rds files to store R objects containing complex statistic models that can be directly used by Shiny apps to produce predictions. To achieve these goals, we explain how to use. The purpose of this tutorial is to build on that foundation for researchers aiming to implement more complicated predictive models, especially those developed with ML methods, and/or more complex interfaces or visualizations. We highly recommend that tutorial to get started making calculators using Shiny. Our goal in this paper is to provide a brief tutorial with examples to aid developers of prediction models in making web-accessible interfaces.Īn excellent tutorial on creating basic risk calculators using the R Shiny package ( 11) has already been published in this journal ( 12). Although many different software environments can be used to create web-accessible user interfaces (UI) for these prediction models, the R Shiny package, along with auxiliary packages such as shinydashboard ( 9) and flexdashboard ( 10), provides easy methods for researchers who are familiar with R to build interactive online interfaces without extensive web development knowledge. In medicine, clinical prediction calculators are increasingly becoming a popular tool for decision making, allowing clinicians and patients to utilize predictive models in a way that is user-friendly and accessible ( 6- 8). As applications of computation and machine learning (ML) become ubiquitous in many areas of science, user interfaces are being developed to allow users to draw meaningful interpretations without extensive expertise in computational methods ( 1- 5).
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