Personalised drug prescription for dental clinics using word embedding

Goh, Wee Pheng and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Zhang, Ji and Yong, Jianming and Oh, Xue Ling and Goh, Elizabeth Zhixin (2020) Personalised drug prescription for dental clinics using word embedding. In: 20th International Conference on Web Information Systems Engineering (WISE 2019), 19-22 Jan 2020, Hong Kong.

[img] Text (Accepted Version)
wiseDemoNov5.pdf
Restricted - Available after 1 February 2021.


Abstract

The number of drugs in drug databases is constantly expanding with novel drugs appearing on the market each year. A dentist cannot be expected to recall all the drugs available, let alone potential drug-drug interactions (DDI). This can be problematic when dispensing drugs to patients especially those with multiple medical conditions who often take a multiple medications. Any new medication
prescribed must be checked against the patient’s medical history, in order to avoid drug allergies and reduce the risk of adverse reactions. Current drug databases allowing the dentist to check for DDI are limited by the lack of integration of the patient’s medical profile with the drug to be prescribed. Hence, this paper introduces a software which predicts the possible DDI of a new medication against
the patient’s medical profile, based on previous findings that associate similarity ratio with DDI. This system is based conceptually on a three-tier framework consisting of a knowledge layer, prediction layer and presentation layer. The novel approach of this system in applying feature vectors for drug prescription will be demonstrated during the conference (http://r.glory.sg/main.php). By engaging with the interactive demonstration, participants will gain first-hand experience in the process from research
idea to implementation. Future work includes the extension of use from dental to medical institutions, and it is currently being enhanced to serve as a training tool for medical students.


Statistics for USQ ePrint 37951
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. Springer Nature Singapore Pte Ltd. 2020. WISE 2019 was postponed until January 2020 because of the problems in Hong Kong. Accepted version embargoed until 1 Feb 2021 (12 months), 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 - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 07 Feb 2020 08:25
Last Modified: 05 Jun 2020 05:16
Uncontrolled Keywords: feature vector, similarity ratio, drug interaction
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1007/978-981-15-3281-8_5
URI: http://eprints.usq.edu.au/id/eprint/37951

Actions (login required)

View Item Archive Repository Staff Only