A Study of Drug Interaction for Personalised Decision Support in Dental Clinics

Goh, Wee Pheng; Tao, Xiaohui; Zhang, Ji; Yong, Jianming

doi:10.1109/wi-iat.2015.28

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…One critical feature in personalised support is in the area of drug prescription, where the system should be able to support the dentist in determining if the drug to be prescribed is safe for the patient by considering the individual's relevant medical history -the drugs the patient is currently taking, the drugs the patient is allergic to, and the medical conditions of the patient (Goh et al, 2015).…”

Section: Classification Of Clinical Decision Support Systemsmentioning

confidence: 99%

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

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While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

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Section: Classification Of Clinical Decision Support Systemsmentioning

confidence: 99%

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

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Mining Drug Properties for Decision Support in Dental Clinics

Goh

Tao

Zhang

et al. 2017

Advances in Knowledge Discovery and Data Mining

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Abstract. With poly-pharmacy becoming more common, it is important for health providers to be aware of the drug profile of patients before prescribing. Although there are many methods on extracting information on drug interactions, they do not integrate with the patients' medical history. This paper describes state-of-the art approaches in extracting the term frequencies of drug properties, and using this knowledge to decide if a drug is suitable for prescription after considering if there is any drug allergy the patient may have and the drugs that the patient is currently taking. An experiment is conducted to evaluate the accuracy of associating the similarity ratio in terms of their term frequencies to the similarity between them. Experimental evaluation of our model yields an accuracy of over 80% which is superior to models that use other methods. Since a drug is to be avoided if it is similar to a drug that patient is allergic to, our model will help dentist decide if a drug is suitable for prescription to the patient. Hence such an approach, when integrated within the clinical workflow will reduce prescription errors thereby increasing the health outcome of the patients.

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A Study of Drug Interaction for Personalised Decision Support in Dental Clinics

Cited by 2 publications

References 11 publications

Decision support systems for adoption in dental clinics: A survey

Decision support systems for adoption in dental clinics: A survey

Mining Drug Properties for Decision Support in Dental Clinics

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