US 2014/0276555 A1Published application

Method and System for Closed-Loop Control of an Artificial Pancreas

AssigneeAnimas Corporation

InventorCarlos Morales

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Abstract

A local extremum of a function ƒ is determined by computing a Jacobian of ƒ at a test point x by adding an imaginary part to x and a Hessian of ƒ by adding two imaginary parts to a multicomplex copy of x and extracting a third imaginary part. Solving a system of equations defined by the Jacobian and Hessian yields a delta; the process is repeated until convergence. This method is used in each of a series of time intervals to compute an insulin-delivery amount for an insulin pump. ƒ is a model-predictive-control cost function; x is a set of successive candidate insulin delivery amounts beginning from a selected time interval. A system includes a glucose monitor and a controller using glucose measurement data therefrom to determine an insulin delivery amount for a time interval by minimizing ƒ; an insulin pump provides insulin corresponding to the delivery amount.

Claims22

Dependent claims

A method of determining a local extremum of a function on an n-dimensional space, the method comprising automatically performing the following steps using a controller:

a) selecting a value for a real-type test point in the n-dimensional space;
b) computing an approximate Jacobian of the function at the test point by, for each dimension i of the n dimensions:
i) setting a complex-type computation point c equal to the test point;
ii) setting an imaginary part of element i of c equal to a nonzero increment;
iii) computing a complex-type value of the function at c;
and
iv) dividing the imaginary part of the complex-type computed function value by the increment to form element i of the Jacobian;
c) computing an approximate Hessian of the function at the test point by, for each pair of dimensions (i,j), each i and j one of the n dimensions:
i) setting a multicomplex-type computation point b equal to the test point;
ii) setting a first imaginary part of element i of b equal to a nonzero first increment;
iii) setting a second imaginary part of element j of b equal to a nonzero second increment;
iv) computing a multicomplex-type value of the function at b;
and
v) dividing the third imaginary part of the computed multicomplex-type function value by the product of the first and second increments to form element (i,j) of the Hessian;
d) solving a system of equations defined by the approximate Hessian and the approximate Jacobian to find a delta;
e) modifying the test point according to the delta to form a next point;
f) determining whether the next point satisfies selected convergence criteria;
and
g) if not, assigning the value of the next point to the test point and repeating the computing steps and the solving through the determining step.

16 more elements

Apparatus for the delivery of insulin, the apparatus comprising:

a) a glucose monitor adapted to measure respective glucose levels of a subject at discrete time intervals and provide respective glucose measurement data indicating each measured glucose level;
b) an insulin infusion pump configured to deliver insulin in response to a delivery control signal;
c) a memory configured to store a plurality of basal insulin delivery amounts at respective ones of the discrete time intervals;
and
d) a model predictive controller adapted to, for each of a plurality of the discrete time intervals:
i) receive the glucose measurement data for that time interval from the glucose monitor;
ii) determine an insulin delivery amount for that time interval using model predictive control based on a selected target glucose concentration range, the received glucose measurement data, the stored basal insulin delivery profile amounts for that time interval and n−1 successive time intervals;
and
iii) provide to the insulin infusion pump a delivery control signal corresponding to the determined insulin delivery amount, so that a corresponding amount of insulin is delivered;
e) in which the model predictive controller is adapted to determine the insulin delivery amount by mathematical minimization of a cost function that computes a cost metric correlated with physiological-fluid glucose-level excursions from a selected target glucose range for a particular set of n successive candidate insulin delivery amounts beginning from the selected one of the time intervals, and the model predictive controller, in order to carry out the mathematical minimization, is adapted to:
i) select n candidate insulin delivery values for a real-type n-dimensional test point;
ii) for each dimension i of the n dimensions:
A) set a complex-type n-dimensional computation point c equal to the test point;
B) set an imaginary part of element i of c equal to a nonzero increment;
C) compute a complex-type value of the cost function at c;
and
D) divide the imaginary part of the complex-type computed function value by the increment to form element i of an approximate Jacobian of the function at the test point;
iii) for each pair of dimensions (i,j), each i and j one of the n dimensions:
A) set a multicomplex-type n-dimensional computation point b equal to the test point;
B) set a first imaginary part of element i of b equal to a nonzero first increment;
C) set a second imaginary part of element j of b equal to a nonzero second increment;
D) compute a multicomplex-type value of the cost function at b;
and
E) divide the third imaginary part of the computed multicomplex-type function value by the product of the first and second increments to form element (i,j) of an approximate Hessian of the function at the test point;
iv) solve a system of equations defined by the approximate Hessian and the approximate Jacobian to find a delta;
v) modify the test point according to the delta to form a next point;
vi) determine whether the next point satisfies selected convergence criteria;
vii) if not, assign the value of the next point to the test point and repeat the computation of the approximate Jacobian and Hessian of the function, solution of the system of equations, modification of the test point, and determination of whether the next point satisfies the selected convergence criteria;
and
viii) if so, select the first element of the next point as the candidate insulin delivery amount for the selected one of the time intervals;
f) and further in which the model predictive controller is adapted to, in order to compute the cost function for each input set of n successive candidate insulin delivery amounts:
i) predict an excursion of the glucose level from a selected target glucose range using at least some of the glucose measurements, a glucose-insulin dynamic model of the subject, and an input set of n candidate insulin delivery amounts;
ii) compute a deviation of the candidate insulin delivery amounts from respective selected basal delivery amounts;
and
iii) form a weighted sum of respective values representing the predicted excursion and the computed deviation using respective selected weights, such that the output of the cost function includes the weighted sum.

