Chronic pain and food trigger tracking system and processes

A computer-implemented method for managing and determining chronic pain triggers:

• providing a user with a visual touch screen 2D/3D body model enabled for interaction with the user and prompting a user to input a first bodily location on the 2D/3D body model of a first pain experienced by the user;
• receiving, by a chronic pain and food trigger tracking app, structured data representing food or supplement intake during a first intake time period and pain symptom data corresponding to at least one symptom reporting time point, wherein the intake time period and the symptom reporting time are not required to occur simultaneously;
• receiving, by the app, user input of a first pain experienced by the user at a first particular time that is one of (i) during the first period of time and (ii) after the first period of time elapses, wherein the user input of the first pain comprise a first bodily location of the first pain experienced by the user, a first type of pain that characterizes the first pain experienced by the user, and a first intensity of the first pain experienced by the user;
• processing the intake data by mapping ingredients to one or more food classification using classification mapping database representing an ingredient hierarchy and collecting composite food records for meals within a prior temporal window;
• generating feature vectors for correlation analysis by computing, for each classification, one or more statistical features derived from occurrence, exposure, or frequency with the window;
• transmitting, to the front-end server by the app, the user input of the food intake information over the first period of time and the user input of the first pain experienced by the user at the first particular time, the composite food records, and intake data;
• transmitting the user input of the food intake information over the first period of time and the user input of the first pain experienced by the user at the first particular time, by the front-end server, to a backend chronic pain and food trigger tracking server, food intake information over the first period of time and the user input of the first pain experienced by the user at the first particular time, the composite food records, and intake data;
• training, by a deep learning processing unit that is communicably connected to the backend chronic pain and food trigger tracking server, an initial machine learning model for the user based on the food intake information over the first period of time and the user input of the first pain at the first particular time;
• receiving, by the app operated by the user subsequent user input of additional food intake information over a second period of time after the first period of time elapses;
• receiving, by the app, subsequent user input of a second pain experienced by the user at a second particular time that is one of (i) during the second period of time and (ii) after the second period of time;
• wherein the subsequent user input of the second pain comprises a second bodily location of the second pain experienced by the user, a second type of pain that characterizes the second pain experienced by the user, and a second intensity of the second pain experienced by the user;
• transmitting, to the front-end server by the chronic pain and food trigger tracking app, the subsequent user input of the additional food intake information over the second period of time and the subsequent user input of the second pain experienced by the user at the second particular time;
• transmitting the subsequent user input of the additional food intake information over the second period of time and the subsequent user input of the second pain experienced by the user at the second particular time, by the front-end server, to the backend chronic pain and food trigger tracking server;
• retraining, by the deep learning processing unit, the initial machine learning model for the user based on the additional food intake information over the second period of time and the subsequent user input of the second pain at the second period of time;
• identifying, by the backend chronic pain and food trigger tracking server, a pain delta that expresses a change in pain for the user between the first pain at the first period of time and the second pain at the second period of time;
• identifying, by the backend chronic pain and food trigger tracking server, correlations between food and pain for the user based on correlating the food intake and additional food intake information to the pain delta;
• and
• generating, by the backend server and based on the correlation between the pain delta and the food intake information and the additional food intake information, a trigger classification for a food that triggered a particular pain comprising one of the first pain and the second pain;
• displaying, based on the trigger classification for a food that triggered a particular pain comprising one of the first pain and the second pain:
• (i) structured dietary or supplement-use plan that removes or substitutes predicted trigger items above a confidence threshold, and/or
• (ii) real-time alerts advising the user to avoid the predicted trigger items.