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Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using

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  • 1Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China.

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|July 5, 2024
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Summary
This summary is machine-generated.

This study developed a privacy-preserving electronic health record (EHR) knowledge graph system. It enables collaborative reasoning on fragmented multicenter patient data for improved clinical decision support, identifying overlooked chronic kidney disease (CKD) cases.

Keywords:
CKDEHRblockchaincollaborationcollaborativedata fragmentationdata privacydata sciencedecision supportelectronic health recordencryptencryptedencryptionhealth recordhypernymkidneyknowledge graphknowledge graphsnephrologyontologiesontologyprivacysecuritysemanticvisualizationvocabulary

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Data Privacy

Background:

  • Medical knowledge graphs offer decision support but struggle with fragmented, multi-hospital patient data.
  • Data security concerns and fragmentation limit the application of knowledge graphs in real-world clinical settings.
  • Integrating knowledge graphs into multicenter environments requires methods that preserve data privacy and security.

Purpose of the Study:

  • To propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning.
  • To utilize multicenter fragmented patient medical data while preserving data privacy.
  • To enhance clinical decision support through secure data integration.

Main Methods:

  • Developed an EHR knowledge graph framework with a collaborative reasoning process.
  • Standardized local EHR data using a unified semantic structure and OMOP vocabulary.
  • Utilized a blockchain network to synchronize intermediate findings and hash-encrypted identities for privacy-preserving multicenter collaboration.

Main Results:

  • Evaluated the system using multicenter fragmented EHR data to identify overlooked chronic kidney disease (CKD) patients.
  • Successfully identified 124 CKD cases from 1185 patients across 3 hospitals, cases missed by individual hospitals.
  • Clinician assessment confirmed 86% of identified patients were indeed CKD positive.

Conclusions:

  • The proposed system effectively leverages multicenter fragmented EHR data for clinical applications.
  • Demonstrated clinical benefits through prompt and comprehensive decision support, improving patient care.
  • The system successfully addresses privacy and security concerns in multicenter medical data integration.