US2015220702A1PendingUtilityA1
Substance monitoring and control in human or animal bodies
Est. expiryOct 12, 2027(~1.2 yrs left)· nominal 20-yr term from priority
Inventors:Roman Hovorka
G16Z 99/00G16H 50/50G16H 50/20G16H 20/17A61M 5/1723G06F 19/3468
48
PatentIndex Score
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Claims
Abstract
Apparatus for monitoring a substance in human or animal in real time, the apparatus comprising a sensor providing a time series of measurements of substance level, said measurements being indicative of an inferred level of said substance in a part of said human or animal and a processor which applies an interacting multiple model strategy to a system model to provide a combined estimate of the inferred substance level from the substance level measurements. The substance may be glucose. The apparatus may also be adapted to control said substance using said interacting multiple model strategy to a system model to provide a combined estimate of a dose to be applied.
Claims
exact text as granted — not AI-modified1 . A system for deriving a glucose level in a blood sample from a human or an animal, the system comprising:
a sensor adapted to provide a time series of measurements of a glucose level in said blood sample, said measurements being indicative of an inferred level of said glucose in a part of said human or animal; and a processor adapted to perform the following steps:
calculate a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory system model,
calculate a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory system model with said second system model being a variation of said first glucoregulatory system model, and
predict a combined estimate of the inferred glucose level based on a combination of the first and second estimates,
wherein the first and second glucoregulatory models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
2 . The system according to claim 1 , wherein the processor is further adapted to calculate a combined estimate by weighting each estimate according to an associated mixing probability representing a respective probability of each said model correctly predicting said glucose level.
3 . The system according to claim 2 , wherein the processor is further adapted to update said mixing probability responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor.
4 . The system according to claim 1 , wherein the processor is a state estimator and is adapted to define a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level.
5 . The system according to claim 1 , further comprising a user monitor to receive inputs from the user and/or to display the status of the apparatus.
6 .- 8 . (canceled)
9 . The system according to claim 1 , further comprising a real-time alarm which is activated when the combined estimate or the combined estimate of future glucose level is below a preset hypoglycemia threshold or above a preset hyperglycaemia threshold.
10 . A system for deriving a glucose level in a blood sample from a human or an animal, the system comprising:
a sensor adapted to provide a time series of measurements of a glucose level in said blood sample, said measurements being indicative of an inferred level of said glucose in a part of said human or animal; and a processor adapted to apply an interacting multiple model strategy to a system model to predict a combined estimate of the inferred glucose level from the glucose level measurements, wherein the interacting multiple model strategy comprises first and second glucoregulatory models each of which comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg), and
wherein said processor is configured to apply the interacting multiple model strategy to calculate a first estimate for said inferred glucose level from said measured substance level using said first glucoregulatory system model, calculate a second estimate for said inferred glucose level from said measured substance level using said second glucoregulatory system model and predict said combined estimate of the inferred glucose level based on a combination of said first and second estimates.
11 . A system for deriving a glucose level in a blood sample from a human or an animal, the system comprising:
a sensor adapted to provide a time series of measurements of a glucose level in said blood sample, said measurements being indicative of an inferred level of said glucose in a part of said human or animal; and a processor adapted to
construct at least two state vectors each comprising a set of variables for a respective one of at least two glucose level models, said state vectors representing states of said at least two models, said state vectors also including a variable representing an uncertainty in a change in glucose level with time, each of said variables having an associated probability distribution;
predict a value of a said glucose level using said probability distribution and said state vectors;
update values of said state vectors responsive to a difference between a predicted glucose level measurement for each said model and a glucose level measurement from said sensor;
update a mixing probability representing a respective probability of each said model correctly predicting said glucose level responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor, and
determine a combined predicted glucose level measurement for said human or animal by combining outputs from said glucose level models according to said updated mixing probability,
wherein said at least two glucose level models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
12 . The system according to claim 1 , further comprising:
a dispenser for delivering a specified amount of medication to a user in response to a command from the processor, wherein the processor is adapted to calculate the specified amount of medication as the amount of medication required to bring the combined estimate or the combined predicted glucose level measurement into line with a desired value.
13 . (canceled)
14 . A system for deriving a glucose level in a blood sample from a human or an animal, the system comprising:
a sensor adapted to provide a time series of measurements of a glucose level in said blood sample, said measurements being indicative of an inferred level of said glucose in a part of said human or animal, a processor adapted to calculate an estimate of said inferred level, a dispenser for delivering a specified amount of medication to a user in response to a command from the processor based on the calculated estimate of said inferred level, wherein the processor is adapted to calculate the specified amount of medication as the amount of medication required to bring the estimate of said inferred level in line with a desired value by calculating a first estimate of said specified amount of medication using a first system model, calculate a second estimate of said specified amount of medication using a second system model with said second system model being a variation of said first system model, and calculate a combined estimate of said specified amount of medication based on a combination of the first and second estimates, wherein said first system model and said second system model each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability unitless u ft) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
15 . (canceled)
16 . The system according to claim 14 , wherein the dispenser delivers insulin, glucagon, a similar medication or combinations thereof.
