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WO2025054131A1 - Commande prédictive de modèle (mpc) variant dans le temps linéaire (ltv) d'un pancréas artificiel avec prédicteurs neuronaux de glucose sanguin affines à pas multiples entraînés par des données - Google Patents

Commande prédictive de modèle (mpc) variant dans le temps linéaire (ltv) d'un pancréas artificiel avec prédicteurs neuronaux de glucose sanguin affines à pas multiples entraînés par des données Download PDF

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Publication number
WO2025054131A1
WO2025054131A1 PCT/US2024/045052 US2024045052W WO2025054131A1 WO 2025054131 A1 WO2025054131 A1 WO 2025054131A1 US 2024045052 W US2024045052 W US 2024045052W WO 2025054131 A1 WO2025054131 A1 WO 2025054131A1
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WIPO (PCT)
Prior art keywords
insulin
algorithm
blood glucose
subject
computing device
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WO2025054131A8 (fr
Inventor
Marzia CESCON
Eleonora Maria AIELLO
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Universita degli Studi di Trento
University of Houston System
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Universita degli Studi di Trento
University of Houston System
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
    • A61M5/172—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic
    • A61M5/1723—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic using feedback of body parameters, e.g. blood-sugar, pressure
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/142—Pressure infusion, e.g. using pumps
    • A61M2005/14208—Pressure infusion, e.g. using pumps with a programmable infusion control system, characterised by the infusion program
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/142—Pressure infusion, e.g. using pumps
    • A61M5/14244—Pressure infusion, e.g. using pumps adapted to be carried by the patient, e.g. portable on the body
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Definitions

  • the computing device includes a non-transitory computer-usable medium having computer-readable program code embodied therein, a closed-loop insulin delivery algorithm trained to predict the blood glucose level of the subject, where the algorithm includes a data-driven multi-step-ahead blood glucose (BG) predictor integrated with a linear time-varying (LTV) model predictive control (MPC) law, and programming instructions operable to implement a method.
  • the method includes feeding the subject’s blood glucose level into the algorithm, predicting if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia using the algorithm, and transmitting insulin administration instructions to the insulin pump based on the prediction.
  • FIG. 5A-5B illustrate the mean and standard deviation of the predicted blood glucose concentrations obtained by the 1-step ahead auto-regressive with exogenous (ARX) predictor and the proposed multi-step ahead affine predictors on the validation dataset.
  • the black solid line represents the mean of the actual continuous glucose monitor (CGM) measurements, and the black dashed line represents the standard deviation of the actual CGM measurements.
  • FIG.6 illustrates Scenario A, as described in Example 1. Top: Mean and standard deviation of glucose concentration [md/dL] using the proposed Model Predictive Control (MPC) based on the multi-step predictor vs.
  • MPC Model Predictive Control
  • FIG. 7 illustrates Scenario B, as described in Example 1. Top: Mean and standard deviation of glucose concentration [md/dL] using the proposed MPC based on the multi-step predictor vs.
  • FIG.8 illustrates Scenario C, as described in Example 1. Top: Mean and standard deviation of glucose concentration [md/dL] using the proposed MPC based on the multi-step predictor vs.
  • FIG.9 illustrates Scenario D. Top: Mean and standard deviation of glucose concentration [md/dL] using the proposed MPC based on the multi-step predictor vs.
  • FIG. 10 illustrates mean and standard deviation of glucose concentration [md/dL] using the proposed MPC based on the multi-step predictor vs. the conventional therapy ; Bottom: Mean and standard deviation of insulin delivery [Ui] using the proposed MPC based on the multi-step predictor vs. the conventional therapy.
  • FIGS. 6-9 are related to continuous subcutaneous insulin infusion (CSII) therapy while FIG.10 is related to multiple daily injection (MDI) therapy.
  • CSII continuous subcutaneous insulin infusion
  • MDI multiple daily injection
  • MPC Model predictive control
  • CGM continuous glucose monitoring
  • the present disclosure pertains to a computer-implemented method of monitoring blood glucose levels of a subject.
  • the methods of the present disclosure include: feeding the subject’s blood glucose level into a closed- loop insulin delivery algorithm trained to predict the blood glucose level of the subject, where the algorithm includes a data-driven multi-step-ahead blood glucose (BG) predictor integrated with a linear time-varying (LTV) model predictive control (MPC) law (step 10); using the algorithm to predict if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia (step 12); and transmitting insulin administration instructions based on the prediction.
  • the methods of the present disclosure occur in real-time. In some embodiments, the methods of the present disclosure occur continuously. Additionally, the methods of the present disclosure may transmit various insulin administration instructions.
  • the insulin administration instructions are based on the solution of a receding horizon (RH) optimal control problem carried out by the LTV-MPC.
  • RH receding horizon
  • the method of the present disclosure may not command a change in insulin delivery (step 16).
  • the blood glucose level of the subject is expected PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 to reach a level representing hyperglycemia (step 18)
  • the insulin administration instructions include increasing insulin administration to the subject (step 20). Thereafter, the method of the present disclosure may command a change in insulin administration (step 16).
