Risk Prediction as a Service: A RESTful Machine Learning Pipeline for the Healthcare Domain


The thesis aims at designing a prototype of a configurable Machine Learning pipeline for the healthcare domain, where risk prediction is provided as a RESTful web service. The pipeline should enable selection of the model to train, the desired performance metrics to achieve, the structure of input data compliant with the model application. Promising tools are Smile and SparkML.

(keywords) machine learning, as a service, REST, healthcare, risk prediction
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Thesis

Risk Prediction as a Service: A RESTful Machine Learning Pipeline for the Healthcare Domain

Supervision

— supervisor

Andrea Omicini

— co-supervisor

Stefano Mariani

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second-cycle thesis

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out-of-date thesis

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Dates

— available since

15/03/2018

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