Document oriented graphical analysis and prediction

Shorabuddin Syed, Mahanazuddin Syed, Hafsa Bareen Syeda, Fred Prior, Meredith Zozus, Melody L. Penning, Mohammed Orloff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In general, small-mid size research laboratories struggle with managing clinical and secondary datasets. In addition, faster dissemination, correlation and prediction of information from available datasets is always a bottleneck. To address these challenges, we have developed a novel approach, Document Oriented Graphical Analysis and Prediction (DO-GAP), a hybrid tool, merging strengths of Not only SQL (NoSQL) document oriented and graph databases. DO-GAP provides flexible and simple data integration mechanism using document database, data visualization and knowledge discovery with graph database. We demonstrate how the proposed tool (DO-GAP) can integrate data from heterogeneous sources such as Genomic lab findings, clinical data from Electronic Health Record (EHR) systems and provide simple querying mechanism. Application of DO-GAP can be extended to other diverse clinical studies such as supporting or identifying weakness of clinical diagnosis in comparison to molecular genetic analysis.

Original languageEnglish (US)
Title of host publicationDigital Personalized Health and Medicine - Proceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
PublisherIOS Press
Pages183-187
Number of pages5
ISBN (Electronic)9781643680828
DOIs
StatePublished - Jun 16 2020
Event30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland
Duration: Apr 28 2020May 1 2020

Publication series

NameStudies in Health Technology and Informatics
Volume270
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference30th Medical Informatics Europe Conference, MIE 2020
CountrySwitzerland
CityGeneva
Period4/28/205/1/20

Keywords

  • Clinical trials
  • Data integration
  • Data processing
  • Data quality

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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