Cheng, X, Yang, P and Yang, Y (2018) Open Knowledge Accessing Method in IoT-based Hospital Information System for Medical Record Enrichment. IEEE Access, 6. pp. 15202-15211. ISSN 2169-3536

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Abstract

For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based Hospital Information System (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer and other medical devices, they are raw, simple, weak-content and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in Linked Open Data (LOD). A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing.

Item Type:	Article
Additional Information:	(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited:	05 Mar 2018 09:24
Last Modified:	04 Sep 2021 03:00
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8170

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