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Related Experiment Video

Updated: Aug 20, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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Predicting no-shows for dental appointments.

Yazeed Alabdulkarim1, Mohammed Almukaynizi1, Abdulmajeed Alameer2

  • 1Information Systems Department, King Saud University, Riyadh, Saudi Arabia.

Peerj. Computer Science
|November 25, 2022
PubMed
Summary

Predicting dental patient no-shows is crucial for healthcare efficiency. Machine learning models can forecast appointment cancellations, enabling clinics to optimize scheduling and reduce costs associated with missed dental visits.

Keywords:
Dental appointmentsDental no-showsMachine learningPatient no-show

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Area of Science:

  • Healthcare Management
  • Machine Learning Applications
  • Dental Health Services

Background:

  • Patient no-shows represent a significant financial burden on healthcare systems, with up to 80% of appointments missed.
  • Understanding variations in no-show behavior across different clinic types and appointment specialties is essential for effective mitigation.
  • Dental appointments, often longer and more complex, present unique challenges in managing patient attendance.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting individual patient no-shows in dental settings.
  • To introduce a novel method for representing patient no-show history to improve predictive accuracy.
  • To explore the practical applications of no-show prediction in optimizing dental appointment scheduling.

Main Methods:

  • Utilized machine learning techniques to build predictive models for dental appointment no-shows.
  • Developed a new approach to encode patient no-show history as binary event sequences.
  • Evaluated model performance using metrics such as Area Under the Curve (AUC) and F1 score.

Main Results:

  • The best predictive model achieved an Area Under the Curve (AUC) of 0.718.
  • The optimal model demonstrated an F1 score of 66.5% in predicting dental no-shows.
  • The novel no-show history representation method enhanced the predictive capabilities of the models.

Conclusions:

  • Machine learning models can effectively predict dental patient no-shows.
  • The proposed no-show history representation aids in learning future behavior patterns.
  • Accurate no-show predictions can inform strategies for appointment reallocation and duration adjustment, improving clinic efficiency.