Similarity Analyzer for Semantic Interoperability of Electronic Health Records Using Artificial Intelligence (AI)

Authors

  • A Naveed Faculty of Engineering and Informatics, University of Bradford, BD7 1DP, United Kingdom
  • Y F Hu Faculty of Engineering and Informatics, University of Bradford, BD7 1DP, United Kingdom
  • T Sigwele Department of Computing and Informatics, Faculty of Science, BIUST University, Private Bag 16, Palapye, Botswana
  • G Mohi-Ud-Din Department of Computer Sciences, Faculty of Science, Liverpool John Moores University, Liverpool, United Kingdom
  • Misfa Susanto Department of Electrical Engineering, Faculty of Engineering, University of Lampung, Jl. Prof. Sumantri Brojonegoro No. 1, Bandar Lampung 35145, Indonesia

DOI:

https://doi.org/10.23960/jesr.v1i2.13

Keywords:

semantic interoperability; interoperability standards;electronic health records (EHR);artifical intelligence techniques

Abstract

The introduction of Electronic Health Records (EHR) has opened possibilities for solving interoperability issues within the healthcare sector. However, even with the introduction of EHRs, healthcare systems like hospitals and pharmacies remain isolated with no sharing of EHRs due to semantic interoperability issues. This paper extends our previous work in which we proposed a framework that dealt with semantic interoperability and security of EHR. The extension is the proposal of a cloud-based similarity analyzer for data structuring, data mapping, data modeling and conflict removal using Word2vec Artificial Intelligence (AI) technique. Different types of conflicts are removed from data in order to model data into common data types which can be interpreted by different stakeholders

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Published

2020-12-17

Issue

Section

Articles