Graph-Based Analytics for Healthcare Data Streams
Keywords:
Graph Neural Networks (GNNs),Healthcare Data Interoperability,Clinical Data Integration,Electronic Health Records (EHR) Analytics,Multimodal Medical Data Fusion,Knowledge Graphs in Healthcare,Temporal Graph Modeling,Real-Time Clinical Data Streams,Predictive Healthcare Analytics,Privacy-Preserving Health AI.Abstract
Graph-level neural architectures provide a unique set of modeling capabilities that can be leveraged toward predictive analytics for rich, heterogeneous healthcare systems. A broad methodological framework defines the steps required to extract semantically interoperable event streams from diverse internal and external clinical data sources. The algorithmic framework is capable of supporting GNN-driven predictive analytics for historical events within institutional data, as well as forward-looking safety-monitoring architectures that continuously evaluate future prognosis during live operations and ignite alerts whenever the predictions rise above user-defined thresholds.
Case studies support safe operations of a temperature-controlled drug-shipping distribution network and an active location-based epidemic containment management., Graph Neural Network (GNN) architectures hold great promise for predictive modeling of large-scale healthcare systems by directly representing the clinical network and incorporating heterogeneous information sources. Nevertheless, the proper data representation and preparation remain challenges that need to be addressed. A methodological framework broadens the horizons for GNN applications beyond conventional settings toward prediction of risk and hazard in rich, heterogeneous environments. Interoperable event streams from different clinical departments constitute the basis for two concrete applications.
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Data Availability Statement
"Only publicly available data sources were used in this study. Clinical event data were derived from the Schizophrenia Research Database and from Sudden Death in Positively Tested COVID-19 Patients data published by the Department of Genomics, University of Genoa, and GENOMICA (a division of P.J. Decker B.V., Spain). All figures, analyses, and case study results are reproduced from publicly available literature and datasets; no new primary patient data were collected by the author."
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