  • the insulin administration instructions include decreasing or suspending insulin administration to the subject (step 22). Thereafter, the method of the present disclosure may command a change in insulin administration (step 16). [0026] In some embodiments, the methods of the present disclosure also include a step of administering insulin based on the insulin administration instructions. In some embodiments, the insulin is administered by a user. In some embodiments, the administration occurs by a method that includes, without limitation, intravenous administration, subcutaneous administration, transdermal administration, percutaneous administration, topical administration, intraarterial administration, intrathecal administration, oral administration, or combinations thereof. In some preferred embodiments, the administration occurs by subcutaneous administration.
  • the insulin administration instructions are transmitted to an insulin pump associated with the subject. Thereafter, the insulin pump implements the instructions.
  • the insulin pump is in the form of a patch. In some embodiments, the insulin pump is operable for subcutaneous insulin administration.
  • the methods of the present disclosure may utilize various types of pumps.
  • the insulin pumps include tethered pumps.
  • the insulin pump includes an insulin reservoir and a processor.
  • the processor is in electronic communication with the algorithm. In some embodiments, the processor actuates or withholds the release of insulin from the insulin reservoir based on received instructions from the algorithm.
  • the methods of the present disclosure also include a step of measuring the blood glucose levels of the subject.
  • the methods of the present disclosure include a step of obtaining a blood sample from the subject and measuring the blood glucose levels from the blood sample.
  • PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 [0031]
  • the blood glucose levels of the subject is measured by a sensor that is associated with the subject. In some embodiments, the sensor transmits the measured blood glucose levels to the algorithm.
  • the methods of the present disclosure may be utilized to monitor the blood glucose levels of various subjects. For instance, in some embodiments, the subject is a human being suffering from type 1 diabetes.
  • the methods of the present disclosure may be used to treat or prevent type 1 diabetes. In some embodiments, the methods of the present disclosure are used to treat type 1 diabetes or prevent the complications arising from type 1 diabetes.
  • the methods of the present disclosure may utilize various types of LTV-MPC models.
  • the LTV-MPC models include parameters that vary with time according to previously specified laws.
  • the LTV-MPC models are also capable of controlling a process while satisfying a set of constraints.
  • the LTV-MPC models are also capable of controlling a process while satisfying a set of constraints that aim to limit the amount of commanded insulin dose to the maximum insulin delivery and limit the glucose levels to the maximum reading value on the glucose sensor.
  • the algorithm includes a machine learning algorithm.
  • the machine learning algorithm is a Root Mean Square Propagation (RMSProp) training algorithm.
  • the machine learning algorithm is a Long Short-Term Memory (LSTM) network.
  • the algorithm does not identify an open-loop algorithm of the glucoregulatory system from available data.
  • the algorithm directly fits the entire BG trajectory over a predefined prediction horizon to be used in the MPC law as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • a Long Short-Term Memory (LSTM) network is used to fit the component of the BG trajectory nonlinear in the state to a predictive nonlinear function incorporated into the algorithm.
  • a linear regression model is used to fit the future insulin control inputs to an affine function incorporated into the algorithm.
  • PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 [0036]
  • the BG trajectory is implemented as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • the algorithm includes customizable user parameters.
  • the customizable user parameters include, without limitation, length of prediction horizon, weight matrices, setpoints, or combinations thereof.
  • blood glucose monitoring systems of the present disclosure are operational to monitor blood glucose levels in accordance with the methods of the present disclosure.
  • An example of a blood glucose monitoring system is illustrated in FIG. 2 as blood glucose monitoring system 30.
  • Blood glucose monitoring system 30 generally includes: an insulin pump 32 associated with a subject 34 and operational to administer insulin to the subject; one or more sensors 33 associated with the subject 34 and operational to measure the blood glucose levels of the subject; and a computing device 40 in electronic communication with insulin pump 32.
  • computing device 40 is in electronic communication with insulin pump 32 through a wireless network 39.
  • computing device 40 is also in electronic communication with one or more sensors 33 through a wireless network 39.
  • one or more sensors 33 are operational to transmit the subject’s blood glucose levels to computing device 40.
  • insulin pump 32 includes an insulin reservoir 36 and a processor 38 that is in electronic communication with computing device 40.
  • processor 38 is operational to actuate or withhold the release of insulin from insulin reservoir 36 upon receiving instructions from computing device 40.
  • the computing devices of the present disclosure generally include one or more computer readable storage mediums having at least one program code embodied therewith.
  • the computing devices of the present disclosure also include a closed-loop insulin delivery algorithm trained to predict the blood glucose level of the subject, where the algorithm includes a data-driven multi- step-ahead blood glucose (BG) predictor integrated with a linear time-varying (LTV) model predictive control (MPC) law.
  • BG data-driven multi- step-ahead blood glucose
  • LTV linear time-varying
  • MPC model predictive control
  • the computing devices of the present disclosure include programming instructions for: feeding the subject’s blood glucose level into the algorithm (e.g., via sensors 33); using the algorithm to predict if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia; and transmitting insulin administration instructions to the insulin pump (e.g., insulin pump 32) based on the prediction.
  • the insulin administration instructions include increasing insulin administration to the subject if the blood glucose level of the subject is expected to reach a level representing hyperglycemia.
