SPUR 2019: Application of machine learning for oral health care


Over the last decade, there has been an increase in the application of machine learning techniques towards medical research. One area that machine learning is well-suited to target is in directing services to areas of the greatest need. Resource mismanagement in primary oral health care places an undue burden on general health service delivery. For example, when emergency departments must address non-traumatic dental problems, patients are treated by providers without specialized dental training, and extensive post-discharge follow up by a dental practitioner is typically still required. The result is added health system burden without any improvements to the patient experience or outcomes.

A steady increase in the prevalence of dental caries and the varying implications of poor oral health21 have prompted the passage of public policy and oral health imperatives that aim to increase access to dental care for all people. To date, policy changes have addressed issues of access for people of different socioeconomic statuses, provided for the creation of school-based dental clinics, interventions at crucial early stages of development, and train dental care providers to service areas of increased need. What has not been addressed in improvements is a method to streamline identification of those at greatest need for dental care. Machine-learning applications that capitalize on information extracted from large stores of public health data are an opportunity to provide resources in a way that is targeted and personalized. This goal of this study is to use machine learning algorithms to create a useful, predictive model for dental care recommendations for individuals based on need.

Student Role

Students will be involved in each phrase of the project from study design, literature review, data management and analysis, to drafting manuscripts and preparation for journal publication and conference presentation. As students develop research skill competency, greater autonomy will be given in completing research tasks. Students also have the opportunity to learn and participate in other ongoing research projects to broaden their knowledge and perspectives.

Student Learning Outcomes & Benefits

Students participated in this project will be actively learning the proper designs of scientific research. They will be trained in the ethnical and practical aspects of human subject research and data management. They will learn how to conduct literature review, cite references, use statistical software to perform appropriate data analysis, write abstracts and manuscripts for publication and deliver professional presentations. They will have the opportunities to be co-authors on abstracts and manuscripts. These hands-on activities will increase students’ research skills and will be beneficial for students who seek further education in medical school or other graduate-level programs.

Man Hung
Associate Professor

School of Medicine
Master of Statistics Program

An important goal for this program is to provide students with rich opportunity to participate in mentored research. To facilitate both the project and the mentoring aims, a regular meeting with all mentored students will be held to provide direction, instructions and guidance regarding the research process, to provide training and to provide skill building opportunities. Such meetings will allow for frequent feedback regarding students’ work. I maintain professional and personal relationships with many of my former undergraduate and graduate students and continue to provide letters of recommendation, mentoring and advice. Cultivating long-term relationships with students provides potential future collaborators as the students move through their academic training and on to graduate school and academic careers. My goal is to form long-term relationships with my students who will become my future colleagues and early stage mentoring is an excellent way to build these relationships.