the experts' analyses revealed that the program failed to correctly identify articles' relevance due to concepts that did not yet exist in the knowledgebase or due to vaguely presented information in the abstracts. NeuroText uses two "evolution" techniques (supervised and unsupervised) that play an important role in the continual improvement of the retrieval results. Software that uses the NeuroText approach can facilitate the creation of curated, special-interest, bibliography databases.

Abstracting and Indexing↗
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↗
PubMedDate not supplied

Metadata-driven ad hoc query of patient data: meeting the needs of clinical studies.

Clinical study data management systems (CSDMSs) have many similarities to clinical patient record systems (CPRSs) in their focus on recording clinical parameters. Requirements for ad hoc query interfaces for both systems would therefore appear to be highly similar. However, a clinical study is concerned primarily with collective responses of groups of subjects to standardized therapeutic interventions for the same underlying clinical condition. The parameters that are recorded in CSDMSs tend to be more diverse than those required for patient management in non-research settings, because of the greater emphasis on questionnaires for which responses to each question are recorded separately. The differences between CSDMSs and CPRSs are reflected in the metadata that support the respective systems' operation, and need to be reflected in the query interfaces. The authors describe major revisions of their previously described CSDMS ad hoc query interface to meet CSDMS needs more fully, as well as its porting to a Web-based platform.

Computer Security↗