Closed-loop control method and system to improve post-prandial glucose response after unannounced source glucose excursions

Abstract

A method, system, and computer readable medium for a dual mode closed loop control (CLC) system is provided that integrates each of (i) an adaptive, individualized model predictive control (MPC) control law that adjusts the control strength of insulin infusion as a function of recent past control actions, glucose measurements, and derivatives thereof, (ii) an automatic bolus priming system (BPS) that commands an extra insulin injection upon detection of a viable metabolic condition (e.g., an unannounced meal), and (iii) a hyperglycemia mitigation system (HMS) to avoid pervasive hyperglycemia.

Description

[0001] Cross-references to related applications

[0002] This international application claims priority and interest in U.S. Provisional Application No. 63 / 091646, filed October 14, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The disclosed implementation scheme involves providing improved glycemic control to individuals with type 1 diabetes (T1DM; T1D in this text), and more specifically, such improvement can be implemented according to closed-loop control (CLC) so as to fully automate the suppression (rejection) of glycemic disturbances due to lack of food. Background Technology

[0004] T1D is a lifelong chronic metabolic disorder that imposes significant economic, physical, social, and psychological costs on patients and their caregivers. 1,2 This autoimmune disease leads to absolute insulin deficiency, requiring exogenous insulin for life to regulate blood glucose levels. 3 Intensive insulin therapy (IIT) has been shown to effectively lower average blood glucose levels, and is typically assessed using hemoglobin A1c (HbA1c), further chronic complications, and possible comorbidities. 4,5 However, intraperitoneal infusion (IIT) is often associated with an increased duration of hypoglycemia (low blood glucose), which can be linked to serious complications and even death. 6 Conversely, systemic exposure to high blood glucose (hyperglycemia) has serious short-term and long-term effects on health and life expectancy. 7 CLC, which can be implemented in an artificial pancreas (AP), typically includes an insulin infusion pump, a continuous glucose monitor (CGM), and control algorithms in between, providing a convenient method for automated insulin dosing. This prolongs the time to normal blood glucose levels while significantly reducing the physical and psychological burden commonly associated with type 1 diabetes (T1D). 8 .

[0005] Over the past decade, computer and in vivo research involving different types of APs has increased dramatically. 9 This growth can likely be attributed to advancements in computer simulations, CGM, insulin pumps, and mobile platforms. 10 During this period, the field has moved from clinical trial platforms... 11 Rapid transition to clinical practice using two (2) commercially available hybrid APs in the United States. Specifically, Medtronic Minimed 670G. 12 and Tandem Control-IQ 13,14While auto-adjusting insulin pumps (APs) are referred to as "hybrid" in their ability to automatically adjust insulin pump infusion, they are not designed to completely replace carbohydrate coverage (i.e., the amount of insulin ingested during a meal to offset the glycemic effects of ingested carbohydrates). Due to the mismatch between the time constants of food absorption and subcutaneous (sc) insulin, the mealtime insulin dose (bolus) must be manually requested 10–15 minutes before the start of a meal and proportional to a predetermined food intake estimate (usually given by the amount of food or carbohydrate content). In contrast, fully automated APs extend the capabilities of hybrid APs by automatically suppressing major disturbances in glycemic levels caused by diet and physical activity.

[0006] In these respects, food intake remains an obstacle to acute pancreatitis (AP) because it has a significant impact on glucose homeostasis. 15 Even under optimal pre-meal conditions, varying amounts of carbohydrates and the overall macronutrient composition of the meal can trigger different glucose dynamics responses. Mixed-type insulin therapy (AP) has been reported to have excellent postprandial results when the parameters used to calculate postprandial insulin dose (such as carbohydrate content, insulin-carbohydrate ratio (CR), and correction factor (CF)) have little or no uncertainty. 13,16,17 However, the relatively high uncertainty in these parameters appears to be the norm rather than the exception, posing a risk to CLC performance and overall glycemic control. 18 The early contributions of fully automated designs to unnotified feeding trials can be found in both engineering (computer simulation) studies

[19] –

[24] and clinical (in vivo) studies

[25] –

[30] . In computer simulation studies, the reported mean percentage of time-in-interval (TIR) ​​in the range of [70–180] mg / dL was 70.4%–90.0%, and the percentage of time-in-interval <70 mg / dL was 0.0%–4.04%, compared with the same percentages reported in clinical studies of 63.6%–84.7% and 0.1%–2.9%, respectively.

