A Graph Framework for Multimodal Medical Information Processing
This addresses the need for more accurate diagnoses and disambiguation in medical cases, particularly for co-morbidity, but appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of multimodal medical information processing by proposing a multilayer graph framework for data fusion, retrieval, analysis, and storage, with a use case on frailty illustrated using Python and Neo4j.
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.