Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics

Wee Pheng Goh, Xiaohui Tao, Ji Zhang, Jianming Yong, Yongrui Qin, Elizabeth Zhixin Goh, Aimin Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With more patients taking multiple medications and the increasing digital availability of diagnostic data such as treatment notes and x-ray images, the importance of decision support systems to help dentists in their treatment planning cannot be over emphasised. Based on the hypothesis that a higher similarity ratio between drugs in a drug-pair indicates that the combination of the drug-pair has a higher chance of an adverse interaction, this paper describes an efficient approach in extracting feature vectors from the drugs in a drug-pair to compute the similarity ratio between them. The feature vectors are obtained through a network model where the information of the drugs are represented as nodes and the relationships between them represented as edges. Experimental evaluation of our model yielded a superior F score of 74%. The use of a network model will drive research efforts into more efficient data-mining algorithms for information retrieval, similarity search and machine learning. Since it is important to avoid drug allergies when prescribing drugs, our work when integrated within the clinical work-flow will reduce prescription errors thereby increasing health outcomes for patients.

Original languageEnglish
Title of host publicationThe 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2018)
EditorsJuan E. Guerrero
PublisherIEEE
Pages168-172
Number of pages5
ISBN (Electronic)9781728102078
ISBN (Print)9781728102085
DOIs
Publication statusPublished - 25 Apr 2019
Event5th International Conference on Behavioral, Economic, and Socio-Cultural Computing - National University of Kaohsiung, Kaohsiung, Taiwan, Province of China
Duration: 12 Nov 201814 Nov 2018
http://besc-conf.org/2018/

Conference

Conference5th International Conference on Behavioral, Economic, and Socio-Cultural Computing
Abbreviated titleBESC 2018
CountryTaiwan, Province of China
CityKaohsiung
Period12/11/1814/11/18
Internet address

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Allergies
Decision support systems
Information retrieval
Data mining
Learning systems
Health
Availability
Planning
X rays

Cite this

Pheng Goh, W., Tao, X., Zhang, J., Yong, J., Qin, Y., Zhixin Goh, E., & Hu, A. (2019). Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics. In J. E. Guerrero (Ed.), The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2018) (pp. 168-172). IEEE. https://doi.org/10.1109/BESC.2018.8697814
Pheng Goh, Wee ; Tao, Xiaohui ; Zhang, Ji ; Yong, Jianming ; Qin, Yongrui ; Zhixin Goh, Elizabeth ; Hu, Aimin. / Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics. The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2018). editor / Juan E. Guerrero. IEEE, 2019. pp. 168-172
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title = "Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics",
abstract = "With more patients taking multiple medications and the increasing digital availability of diagnostic data such as treatment notes and x-ray images, the importance of decision support systems to help dentists in their treatment planning cannot be over emphasised. Based on the hypothesis that a higher similarity ratio between drugs in a drug-pair indicates that the combination of the drug-pair has a higher chance of an adverse interaction, this paper describes an efficient approach in extracting feature vectors from the drugs in a drug-pair to compute the similarity ratio between them. The feature vectors are obtained through a network model where the information of the drugs are represented as nodes and the relationships between them represented as edges. Experimental evaluation of our model yielded a superior F score of 74{\%}. The use of a network model will drive research efforts into more efficient data-mining algorithms for information retrieval, similarity search and machine learning. Since it is important to avoid drug allergies when prescribing drugs, our work when integrated within the clinical work-flow will reduce prescription errors thereby increasing health outcomes for patients.",
keywords = "Drug adverse interaction, Clinical decision support, Network model, Drug prescription",
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Pheng Goh, W, Tao, X, Zhang, J, Yong, J, Qin, Y, Zhixin Goh, E & Hu, A 2019, Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics. in JE Guerrero (ed.), The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2018). IEEE, pp. 168-172, 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing , Kaohsiung, Taiwan, Province of China, 12/11/18. https://doi.org/10.1109/BESC.2018.8697814

Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics. / Pheng Goh, Wee; Tao, Xiaohui; Zhang, Ji; Yong, Jianming; Qin, Yongrui; Zhixin Goh, Elizabeth; Hu, Aimin.

The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2018). ed. / Juan E. Guerrero. IEEE, 2019. p. 168-172.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Hu, Aimin

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N2 - With more patients taking multiple medications and the increasing digital availability of diagnostic data such as treatment notes and x-ray images, the importance of decision support systems to help dentists in their treatment planning cannot be over emphasised. Based on the hypothesis that a higher similarity ratio between drugs in a drug-pair indicates that the combination of the drug-pair has a higher chance of an adverse interaction, this paper describes an efficient approach in extracting feature vectors from the drugs in a drug-pair to compute the similarity ratio between them. The feature vectors are obtained through a network model where the information of the drugs are represented as nodes and the relationships between them represented as edges. Experimental evaluation of our model yielded a superior F score of 74%. The use of a network model will drive research efforts into more efficient data-mining algorithms for information retrieval, similarity search and machine learning. Since it is important to avoid drug allergies when prescribing drugs, our work when integrated within the clinical work-flow will reduce prescription errors thereby increasing health outcomes for patients.

AB - With more patients taking multiple medications and the increasing digital availability of diagnostic data such as treatment notes and x-ray images, the importance of decision support systems to help dentists in their treatment planning cannot be over emphasised. Based on the hypothesis that a higher similarity ratio between drugs in a drug-pair indicates that the combination of the drug-pair has a higher chance of an adverse interaction, this paper describes an efficient approach in extracting feature vectors from the drugs in a drug-pair to compute the similarity ratio between them. The feature vectors are obtained through a network model where the information of the drugs are represented as nodes and the relationships between them represented as edges. Experimental evaluation of our model yielded a superior F score of 74%. The use of a network model will drive research efforts into more efficient data-mining algorithms for information retrieval, similarity search and machine learning. Since it is important to avoid drug allergies when prescribing drugs, our work when integrated within the clinical work-flow will reduce prescription errors thereby increasing health outcomes for patients.

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Pheng Goh W, Tao X, Zhang J, Yong J, Qin Y, Zhixin Goh E et al. Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics. In Guerrero JE, editor, The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2018). IEEE. 2019. p. 168-172 https://doi.org/10.1109/BESC.2018.8697814