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Proceedings Paper

Pattern search in multi-structure data: a framework for the next-generation evidence-based medicine
Author(s): Sreenivas R. Sukumar; Keela C. Ainsworth
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Paper Abstract

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. Addressing this need, we pose and answer the following questions: (i) How can we jointly analyze and explore measurement data in context with qualitative domain knowledge? (ii) How can we search and hypothesize patterns (not known apriori) from such multi-structure data? (iii) How can we build predictive models by integrating weakly-associated multi-relational multi-structure data? We propose a framework towards answering these questions. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Paper Details

Date Published: 19 March 2014
PDF: 6 pages
Proc. SPIE 9039, Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations, 90390O (19 March 2014); doi: 10.1117/12.2044378
Show Author Affiliations
Sreenivas R. Sukumar, Oak Ridge National Lab. (United States)
Keela C. Ainsworth, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 9039:
Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations
Maria Y. Law; Tessa S. Cook, Editor(s)

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