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Personalized Health Knowledge Graph.

Amelie Gyrard1, Manas Gaur1, Saeedeh Shekarpour2

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Summary
This summary is machine-generated.

Personalized healthcare requires integrating patient data with environmental context. This study proposes a Personalized Healthcare Knowledge Graph (PHKG) to manage chronic diseases by combining IoT data and explicit knowledge.

Keywords:
ContextualizationData ManagementHealthcareKnowledge Graph (KG)Linked Open Data (LOD)OntologyPersonalized Knowledge GraphReasoning and Integration

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Data Science

Background:

  • Current health applications lack personalized and contextual patient knowledge.
  • Effective chronic disease management requires a deeper understanding of individual patient conditions.
  • Existing systems fail to integrate diverse data sources for comprehensive patient insights.

Purpose of the Study:

  • To propose a Personalized Healthcare Knowledge Graph (PHKG) for advanced chronic disease management.
  • To address the need for integrating personalized patient data with contextual information.
  • To lay the foundation for "Personalized Coach for Healthcare" applications.

Main Methods:

  • Aggregating heterogeneous data from Internet of Things (IoT) devices, clinical notes, and Electronic Medical Records (EMRs).
  • Developing a PHKG by combining IoT data analytics with explicit knowledge.
  • Analyzing challenges in data collection, management, analysis, and integration following the Data, Information, Knowledge, and Wisdom (DIKW) pyramid.

Main Results:

  • Identified key challenges in creating a comprehensive healthcare knowledge graph.
  • Outlined a solution framework combining IoT data analytics and explicit knowledge.
  • Demonstrated the approach with use cases for asthma, obesity, and Parkinson's disease.

Conclusions:

  • A PHKG is essential for personalized chronic disease management applications.
  • Integrating diverse data sources through advanced analytics and knowledge representation is feasible.
  • The proposed framework supports the development of intelligent healthcare coaching systems.