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Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health

Chuan Hong1,2, Everett Rush3, Molei Liu4

  • 1Harvard Medical School, Boston, MA, USA.

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Summary

Large-scale electronic health record code embeddings enable efficient feature identification for translational research. This method, knowledge extraction via sparse embedding regression (KESER), bypasses patient-level data sharing for multi-center studies.

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Area of Science:

  • Computational biology
  • Health informatics
  • Translational research

Background:

  • Electronic Health Record (EHR) systems offer vast potential for translational research.
  • Identifying phenotype-related codes is challenging due to the large volume of available codes.
  • Traditional data mining requires patient-level data, limiting cross-institutional data sharing.

Purpose of the Study:

  • To demonstrate the efficacy of multi-center, large-scale code embeddings for identifying disease-related features.
  • To develop and evaluate a novel method for feature selection and knowledge extraction from EHR data.
  • To create an integrated clinical knowledge map for enhanced multi-institutional analysis.

Main Methods:

  • Constructed large-scale code embeddings from EHR data across two medical centers.
  • Developed Knowledge Extraction via Sparse Embedding Regression (KESER) for feature selection.
  • Performed integrative network analysis and evaluated KESER's performance on eight diseases.

Main Results:

  • KESER identified comprehensive features, comparable to expert-curated lists.
  • Features selected by KESER achieved performance similar to manual or patient-level data methods.
  • An integrated knowledge map improved accuracy in identifying disease-disease and disease-drug relationships compared to single-institution data.

Conclusions:

  • Analysis of code embeddings via KESER effectively reveals clinical knowledge and concept relatedness.
  • KESER facilitates multi-center EHR studies by eliminating the need for patient-level data sharing.
  • This approach significantly advances the utilization of EHR data for large-scale research.