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Related Experiment Videos

Tutorial: translating a validated breast cancer prediction model into a web-based decision aid using R Shiny.

Emily A Wolfson1, Long H Ngo1,2, Mara A Schonberg1

  • 1Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Annals of Translational Medicine
|July 11, 2026
PubMed
Summary

Developing patient-facing breast cancer risk prediction tools is crucial for informed decision-making. This study presents a framework for creating interactive online decision aids using R Shiny, enhancing clinical utility.

Keywords:
Risk calculatorShiny applicationbreast cancer screeningclinical decision supportdecision aid

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Area of Science:

  • Biomedical Informatics
  • Health Services Research
  • Cancer Prevention

Background:

  • Risk prediction models are underutilized in patient-facing tools for informed decision-making.
  • Existing breast cancer risk models lack guidance for screening and prevention medication choices.
  • Translating complex risk models into accessible patient tools remains a challenge.

Purpose of the Study:

  • To present a framework for developing online decision aids using R Shiny.
  • To demonstrate integrating a validated competing risk regression model into an interactive web application.
  • To provide practical guidance for building multi-page, dynamic clinical decision support tools.

Main Methods:

  • Developed a web-based decision aid application integrating a competing risk regression model.
  • Utilized R Shiny to create an interactive, multi-page online tool.
  • Implemented reactive programming, data visualization, and conditional navigation for user engagement.

Main Results:

  • Successfully created a case study application demonstrating the framework.
  • Showcased real-time calculation and clear presentation of individualized risk estimates.
  • Illustrated dynamic updates and interactive features for enhanced user experience.

Conclusions:

  • The R Shiny framework facilitates the development of comprehensive, clinically useful online decision aids.
  • This approach extends basic risk calculators into dynamic tools supporting informed patient decisions.
  • The presented methods address the gap in practical guidance for building sophisticated risk-based decision support systems.