Database management and graphical user interfaces for measurements collected by analyzing blood

A system for managing blood glucose levels of a user, the system comprising:

• a memory having processor-readable instructions stored therein;
• and
• a processor configured to access the memory and execute the processor-readable instructions, which, when executed by the processor configures the processor to perform a method, the method comprising:
• electronically receiving at a server, using one or more processors, data including types and dosages of medications consumed by the user, and A1C values of the user;
• storing, using the one or more processors, in a database connected to the one or more processors, the data received at the server;
• extracting metadata, using the one or more processors, from the stored data;
• inputting, using the one or more processors, the metadata into one or more machine learning algorithms to generate (a) a goal for the user, wherein the goal includes a reduction in the A1C value of the user, wherein the machine learning algorithms generate the goal based at least on cohort data for a cohort associated with the user and (b) a treatment plan for the user to achieve the goal based on the types and dosages of medications consumed by the user, wherein the treatment plan includes instructions for tasks to be performed by the user, wherein the tasks include one or more of prescribed blood glucose measurement pairs to be measured, prescribed timing and dosage of medication to be consumed by the user, a prescribed amount of carbohydrates to be consumed by the user, or prescribed exercise for the user to perform, wherein the one or more machine learning algorithms include one or more of artificial neural networks, Bayesian statistics, case-based reason, decision trees, inductive logic processing, Gaussian process regression, Gene expression programming, Logistic model trees, stochastic modeling, or statistical modeling;
• outputting the treatment plan, using the one or more processors, to the user via an electronic device of the user;
• electronically receiving, using the one or more processors, data relating to the treatment plan, the data relating to the treatment plan including types, timing, and dosages of medications consumed by the user, times and amounts of carbohydrates consumed by the user, amount of sleep of the user, amount of exercise performed by the user, and blood glucose levels of the user;
• revising the treatment plan, using the one or more processors based on at least one of determined medication compliance, determined diet compliance, determined exercise compliance, and identified patterns;
• outputting the revised treatment plan to the user, using the one or more processors, via the electronic device, wherein the revised treatment plan includes one or more tasks to be performed by the user, wherein the tasks include a change in the one or more of the prescribed blood glucose measurement pairs, the prescribed timing and dosage of medication to be consumed by the user, the prescribed amount of carbohydrates for the user to consume, or the prescribed exercise for the user to perform;
• determining, using the one or more processors, tendency of the user to follow at least one of a medication regimen, a diet regimen, and an exercise regimen, wherein the generating the treatment plan for the user to achieve the goal also is based on the determined tendency of the user to follow the medication regimen, the diet regimen, and the exercise regimen;
• determining, using the one or more processors, based on the identified patterns, a trigger event that occurs before an adverse effect on the blood glucose levels of the user;
• and
• sending a notification to the user, using the one or more processors, via the electronic device of the user upon detecting a subsequent instance of the trigger event, wherein the notification includes an identification of the trigger event to the user and an identification of the adverse effect on the blood glucose levels that occur after the trigger event.

A computer-implemented method for managing blood glucose levels of a user, the method comprising:

• electronically receiving at a server, using one or more processors, data, including types and dosages of medications consumed by the user, and A1C values of the user;
• storing, using the one or more processors, in a database connected to the one or more processors, the data received at the server;
• extracting metadata, using the one or more processors, from the stored data;
• inputting, using the one or more processors, the metadata into one or more machine learning algorithms to generate (a) a goal for the user based at least on cohort data for a cohort associated with the user and (b) a treatment plan for the user to achieve the goal based on the types and dosages of medications consumed by the user, wherein the treatment plan includes instructions for tasks to be performed by the user, wherein the tasks include one or more of prescribed blood glucose measurement pairs to be measured, prescribed timing and dosage of medication to be consumed by the user, a prescribed amount of carbohydrates to be consumed by the user, or prescribed exercise for the user to perform, wherein the one or more machine learning algorithms include one or more of artificial neural networks, Bayesian statistics, case-based reason, decision trees, inductive logic processing, Gaussian process regression, Gene expression programming, Logistic model trees, stochastic modeling, or statistical modeling;
• outputting the treatment plan, using the one or more processors, to the user via an electronic device of the user;
• electronically receiving, using the one or more processors, data relating to the treatment plan, the data relating to the treatment plan including types, timing, and dosages of medications consumed by the user, times and amounts of carbohydrates consumed by the user, and blood glucose levels of the user;
• outputting a revised treatment plan to the user, using the one or more processors, via the electronic device, wherein the revised treatment plan includes one or more tasks to be performed by the user, wherein the tasks include a change in the one or more of the prescribed blood glucose measurement pairs to be measured, the prescribed timing and dosage of medication to be consumed by the user, the prescribed amount of carbohydrates for the user to consume;
• receiving, using the one or more processors, global positioning system (GPS) data from the electronic device of the user;
• receiving an indication from the user that the user would like to exercise, and, after receiving the indication from the user, retrieving GPS data from the electronic device of the user, and generating a route for the user to walk along, wherein a distance of the route corresponds to the prescribed exercise to the user in the treatment plan;
• administering, via a software application downloaded to the electronic device of the user, and using the one or more processors, a questionnaire to the user;
• electronically receiving, using the one or more processors, user answers to the questionnaire;
• determining, using the one or more processors, based on the received user answers to the questionnaire, a tendency of the user to follow a medication regimen and a diet regimen, wherein the generating the treatment plan for the user to achieve the goal also is based on the tendency of the user to follow the medication regimen and the diet regimen;
• determining, using the one or more processors, a trigger event that occurs before an adverse effect on the blood glucose levels of the user;
• and
• sending a notification to the user, using the one or more processors, via the electronic device of the user upon detecting an instance of the trigger event, wherein the notification includes an identification of the trigger event to the user and an identification of the adverse effect on the blood glucose levels, wherein the trigger event includes a day of the week and the adverse effect on the blood glucose levels includes hyperglycemia.