Systems and methods for processing electronic images for health monitoring and forecasting

A system for monitoring health of a population, the system comprising:

• at least one memory storing instructions;
• and
• at least one processor configured to execute the instructions to perform operations comprising:
• receiving a query from a user requesting monitoring health of a population, wherein the query identifies at least one geographic region of a plurality of geographic regions;
• in response to receiving the query, determining a current prevalence of at least one data variable or at least one health variable in the at least one geographic region, via a machine learning model trained to determine whether the at least one data variable or the at least one health variable is present in the at least one geographic region, by:
• determining that the at least one data variable or the at least one health variable is present in a plurality of digital pathology images and/or reports input into the machine learning model over an interval of time, wherein the machine learning model is trained using whole slide images and diagnostic data corresponding to the plurality of geographic regions;
• determining a relationship between the at least one geographic region, the at least one data variable, or the at least one health variable;
• determining a first trend in the determined relationship at a first period of time based on the current prevalence of the at least one data variable or the at least one health variable in the at least one geographic region;
• determining a second trend in the determined relationship at a second period of time;
• generating an alert based on an irregularity between the first trend, the second trend, and a timing of the received query in the determined relationship being detected;
• training the machine learning model to predict future relationships over time in the at least one geographic region;
• and
• determining, based on the predicted future relationships, whether to continue monitoring health of a population.

A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for monitoring health of a population, the operations comprising:

• receiving a query from a user requesting monitoring health of a population, wherein the query identifies at least one geographic region of a plurality of geographic regions;
• in response to receiving the query, determining a current prevalence of at least one data variable or at least one health variable in the at least one geographic region, via a machine learning model trained to determine whether the at least one data variable or the at least one health variable is present in the at least one geographic region, by:
• determining that the at least one data variable or the at least one health variable is present in a plurality of digital pathology images and/or reports input into the machine learning model over an interval of time, wherein the machine learning model is trained using whole slide images and diagnostic data corresponding to the plurality of geographic regions;
• determining a relationship between the at least one geographic region, the at least one data variable, or the at least one health variable;
• determining a first trend in the determined relationship at a first period of time based on the current prevalence of the at least one data variable or the at least one health variable in the at least one geographic region;
• determining a second trend in the determined relationship at a second period of time;
• generating an alert based on an irregularity between the first trend, the second trend, and a timing of the received query in the determined relationship being detected;
• training the machine learning model to predict future relationships over time in the at least one geographic region;
• and
• determining, based on the predicted future relationships, whether to continue monitoring health of a population.