[0007] However, although APs implementing CLC perform very well in managing blood glucose in the absence of pre-meal glucose shifts (e.g., at night), it is difficult to prevent chronic hyperglycemia after unannounced carbohydrate intake without a mealtime dose. 55 This is partly because CLC systems inherently experience a delay in the perception of pre-meal blood glucose rise by the CGM and initiation of post-infusion insulin action, and also because CLCs must avoid hypoglycemia due to overly aggressive insulin administration. For these reasons, all currently commercially available CLC systems are effectively hybrid closed-loop (HCL) systems, requiring the user to input the amount of carbohydrates ingested; post-meal CR is then administered to avoid severe hyperglycemia. 56-60 .

[0008] Unfortunately, neglecting carbohydrate intake is common among individuals with type 1 diabetes (T1D). This affects 65% of adolescents who consume carbohydrates at least once a week. 52 Of these, 38% of adolescents missed at least 15% of the dose. 53 Adolescents who missed four doses per week had a 1% increase in HbA1c. 52 This could result in a large number of adolescents failing to reach the recommended HbA1c level. 54 .

[0009] Therefore, there is a need to provide optimized CLC that automatically delivers mealtime doses when appropriate and suppresses (i.e., mitigates) glycemic disorders that tend toward hyperglycemia in a manner that automatically regulates insulin delivery, while also suppressing the possibility of hypoglycemia.

[0010] In these respects, the results based on the implementation scheme described herein will be compared with those obtained based on the established hybrid CLC controller USS-Virginia. 32 .

[0011] Specifically, such analysis and results are based on an adult cohort of 100 subjects using an FDA-approved UVA / Padova simulator, which correlates with various metabolic responses and population characteristics. 33 . Summary of the Invention

[0012] It should be understood that the following overview and detailed description are exemplary and illustrative, and are intended to provide further explanation of the claimed embodiments. Neither the abstract nor the following description is intended to define or limit the scope of the embodiments to the specific features mentioned in the abstract or description. Rather, the scope of the embodiments is defined by the appended claims.

[0013] One implementation may include, within an artificial pancreas (AP), a processor-implemented method for regulating blood glucose in a subject with type 1 diabetes (T1D), comprising: predicting the subject's blood glucose value based on continuous glucose monitoring (CGM) measurements; determining a basal insulin dosing schedule based on the predicted value; modifying the schedule based on predetermined values ​​of one or more CGM measurements detected from the CGM measurements and a rate of change in the CGM measurements, and defining the modified schedule based on the modification; and delivering the schedule or the modified schedule to the subject. The implementation may further include calculating the probability of an unnotified glycemic disturbance occurring within a predetermined time period; and supplementing the delivery of the schedule or the modified schedule with the automated delivery of a first mealtime insulin dose in response to the calculated probability.

[0014] The planned and modified plans can each minimize a cost function that includes (a) correcting the subject’s blood glucose level to a predetermined target level, (b) penalizing predicted blood glucose values ​​that tend to be hypoglycemic, and (c) weighting the difference between predictions of two consecutive basal insulin doses.

[0015] Glucose disturbance can be defined by including at least one source of glucose fluctuation, which is not considered in the predicted glucose value on which the planned and modified plans are based, and the calculated probability can be based on CGM measurements over a predetermined period.

[0016] The calculated probability can be calculated at each consecutive interval of the CGM measurement, each interval being included within a predetermined period.

[0017] The first meal dose can be defined as a predetermined percentage of the subject’s total daily insulin (TDI).

[0018] The predetermined percentage can increase as the calculated probability increases.

[0019] Regarding a series of first-meal doses, subsequent doses can be reduced by an amount of insulin on board (IOB) equal to the sum of the doses of each previous first meal.

[0020] Based on predicted blood glucose values ​​indicating hypoglycemia, this aspect can include reducing the basal insulin dose to a portion of its average value.

[0021] On the other hand, it may include (a) in response to the current blood glucose value and the predicted blood glucose value indicating hyperglycemia, and (b) after the delivery of the first mealtime dose, automatically supplementing the delivery of the planned or modified plan with the delivery of a second mealtime insulin dose.

[0022] The delivery of the second meal dose can be blocked within two (2) hours after the delivery of the first meal dose.

[0023] The frequency of delivery of the second meal dose can be limited to once per hour.

[0024] On the other hand, it may include suspending the automatic delivery of the first meal dose in response to an AP notification to eat, and supplementing the planned or modified delivery of the third meal dose with a third meal dose calculated based on the subject's insulin-carbohydrate ratio (CR) and correction factor (CF) to reach half of the meal dose.

[0025] Each implementation scheme may further include related systems and computer-readable media corresponding to the specific methods described above.

