Can A Generative Ai Model Help Us Improve The Performance Of A Predictive Modeling Task With Limited Data: A Case Study Of Dental Implant Failure Prediction?
Location
Hager-Lubbers Exhibition Hall
Description
PURPOSE: Data scarcity is a significant challenge in medical domains, impacting machine learning models like those predicting dental implant failure. This study investigates whether a hybrid generative-discriminative approach can enhance the predictive performance of a feedforward deep neural network using synthetic data. Additionally, it explores demographic factors, lifestyle choices, and procedural variables, including surgeon experience, to assess their impact on implant success. METHODS AND MATERIALS: A hybrid model combining a generative and discriminative network was developed. The generative component creates synthetic data by injecting random noise into Gaussian distributions for continuous variables and learning conditional distributions for categorical variables. An additional neural network attached to a Generative Adversarial Network (GAN) architecture enables integration. Statistical analysis using standard techniques was conducted on a publicly available dental implant failure dataset to identify influential factors. ANALYSES: The supervised neural network module outperformed previously published results in predicting implant failure. The generative-discriminative model is still under evaluation, with synthetic data performance assessments ongoing. Statistical tests revealed significant associations between demographic factors, lifestyle choices, and procedural variables with implant success. Surgeon experience was particularly influential, emphasizing the role of practitioner expertise.CONCLUSIONS: The study demonstrates the potential of deep learning models, particularly hybrid approaches, to enhance predictive accuracy in data-scarce scenarios. Key insights from the statistical analysis underscore the importance of personalized predictive models to improve patient outcomes.
Can A Generative Ai Model Help Us Improve The Performance Of A Predictive Modeling Task With Limited Data: A Case Study Of Dental Implant Failure Prediction?
Hager-Lubbers Exhibition Hall
PURPOSE: Data scarcity is a significant challenge in medical domains, impacting machine learning models like those predicting dental implant failure. This study investigates whether a hybrid generative-discriminative approach can enhance the predictive performance of a feedforward deep neural network using synthetic data. Additionally, it explores demographic factors, lifestyle choices, and procedural variables, including surgeon experience, to assess their impact on implant success. METHODS AND MATERIALS: A hybrid model combining a generative and discriminative network was developed. The generative component creates synthetic data by injecting random noise into Gaussian distributions for continuous variables and learning conditional distributions for categorical variables. An additional neural network attached to a Generative Adversarial Network (GAN) architecture enables integration. Statistical analysis using standard techniques was conducted on a publicly available dental implant failure dataset to identify influential factors. ANALYSES: The supervised neural network module outperformed previously published results in predicting implant failure. The generative-discriminative model is still under evaluation, with synthetic data performance assessments ongoing. Statistical tests revealed significant associations between demographic factors, lifestyle choices, and procedural variables with implant success. Surgeon experience was particularly influential, emphasizing the role of practitioner expertise.CONCLUSIONS: The study demonstrates the potential of deep learning models, particularly hybrid approaches, to enhance predictive accuracy in data-scarce scenarios. Key insights from the statistical analysis underscore the importance of personalized predictive models to improve patient outcomes.