Towards a Personal Health Knowledge Graph Framework for Patient Monitoring

Daniel Bloor, Nnamdi Valbosco Ugwuoke, David Taylor, Kier Lewis, Luis Mur, Chuan Lu*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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Abstract

Healthcare providers face significant challenges with managing and monitoring patient data outside of clinics, particularly with limited resources and insufficient feedback on their patients' conditions. Effective management of these symptoms and exploration of larger bodies of data are vital for maintaining long-term quality of life and preventing late interventions. In this paper, we propose a framework for constructing personal health knowledge graphs from heterogeneous data sources. Our approach integrates clinical databases, relevant ontologies, and standard healthcare guidelines to support alert generation, clinicians' interpretation and querying of patient data. Through a use case focusing on monitoring Chronic Obstructive Lung Disease (COPD) patients, we demonstrate that inference and reasoning on personal health knowledge graphs built with our framework can aid in patient monitoring and enhance the efficacy and accuracy of patient data queries.
Original languageEnglish
Number of pages6
Publication statusPublished - Sept 2023
EventCONFERENCE ON COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS & BIOSTATISTICS - Padova, Italy
Duration: 06 Sept 202308 Sept 2023
Conference number: 18th
https://cibb2023.dei.unipd.it/

Conference

ConferenceCONFERENCE ON COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS & BIOSTATISTICS
Abbreviated titleCIBBB 2023
Country/TerritoryItaly
CityPadova
Period06 Sept 202308 Sept 2023
Internet address

Keywords

  • personal health knowledge graph
  • COPD
  • patient monitoring
  • knowledge graph

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