Deploying Predictive Models In A Healthcare Environment - An Open Source Approach
| Autor: | Dennis H. Murphree, Curtis B. Storlie, Daniel J. Quest, Che Ngufor, Ryan M. Allen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Modern medicine
Source code 020205 medical informatics business.industry Computer science media_common.quotation_subject 020207 software engineering 02 engineering and technology Skilled Nursing Python (programming language) computer.software_genre Data modeling Open source Health care 0202 electrical engineering electronic engineering information engineering Data Mining Web service Software engineering business computer Software media_common computer.programming_language |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc.2018.8513689 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|