Machine learning-based prediction of physiological parameters in remote medical information exchange

A computer-implemented method of predicting a physiological parameter value of a patient based on a facial image of the patient, the method including:

• receiving the facial image of the patient from a captured video stream including facial and upper body features of the patient;
• processing the facial image to generate a frontal facial image;
• providing the frontal facial image, comprising facial and upper body features, to a first trained neural network model configured to predict a sentiment of the patient based on the facial and upper body features;
• receiving the sentiment of the patient from the first neural network model indicating how the patient is feeling;
• wherein the first trained neural network model is a regression deep learning convolutional neural network model;
• and
• providing the predicted physiological parameter value including the sentiment of the patient to aid in real time diagnosis of the patient.

A non-transitory computer readable medium including a set of instructions that are executable by one or more processors of a computer to cause the computer to perform a method for predicting a physiological parameter value of a patient based on a facial image of the patient, the method including:

• receiving the facial image of the patient from a captured video stream including facial and upper body features of the patient;
• processing the facial image to generate a frontal facial image;
• providing the frontal facial image, comprising facial and upper body features, to a trained neural network model configured to predict a sentiment of the patient based on the facial and upper body features;
• receiving the sentiment of the patient from the trained neural network model indicating how the patient is feeling;
• wherein the trained neural network model is a regression deep learning convolutional neural network model;
• and
• providing the predicted physiological parameter value including the sentiment of the patient to aid in real time diagnosis of the patient.