Autonomous Clinical Analytics via GNN and Generative AI over Interoperable FHIR Networks
Keywords:
FHIR-Based Healthcare Analytics, Generative Adversarial Networks, Graph Neural Networks, Clinical Decision Support, Healthcare Data Interoperability, FHIR Data Integration, AI in Healthcare Analytics, Clinical Inference Engines, Healthcare Data Standardization, Predictive Clinical Modeling, Healthcare Graph Analytics, AI-Driven Diagnostics, Clinical Data Normalization, Healthcare AI Frameworks, Medical Data Networks, Quality Assessment in AI, Synthetic Clinical Data Generation, Interoperable Health Systems, Data-Driven Clinical Insights, Advanced Healthcare Analytics.Abstract
The rapidly evolving landscape of healthcare analytics requires effective solutions to operationalize the plethora of increasingly available and interoperable data sources. However, much of the FHIR-enabled research to date has focused purely on the standardization and sharing of data-evidence resources rather than the seamless integration of healthcare analytics with such resources. To mitigate this gap, this study explores the Generative-adversarial Networks, Graph Neural Networks, and the Fast Healthcare Interoperability Resources (FHIR) for Healthcare Analytics. The proposed architectural framework, operating over a broad spectrum of FHIR resources, opens the door for FHIR-enabled healthcare analytics. Inference engines can be readily designed by simply constructing the necessary clinical datasets conformant to the desired analytics task. Beyond replacing those engines, a new capability to assess output quality and safety, and a Generative model to synthesize new output, can be layered in a natural step.
Existing research has largely concentrated on healthcare data sharing, whereas analytics, blooming in other domains, remains undeveloped in healthcare. The study thus introduces a two-layer architectural framework that deploys Generative and Graph Neural Network methods over FHIR networks. The compute layer applies these healthcare analytics methods to any Healthcare FHIR dataset, while the data layer normalizes a broad set of FHIR resources either in standard form or in specific use cases. The first focal task—decision support for given input clinical scenarios—demonstrates the framework’s prospective power and offers a GCN-based engine and a quality-assessment module to evaluate the accuracy of decisions.
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