Mining drug properties for decision support in dental clinics
Conference or Workshop item
Paper/Presentation Title | Mining drug properties for decision support in dental clinics |
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Authors | Goh, Wee Pheng (Author), Tao, Xiaohui (Author), Zhang, Ji (Author) and Yong, Jianming (Author) |
Editors | Kim, J., Shim, K., Cao, L., Lee, J., Lin, X. and Moon, Y. |
Journal or Proceedings Title | Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017) |
Journal Citation | 10235, pp. 375-387 |
Number of Pages | 13 |
Year | 2017 |
Publisher | Springer |
Place of Publication | Germany |
ISBN | 9783319575292 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-57529-2_30 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-319-57529-2_30 |
Conference/Event | 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017) |
Event Details | Rank A A A A A A A A A A A A A A A A A A A |
Event Details | 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017) Parent Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Delivery In person Event Date 23 to end of 26 May 2017 Event Location Jeju, South Korea |
Abstract | 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 79%. 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 workflow, will reduce prescription errors thereby increasing the health outcome of the patient. |
Keywords | adverse relationship; drug allergy; drug properties; knowledge-base; personalised prescription; similarity ratio; term frequency |
ANZSRC Field of Research 2020 | 460902. Decision support and group support systems |
460103. Applications in life sciences | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Series | Lecture Notes in Artificial Intelligence (Book series) |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
School of Management and Enterprise | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3w27/mining-drug-properties-for-decision-support-in-dental-clinics
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