17 . The system according to claim 1 , further comprising a user interface for displaying a suggested insulin bolus to be applied,
wherein the suggested insulin bolus is calculated by the processor as the amount of medication required to bring the combined estimate into line with a desired glucose value.
18 . The system according to claim 17 , wherein the desired glucose value varies with time to define a trajectory of values.
19 . The system according to claim 16 , wherein the processor applies the interacting multiple model strategy to the glucoregulatory model to determine the amount of medication required.
20 . A method for deriving a glucose level in a blood sample from a living human or an animal, the method comprising:
inputting a time series of glucose level measurements obtained from said blood sample by a sensor, said glucose level measurements being indicative of a inferred level of said glucose in a part of said human or animal, calculating a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory system model, calculating a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory system model with said second system model being a variation of said first system model, and predicting a combined estimate of the inferred glucose level based on a combination of the first and second estimates, wherein the first and second glucoregulatory models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
21 . The method according to claim 20 , further comprising applying a Kalman filter.
22 . The method according to claim 21 , wherein each estimate is weighted according to a mixing probability representing a respective probability of each said model correctly predicting said glucose level when combining the multiple estimates.
23 . The method according to claim 22 , comprising updating said mixing probability responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor.
24 . The method according to claim 22 , comprising determining the mixing probability from the model probability.
25 . The method according to claim 20 , comprising defining a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level.
26 .- 30 . (canceled)
31 . A method for deriving a glucose level in a blood sample from a living human or an animal, the method comprising:
inputting a time series of glucose level measurements obtained from said blood sample by a sensor, said glucose level measurements being indicative of a inferred level of said glucose in a part of said human or animal, defining a system model which estimates said inferred glucose level from said measured glucose level, and applying an interacting multiple model strategy to the system model to provide multiple estimates for the inferred glucose level, wherein the interacting multiple model strategy comprises first and second glucoregulatory models each of which comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg), and
wherein applying the interacting multiple model strategy comprises calculating a first estimate for said inferred glucose level from said measured substance level using said first glucoregulatory system model, calculating a second estimate for said inferred glucose level from said measured substance level using said second glucoregulatory system model and predicting said combined estimate of the inferred glucose level based on a combination of said first and second estimates.
32 . A method for deriving a glucose level in a blood sample from a living human or an animal, the method comprising:
inputting a time series of glucose level measurements from obtained from said blood sample by a glucose sensor; constructing at least two state vectors each comprising a set of variables for a respective one of at least two glucose level models, said state vectors representing states of said at least two models, said state vectors also including a variable representing an uncertainty in a change in glucose level with time, each of said variables having an associated probability distribution; predicting a value of a said glucose level using said probability distribution and said state vectors; updating values of said state vectors responsive to a difference between a predicted glucose level measurement for each said model and a glucose level measurement from said sensor; and updating a mixing probability representing a respective probability of each said model correctly predicting said glucose level responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor, and determining a combined predicted glucose level measurement for said human or animal by combining outputs from said glucose level models according to said updated mixing probability, wherein said at least two models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability unitless u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
33 . The method according to claim 32 , further comprising using the value of a first of state vectors to modify a second of said state vectors to thereby represent a transition between said models.
34 . The method according to claim 32 , further comprising:
calculating a specified amount of medication as the amount of medication required to bring the combined estimate into line with a desired value and delivering said specified amount of medication to a user.
35 . (canceled)
36 . A method for deriving a glucose level in a blood sample from a living human or an animal, the method comprising;
providing a time series of measurements of glucose level obtained from said blood sample, said measurements being indicative of an inferred level of said glucose in a part of said human or animal, calculating an estimate of said inferred level, calculating a specified amount of medication to be the amount of medication required to bring the estimate of said inferred level in line with a desired value by calculating a first estimate of said specified amount of medication using a first glucoregulatory system model, calculate a second estimate of said specified amount of medication using a second glucoregulatory system model with said second system model being a variation of said first system model, calculating said specified amount of medication based on a combination of said estimates and delivering said specified amount of medication to a user, wherein said at first and second glucoregulatory system models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
37 . (canceled)
38 . The method according to claim 20 , further comprising:
displaying a suggested insulin bolus to be applied on a user interface, wherein the suggested insulin bolus is calculated by the processor as the amount of medication required to bring the combined estimate into line with a desired glucose value.
