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Maki Koyama Dental Risk Predictor
- 1. Dental Risk Preditor Keeping Appointments, Keeping Happy Smiles Maki S. Koyama Insight Data Science
- 2. PROBLEM SOLUTION • Dentists suffer cancellations/no show-up, reducing practice efficiency and profits • No perceived need in patients • Educate patients and fill a gap between the perceived need and the actual-need • Tools/models to estimate dental risks – the probability of tooth loss
- 3. Data Features Outcomes Binary Classification Analysis US National Health & Nutrition Survey Age: 20-80yr Cross- sectional 7 features: 1) Age 2) Sex 3) Insurance 4) Annual Income 5) Smoking 6) Drinking 7) Avoided food Permanent tooth present or absent 1) Logistic Reg 2) Naïve Bayes 3) Random Forest
- 4. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 0 20 40 60 80 100 Tooth Location Percentage% Tooth Loss Percentage (%) Wisdom Teeth Upper Lower 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 0 20 40 60 80 100 Tooth Location Percentage% Wisdom Teeth
- 5. Cross-Validation: LR AUC Summary Wisdom Teeth 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 0.0 0.2 0.4 0.6 0.8 1.0 Tooth Location Probability The Upper Jaw 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 0.0 0.2 0.4 0.6 0.8 1.0 Tooth Location Probability The Lower Jaw LR AUC: Range = 0.72 ~ 0.85 10-fold cross validation: Accuracy = 70~85% Across 32 tooth location Tooth #2 LRAUCLRAUC LR is the best model across 32 locations
- 6. Important Features At risk for losing a tooth (28 teeth) Wisdom Teeth More Lost More Present -4 -3 -2 -1 0 1 2 Food Avoided Smoke Now High Income Low Income Aging Coefficients -4 -3 -2 -1 0 1 2 Smoke Now High Income Insurance Sex (W) Aging Coefficients More Lost More Present < 10k 100k >
- 7. What is your age (yrs)? Are you a male or female? Select your annual House Income ($) Do you currently smoke? If so, how many per day? Did you avoid any particular food due to teeth problems in the last 1 year? How often did you have a dental checkup in the last 1 year? How often do you brush and floss your teeth per day? How often do you have sugary snacks per day? ”Please complete this survey” 35 0 1 M F Yes No Yes No 0 12
- 8. Dentists can spot patient’s dental risks Amy Smith: 35 years old, female The estimated probability of “losing tooth” 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132 0.0 0.2 0.4 0.6 0.8 1.0 Tooth Location ProbabiityofToothLoss
- 9. What is your age (yrs)? Are you a male or female? Select your annual House Income ($) Do you currently smoke? If so, how many per day? Did you avoid any particular food due to teeth problems in the last 1 year? How often did you have a dental checkup in the last 1 year? How often do you brush and floss your teeth per day? How often do you have sugary snacks per day? Additions to improve prediction capacity 35 0 1 M F Yes No Yes No 0 12
- 10. About Me Maki S. Koyama PhD in Physiology at University of Oxford, UK Neuroimaging research, including infant scanning, for neurodevelopmental disorders
- 11. 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132 0.0 0.2 0.4 0.6 0.8 1.0 Tooth LocationProbabiityofToothLoss Patient 1 Patient 2 Age/Sex: 35 yrs / M 35 yrs / M Insurance: No Yes Income: Low High Smoke now : Yes No 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132 0.0 0.2 0.4 0.6 0.8 1.0 Tooth Location ProbabiityofToothLoss Two Extreme Cases Patient 1 Patient 2
Editor's Notes
- Hi I am Maki. My project aims to provide dentists a tool for predicting dental risks in their patients.
- Make potential dental problems VISIBLE to patients
- Dental patients often cancel their appointment or even do not show up. This result in reduced practice efficiency and profits in dental offices. To solve this problem I built models to predict the probability of an extreme dental problem, teeth loss, based on individuals’ information such as age, income, and smoking habits. This information is visible and concrete, and thus dentists can effectively communicate with pateitns o motivate them to comply with dental recommendation. As a result, dentists can improve their practice efficiency and profits, while patients can also maintain and improve their dental health.
- Wisdom teeth removal % are outliers, and the decision may depend on many factors beyond demographic and lifestyle risk factors
- 28th Age, Income, Smoking, Food Avoid WT Income, Age, Smoking, Insurance
- I used a term “predictor” in my project, but the dataset available for this analysis was cross-sectional. That is, the predictive models can provide the estimates of prevalence ratio as potential risks. To understand outcome exactly 5 years or 10 years time, longitudinal data is necessary, and if the data becomes available, I want to revisit this project.
- The insight extracted from this dataset is that inclusion of other features, particularly dental hygiene habits, would improve the accuracy of the model. For this, this app can help dentists to collect the data from their patients, and the data will be used to re-build the models.
- The insight extracted from this dataset is that inclusion of other features, particularly dental hygiene habits, would improve the accuracy of the model. For this, this app can help dentists to collect the data from their patients, and the data will be used to re-build the models.
- This type of analysis and the predictive framework can be applied to other medical business beyond dental practice. For example, prediction of infertility, infertility treatment, and IVF success.