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Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data.

William Van Woensel1, Chad Armstrong2, Malavan Rajaratnam2

  • 1NICHE Research Group, Faculty of Computer Science, Dalhousie University, Canada.

Studies in Health Technology and Informatics
|May 27, 2021
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Summary
This summary is machine-generated.

This study infers missing patient data in Electronic Medical Records (EMRs) by using medical knowledge graphs. This approach enhances incomplete EMRs for better clinical insights when data is scarce.

Keywords:
Electronic Medical RecordsKnowledge GraphsKnowledge-based ReasoningMedical Taxonomies

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Area of Science:

  • Medical Informatics
  • Knowledge Representation and Reasoning
  • Health Data Science

Background:

  • Electronic Medical Records (EMRs) are vital for digital record-keeping in healthcare, aiming to boost productivity and communication.
  • However, EMR systems often suffer from data fragmentation and incomplete entries due to clinician time constraints and lack of data validation.
  • This results in an incomplete patient profile, hindering comprehensive clinical decision-making.

Purpose of the Study:

  • To address the challenge of incomplete Electronic Medical Records (EMRs).
  • To develop a method for inferring missing causal associations between medical concepts within EMRs.
  • To enhance patient data completeness for improved clinical insights, particularly when machine learning is not feasible.

Main Methods:

  • A knowledge-based approach was employed, leveraging open medical terminologies like SNOMED-CT and ICD.
  • A semantic Extract-Transform-Load (ETL) process was utilized to convert EMR databases into a domain-specific Knowledge Graph.
  • Knowledge-based reasoning with explainable inferences was applied to infer missing clinical encounter information.

Main Results:

  • Successfully converted EMR data into an enriched, domain-specific Knowledge Graph.
  • Demonstrated the ability to plausibly infer missing causal associations between diagnoses and treatments from incomplete EMR data.
  • Provided a method for generating explainable inferences to complete patient profiles.

Conclusions:

  • The knowledge-based approach effectively infers missing information in Electronic Medical Records (EMRs).
  • This method enhances the completeness of patient data by leveraging external medical knowledge and reasoning.
  • The enriched Knowledge Graph and inference techniques offer a valuable tool for improving clinical data quality and understanding.