Systems, methods and devices for predicting personalized biological state, predicting personalized behavior, and recommending personalized behavior with models produced with meta-learning

A method for personalized blood glucose monitoring for a test subject, comprising:

• (a) acquiring a plurality of task data sets from a plurality of subjects, each of the plurality of task data sets comprising input values and output values from a different source associated with a subject in the plurality of subjects,
• wherein the acquiring comprises (i) using a continuous glucose monitor (CGM) sensor to obtain time-series blood glucose measurements of the subject, and (ii) using a heart rate monitor (HRM) sensor to obtain time-series heart rate monitor values of the subject;
• wherein the task data sets comprise time series data, with a value for one time being an input value, and a value for a subsequent time being an output value;
• (b) performing a meta-learning operation comprising:
• (1) processing at least a portion of the input values with a biology model that generates predicted output values comprising at least one blood glucose level corresponding to the input values, wherein the biology model comprises a neural network configured to process the time series data, such that a value for one time of the time series data is processed as an input value to the neural network and another value for a subsequent time of the time series data is produced as an output value of the neural network,
• (2) generating meta-learning error values at least in part by comparing each predicted output value to the output value corresponding to the respective input value,
• (3) training the biology model based at least in part on the meta-learning error values, wherein the training comprises adjusting parameters for the neural network of the biology model, such that the biology model reaches convergence with a meta-error target of the biology model, wherein
• the parameters are stored as meta-learned parameters after all task data sets have been processed by the meta-learning operation;
• (c) training a population-based meta-learned model, at least in part by configuring the neural network of the biology model with the meta-learned parameters;
• (d) acquiring test subject data from the test subject by (i) using the CGM sensor to obtain time-series blood glucose measurements of the test subject, and (ii) using the HRM sensor to obtain time-series heart rate monitor values of the test subject, wherein the test subject data was not used for performing the meta-learning operation in (c), and wherein the test subject is not among the plurality of subjects in (a);
• and
• (e) subsequent to the training of (c), training a personalized subject prediction model to predict at least one blood glucose level for the test subject, at least in part by re-training the population-based meta-learned model with the test subject data, the test subject data set comprising input values and output values and being different from each of the task data sets,
• wherein the re-training comprises adjusting the meta-learned parameters of the population-based meta-learned model, such that the personalized subject prediction model reaches convergence with an error target of the personalized subject prediction model,
• thereby producing a personalized subject prediction model that is optimized for the test subject;
• and
• (f) predicting a blood glucose level for the test subject with the personalized subject prediction model.