39 . A carrier carrying computer program code to, when running, implementing the method of claim 20 .
40 . The system according to claim 1 , wherein the interstitial glucose kinetics sub-model is described as
q
3
(
t
)
t
-
k
31
(
q
3
(
t
)
-
q
1
(
t
)
)
g
IG
(
t
)
=
q
3
(
t
)
V
G
where q 3 (t) is the mass of glucose in the interstitial fluid (mmol/kg), k 31 is the fractional transfer rate (/min), and g IG (t) is interstitial glucose concentration.
41 . The system according to claim 4 , wherein the state vector comprises a subset of the parameters of each model.
42 . The system according to claim 41 , wherein the state vector is
x k =( g 1f,k ,g 2f,k ,u S,k ,F k ,q 3f,k ) T (1)
where q 1f,k , q 2f,k , and q 3f,k represent glucose amounts in the accessible, non-accessible, and interstitial compartments excluding the contribution from meals, u s,k is the unexplained glucose influx and F k is the state transition model which is applied to the previous state x k-1 .
43 . The system according to claim 1 , wherein the insulin absorption sub-model is described by a two compartment model
i
1
(
t
)
t
=
-
1
t
xI
i
1
(
t
)
+
u
(
t
)
60
+
δ
t
j
(
t
)
v
(
t
)
i
2
(
t
)
t
=
-
1
t
x
I
[
i
2
(
t
)
-
i
1
(
t
)
]
i
(
t
)
=
1
t
xI
1000
MCR
I
W
i
2
(
t
)
where i 1 (t) and i 2 (t) is the amount of insulin in the two subcutaneous insulin depots (U), i(t) is the plasma insulin concentration (mU/l), u(t) denotes insulin infusion (U/h), v(t) denotes insulin boluses given at time t j (U), t max,I is the time-to-peak of insulin absorption (min), MCR I is the metabolic clearance rate of insulin (L/kg/min), and W is subject's weight (kg).
44 . The system according to claim 1 , wherein the insulin action sub-model is described as
r
D
(
t
)
t
=
p
2
D
(
i
(
t
)
-
r
D
(
t
)
)
r
E
(
t
)
t
=
p
2
E
(
i
(
t
)
-
r
E
(
t
)
)
where γ D and γ EGP are the remote insulin actions affecting glucose disposal and endogenous glucose production, respectively, (mU/l), and p 2,D and p 2,EGP are the fractional disappearance rates associated with the remote insulin actions (/min).
45 . The system according to claim 1 , wherein the gut absorption sub-model is described by a two compartment model
a
1
(
t
)
t
=
-
1
t
xG
a
1
(
t
)
+
v
G
(
t
)
a
2
(
t
)
t
=
-
1
t
xG
[
a
2
(
t
)
-
a
1
(
t
)
]
u
A
(
t
)
=
5.551
Wt
xG
a
2
(
t
)
where a 1 (t) and a 2 (t) is the amount of glucose in the two absorption compartments (g), u A (t) is the gut absorption rate (mmol/kg/min), v G (t) denotes meal ingestion (g/min), and t max,G is the time-to-peak of the gut absorption (min).
46 . A method according to claim 25 , wherein the state vector comprises a subset of the parameters of each model.
47 . The method according to claim 46 , wherein the state vector is
x k e =( i 1,k ,i 2,k ,r D,k ,r E,k ,a 1,k ,a 2,k ,q 1,k ,q 2,k ,q 3,k u S,k ) T
where i i,k and i 2,k is the amount of insulin in the two subcutaneous insulin depots at time k, γ D,k and γ E,k are the remote insulin actions affecting glucose disposal and endogenous glucose production, a 1,k and a 2,k are the amount of glucose in the two absorption compartments, q 1,k , q 2,k , q 3,k represent glucose amounts in the accessible, non-accessible, and interstitial compartments and u s,k is the unexplained glucose influx.
