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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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Abstract

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of Science articles discussing semantic interoperability solutions.

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Data availability

As the data used for this study is downlaoded from MIMIC III database after attending city training program provided by physionet. Demo version of this dataset is available publically. It is unethical to provide the dataset without undergoing city training program offered by them.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sony Purushothaman & G. Siva Shanmugam

  2. Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Krishnakovil, Tamil Nadu, India

    Sureshkumar Nagarajan

  3. Department of Computer Science, Govt. Model Engineering College, Kerala, Kochi, India

    Sony Purushothaman

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  1. Sony Purushothaman
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  2. G. Siva Shanmugam
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  3. Sureshkumar Nagarajan
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Correspondence to G. Siva Shanmugam.

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Purushothaman, S., Shanmugam, G.S. & Nagarajan, S. Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey. SN COMPUT. SCI. 5, 99 (2024). https://doi.org/10.1007/s42979-023-02392-x

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