System and method for wearable medical sensor and neural network based diabetes analysis

A machine-learning based method for diabetes analysis based on one or more processors configured to interact with a plurality of wearable medical sensors (WMSs), the method comprising:

• receiving physiological data from the WMSs and demographic data from a user interface;
• training at least one neural network based on a grow-and-prune paradigm to generate at least one diabetes inference model, the neural network to grow at least one of connections and neurons based on gradient information and to prune away at least one of connections and neurons based on magnitude information;
• and
• outputting a diabetes-based decision by inputting the received physiological data and demographic data into the generated diabetes inference model,
• wherein the growing at least one of connections and neurons based on gradient information comprises adding connection or neuron when its gradient magnitude is greater than a predefined percentile of gradient magnitudes based on a growth ratio, and
• wherein the pruning away at least one of connections and neurons based on magnitude information comprises removing a connection or neuron when its magnitude is less than a predefined percentile of magnitudes based on a pruning ratio.

A non-transitory computer-readable medium having stored thereon a computer program for execution by a processor configured to perform a machine-learning based method for diabetes analysis, the method comprising:

• receiving physiological data from one or more wearable medical sensors (WMSs) and demographic data from a user interface;
• training at least one neural network based on a grow-and-prune paradigm to generate at least one diabetes inference model, the neural network to grow at least one of connections and neurons based on gradient information and to prune away at least one of connections and neurons based on magnitude information;
• and
• outputting a diabetes-based decision by inputting the received physiological data and demographic data into the generated diabetes inference model,
• wherein the growing at least one of connections and neurons based on gradient information comprises adding connection or neuron when its gradient magnitude is greater than a predefined percentile of gradient magnitudes based on a growth ratio, and
• wherein the pruning away at least one of connections and neurons based on magnitude information comprises removing a connection or neuron when its magnitude is less than a predefined percentile of magnitudes based on a pruning ratio.