Glucose level management without carbohydrate counting
Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.
1. A system for glucose level management without carbohydrate counting, the system comprising:
one or more processors; and
one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of:
obtaining contextual information for a meal;
predicting amounts of glucose to be absorbed into a bloodstream of a patient over a duration of time due to consumption of the meal based at least in part on a correlation between the contextual information for the meal and amounts of glucose absorbed into the bloodstream due to the consumption of the meal;
determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose; and
affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.
2. The system of claim 1 , wherein the amounts of glucose to be absorbed into the bloodstream comprises a first amount of glucose to be absorbed into the bloodstream at a first time instance within the duration of time, and a second amount of glucose to be absorbed into the bloodstream at a second time instance within the duration of time.
3. The system of claim 1 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
prior to predicting the amounts of glucose to be absorbed into the bloodstream, determining a second correlation between contextual information for one or more previous meals and amounts of glucose absorbed into the bloodstream of the patient over a duration of time due to consumption of the one or more previous meals, wherein determining the second correlation is performed without information from the patient relating to carbohydrates consumed from the one or more previous meals.
4. The system of claim 1 , wherein determining the one or more amounts of insulin comprises:
obtaining an insulin sensitivity factor for the patient; and
determining the one or more amounts of insulin to counteract effects of the predicted amounts of glucose based on the insulin sensitivity factor.
5. The system of claim 1 , wherein determining the one or more amounts of insulin comprises utilizing a mathematical model to predict a glycemic response of the patient based on a candidate amount of insulin and the predicted amounts of glucose to be absorbed into the bloodstream over the duration of time.
6. The system of claim 1 , wherein determining the one or more amounts of insulin comprises using an artificial intelligence technique to determine a dosage of insulin that corresponds to a maximum amount of time within a target range for the patient's glucose levels.
7. The system of claim 1 , wherein predicting the amounts of glucose to be absorbed comprises utilizing one or more of a group comprising a statistical averaging technique, a pattern matching technique, and a machine learning technique.
8. The system of claim 1 , wherein the contextual information comprises one or more of a group comprising a time of day, a day of week, and a patient location.
9. The system of claim 1 , wherein affecting the insulin therapy comprises:
communicating towards an insulin delivery device the information indicative of the determined one or more amounts of insulin.
10. The system of claim 9 , wherein the insulin delivery device is an injection device configured to automatically set an insulin level based on the information indicative of the determined one or more amounts of insulin.
11. The system of claim 9 , wherein the insulin delivery device is an insulin pump configured to automatically deliver the determined one or more amounts of insulin to the patient.
12. The system of claim 1 , wherein the one or more processors are included in one or more network devices.
13. A processor-implemented method for glucose level management without carbohydrate counting, the method comprising:
obtaining contextual information for a meal;
predicting amounts of glucose to be absorbed into a bloodstream of a patient over a duration of time due to consumption of the meal based at least in part on a correlation between the contextual information for the meal and amounts of glucose absorbed into the bloodstream due to the consumption of the meal;
determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose; and
affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.
14. The method of claim 13 , wherein the amounts of glucose to be absorbed into the bloodstream comprises a first amount of glucose to be absorbed into the bloodstream at a first time instance within the duration of time, and a second amount of glucose to be absorbed into the bloodstream at a second time instance within the duration of time.
15. The method of claim 13 , further comprising prior to predicting the amounts of glucose to be absorbed into the bloodstream, determining a second correlation between contextual information for one or more previous meals and amounts of glucose absorbed into the bloodstream of the patient over a duration of time due to consumption of the one or more previous meals, wherein determining the second correlation is performed without information from the patient relating to carbohydrates consumed from the one or more previous meals.
16. The method of claim 13 , wherein determining the one or more amounts of insulin comprises:
obtaining an insulin sensitivity factor for the patient; and
determining the one or more amounts of insulin to counteract effects of the predicted amounts of glucose based on the insulin sensitivity factor.
17. The method of claim 13 , wherein determining the one or more amounts of insulin comprises utilizing a mathematical model to predict a glycemic response of the patient based on a candidate amount of insulin and the predicted amounts of glucose to be absorbed into the bloodstream over the duration of time.
18. The method of claim 13 , wherein predicting the amounts of glucose to be absorbed comprises utilizing one or more of a group comprising a statistical averaging technique, a pattern matching technique, and a machine learning technique.
19. The method of claim 13 , wherein affecting the insulin therapy comprises:
communicating towards an insulin delivery device the information indicative of the determined one or more amounts of insulin.
20. One or more non-transitory processor-readable storage media storing instructions which, when executed by one or more processors, cause performance of:
obtaining contextual information for a meal;
predicting amounts of glucose to be absorbed into a bloodstream of a patient over a duration of time due to consumption of the meal based at least in part on a correlation between the contextual information for the meal and amounts of glucose absorbed into the bloodstream due to the consumption of the meal;
determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose; and
affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.