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.

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients' personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients' health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the

Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered frameworkthe prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes. Keywords Feature vector • Similarity ratio • Word embedding • Adverse network model • Personalised drug prescription This work is partially supported by Glory Dental Surgery Pte Ltd and undertaken collaboratively with their panel of dentists. We would like to thank Dr. Xueling Oh and Dr. Elizabeth Goh for enriching the authors understanding of drug prescription within the medical domain.

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.

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.

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug-pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug-pairs, by using feature vectors generated from term similarities and word embeddings of bio-medical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model's association of the similarity ratio between two drugs yielded a superior F score of 89%.Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors and thereby increase the health outcomes of patients.

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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.