Model guided deep learning approach towards prediction of physical system behavior

A method for controlling a predictive medical monitor, the method comprising:

• storing, in a memory, a guide model including a non-linear predictive model for estimating a guiding first patient parameter;
• storing, in the memory, a deep neural network, the deep neural network including:
• an input layer configured to receive the guiding first estimated patient parameter;
• a plurality of hidden layers, wherein each hidden layer comprises a respective plurality of artificial neurons, each artificial neuron in a hidden layer being configured to perform a linear or nonlinear transformation on output of at least one artificial neuron from an adjacent layer in the deep neural network;
• and
• an output layer configured to receive at least one output from a hidden layer;
• and with an electronic processor coupled to the memory:
• receiving patient input data;
• inputting the patient input data into the guide model and generating the guiding first estimated patient parameter based on the guide model and the patient input data;
• and
• inputting the patient input data and the guiding first estimated patient parameter into the deep neural network input layer and generating the second estimated patient parameter based on the deep neural network, the patient input data, and the guiding first estimated patient parameter.

A transitory computer-readable medium comprising instructions that when executed by an electronic processor, cause the electronic processor to:

• store, in a memory, a guide model including a non-linear predictive model for estimating a guiding first patient parameter;
• store, in the memory, a deep neural network, the deep neural network including:
• an input layer configured to receive the guiding first estimated patient parameter;
• a plurality of hidden layers, wherein each hidden layer comprises a respective plurality of artificial neurons, each artificial neuron in a hidden layer being configured to perform a linear or nonlinear transformation on output of at least one artificial neuron from an adjacent layer in the deep neural network;
• and
• an output layer configured to receive at least one output from a hidden layer;
• receive patient input data;
• input the patient input data into the guide model and generate the guiding first estimated patient parameter based on the guide model and the patient input data;
• and
• input the patient input data and the guiding first estimated patient parameter into the deep neural network input layer and generate the second estimated patient parameter based on the deep neural network, the patient input data, and the guiding first estimated patient parameter.