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Updated: May 30, 2025

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ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.

Ziming Gan1, Doudou Zhou2, Everett Rush3

  • 1Department of Statistics, University of Chicago, 5801 S Ellis Ave, Chicago, 60615, IL, USA.

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|January 25, 2025
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Summary

The ARCH algorithm creates a knowledge graph from electronic health records, improving clinical data analysis and predictive modeling for better patient outcomes.

Keywords:
Electronic health recordsKnowledge graphNatural language processingRepresentation learning

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Area of Science:

  • Biomedical Informatics
  • Computational Health

Background:

  • Electronic health record (EHR) systems contain complex, heterogeneous data including codified entries and free-text clinical notes.
  • Extracting meaningful insights from EHR data is challenging due to feature representation and information extraction complexities.

Purpose of the Study:

  • To develop an efficient Aggregated naRrative Codified Health (ARCH) records analysis method.
  • To generate a large-scale knowledge graph (KG) integrating both codified and narrative EHR features.

Main Methods:

  • ARCH analyzes EHR data from 12.5 million patients, deriving embedding vectors and quantifying feature relatedness with p-values.
  • A sparse embedding regression is employed to build a KG by removing indirect feature linkages.
  • The method was validated on tasks including relationship detection, drug side effect prediction, and disease phenotyping.

Main Results:

  • ARCH generated high-quality clinical embeddings and a KG for over 60,000 EHR concepts.
  • The system achieved high accuracy in detecting EHR concept relationships (AUCs up to 0.926) and predicting drug side effects (AUC 0.826).
  • ARCH successfully sub-typed Alzheimer's disease patients into groups with different mortality rates.

Conclusions:

  • The ARCH algorithm effectively generates large-scale, high-quality semantic representations and a KG from EHR data.
  • These resources are valuable for diverse predictive modeling tasks in healthcare.
  • ARCH enhances the performance of weakly supervised phenotyping algorithms.