@inproceedings{e0bf2747f6344359acf7fb60e60e66a7,
title = "Document oriented graphical analysis and prediction",
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.",
keywords = "Clinical trials, Data integration, Data processing, Data quality",
author = "Shorabuddin Syed and Mahanazuddin Syed and Syeda, {Hafsa Bareen} and Fred Prior and Meredith Zozus and Penning, {Melody L.} and Mohammed Orloff",
note = "Publisher Copyright: {\textcopyright} 2020 European Federation for Medical Informatics (EFMI) and IOS Press.; 30th Medical Informatics Europe Conference, MIE 2020 ; Conference date: 28-04-2020 Through 01-05-2020",
year = "2020",
month = jun,
day = "16",
doi = "10.3233/SHTI200147",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "183--187",
editor = "Pape-Haugaard, {Louise B.} and Christian Lovis and Madsen, {Inge Cort} and Patrick Weber and Nielsen, {Per Hostrup} and Philip Scott",
booktitle = "Digital Personalized Health and Medicine - Proceedings of MIE 2020",
}