[0026] In some implementations, the disclosed implementation may include one or more features described herein. Attached Figure Description

[0027] The accompanying drawings, which are incorporated herein and form part of this specification, illustrate exemplary embodiments and, together with the specification, further enable those skilled in the art to implement and carry out these embodiments, as well as other embodiments that are obvious to those skilled in the art. The embodiments herein will be described in more detail with reference to the following drawings, wherein:

[0028] Figure 1 A closed-loop control (CLC) system based on the implementation model predictive control (MPC) according to the embodiment described herein is shown;

[0029] Figure 2A The embodiments described herein are illustrated. Figure 1 The MPC tuning / detuning surface, and Figure 2B It shows according to Figure 1 MPC and Figure 2A Tuning / detuning rules for the tuning / detuning surfaces;

[0030] Figure 3 The correlation between the amount of hypoglycemic events induced by total daily insulin (TDI) and the probability of glycemic disturbances is shown.

[0031] Figure 4A and 4B Histograms of the root mean square error (RMSE) for the identification and validation datasets of the research subjects are shown respectively, relative to the computer simulation study of CLC conducted in this paper.

[0032] Figure 5 A performance example of the Bolus Priming System (BPS) provided by the MPC of the representative subjects used in this paper for computer simulation studies is shown.

[0033] Figure 6 The time distribution of sequential injections for unnotified feeding in the entire group is shown in a computer simulation study.

[0034] Figure 7 The evolution of the probability of glycemic disturbances associated with having the entire computer simulation study group eat individually is shown;

[0035] Figure 8 A timeline for the computer simulation research is shown;

[0036] Figure 9A and 9B The computer simulation study group shows glucose readings related to insulin delivery;

[0037] Figure 10A and10B An error plot is shown relative to the conventional control for the MPC according to the embodiment of this paper, showing the time in range (TIR) ​​and time out of range within a six (6) hour window following the variation in food intake;

[0038] Figure 11 An exemplary construction of a CLC for use in the embodiments described herein is shown;

[0039] Figure 12A An exemplary computing device is shown that can implement one or more portions of a CLC of the embodiments described herein. Figure 12B A network system is shown that can implement and / or be used to implement one or more parts of the CLC of the various embodiments herein;

[0040] Figure 13 A block diagram is shown that can be implemented and / or used to implement one or more parts of the CLC related to Internet connectivity described herein;

[0041] Figure 14 The present invention illustrates a system that can be implemented and / or used to implement one or more parts of the CLC described herein, based on one or more clinical settings and an internet connection; and

[0042] Figure 15 An exemplary structure embodying one or more parts of the CLC described herein is shown. Detailed Implementation

[0043] This application will now be described with reference to various exemplary embodiments. This specification discloses one or more embodiments that include features of this embodiment. The described embodiments and references in the specification to “an embodiment,” “an embodiment,” and “example embodiment,” etc., indicate that the described one or more embodiments may include specific features, structures, or characteristics. Such phrases do not necessarily refer to the same embodiment. Those skilled in the art will understand that a specific feature, structure, or characteristic described in connection with an embodiment is not necessarily limited to that embodiment, but is generally relevant and applicable to one or more other embodiments.

[0044] In several figures, and even in different figures, the same reference numerals may be used for the same elements having the same function. The described embodiments, along with their detailed construction and elements, are merely intended to aid in a comprehensive understanding of this embodiment. Therefore, it will be apparent that this embodiment can be implemented in various ways and does not require any of the specific features described herein. Furthermore, well-known functions or constructions are not described in detail because they would obscure this embodiment with unnecessary detail.

[0045] The description should not be considered restrictive, but merely for illustrating the general principles of this embodiment, as the scope of this embodiment is best defined by the appended claims.

[0046] It should also be noted that in some alternative implementations, blocks in the flowchart, connections in the sequence diagram, states in the state diagram, etc., can occur in the order shown in the diagram. That is, the order of the blocks / connections / states shown is not intended to be restrictive. On the contrary, the blocks / connections / states shown can be reordered to any suitable order, and some blocks / connections or states can occur simultaneously.

[0047] All definitions used in this document should be understood as a grasp of dictionary definitions, definitions in merged files by reference, and / or the general meaning of the definition terms.

[0048] The indefinite articles “a” and “an” used in the specification and claims shall be understood as “at least one” unless expressly stated otherwise.

