Database management and graphical user interfaces for measurements collected by analyzing blood
Inventors
McRAITH, Kevin • KESANI, Hari • Iyer, Anand • SUSAI, Gabriel • SHOMALI, Mansur • RAO, Prasad Matti
Assignees
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Publication Number
US-10854337-B2
Publication Date
2020-12-01
Expiration Date
Abstract
Methods and devices include database management and graphical user interfaces for measurements collected by analyzing blood.
Core Innovation
The invention relates to a computer-implemented method and system for managing blood glucose levels of a user by electronically receiving blood-glucose-related data in a server before generation of a treatment plan. The received data includes a length of time that the user has been diagnosed with a blood glucose condition, types and dosages of medications consumed by the user, and A1C values. The method stores the received data in a database connected to one or more processors and extracts metadata from the stored data.
The invention inputs the metadata into one or more machine learning algorithms to generate a goal and a treatment plan. The goal includes a reduction in the A1C value of the user at an end of a treatment period compared to an A1C value at a beginning of the treatment period, and the goal is generated based at least on cohort data associated with the user. The treatment plan includes instructions for tasks during a first subset of the treatment period, including prescribed blood glucose measurement pairs, prescribed timing and dosage of medication, a prescribed amount of carbohydrates to be consumed, or prescribed exercise.
After outputting the generated treatment plan to the user via an electronic device, the invention electronically receives data relating to the treatment plan during the first subset of the treatment period. It applies machine learning algorithms to determine medication compliance, diet compliance, and exercise compliance, analyzes patterns between medication, carbohydrate, sleep, exercise, and blood glucose levels, revises the treatment plan for a subsequent subset of the treatment period based on the compliance determinations and identified patterns, and outputs the revised treatment plan with changed tasks.
The invention further integrates GPS data with the treatment plan by identifying restaurants in proximity to the user based on time proximity to scheduled meals, using a restaurant database that includes meals offered by cataloged restaurants and a carbohydrate content of each meal. It outputs a list of recommended meals and generates a walking route for travel from a current location to a selected restaurant. The invention administers a questionnaire to determine a tendency of the user to follow a medication regimen, a diet regimen, and an exercise regimen, and determines a trigger event that occurs before an adverse effect on blood glucose levels.
Claims Coverage
The partial content provides three independent claims (clm-00001, clm-00003, clm-00006). Across these claims, the inventive features center on machine learning-generated blood glucose treatment planning, compliance and pattern analysis, GPS-based meal and route assistance, questionnaire-based regimen tendency input, and trigger-event notifications for adverse blood glucose effects including hyperglycemia.
Machine-learning goal and treatment plan for A1C reduction
inputting metadata into one or more machine learning algorithms to generate a goal for the user wherein the goal includes a reduction in the A1C value of the user at an end of a treatment period as compared to an A1C value of the user at a beginning of the treatment period, and to generate the treatment plan based on the length of time that the user has been diagnosed with the blood glucose condition, and the types and dosages of medications consumed by the user.
Task-based treatment plan instructions for a subset of the treatment period
the treatment plan includes instructions for tasks to be performed by the user during a first subset of the treatment period, wherein the tasks include one or more of prescribed blood glucose measurement pairs to be measured before and after meals, 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.
Compliance determination and pattern identification from received treatment-plan data
applying the one or more machine learning algorithms to the received data relating to the treatment plan to determine medication compliance, diet compliance, and exercise compliance by comparing received types, timing, and dosages of medication, received amounts of carbohydrates consumed, and received amounts of exercise performed to prescribed counterparts, and to analyze medications consumed, carbohydrates consumed, sleep, exercise, and blood glucose levels to identify patterns.
Revising treatment plan and updating subset tasks
revising the treatment plan for a subsequent subset of the treatment period based on the determined compliance and identified patterns, and outputting the revised treatment plan to the user via the electronic device, wherein the revised treatment plan includes changed tasks compared to the first subset of the treatment period.
GPS-based restaurant identification using meal carbohydrate content
receiving GPS data from the electronic device of the user; identifying, based on the received GPS data and a time proximity to scheduled meals of the treatment plan, restaurants in proximity to the user and cataloged in a database, wherein the restaurant database includes meals offered by the cataloged restaurants and a carbohydrate content of each meal; and outputting a list of the identified restaurants, including recommended meals based on carbohydrate content.
Walking route generation to a selected restaurant
receiving a selection of a catalogued restaurant from the user; generating a walking route for the user to travel along from a current location of the user to the selected restaurant.
Questionnaire-based regimen-following tendency
administering a questionnaire to the user via a software application downloaded to the electronic device of the user, receiving user answers, and determining a tendency of the user to follow a medication regimen, a diet regimen, and an exercise regimen, with the tendency used in generating the treatment plan.
Trigger event detection and notification for hyperglycemia
determining, based on the identified patterns, a trigger event that occurs before an adverse effect on blood glucose levels of the user; sending a notification to the user upon detecting a subsequent instance of the trigger event, wherein the notification includes an identification of the trigger event and an identification of the adverse effect on blood glucose levels that occur after the trigger event, and the adverse effect includes hyperglycemia.
Goal generation based on cohort data
the machine learning algorithms generate the goal based at least on cohort data for a cohort associated with the user.
Across the independent claims, the inventive core is machine-learning-driven generation of an A1C-reduction goal and a task-based treatment plan that is revised based on medication, diet, and exercise compliance and identified patterns from received treatment-plan data. Additional inventive elements include GPS-based restaurant recommendation using meal carbohydrate content, walking route generation, questionnaire-based determination of regimen-following tendency, and trigger-event notifications that identify adverse effects such as hyperglycemia.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Managing blood glucose levels of a user by generating and revising a treatment plan for a treatment period, including tasks such as blood glucose measurement pairs, medication timing and dosage, carbohydrate consumption, and exercise.
Recommended restaurant meals for planned meals using nearby restaurants and carbohydrate content of meals.
Walking route generation from current location to a selected restaurant.
Questionnaire-driven determination of a tendency to follow medication, diet, and exercise regimens used in generating the treatment plan.
Notification to a user when a trigger event occurs before an adverse effect on blood glucose levels, including hyperglycemia, with identification of the trigger event and subsequent adverse effect.
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