US2014365138A1PendingUtilityA1
Systems and methods for pattern recognition in diabetes management
Est. expiryMar 20, 2027(~0.7 yrs left)· nominal 20-yr term from priority
G06F 17/18G16H 50/20G16H 15/00G06F 19/3487G01N 33/66G16H 40/67G16H 20/10
47
PatentIndex Score
0
Cited by
0
References
0
Claims
Abstract
A diabetes management system or process is provided herein that may be used to analyze and recognize patterns for a large number of blood glucose concentration measurements and other physiological parameters related to the glycemia of a patient. In particular, a method of monitoring glycemia in a patient may include storing a patient's data on a suitable device, such as, for example, a blood glucose meter. The patient's data may include blood glucose concentration measurements. The diabetes management system or process may be installed on, but is not limited to, a personal computer, an insulin pen, an insulin pump, or a glucose meter. The diabetes management system or process may identify a plurality of pattern types from the data including a testing/dosing pattern, a hypoglycemic pattern, a hyperglycemic pattern, a blood glucose variability pattern, and a comparative pattern. After identifying a particular pattern with the data management system or process, a warning message may be displayed on a screen of a personal computer or a glucose meter. Other messages can also be provided to ensure compliance of any prescribed diabetes regiments or to guide the patient in managing the patient's diabetes.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method of monitoring glycemia of a patient, the method comprising:
storing a patient's data that includes glucose measurements; generating a hypoglycemic pattern, wherein the generating of hypoglycemic patterns comprises: calculating chi-squared χ 2 using the following:
χ
2
=
∑
i
=
1
n
(
L
i
-
L
i
,
pre
)
2
L
i
.
pre
+
∑
i
=
1
n
(
L
i
′
-
L
i
,
pre
′
)
2
L
i
.
pre
′
where χ 2 =chi-squared, i represents a particular time interval, n is a total number of time intervals, L i is a number of substantially hypoglycemic glucose concentration measurements that occur during time interval i, L i,pre is a predicted number of substantially hypoglycemic glucose concentration measurements that will occur during time interval i, and L i,pre ′ is a predicted number of non-hypoglycemic glucose concentration measurements that will occur interval time i;
comparing a calculated χ 2 to a χ 2 value in a table based on a number of degrees of freedom for each of the time intervals i; and
determining that at least one of the time intervals are statistically significantly different if the calculated χ 2 is greater than the χ 2 value on the table calculating L i,pre and L i,pre using estimation equations comprising:
L
i
,
pre
=
∑
i
=
1
n
L
i
∑
i
=
1
n
N
i
*
N
i
and
L
i
,
pre
′
=
∑
i
=
1
n
L
i
′
∑
i
=
1
n
N
i
*
N
i
where N i represents the total number of glucose concentration measurements performed during timer interval i
calculating Z i using a Z test, the Z test comprising:
Z
i
=
(
L
i
-
L
i
,
pre
)
SE
i
where Z i represents a Z test at a particular time interval i and SE, represents a standard error for a particular time interval i;
comparing a calculated Z i to a Z value in a table;
identifying that one of the time intervals is statistically significantly different if the calculated Z i is greater than the Z test; and
displaying a message indicating a high incidence of hypoglycemia occurring on at least one of the time intervals if the percentage of hypoglycemic incidence is greater than a pre-determined threshold, wherein the pre-determined threshold ranges from about 5% to about 15%.
2 . The method of claim 1 , wherein the Z test includes a threshold of about two.
3 . The method of claim 1 further comprising:
calculating SE i , using a standard error equation comprising:
SE
i
=
1
N
i
*
L
i
,
pre
*
(
N
i
-
L
i
,
pre
)
4 . The method of claim 1 , wherein the time interval is a day of a week.
5 . A method of monitoring glycemia of a patient, the method comprising:
storing a patient's data that includes glucose measurements; generating a plurality of patterns including multiple glucose variability patterns by:
obtaining a number of glucose measurements over a total time period;
dividing the total time period into a plurality of time intervals wherein the time interval recurs daily and is equal to about one eighth of a day;
determining a percentage of hyperglycemic incidence for each of the time intervals;
determining whether the percentage of hyperglycemic incidence for at least one of the time intervals is statistically significantly different via utilization of a chi-squared test to determine if any of the time intervals is statistically significantly different, wherein the chi-squared test uses a confidence level ranging from about 95% to about 99% and includes:
χ
2
=
∑
i
=
1
n
(
H
i
-
H
i
,
pre
)
2
H
i
,
pre
+
∑
i
=
1
n
(
H
i
′
-
H
i
,
pre
′
)
2
H
i
,
pre
′
where χ 2 =chi-squared, i represents a particular time interval, n is a total number of time intervals, H i is a number of substantially hyperglycemic glucose concentration measurements that occur during time interval i, H i,pre is a predicted number of substantially hyperglycemic glucose concentration measurements that will occur during time interval i, and H i,pre ′ is a predicted number of non-hyperglycemic glucose concentration measurements that will occur during time interval i;
comparing a calculated χ 2 to a χ 2 value in a table based on a number of degrees of freedom for each of the time intervals i; and
determining that at least one of the time intervals are statistically significantly different if the calculated χ 2 is greater than the χ 2 value on the table; and
displaying a message indicating a high incidence of hyperglycemia occurring on at least one of the time intervals in at least one of graphic, text or symbols.
6 . The method of claim 5 , further comprising:
calculating H i,pre and H i,pre ′ using estimation equations comprising:
H
i
,
pre
=
∑
i
=
1
n
H
i
∑
i
=
1
n
N
i
*
N
i
and
H
i
,
pre
′
=
∑
i
=
1
n
H
i
′
∑
i
=
1
n
N
i
*
N
i
where N i represents the total number of glucose concentration measurements performed during timer interval i.
7 . The method of claim 6 further comprising: identifying one of the time intervals as being statistically significantly different using a Z test comprising:
Z
i
=
(
H
i
-
H
i
,
pre
)
SE
i
where Z i represents a Z test at a particular time interval i and SE i represents a standard error for a particular time interval i;
comparing a calculated Z i to a Z value in a table; and
identifying that one of the time intervals are statistically significantly different if the calculated Z i is greater than the Z test.
8 . The method of claim 7 , wherein the Z test includes a threshold of about two.
9 . The method of claim 7 further comprising:
calculating SE i , using a standard error equation, the standard error equation comprising:
SE
i
=
1
N
i
*
H
i
,
pre
*
(
N
i
-
H
i
,
pre
)
10 . The method of claim 9 , wherein the time interval is a day of a week.Cited by (0)
No later patents cite this yet.
References (0)
No backward citations on record.