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

Dennis H. Murphree, Daniel J. Quest, Ryan M. Allen, Che Ngufor, Curtis B. Storlie

Research output: Contribution to journalArticle

Abstract

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.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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