Entity Event Knowledge Graph for Powerful Health Informatics

Ravi Bajracharya, Richard Wallace, Jans Aasman, Parsa Mirhaji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces the Entity-Event Knowledge Graph (EEKG) model for clinical data stored in graph databases. We describe how the EEKG model dramatically simplifies the representation of patient data, facilitates temporal queries, enables a 360 view of patients and promotes scalability by partitioning patient data into shards. We solved the practical problem that not all clinical data and life science knowledge can be sharded. The solution is to federate each individual shard with common shared data in a knowledge graph. One such shared data source is the UMLS (Unified Medical Language System) knowledge base, which contains genetic, drug clinical trials and Metathesaurus data that we link to individual patient records. We report on several use cases including EMR patient retrieval, matching patients with clinical trials, patient control group selection, and care quality measures.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages456-460
Number of pages5
ISBN (Electronic)9781665468459
DOIs
StatePublished - 2022
Event10th IEEE International Conference on Healthcare Informatics, ICHI 2022 - Rochester, United States
Duration: Jun 11 2022Jun 14 2022

Publication series

NameProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022

Conference

Conference10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Country/TerritoryUnited States
CityRochester
Period6/11/226/14/22

Keywords

  • clinical trials knowledge base
  • distributed graph database
  • entity-event model
  • knowledge graph
  • umls skos knowledge graph

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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