Feature-based learning in drug prescription system for medical clinics

Goh, Wee Pheng and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Zhang, Ji and Yong, Jianming (2020) Feature-based learning in drug prescription system for medical clinics. Neural Processing Letters. pp. 1-19. ISSN 1370-4621


Official URL: https://rdcu.be/b5oAb

Abstract

Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles.Within our three-tiered framework - the prediction layer, the knowledge layer and the presentation layer—we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 2 July 2020. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences (1 Aug 2018 -)
Date Deposited: 31 Jul 2020 02:19
Last Modified: 31 Aug 2020 04:20
Uncontrolled Keywords: feature vector; similarity ratio; word embedding; adverse network model; personalised drug prescription
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software
Identification Number or DOI: 10.1007/s11063-020-10296-7
URI: http://eprints.usq.edu.au/id/eprint/39135

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