48 . A method of receiving treatment for abnormal glucose levels circulating in one's blood, the method comprising:
providing a blood sample; and receiving a recommendation for an administration of an effective amount of insulin, the recommendation arising from a determination of glucose levels in said blood sample and calculations derived by:
inputting a time series of glucose level measurements obtained from said blood sample by a sensor, said glucose level measurements being indicative of a inferred level of said glucose in a part of said human or animal,
calculating a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory system model,
calculating a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory system model with said second system model being a variation of said first system model, and
predicting a combined estimate of the inferred glucose level based on a combination of the first and second estimates,
wherein the first and second glucoregulatory models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and
wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
49 . A method of receiving treatment for abnormal glucose levels circulating in one's blood, the method comprising:
providing a blood sample; and receiving a recommendation for an administration of an effective amount of insulin, the recommendation arising from a determination of glucose levels in said blood sample and calculations derived by:
inputting a time series of glucose level measurements obtained from said blood sample by a sensor, said glucose level measurements being indicative of a inferred level of said glucose in a part of said human or animal,
defining a system model which estimates said inferred glucose level from said measured glucose level, and
applying an interacting multiple model strategy to the system model to provide multiple estimates for the inferred glucose level, and
wherein the interacting multiple model strategy comprises first and second glucoregulatory models each of which comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption,
wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg), and
wherein applying the interacting multiple model strategy comprises calculating a first estimate for said inferred glucose level from said measured substance level using said first glucoregulatory system model, calculating a second estimate for said inferred glucose level from said measured substance level using said second glucoregulatory system model and predicting said combined estimate of the inferred glucose level based on a combination of said first and second estimates.
50 . A method of receiving treatment for abnormal glucose levels circulating in one's blood, the method comprising:
providing a blood sample; and receiving a recommendation for an administration of an effective amount of insulin, the recommendation arising from a determination of glucose levels in said blood sample and calculations derived by:
inputting a time series of glucose level measurements from obtained from said blood sample by a glucose sensor;
constructing at least two state vectors each comprising a set of variables for a respective one of at least two glucose level models, said state vectors representing states of said at least two models, said state vectors also including a variable representing an uncertainty in a change in glucose level with time, each of said variables having an associated probability distribution;
predicting a value of a said glucose level using said probability distribution and said state vectors;
updating values of said state vectors responsive to a difference between a predicted glucose level measurement for each said model and a glucose level measurement from said sensor; and
updating a mixing probability representing a respective probability of each said model correctly predicting said glucose level responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor, and
determining a combined predicted glucose level measurement for said human or animal by combining outputs from said glucose level models according to said updated mixing probability,
wherein said at least two models each comprise a sub-model of glucose kinetics in the blood of said human or animal, a sub-model of interstitial glucose kinetics, a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption, and
wherein the glucose kinetics sub-model is described as
q
1
(
t
)
t
=
-
(
S
ID
x
D
(
t
)
+
k
21
)
q
1
(
t
)
+
k
12
q
2
-
F
01
+
E
G
P
(
t
)
+
Fu
A
(
t
)
+
u
S
(
t
)
q
2
(
t
)
t
=
+
k
21
q
1
(
t
)
-
k
12
q
2
(
t
)
g
p
(
t
)
=
q
1
(
t
)
V
G
where q 1 (t) and q 2 (t) are the masses of glucose in the accessible and non-accessible glucose compartments (mmol/kg), k 21 and k 12 are the fractional transfer rates (/min), F 01 is the non-insulin dependent glucose utilisation (mmol/kg/min), EGP(t) is the endogenous glucose production (mmol/kg/min), S I,D is peripheral insulin sensitivity (/min per mU/l), F is glucose bioavailability (unitless), u S (t) is unexplained glucose influx (mmol/kg/min), g P (t) is plasma glucose concentration, V G is the glucose distribution volume in the accessible compartment (l/kg).
51 . The system according to claim 10 , further comprising:
a dispenser for delivering a specified amount of medication to a user in response to a command from the processor, wherein the processor is adapted to calculate the specified amount of medication as the amount of medication required to bring the combined estimate or the combined predicted glucose level measurement into line with a desired value.
52 . The system according to claim 12 , wherein the dispenser delivers insulin, glucagon, a similar medication or combinations thereof.
53 . The system according to claim 51 , wherein the dispenser delivers insulin, glucagon, a similar medication or combinations thereof.
54 . The system according to claim 11 , further comprising a user interface for displaying a suggested insulin bolus to be applied,
wherein the suggested insulin bolus is calculated by the processor as the amount of medication required to bring the combined estimate into line with a desired glucose value.
55 . The system according to claim 54 , wherein the desired glucose value varies with time to define a trajectory of values.
56 . The system according to claim 18 , wherein the processor applies the interacting multiple model strategy to the glucoregulatory model to determine the amount of medication required.
57 . The system according to claim 52 , wherein the processor applies the interacting multiple model strategy to the glucoregulatory model to determine the amount of medication required.
58 . The system according to claim 53 , wherein the processor applies the interacting multiple model strategy to the glucoregulatory model to determine the amount of medication required.
59 . The method according to claim 32 , further comprising:
displaying a suggested insulin bolus to be applied on a user interface, wherein the suggested insulin bolus is calculated by the processor as the amount of medication required to bring the combined estimate into line with a desired glucose value.Join the waitlist — get patent alerts
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