Mining drug properties for decision support in dental clinics

Goh, Wee Pheng and Tao, Xiaohui and Zhang, Ji and Yong, Jianming (2017) Mining drug properties for decision support in dental clinics. In: 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017) , 23-26 May 2017, Jeju, South Korea .

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The rise of polypharmacy requires from health providers an
awareness of a patient's drug profile before prescribing. Existing methods to extract information on drug interactions do not integrate with the patient's medical history. This paper describes state-of-the-art approaches
in extracting the term frequencies of drug properties and combining this knowledge with consideration of the patient's drug allergies and current medications to decide if a drug is suitable for prescription. Experimental
evaluation of our models association of the similarity ratio between two drugs (based on each drug's term frequencies) with the similarity between them yields a superior accuracy of 77%. Similarity to a drug the patient
is allergic to or is currently taking are important considerations as to the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work ow, will reduce prescription errors
thereby increasing the health outcome of the patient.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 24 Jan 2017 01:37
Last Modified: 19 Sep 2018 01:17
Uncontrolled Keywords: adverse relationship; drug allergy; drug properties; knowledge-base; personalised prescription; similarity ratio; term frequency
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software
Identification Number or DOI: 10.1007/978-3-319-57529-2 30

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