  • the insulin administration instructions include decreasing or suspending insulin administration to the subject if the blood glucose level of the subject is expected to reach a level representing hypoglycemia. In some embodiments, the insulin administration instructions are based on the solution of a receding horizon (RH) optimal control problem carried out by the LTV-MPC.
  • the blood glucose monitoring systems of the present disclosure may include various insulin pumps. For instance, in some embodiments, the insulin pump is in the form of a patch. In some embodiments, the insulin pump is operable for subcutaneous insulin administration. In some embodiments, the insulin pumps include tethered pumps.
  • the blood glucose monitoring systems of the present disclosure may also include various types of computing devices. For instance, in some embodiments, the computing device includes a computer.
  • the computing device includes a mobile device. In some embodiments, the computing device includes an app on a mobile device. In some embodiments, the computing device is in electronic communication with the insulin pump through a wireless network. In some embodiments, the computing device includes a chip on the insulin pump.
  • the blood glucose monitoring systems of the present disclosure may also include various types of algorithms. Suitable algorithms were described supra and are incorporated herein by reference. [0046] The blood glucose monitoring systems of the present disclosure may be suitable for use in monitoring the blood glucose levels of various subjects. For instance, in some embodiments, the subject is a human being suffering from type 1 diabetes. PCT Application Attorney Docket No.
  • the present disclosure relates to a blood glucose monitoring system having an insulin pump associated with a subject and operational to administer insulin to the subject, one or more sensors associated with the subject and operational to measure the blood glucose levels of the subject, and a computing device in electronic communication with the insulin pump.
  • the computing device includes a non-transitory computer-usable medium having computer-readable program code embodied therein and a closed-loop insulin delivery algorithm trained to predict the blood glucose level of the subject.
  • the algorithm comprises a data-driven multi-step-ahead BG predictor integrated with an LTV- MPC law.
  • the computing device further includes programming instructions operable to implement a method.
  • the method includes feeding the subject’s blood glucose level into the algorithm, predicting if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia using the algorithm, and transmitting insulin administration instructions to the insulin pump based on the prediction.
  • the insulin administration instructions are based on the solution of a RH optimal control problem carried out by the LTV-MPC.
  • the algorithm is a machine learning algorithm.
  • the machine learning algorithm is an LSTM network.
  • the algorithm does not identify an open-loop algorithm of the glucoregulatory system from available data.
  • the algorithm directly fits the entire BG trajectory over a predefined prediction horizon to be used in the MPC law as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • an LSTM network is used to the component of the BG trajectory nonlinear in the state to a predictive nonlinear function incorporated into the algorithm.
  • a linear regression model is used to fit the future insulin control inputs to an affine function incorporated into the algorithm.
  • the BG prediction is implemented as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • the feeding of the subject’s blood glucose level into the algorithm occurs in real-time.
  • the insulin pump includes an insulin reservoir and a processor.
  • the processor is in electronic communication with the computing device.
  • the processor is operational to actuate or withhold the release of insulin from PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 the insulin reservoir.
  • the computing device is a mobile device.
  • the computing device is an app on a mobile device.
  • the computing device is a chip on an insulin pump.
  • the computing device is in electronic communication with the insulin pump through a wireless network.
  • the insulin pump is operable for subcutaneous insulin administration.
  • Computing Devices include one or more computer readable storage mediums having at least one program code embodied therewith.
  • the computing device includes a closed-loop insulin delivery algorithm trained to predict the blood glucose level of a subject, where the algorithm includes a data-driven multi-step-ahead blood glucose (BG) predictor integrated with a linear time-varying (LTV) model predictive control (MPC) law.
  • BG data-driven multi-step-ahead blood glucose
  • LTV linear time-varying
  • MPC model predictive control
  • the computing device also includes programming instructions for: feeding the subject’s blood glucose level into the algorithm; using the algorithm to predict if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia; and transmitting insulin administration instructions based on the prediction.
  • the computing devices of the present disclosure can have numerous architectures.
  • the computing device of the present disclosure can include various types of computer readable storage mediums.
  • the computer readable storage mediums can be a tangible device that can retain and store instructions for use by an instruction execution device.
  • the computer readable storage medium may include, without limitation, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or combinations thereof.
  • a non-exhaustive list of more specific examples of suitable computer readable storage medium includes, without limitation, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device, or combinations thereof.
  • a computer readable storage medium is not to be construed as being transitory signals per se. Such transitory signals may be represented by radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
  • computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network and/or a wireless network.
  • the network may include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
  • computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected in some embodiments to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • electronic circuitry PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry to perform aspects of the present disclosure.
  • FIG.3 illustrates an embodiment of the present disclosure of the hardware configuration of a computing device 50, which is representative of a hardware environment for practicing various embodiments of the present disclosure.
  • Computing device 50 has a processor 51 connected to various other components by system bus 52.
  • An operating system 53 runs on processor 51 and provides control and coordinates the functions of the various components of FIG. 3.
  • An application 54 in accordance with the principles of the present disclosure runs in conjunction with operating system 53 and provides calls to operating system 53, where the calls implement the various functions or services to be performed by application 54.
