Deploying Predictive Models In A Healthcare Environment - An Open Source Approach

Despite dramatic progress in the application of predictive modeling and data mining techniques to problems in modern medicine, a major challenge facing technical practitioners is that of delivering models to clinicians. We have developed an easily implementable framework for publishing predictive models written in R or Python in a way that allows them to be consumed by practically any downstream clinical application, as well as allowing them to be reused in a wide variety of environments without modification. The approach makes models available as web services embedded in containers and uses only open source technology. We provide a template, practical explanation and discussion of involved technologies for a model production framework. We currently use this framework to deliver a model for predicting readmission to hospital following discharge to skilled nursing facilities. The flexibility and simplicity of this methodology will allow it to be readily adopted at a wide variety of institutions. We also provide source code for an example model.