In “Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching“, published in the International Journal of Medical Informatics 132:104002 DOI: 10.1016/j.ijmedinf.2019.104002, Kiourtis et al. (2019), address the following objective, and quote:
“Healthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and human interaction fields.”
The authors propose an elegant mechanism “that promises healthcare interoperability through the transformation of healthcare data into the corresponding HL7 FHIR structure.”
These are great news! Very cool and practical research that can solve so many problems in the healthcare informatics field.
Many thanks for citing our cosine similarity tutorial as reference 52.
My only reserve with the paper is that early in the article they suggest adding and averaging similarities, which is a mathematically invalid exercise. Distances are arithmetically additive, but similarities (of the same or different kind or source) are not. We can make similarities additive and average them, but not in the arithmetic sense. Other than that, they work is a noble effort.
