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Patient-centric knowledge graphs: a survey of current methods, challenges, and applications.

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Area of Science:

  • * Health Informatics
  • * Artificial Intelligence in Medicine
  • * Knowledge Representation

Background:

  • * Healthcare data is often fragmented across disparate systems.
  • * A unified patient health perspective is crucial for individualized care.
  • * Patient-Centric Knowledge Graphs (PCKGs) emerge as a solution.

Purpose of the Study:

  • * To review methodologies, challenges, and opportunities in PCKG development.
  • * To explore PCKG applications in personalized medicine and disease prediction.
  • * To provide a foundational perspective on the state-of-the-art in PCKGs.

Main Methods:

  • * Literature review of PCKG methodologies.
  • * Analysis of data integration, knowledge extraction, and representation techniques.
  • * Exploration of reasoning, semantic search, and inference mechanisms.

Main Results:

  • * PCKGs integrate diverse health data for a comprehensive patient view.
  • * Advanced techniques enhance PCKG construction and evaluation for actionable insights.
  • * PCKGs show significant potential in personalized medicine and treatment planning.

Conclusions:

  • * PCKGs represent a significant shift towards individualized patient care.
  • * Effective PCKG development requires addressing complexities in ontology design and data integration.
  • * Future research should focus on optimizing PCKG applications for improved healthcare outcomes.