  • Application 54 may include, for example, a program for monitoring blood glucose levels of a subject, as discussed in the present disclosure, such as in connection with FIGS.1-2.
  • ROM read-only memory
  • BIOS basic input/output system
  • RAM random access memory
  • Disk adapter 57 may be an integrated drive electronics (“IDE”) adapter that communicates with a disk unit 58 (e.g., a disk drive).
  • IDE integrated drive electronics
  • Computing device 50 may further include a communications adapter 59 connected to bus 52. Communications adapter 59 interconnects bus 52 with an outside network (e.g., wide area network) to communicate with other devices.
  • an outside network e.g., wide area network
  • These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein includes an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the blocks may occur out of the order noted in the Figures.
  • two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • Model Predictive Control (MPC) of an Artificial Pancreas with Data- Driven Learning of Multi-Step-Ahead Blood Glucose Predictors [0067] Applicants present the design and in-silico evaluation of a closed-loop insulin delivery algorithm to treat type 1 diabetes (T1D) in a data-driven multi-step-ahead blood glucose (BG) predictor integrated into a linear time-varying (LTV) model predictive control (MPC) framework. Instead of identifying an open-loop model of the glucoregulatory system from available data, Applicants propose to directly fit the entire BG prediction over a predefined prediction horizon to be used in the MPC, as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • LSTM Long Short-Term Memory
  • ARX auto-regressive with exogenous
  • the mean ⁇ standard deviation percent time in the range 70-180 [mg/dL] was 74.99 ⁇ 7.09 vs.54.15 ⁇ 14.89
  • the mean ⁇ standard deviation percent time in the tighter range 70-140 [mg/dL] was 47.78 ⁇ 8.55 vs. 34.62 ⁇ 9.04
  • the mean ⁇ standard deviation percent time in sever hypoglycemia, i.e., ⁇ 54 [mg/dl] was 1.00 ⁇ 3.18 vs.9.45 ⁇ 11.71, for the proposed LSTM-MPC controller and the traditional ARX-MPC, respectively.
  • This approach provided accurate predictions of future glucose concentrations and good closed-loop performances of the overall MPC controller.
  • Type 1 diabetes is a metabolic condition characterized by high blood glucose levels (hyperglycemia), caused by the autoimmune irreversible destruction of the pancreatic ⁇ -cells, which are responsible for the production and release of the hormone insulin.
  • the chronic diabetes hyperglycemia i.e. blood glucose (BG) levels ⁇ 180 mg/dL, leads to an increased risk of life- threatening events, such as diabetes ketoacidosis, and has serious long-term complications associated with damage, dysfunction and failure of various organs.
  • BG blood glucose
  • Exogenous insulin is therefore required for individuals with T1D to adequately regulate their BG concentration in the euglycemic range, i.e.70-180 mg/dL.
  • individuals with T1D experience difficulties in maintaining healthy BG levels and fail to meet the recommended glycemic targets.
  • Model predictive control (MPC) is an attractive control strategy for closed loop insulin delivery and has been considered in this context showing promising results in the management of diabetes in a hybrid fashion (so-called hybrid closed-loop systems).
  • the MPC algorithm oversees adjusting the clinical defined basal insulin profile during fasting periods based on real-time measurements from continuous glucose monitoring (CGM) device, while a linear feed-forward control action is included in the control scheme based on the announcement of the disturbances provided by the user.
  • CGM continuous glucose monitoring
  • the MPC algorithms currently adopted in clinical trials rely on linear models to describe the process under control, to optimize the control performance and ensure constraint satisfaction over a prediction horizon. From the algorithmic point of view, the major limitation affecting glucose control schemes, lies in the inaccuracies of the linear models used for generating the BG predictions. The use of a linear model is justified by its simplicity and small computational load, but a linear model can only approximate the complex non-linear dynamic of the human metabolism.
  • Applicants explore a different way of approaching the modeling step in a linear time- varying (LTV) MPC framework.
  • LTV linear time- varying
  • Applicants consider the estimation of long-term horizons multi-step ahead predictors of BG dynamics for receding horizon control starting from input-output data, with the goal of increasing the prediction accuracy for longer prediction horizons.
  • ANNs Artificial Neural Networks
  • LSTM Long Short-Term Memory
  • RNNs Recursive Neural Networks
  • LSTM Long Short-Term Memory
  • Applicants build predictors as the superposition of a nonlinear function of past inputs and past outputs and an affine function of future control moves.
  • This predictor construction allows Applicants to solve the MPC problem via quadratic programming (QP) despite the nonlinearity of the system.
  • QP quadratic programming
  • Example 1.2 Data-Driven Multi-Step-Ahead Blood Glucose Predictors for MPC
  • Insulin variations are obtained by injecting insulin boluses, and aim at minimizing the occurrence of hypo- and hyperglycemia in presence of disturbances (e.g., meals, physical activity).
  • disturbances e.g., meals, physical activity.
  • the insulin bolus dose is computed as the ratio between the meal amount and the carbohydrate-to-insulin (CR) factor.
  • Each LSTM layer receives input from the previous layer, enabling the model to learn hierarchical representations of the data.
  • the output of the last LSTM layer is then passed to a stack of two fully connected (FC) layers.
