Multivariate Data-Driven Decision Guidance for clinical scientists

Frada Burstein, Daswun De Silva, Herbert Jelinek, Andrew Stranieri

Research output: Book chapter/Published conference paperConference paper

5 Citations (Scopus)
4 Downloads (Pure)

Abstract

Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
Original languageEnglish
Title of host publicationProceedings of 29th IEEE International Conference of Data Engineering
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages193-199
Number of pages7
ISBN (Electronic)9781467353021
DOIs
Publication statusPublished - 2013
Event29th IEEE International Conference of Data Engineering - Sofitel Brisbane Central Hotel, Brisbane, Australia
Duration: 08 Apr 201311 Apr 2013
http://www.icde2013.org/index.html

Conference

Conference29th IEEE International Conference of Data Engineering
CountryAustralia
CityBrisbane
Period08/04/1311/04/13
OtherThe annual ICDE conference addresses research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. It is a leading forum for researchers, practitioners, developers, and users to explore cutting-edge ideas and to exchange techniques, tools, and experiences. We invite the submission of original research contributions and industry papers, as well as proposals for workshops, panels, tutorials, and demonstrations.
Internet address

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    Burstein, F., De Silva, D., Jelinek, H., & Stranieri, A. (2013). Multivariate Data-Driven Decision Guidance for clinical scientists. In Proceedings of 29th IEEE International Conference of Data Engineering (pp. 193-199). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDEW.2013.6547449