Assessing the readiness of dental electronic health records for machine learning prediction of procedure outcomes: Insights from the bigmouth repository on composite and amalgam restoration survival rates.

Journal: Journal of dentistry
Published Date: Jun 6, 2025

Abstract

OBJECTIVE: Dental electronic health records (EHRs) often lack comprehensive data for evaluating procedure outcomes. Machine learning (ML) enables predictive modeling but its applicability to dental EHR data remains unclear. This study assessed the readiness of dental EHRs for predicting restoration failure using classical and ML models.

Authors

  • Hend Alqaderi
    Tufts University School of Dental Medicine, Boston, MA, USA. Electronic address: hend.alqaderi@tufts.edu.
  • Hesham Alhazmi
    Umm Al-Qura University, Makkah, Saudi Arabia. Electronic address: haahazmi@uqu.edu.sa.
  • Lauren Gritzer
    UCSF School of Dentistry, San Francisco, CA, USA. Electronic address: Lauren.Gritzer@ucsf.edu.
  • Narjes Bencheikh
    Harvard School of Dental Medicine, Boston, MA, USA. Electronic address: narjesbencheikh@hsdm.harvard.edu.
  • Mary Tavares
    Boston University Goldman School of Dental Medicine, Boston, MA, USA. Electronic address: mtavar@bu.edu.
  • Jay Patel
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
  • Athanasios Zavras
    Tufts University School of Dental Medicine, Boston, MA, USA. Electronic address: Athanasios.Zavras@tufts.edu.

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