  • FC layers are responsible for mapping the LSTM’s hidden representations to a suitable format for prediction. They introduce additional nonlinearity and complexity to the model, enabling it to capture more intricate patterns in the data.
  • the output size of each predictor ⁇ ! is set equal to j such that the number of neurons in the output layer equals the number of samples up to ⁇ .
  • ⁇ ! is the ⁇ @: sample from ⁇ ! ( ⁇ ⁇ ).
  • the training process uses a Mean Absolute Error (MAE) loss function, with the Root Mean Square Propagation (RMSProp) algorithm to minimize the loss function and update the model’s parameters.
  • the MAE loss function is defined as: PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 where A BCD is the loss, E dataset, ⁇ 7 and ⁇ G F are the actual and the predicted glucose samples, respectively.
  • the learning rate was set to 0.0001 to ensure stable and effective convergence during training.
  • the forward chaining methodology was applied for training and validation of each ⁇ ! by using the entire 10-adult cohort. This approach is specifically designed to handle temporal dependencies and ensures realistic evaluation of the model’s performance. Forward chaining involves iteratively training the model on a subset of the subjects’ data and testing it on remaining subjects of the cohort. For each iteration in the training, the model is recalibrated using all available subjects in the training set, which means to use the overall data cohort for the identification of the model. This validation step assesses the model’s ability to generalize and make accurate predictions for new patients. To enhance the performance of the LSTM model for blood glucose prediction, Applicants undertook a comprehensive process involving hyperparameter tuning, early stopping, and meticulous model evaluation.
  • the CF is a clinical parameter that describes how much 1 unit of rapid-acting insulin will reduce the blood glucose from the current level.
  • the CF is an estimate of the so-called insulin sensitivity, which is unknown, but it affects the observed blood glucose trends. That is, the following is assumed: the flatter the glucose trend, the lower the insulin sensitivity.
  • the mismatch between the glucose data generated in Scenario-II and the predicted open-loop glucose excursions can be calculated as: the error samples at time instant ⁇ , and samples at time instant ⁇ . From Eq.3, Applicants can derive that 3 % describes the ratio between K' 456,LM and K-.
  • the basal insulin - %.* is the subject-dependent basal insulin rate.
  • a time-dependent set point ' % was employed and set to 110 mg/dl during the day, and 125 mg/dl at night. Daytime is defined to be the interval 5:00 am to 10:00 pm. All other times are nighttime and there is no transition period.
  • the stochastic meal generator is based on a Markov Chain, whose state is the fasting period, and the transition probabilities depend on daytime and carbohydrate intake of the previous meal.
  • Recommended glucose targets include percent time in range of 70-180 mg/dL > 70%, percent time below 70 mg/dL ⁇ 4%, and percent time above 180 mg/dL ⁇ 25%.
  • PEM Prediction Error Method
  • the parameters of the proposed ARX are identified, using the identification data from Scenario-II, for consistency with the training of the T-step-ahead predictor.
  • the identified parameters are: [0094]
  • a state space realization of the proposed ARX is required. Applicants chose the realization corresponding to the canonical controllability form.
  • the state vector of the ARX model is not measurable, thus a steady-state Kalman filter has been incorporated.
  • the process noise covariance U bL is set to the identity matrix, W Q , and the measurement noise covariance ⁇ bL is defined equal to 1c .d .
  • the weight rARX always penalizes the deviation of the control action from the basal rate, i.e. ⁇ uins.
  • the input and output constraints are consistent with those defined in Eq.18.
  • PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 [0097] Applicants conduct statistical analyses for each metric and scenario to evaluate the significance of the difference between controller designs.
  • FIGS. 5A-5B illustrate the predicted population-level CGM time-series on a representative day of the validation dataset. Meals occur at 7 PM, at 8 AM, and 1 PM, respectively.
  • step ahead index leads to a deterioration in the accuracy of the prediction for a given model, as shown in FIGS.5A-5B.
  • 1-step ahead i.e.15 minutes
  • the ARX and multi-step predictions are overlapping with the CGM data: this result is expected because the ARX predictor was identified to maximize the 1-step ahead prediction performance.
  • 2-step ahead i.e. 30 minutes
  • the ARX and multi-step predictions are almost overlapping, but the prediction of the ARX model slightly tends to underestimate the glucose excursion around the postprandial phase. This holds also for 3-step ahead case, i.e. 45 minutes, where the ARX model starts to underestimate the glucose level also during the nocturnal period.
  • the multi-step predictor shows a faster response to insulin bolus than to meal intake. From 75 up 120 minutes, the aforementioned issues become more evident: the ARX predictor heavily underestimates the glucose levels, while the multistep predictor captures the glucose trend but with an wider excursion around the meals. In particular, it is important to note is that the proposed multi-step predictor is able to capture the downward slope of a prandial hyperglycemic excursion as well as the overnight steady-state equilibrium glucose concentration consistently well, regardless of prediction horizon.
  • the best tuning of the ARX-based MPC resulted in being more aggressive with respect to the best tuning of the multi-step-based MPC.
  • Applicants compared the performance of the MPC constructed with the proposed multi-step predictors with those of the ARX-based MPC for each scenario, i.e., Scenario A, Scenario B, Scenario C, and Scenario D.
