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Multivariate and Online Transfer Learning With Uncertainty Quantification.

Jimmy Hickey1, Jonathan P Williams1, Brian J Reich1

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.

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This study introduces a new Bayesian transfer learning framework to improve periodontal outcome modeling for underrepresented groups. The enhanced method ensures accurate predictions without compromising data privacy, crucial for dental health applications.

Keywords:
Bayesian transfer learningdental recordsinformative Bayesian prioronline learningracial bias

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Area of Science:

  • Biostatistics
  • Dental Research
  • Machine Learning

Background:

  • Periodontitis, a common dental condition, can lead to tooth loss if untreated.
  • Accurate modeling of periodontal outcomes is challenging due to measurement difficulties.
  • Existing models may fail or pose risks when applied to underrepresented demographic groups.

Purpose of the Study:

  • To extend the RECaST Bayesian transfer learning framework for improved periodontal outcome modeling.
  • To address disparities in representation within demographic groups for predictive modeling.
  • To develop a method that enhances model performance for underrepresented populations without data sharing.

Main Methods:

  • Proposed an extension to the RECaST Bayesian transfer learning framework.
  • Developed a joint multivariate outcome modeling approach.
  • Introduced an online method for sequential datasets and mitigated negative transfer.

Main Results:

  • The proposed method significantly improved upon the previous univariate RECaST approach.
  • Demonstrated effective predictive performance and uncertainty quantification on simulated and real dental data.
  • Successfully mitigated negative transfer, protecting underrepresented groups from detrimental model application.

Conclusions:

  • The novel Bayesian transfer learning framework enhances periodontal outcome prediction accuracy and reliability.
  • The method is particularly valuable for applications in healthcare where demographic representation is critical.
  • The approach offers robust uncertainty quantification and ensures data privacy by not sharing data between domains.