32 more elements

A method to control an infusion pump responsive to a controller that receives data from a glucose sensor, the method comprising:

a) measuring respective glucose levels of a physiological fluid from a subject for each time interval of a series of discrete time intervals using the glucose sensor;
and
b) using a controller, automatically calculating an insulin delivery amount for a selected one of the time intervals by mathematically minimizing a cost function that computes a cost metric correlated with physiological-fluid glucose-level excursions from a selected target glucose range for a particular set of n successive candidate insulin delivery amounts beginning from the selected one of the time intervals, the minimizing including:
i) selecting n candidate insulin delivery values for a real-type n-dimensional test point;
ii) computing an approximate Jacobian of the cost function at the test point by evaluating the cost function, in which the model predictive controller computes the cost function for each input set of n successive candidate insulin delivery amounts by:
A) predicting an excursion of the glucose level from a selected target glucose range using at least some of the glucose measurements, a glucose-insulin dynamic model of the subject, and the candidate insulin delivery amounts;
B) computing a deviation of the candidate insulin delivery amounts from respective selected basal delivery amounts;
and
C) forming a weighted sum of respective values representing the predicted excursion and the computed deviation using respective selected weights such that the output of the cost function includes the weighted sum;
iii) computing an approximate Hessian of the cost function at the test point by evaluating the cost function;
iv) solving a system of equations defined by the approximate Hessian and the approximate Jacobian to find a delta;
v) modifying the test point according to the delta to form a next point;
vi) determining whether the next point satisfies selected convergence criteria;
vii) if not, assigning the value of the next point to the test point and repeating the computing of the approximate Jacobian and Hessian, solving the system of equation, modifying the test point and the determining step;
and
viii) if so, selecting a first element of the next point as the candidate insulin delivery amount for the selected one of the time intervals;
c) determining an approved insulin delivery amount from the insulin delivery amount;
and
d) commanding the infusion pump to deliver the approved insulin delivery amount to the subject.

16 more elements

Dependencies are inferred from the claim text.

Facts

Application: 13/840,429Filed 15 Mar 2013
Published: 18 Sep 2014
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

A61M 5/1782
Syringes: devices aiding filling of syringes in situ
A61M 2205/3569
Range: sublocal, e.g. between console and disposable
G16H 20/17
Relating to drugs or medications, e.g. for ensuring correct administration to patients: delivered via infusion or injection
G16H 40/60
ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or…:…

Family

US 9,517,306 B2Related patent
US 2014/0276555 A1This patent

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