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Translational Medicine Informatics Services from the Bedside Over QL4POMR

In: Liss 2022

Author

Listed:
  • Sabah Mohammed

    (Lakehead University)

  • Jinan Fiaidhi

    (Lakehead University)

  • Darien Sawyer

    (Lakehead University)

  • Peter Sertic

    (Lakehead University)

Abstract

Translational medicine informatics (TMI) requires data integration on a massive scale to promote medical discovery. To accomplish this, the integration of data about drugs, diseases, targets, and genetics needs to happen at the physician point of care level via electronic health records and other care applications, but the legacy REST APIs and unstructured nature of current healthcare data poses a great challenge for this integration. Previously we have developed QL4POMR as an updated representation of the electronic healthcare record that uses GraphQL and the problem oriented medical record (POMR) to provide better structure and interoperability to healthcare data. We have now also developed a data layer using Gatsby and GraphQL gateways to pull medical data from outside sources into the QL4POMR ecosystem. In this paper, we outline how we leverage the QL4POMR data layer to apply TMI at the physician level EHR using information from trusted sources like DrugBank and OpenTargets. The TMI processes described can be applied to develop a wide range of microservices for physicians and patients.

Suggested Citation

  • Sabah Mohammed & Jinan Fiaidhi & Darien Sawyer & Peter Sertic, 2023. "Translational Medicine Informatics Services from the Bedside Over QL4POMR," Lecture Notes in Operations Research, in: Xiaopu Shang & Xiaowen Fu & Yixuan Ma & Daqing Gong & Juliang Zhang (ed.), Liss 2022, pages 45-57, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-2625-1_4
    DOI: 10.1007/978-981-99-2625-1_4
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