  • Scenario A the effect of the meal disturbance is rejected with a static feed-forward action based on the conventional therapy, which relies on clinical parameters.
  • the controller capabilities although the meal is announced to the controller, no additional feed-forward control action is included in the control scheme and the both MPC are completely in charge of disturbance rejection.
  • FIGS.6-9 show population-level trajectories on Scenario A, Scenario B, Scenario C, and Scenario D, respectively.
  • the proposed MPC results in BG curves that are less steep from peak to through, and generally in tighter standard-deviation and min-max envelopes.
  • the controller shows a steady insulin delivery with no suspension.
  • the ARX-based MPC tends to command extremely large insulin boluses to compensate the meal effect, followed by periods of suspension of insulin delivery, which is an undesirable situation in this application.
  • the proposed MPC resulted in a significant reduction in hypoglycemia risk at the cost of a non-significant increase in the time in hyperglycemia and a slight reduction in the time in acceptable ranges.
  • the notable fact here is that the controller avoided over-delivery of insulin and prevented the dangerously low glucose levels in situation of potential increase of the insulin sensitivity.
  • the proposed MPC achieved a significant reduction in the coefficient of variation and in the percentage of time in the hypoglycemic zone, i.e. below 70 mg/dL, as well as an increase in the percentage of time between 70 and 180 mg/dL, with a non-significant increase in the time in hyperglycemic zone, i.e. above 180 mg/dL, as reported in Table 2.
  • Example 1.6 Summary and Conclusions
  • Applicants proposed a novel closed-loop insulin delivery algorithm for the treatment of T1D.
  • the novelty in the approach lies in the modeling step of the model-based control architecture.
  • Applicants learn a multi-step-ahead predictor of the output which is affine in the control input via a LSTM network.
  • the predictor structure is conceived to increase the accuracy of long-term predictions, allowing at the same time for an efficient PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 formulation and solution of the resulting LTV-MPC.
  • Applicants showed in numerical simulations the better performances of the proposed predictor, compared to that of an ARX-based.
  • control performances Applicants have demonstrated an improvement overall, increased time in target range with significant reduction in glucose variability and both hypo- and hyperglycemia risks, for a nominal scenario and a scenario testing the robustness against random meal disturbances.
  • the proposed approach significantly reduced hypoglycemia risk.
  • a population predictor could ideally limit the performance since it describes the average dynamics of the population.
  • the well-known inter-patient variability increases the need of patient-tailored models as an individualized description of the glucose metabolism can improve the effectiveness of the control algorithm.
  • training the model using data from a single patient may result in a lack of generalization for the model.
  • a trade-off may be achieved by pre-training the multi-step-ahead predictor using the population data and then fine-tuning the model parameters with individual data.
  • the same approach can be replicated in an in vivo setting with a first phase during which the model is pre-trained using in silico data to maximize the generalization capability of the predictor and a second phase of fine-tuning by using the clinical data collected in a real-life setting.
  • MDI Multiple Daily Injection
  • Treatment Regime [00106] In the context of type 1 diabetes (T1D), the most common platforms adopted for sensing and actuation, respectively, are continuous glucose monitors (CGM) and continuous subcutaneous insulin infusion (CSII) pumps.
  • CGM continuous glucose monitors
  • CSII continuous subcutaneous insulin infusion
  • the MDI treatment comprises the delivery of two types of insulin formulations: (1) a long-acting analog once per day, e.g., every morning before breakfast; and (2) a rapid-acting analog at mealtimes.
  • Embodiment 1 improves the postprandial glucose regulation by reducing the time spent in hyperglycemic range (>180 mg/dL) and increasing the time in euglycemic range (70-180 mg/dL), as reported in Table 3.
  • Embodiment 1 Additional embodiments of the present disclosure are provided herein below: Embodiment 1.
  • a computer-implemented method of monitoring blood glucose levels of a subject comprising: feeding the subject’s blood glucose level into a closed-loop insulin delivery algorithm trained to predict the blood glucose level of the subject, wherein the algorithm comprises a data-driven multi-step-ahead blood glucose (BG) predictor integrated with a Linear time-varying (LTV) model predictive control (MPC) law; using the algorithm to predict if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia; and transmitting insulin administration instructions based on the prediction.
  • Embodiment 2 The method of Embodiment 1, wherein the insulin administration instructions are based on the solution of a receding horizon (RH) optimal control problem carried out by the LTV- MPC.
  • RH receding horizon
  • Embodiment 3 The method of Embodiment 1, wherein the insulin administration instructions comprise increasing insulin administration to the subject if the blood glucose level of the subject is expected to reach a level representing hyperglycemia.
  • Embodiment 4 The method of Embodiment 1, wherein the insulin administration instructions comprise decreasing or suspending insulin administration to the subject if the blood glucose level of the subject is expected to reach a level representing hypoglycemia.
  • Embodiment 5. The method of Embodiment 1, wherein the method further comprises a step of measuring the blood glucose levels of the subject.
  • Embodiment 1 wherein the method further comprises a step of obtaining a blood sample from the subject and measuring the blood glucose levels from the blood sample.
