Methodology for developing OpenEHR archetypes: a narrative literature review

Abstract

Objective: To present a narrative literature review to identify, analyze, and characterize the state of the art about methodologies for developing openEHR archetypes. Method: An exhaustive literature search in the computer science field. We used the databases: IEEE Digital Library, ACM Digital Library, Science Direct, Scopus and Springer Link. The screening process involved applying suitable selection criteria to 361 publications to define the scope for selecting the appropriate papers. Results: The nine selected papers were grouped into five categories, in which we identified some connection points between the papers, and we realized that any gaps in one paper are complemented by the other papers. Conclusion: The research contributed to the construction of a theoretical reference on methodologies for developing openEHR archetypes, as well as showing that it is a growing research topic and there are some aspects that require further study.

Adaption BERT for Medical Information Processing with ChatGPT and Contrastive Learning

“…Semantic similarity computation of medical information, as one of the cores in medical information processing, aims to calculate the similarity of medical professional terms within massive datasets [1][2][3][4]. Semantic similarity computation of medical information finds essential applications in various areas, including medical information modeling [5], semantic retrieval [6], intelligent decision support [7], and medical knowledge graph construction [8].…”