Abstract
As medical services increasingly trying to harness advances in connected medical devices and the use of artificial intelligence, a new modeling strategy is essentially required to enable developers of the emerging medical applications to organize, integrate, and retain information in this new era of service-oriented healthcare. Such strategy needs to adhere to the principle of knowledge coupling that was advocated by Lawrence Weed, the father of modern problem-oriented medical records, in early 1970s where he defined the way medical information should be described for higher decision making. There has never been a more compelling time to use knowledge coupling related to both medical knowledge and the services build around them. The new digital technologies such as microservices, graph-based databases, Internet of Healthcare Things, and Cloud Computing as well as the non-digital disruptive events such as the pandemic have accelerated the adoption of new notions of service integration and knowledge coupling to provide the long waited solution for interoperability in healthcare as well as higher level of knowledge integration and analytics. The cornerstone of every new change is pointing toward the use of microservices and knowledge graph APIs to be able to thrive and lead in the uncertainty and healthcare change. This research paper uses the notion of mindmap to push conversation and guide scholars in developing effective microservice-based care systems that utilize knowledge graphs and the new care standards including the HL7 FHIR. Central to our mindmap is the GraphQL graph-based technology and the medical diagnosis as a service. This mindmap is the starting research point of our MITACS 2021 and NSERC DDG 2021 projects.
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This research is funded by the first author NSERC Discovery Grant 2021 (Discovery Development Grant DDG-2021-00014) and the MITACS research project of both authors (MITACS_IT22305).
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Mohammed, S., Fiaidhi, J. Establishment of a mindmap for medical e-Diagnosis as a service for graph-based learning and analytics. Neural Comput & Applic 35, 16089–16100 (2023). https://doi.org/10.1007/s00521-021-06200-6
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DOI: https://doi.org/10.1007/s00521-021-06200-6