  • Embodiment 7. The method of Embodiment 6, wherein the blood glucose levels of the subject is measured by a sensor associated with the subject, wherein the sensor transmits the measured blood glucose levels to the algorithm.
  • Embodiment 8. The method of Embodiment 1, wherein the method occurs in real-time.
  • Embodiment 9. The method of Embodiment 1, wherein the method occurs continuously.
  • Embodiment 10 The method of Embodiment 1, further comprising a step of administering insulin based on the instructions.
  • Embodiment 11. The method of Embodiment 10, wherein the insulin is administered by a user.
  • Embodiment 29 PCT Application Attorney Docket No. AF23853.P198WO UH ID No.2023-061 Embodiment 29.
  • the method of Embodiment 28 wherein the method is used to treat type 1 diabetes or prevent the complications arising from type 1 diabetes.
  • a blood glucose monitoring system comprising: an insulin pump associated with a subject and operational to administer insulin to the subject; one or more sensors associated with the subject and operational to measure the blood glucose levels of the subject; and a computing device in electronic communication with the insulin pump, the computing device comprising: a non-transitory computer-usable medium having computer-readable program code embodied therein; a closed-loop insulin delivery algorithm trained to predict the blood glucose level of the subject, wherein the algorithm comprises a data-driven multi-step-ahead blood glucose (BG) predictor integrated with a linear time-varying (LTV) model predictive control (MPC) law; and programming instructions operable to implement a method comprising: feeding the subject’s blood glucose level into the algorithm; predicting if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia using the algorithm; and transmitting insulin administration instructions to the insulin pump based on the prediction.
  • BG data-driven multi-step-ahead blood glucose
  • LTV linear time-varying
  • MPC
  • Embodiment 31 The system of Embodiment 30, wherein the insulin administration instructions are based on the solution of a receding horizon (RH) optimal control problem carried out by the LTV-MPC.
  • Embodiment 32 The system of Embodiment 30, wherein the insulin administration instructions comprise increasing insulin administration to the subject if the blood glucose level of the subject is expected to reach a level representing hyperglycemia.
  • Embodiment 33 The system of Embodiment 30, wherein the insulin administration instructions comprise decreasing or suspending insulin administration to the subject if the blood glucose level of the subject is expected to reach a level representing hypoglycemia.
  • Embodiment 34 The system of Embodiment 30, wherein the insulin pump is in the form of a patch.
  • Embodiment 35 The system of Embodiment 35.
  • Embodiment 30 wherein the insulin pump is operable for subcutaneous insulin administration.
  • Embodiment 36 The system of Embodiment 30, wherein the insulin pump comprises an insulin reservoir and a processor, wherein the processor is in electronic communication with the computing device, and wherein the processor is operational to actuate or withhold the release of insulin from the insulin reservoir.
  • Embodiment 37 The system of Embodiment 30, wherein the one or more sensors are operational to transmit the subject’s blood glucose levels to the computing device.
  • Embodiment 38 The system of Embodiment 30, wherein the computing device comprises a computer.
  • Embodiment 39 The system of Embodiment 39.
  • Embodiment 30 wherein the computing device comprises a mobile device.
  • Embodiment 40 The system of Embodiment 30, wherein the computing device comprises an app on a mobile device.
  • Embodiment 41 The system of Embodiment 30, wherein the computing device comprises a chip on an insulin pump.
  • Embodiment 42 The system of Embodiment 30, wherein the computing device is in electronic communication with the insulin pump through a wireless network.
  • the system of Embodiment 30, wherein the algorithm is a machine learning algorithm.
  • Embodiment 44 The system of Embodiment 43, wherein the machine learning algorithm is a Root Mean Square Propagation (RMSProp) training algorithm.
  • RMSProp Root Mean Square Propagation
  • Embodiment 43 wherein the machine learning algorithm is a Long Short-Term Memory (LSTM) network.
  • Embodiment 46 The system of Embodiment 30, wherein the algorithm does not identify an open- loop algorithm of the glucoregulatory system from available data.
  • Embodiment 47 The system of Embodiment 30, wherein the algorithm directly fits the entire BG trajectory over a predefined prediction horizon to be used in the MPC law as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • Embodiment 48 Embodiment 48.
  • Embodiment 47 wherein a Long Short-Term Memory (LSTM) network is used to the component of the BG trajectory nonlinear in the state to a predictive nonlinear function incorporated into the algorithm.
  • Embodiment 49 The system of Embodiment 47, wherein a linear regression model is used to fit the future insulin control inputs to an affine function incorporated into the algorithm.
  • Embodiment 50 The system of Embodiment 30, wherein the algorithm comprises customizable user parameters.
  • Embodiment 50 wherein the customizable user parameters are selected from the group consisting of length of prediction horizon, weight matrices, setpoints, or combinations thereof.
  • Embodiment 52 The system of Embodiment 50, wherein the customizable user parameters comprise length of prediction horizon.
  • Embodiment 53. The system of Embodiment 30, wherein the BG prediction is implemented as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • Embodiment 54 The system of Embodiment 30, wherein the feeding of the subject’s blood glucose level into the algorithm occurs in real-time.
