Clinical research and practice are rapidly changing mostly due to Information and Communication Technology, especially, as Machine Learning (ML) offers great potential for predictive and personalised medicine. Nevertheless, barriers are still existing for widespread adoption of ML tools, as highlighted by studies from the European Union. In this paper, we propose an architecture for a Decision Support Systems assisting clinicians in assessing health risk of patients by delivering “Risk Prediction as a Service”. By leveraging standard web technologies as well as the PMML and PFA formats for exchange of trained models, we achieve ubiquitous access to predictions, ease of deployment, seamless interoperability, while promoting collaboration. |
