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Decision fusion in healthcare and medicine : a narrative review
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Nazari, Elham, Biviji, Rizwana, Roshandel, Danial, Pour, Reza, Shahriari, Mohammad Hasan, Mehrabian, Amin and Tabesh, Hamed (2022) Decision fusion in healthcare and medicine : a narrative review. mHealth, 8 (8). doi:10.21037/mhealth-21-15 ISSN 2306-9740.
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WRAP-Decision-fusion-in-healthcare-and-medicine-a-narrative-review-Mehrabian-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (332Kb) | Preview |
Official URL: http://dx.doi.org/10.21037/mhealth-21-15
Abstract
Objective: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels.
Background: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.
Methods: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.
Conclusions: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.
Item Type: | Journal Article | ||||||||
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Subjects: | R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School | ||||||||
Library of Congress Subject Headings (LCSH): | Medicine -- Data processing, Health -- Data processing, Neural networks (Computer science), Multisensor data fusion, Health promotion, Big data | ||||||||
Journal or Publication Title: | mHealth | ||||||||
Publisher: | AME Publishing | ||||||||
ISSN: | 2306-9740 | ||||||||
Official Date: | 20 January 2022 | ||||||||
Dates: |
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Volume: | 8 | ||||||||
Number: | 8 | ||||||||
Number of Pages: | 17 | ||||||||
DOI: | 10.21037/mhealth-21-15 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 28 March 2022 | ||||||||
Date of first compliant Open Access: | 29 March 2022 | ||||||||
RIOXX Funder/Project Grant: |
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