  • Embodiment 55 The system of Embodiment 30, wherein the system is suitable for use in monitoring the blood glucose levels of the subject.
  • Embodiment 56 The system of Embodiment 30, wherein the subject is a human being suffering from type 1 diabetes.
  • a computing device comprising one or more computer readable storage mediums having at least one program code embodied therewith, wherein the computing device comprises: a closed-loop insulin delivery algorithm trained to predict the blood glucose level of a subject, wherein the algorithm comprises a data-driven multi-step-ahead blood glucose (BG) predictor integrated with a linear time-varying (LTV) model predictive control (MPC) law; and programming instructions for: feeding the subject’s blood glucose level into the algorithm, using the algorithm to predict if the blood glucose level of the subject is expected to reach a level representing hypoglycemia or hyperglycemia, and transmitting insulin administration instructions based on the prediction.
  • BG data-driven multi-step-ahead blood glucose
  • LTV linear time-varying
  • MPC model predictive control
  • Embodiment 61 The computing device of Embodiment 57, wherein the computing device comprises a computer.
  • Embodiment 62. The computing device of Embodiment 57, wherein the computing device comprises a mobile device.
  • Embodiment 63 The computing device of Embodiment 57, wherein the computing device comprises an app on a mobile device.
  • Embodiment 64. The computing device of Embodiment 57, wherein the computing device comprises a chip on an insulin pump.
  • Embodiment 65 The computing device of Embodiment 57, wherein the computing device is in electronic communication with the insulin pump through a wireless network.
  • Embodiment 57 wherein the algorithm directly fits the entire BG prediction over a predefined prediction horizon to be used in the MPC law as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • Embodiment 71 The computing device of Embodiment 70, wherein a Long Short-Term Memory (LSTM) network is used to fit the component of the BG trajectory nonlinear in the state to a predictive nonlinear function incorporated into the algorithm.
  • Embodiment 72 The computing device of Embodiment 70, wherein a linear regression model is used to fit the future insulin control inputs to an affine function incorporated into the algorithm.
  • Embodiment 73 Embodiment 73.
  • Embodiment 57 wherein the algorithm comprises customizable user parameters.
  • Embodiment 74 The computing device of Embodiment 73, wherein the customizable user parameters are selected from the group consisting of length of prediction horizon, weight matrices, setpoints, or combinations thereof.
  • Embodiment 75 The computing device of Embodiment 74, wherein the customizable user parameters comprise length of prediction horizon.
  • Embodiment 76 The computing device of Embodiment 57, wherein the BG prediction is implemented as a nonlinear function of past input-output data and an affine function of future insulin control inputs.
  • Embodiment 77 Embodiment 77.
  • Embodiment 57 wherein the feeding of the subject’s blood glucose level into the algorithm occurs in real-time.
  • Embodiment 78. The computing device of Embodiment 57, wherein the computing device is suitable for use in monitoring the blood glucose levels of the subject.
  • Embodiment 79. The computing device of Embodiment 78, wherein the subject is a human being suffering from type 1 diabetes. [00112] Without further elaboration, it is believed that one skilled in the art can, using the description herein, utilize the present disclosure to its fullest extent. The embodiments described herein are to be construed as illustrative and not as constraining the remainder of the disclosure in any way whatsoever.

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Abstract

L'invention concerne un système de surveillance de la glycémie ayant une pompe à insuline opérationnelle pour administrer de l'insuline au sujet, des capteurs associés au sujet et opérationnels pour mesurer les taux de glycémie d'un sujet, et un dispositif informatique en communication électronique avec la pompe à insuline. Le dispositif informatique comprend des supports de stockage lisibles par ordinateur auxquels est incorporé un code de programme. Le dispositif informatique comprend un algorithme d'administration d'insuline en boucle fermée entraîné pour prédire le taux de glycémie du sujet. L'algorithme comprend un prédicteur de glucose sanguin à pas multiples basé sur des données intégré à une loi de commande prédictive de modèle variant dans le temps linéaire. Le dispositif informatique comprend des instructions de programmation pour introduire le taux de glycémie du sujet dans l'algorithme, utiliser l'algorithme pour prédire s'il est attendu que le taux de glycémie du sujet atteigne un niveau représentant l'hypoglycémie ou l'hyperglycémie, et transmettre des instructions d'administration d'insuline à la pompe à insuline sur la base de la prédiction.
PCT/US2024/045052 2023-09-05 2024-09-03 Commande prédictive de modèle (mpc) variant dans le temps linéaire (ltv) d'un pancréas artificiel avec prédicteurs neuronaux de glucose sanguin affines à pas multiples entraînés par des données Pending WO2025054131A1 (fr)

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US20210260289A1 (en) * 2020-02-20 2021-08-26 Dexcom, Inc. Machine learning in an artificial pancreas
US20220031949A1 (en) * 2016-01-14 2022-02-03 Bigfood Biomedical, Inc. Adjusting insulin delivery rates
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US20170095612A1 (en) * 2010-10-31 2017-04-06 Trustees Of Boston University Blood glucose control system
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US20220031949A1 (en) * 2016-01-14 2022-02-03 Bigfood Biomedical, Inc. Adjusting insulin delivery rates
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