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A Graph Framework for Multimodal Medical Information Processing

Authors:
Georgios Drakopoulos
Vasileios Megalooikonomou

Keywords: Frailty index; Co-morbidity; Neo4j; Tensor analysis; Multimodal data mining

Abstract:
Multimodal medical information processing is cur- rently the epicenter of intense interdisciplinary research, as proper data fusion may lead to more accurate diagnoses. Moreover, multimodality may disambiguate cases of co-morbidity. This paper presents a framework for retrieving, analyzing, and storing medical information as a multilayer graph, an abstract format suitable for data fusion and further processing. At the same time, this paper addresses the need for reliable medical information through co-author graph ranking. A use case pertaining to frailty based on Python and Neo4j serves as an illustration of the proposed framework.

Pages: 278 to 282

Copyright: Copyright (c) IARIA, 2016

Publication date: April 24, 2016

Published in: conference

ISSN: 2308-4359

ISBN: 978-1-61208-470-1

Location: Venice, Italy

Dates: from April 24, 2016 to April 28, 2016