Semantic Interoperability for an AI-based Applications Platform for Smart Hospitals Using HL7 FHIR – The Journal of Systems & Software
Highlights
- Verified efficiency of the approach in both a medical scenario and for managing KIPs.
- Semantic interoperability for AI-based applications platform for smart hospitals.
- FHIR server to ensure interoperability
- Assessment of the coherence of the FHIR server to the FAIR principles
Abstract
The digitization of the healthcare domain has the potential to drastically improve healthcare services, reduce the time to diagnosis, and lower costs. However, digital applications for the healthcare domain need to be interoperable to maximize their potential. Additionally, with the rapid expansion of Artificial Intelligence (AI) and, specifically, Machine Learning (ML), large amounts of diverse types of data are being utilized. Thus, to achieve interoperability in such applications, the adoption of common semantic data models becomes imperative. In this paper, we describe the adoption of such a common semantic data model, using the well-known Health Level Seven Fast Health Interoperability Resources (HL7 FHIR) standard, in a platform that assists in the creation and storage of a plethora of AI-based applications for several medical conditions. The FHIR server’s efficiency is being showcased by using it in an application predicting coronary artery stenosis as well as for managing the platform’s key performance indicators.
Emmanouil S. Rigas, Paris Lagakis, Makis Karadimas, Evangelos Logaras, Dimitra Latsou, Magda Hatzikou, Athanasios Poulakidas, Antonis Billis, Panagiotis D. Bamidis (2024), Semantic interoperability for an AI-based applications platform for smart hospitals using HL7 FHIR,
Journal of Systems and Software, Volume 215:112093, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2024.112093
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