III: Medium: Collaborative Research: KMELIN: Knowledge Mining and Embedding Learning for Complex Dynamic Information Networks

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Complex dynamic information networks (CDINs) consist of data objects that are highly correlated with a variety of dependency relationships, such as patient-physician interactions or patient-medication-insurance claims. Each data object as a CDIN node has rich contents, such as biometric information of a patient, disease symptoms, or hospital logistics. Data objects and their relationships also continuously evolve and change. Many health, social, physical, and biological systems share the CDIN essence that the multifaceted and dynamic nature of individual nodes imposes significant challenges for modeling a complex and evolving network as a whole. Although data relationships are becoming rich and comprehensive than ever, existing systems are mostly relational-database driven, and cannot integrate complex relationships of networked data for Big Data analytics.This project aims to design a knowledge mining and embedding learning platform for CDINs that will (1) extract and represent complex structure and rich-content information in the health domain as a CDIN; (2) perform knowledge mining, including clustering and classification, on CDIN networks; (3) enable feature embedding learning with CDINs, so the users can interact with CDINs for content access, and (4) provide a prototype system for hospital re-admission decision support. The spectrum of the methods from the project will not only enrich algorithms and solutions for mining complex structure and rich content networks, as opposed to static networks, but also shift existing health information systems from traditional databases towards becoming network centered systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.