  Multimodal medical information processing is currently 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.
