Systems, methods, and devices for biophysical modeling and response prediction

Abstract

Various systems and methods are disclosed. One or more of the methods disclosed uses machine learning algorithms to predict biophysical responses from biophysical data, such as heart rate monitor data, food logs, or glucose measurements. Biophysical responses may include behavioral responses. Additional systems and methods extract nutritional information from food items by parsing strings containing names of food items.

What is claimed is: 
     
         1 . A method, comprising:
 training a neural network with time series training data of a first modality and time series training data of a second modality to create a first model that generates time series data of the second modality from time series data of the first modality;   training a second model with the generated time series of the second modality, time series training data of a third modality, and time series data of a fourth modality to generate time series data of the fourth modality;   until a convergence condition is reached, iteratively testing the second model on the time series data of the first modality and the time series data of the third modality; and   responsive to reaching the convergence condition, predicting second modality data by testing the second model with data of the first modality.   
     
     
         2 . The method of  claim 1 , wherein the time series training data of the second modality has gaps. 
     
     
         3 . The method of  claim 1 , further comprising:
 acquiring the time series training data of the first modality with a first type sensor; and   acquiring the time series training data of the second modality with a second type sensor.   
     
     
         4 . The method of  claim 3 , wherein the second type sensor is a glucose meter, and wherein the time series data of the second modality includes glucose levels over time. 
     
     
         5 . The method of  claim 1 , wherein training the neural network to create the first model includes training with N sets of time series training data, and wherein training the first model with the estimated time series training data of the first modality and time series training data of at least the third modality includes training with M sets of time series data. 
     
     
         6 . The method of  claim 5 , further comprising testing the first model with the N sets of time series data and the M sets of time series data and updating the first model in response to error values of the testing, and wherein the trained first model is the first model with the smallest error. 
     
     
         7 . The method of  claim 1 , wherein reaching a convergence condition includes calculating an error value not greater than a threshold. 
     
     
         8 . A system, comprising:
 an initial model section that includes a first model trained to generate time series data of a second modality from time series data of a first modality with M sets of training data;   a training section that includes:
 a second model derived from the first model and configured to generate time series data of at least a third modality from at least time series data of a fourth modality with N sets of training data, and 
 a testing section configured to test the second model with the M and N sets of training data, and update the second model in response to test error values; and 
   an inference model that is the second model with the lowest test error value, configured to infer time series data of the second modality from time series data of the first modality.   
     
     
         9 . The system of  claim 8 , wherein the first model, the second model and the inference model comprise neural networks. 
     
     
         10 . The system of  claim 8 , wherein the time series data of the first and second modalities are biophysical sensor data. 
     
     
         11 . The system of  claim 10 , wherein at least the time series data of the first and second modalities are glucose levels corresponding to glucose meters. 
     
     
         12 . The system of  claim 11 , wherein the third and fourth modalities are glucose levels. 
     
     
         13 . The system of  claim 8 , wherein the training section comprises:
 an inverse model that is an inverse of the first model and configured to generate estimated time series data of the first modality from the time series data of the third and a fourth modality;   an estimator section configured to generate linear parameters from the estimated time series data of the first modality and the time series data of the third modality;   
       section configured to generate mapped time series data of the first modality from time series data of the third modality using the linear parameters,
 wherein the second model is trained with the mapped time series data of the first modality. 
 
     
     
         14 . A method, comprising:
 training a neural network with time series training data of a first modality and time series training data of a second modality to create a first model that generates time series data of the second modality from time series data of the first modality;
 until a convergence condition is reached:
 using a second model to generate estimated time series data of the first modality from a mixture of time series data from a third modality and a fourth modality, wherein the second model is initiated as an inverse model of the first model; 
 using the estimated time series data of the first modality and time series data of the third modality, training the second model to estimate linear fitting parameters; 
 using the estimated linear fitting parameters to generate analogous time series data of the first modality from the time series data of the third modality; 
 linearly mapping the analogous time series data of the first modality to the time series data of the third modality; 
 training a third model using the linearly mapped analogous time series data from the first modality mixture of time series data of the third modality and time series data of the fourth modality to generate a mixture of time series data from the third modality and time series data from the fourth modality, wherein the third model is an inverse of the second model; 
 modifying the second model to be an inverse model of the third model; and 
 evaluating whether the convergence condition has been reached. 
 
   
     
     
         15 . The method of  claim 14 , wherein training the third model includes initializing the third model as the first model. 
     
     
         16 . A method for training a neural network to calibrate time series data, comprising:
 receiving calibrated time series data for a biophysical response and corresponding raw time series data for the biophysical response;   training, on the calibrated time series data and the corresponding raw time series data for the biophysical response, a neural network to generate calibrated time series data, which training comprises updating parameters of the neural network based on a difference between (i) an output of the neural network for a given raw time series and (ii) a corresponding calibrated times series;   receiving raw input time series data generated by a biophysical sensor; and   generating calibrated time series data by applying the raw input time series data to the neural network.   
     
     
         17 . The method of  claim 16 , wherein the raw input time series data is generated by a glucose meter. 
     
     
         18 . The method of  claim 16 , wherein the neural network is trained to cancel drift present in the raw input time series data. 
     
     
         19 . The method of  claim 18 , wherein the raw time series data and raw input time series data are generated by glucose meters. 
     
     
         20 . The method of  claim 16 , wherein training the neural network further comprises domain specific feature engineering. 
     
     
         21 . The method of  claim 16 , wherein training the neural network comprises unsupervised training.