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Title: PAUSE: Predictive Analytics Using SPARQL-Endpoints

Abstract

This invention relates to the medical industry and more specifically to methods of predicting risks. With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Authors:
; ;
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Sreenivas R. Sukumar, Keela Ainsworth, and Nathaniel Bond
OSTI Identifier:
1324318
Report Number(s):
PAUSE; 004921IBMPC00
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Software
Software Revision:
00
Software Package Number:
004921
Software CPU:
IBMPC
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

Sukumar, Sreenivas R, Ainsworth, Keela, and Bond, Nathaniel. PAUSE: Predictive Analytics Using SPARQL-Endpoints. Computer software. Vers. 00. USDOE. 11 Jul. 2014. Web.
Sukumar, Sreenivas R, Ainsworth, Keela, & Bond, Nathaniel. (2014, July 11). PAUSE: Predictive Analytics Using SPARQL-Endpoints (Version 00) [Computer software].
Sukumar, Sreenivas R, Ainsworth, Keela, and Bond, Nathaniel. PAUSE: Predictive Analytics Using SPARQL-Endpoints. Computer software. Version 00. July 11, 2014.
@misc{osti_1324318,
title = {PAUSE: Predictive Analytics Using SPARQL-Endpoints, Version 00},
author = {Sukumar, Sreenivas R and Ainsworth, Keela and Bond, Nathaniel},
abstractNote = {This invention relates to the medical industry and more specifically to methods of predicting risks. With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.},
doi = {},
year = {Fri Jul 11 00:00:00 EDT 2014},
month = {Fri Jul 11 00:00:00 EDT 2014},
note =
}

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