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Patient-Level Prediction: Implementing Trained Model as an API in Clinical Decision Support System

Hello OHDSI community,
I have a question regarding the implementation of a trained model as an API for patient-level prediction in order to develop a prototype of a Clinical Decision Support System (CDSS).

I have successfully trained a Gradient Boosting Machine model that predicts death as an outcome. Now, I would like to integrate this model into our CDSS prototype. My goal is to create a microservice that can receive input data related to a patient and provide the predicted score as an API response.

I would appreciate any guidance or recommendations on how to proceed with this implementation. Specifically, I am looking for insights on the following:

  • What technologies or frameworks would be suitable for creating the microservice to host the trained model as an API (R, Python, etc.)?
  • Are there any existing projects or examples that demonstrate the appropriate format and structure of the input data (feature vector) to be sent to the API for obtaining predictions?

Thank you in advance for your support!

Best regards,