[0049] The phrase “and / or” as used in the specification and claims should be understood as “any one or both” of the elements combined in this way, i.e., elements that exist together in some cases and separately in others. Multiple elements listed with “and / or” should be interpreted in the same way, i.e., “one or more” of the elements combined in this way. In addition to the elements specifically identified by the word “and / or”, other elements may optionally be present, whether related to or unrelated to those specifically identified. Thus, as a non-limiting example, in one embodiment, when used in conjunction with open-ended language such as “comprising,” a reference to “A and / or B” may refer only to A (optionally including elements other than B); in another embodiment only to B (optionally including elements other than A); in yet another embodiment both A and B (optionally including other elements); and so on.

[0050] As used in the specification and claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” should be interpreted as inclusive, meaning it includes at least one but more than one number or list of elements, as well as optional other unlisted items. Only terms that explicitly indicate the opposite, such as “only one” or “exactly one”, or when used in the claims, “consisting of…”, will refer to containing only one element or one element in a list of elements. Generally, the term “or” as used herein should only be interpreted as indicating an exclusive alternative (i.e., “one or the other, not both”) when preceded by an exclusive term, such as “any,” “one,” “only one,” or “exactly one” as used in the claims, and should have the general meaning used in the field of patent law.

[0051] As used herein in the specification and claims, the phrase “at least one,” referring to a list of one or more elements, should be understood to mean at least one element selected from any one or more elements in the list, but not necessarily including at least one of each element specifically listed in the list, and does not exclude any combination of elements in the list. This definition also allows for the optional presence of elements other than those specifically identified in the list of elements referred to by the phrase “at least one,” whether related to or unrelated to those specifically identified elements. Thus, as a non-limiting example, in one embodiment, “at least one of A and B” (or equivalently, “at least one of A or B,” or equivalently, “at least one of A and / or B”) may mean at least one, optionally including more than one A, with no B (and optionally including elements other than B); in another embodiment, it means at least one, optionally including more than one B, with no A (and optionally including elements other than A); in yet another embodiment, it means at least one, optionally including more than one A, and at least one (optionally including more than one B) (and optionally including other elements); etc.

[0052] In the claims and the foregoing description, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “consisting of,” etc., should be understood as open-ended, meaning including but not limited to.

[0053] It should be understood that although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as being more preferred or advantageous than other embodiments. Furthermore, unless otherwise stated, all embodiments described herein should be considered exemplary.

[0054] It should be understood that any component or module mentioned in any implementation discussed herein may be formed integrally with or separately from each other. Furthermore, redundant functionality or structure of components or modules may be implemented. Moreover, various components may communicate locally and / or remotely with any user / clinician / patient or machine / system / computer / processor. Furthermore, various components may communicate wirelessly and / or via hardwired or other desired and available means of communication, systems, and hardware. Furthermore, various components and modules may be replaced by other modules or components that provide similar functionality.

[0055] It should be understood that the apparatus and related components discussed herein can take on all shapes along the entire continuous geometric spectrum of operation in the x, y, and z planes to provide for and meet anatomical, environmental, and structural requirements, as well as operational demands. Furthermore, the positions and alignments of the various components can be varied as needed or required.

[0056] It should be understood that the various sizes, dimensions, profiles, stiffness, shape, flexibility, and materials of any component or part of a component in the various embodiments discussed throughout the discussion may vary and be used as needed or required.

[0057] It should be recognized that although some dimensions are provided in the above figures, the device can be configured in various sizes, dimensions, profiles, stiffness, shapes, flexibility and materials, as it is a component or part of the device and can therefore be varied and utilized as needed or required.

[0058] While exemplary embodiments of this application have been explained in detail in certain respects herein, it should be understood that other embodiments may also be considered. Therefore, the scope of this application is not limited to the details of the construction and arrangement of the components described below or shown in the accompanying drawings. This application is capable of having other embodiments and can be practiced or implemented in various ways.

[0059] A range may be expressed herein as from “about” or “approximately” a particular value and / or to “about” or “approximately” another particular value. Other exemplary embodiments, when expressing such a range, include from one particular value and / or to another particular value.

[0060] In describing example embodiments, terminology will be used for clarity. Each term is considered to have the broadest meaning as understood by one of ordinary skill in the art and includes all technical equivalents that operate in a similar manner to achieve a similar purpose. It should also be understood that reference to one or more steps of a method does not exclude the presence of additional or intermediate method steps between those explicitly identified steps. The steps of a method may be performed in a different order than that described herein without departing from the scope of this application. Similarly, it should be understood that reference to one or more components in a device or system does not exclude the presence of additional or intermediate components between those explicitly identified components.

[0061] The reference list includes a number of references, which may include various patents, patent applications, and publications, and are discussed within the disclosure provided herein. The citations and / or discussions of these references are provided solely for the purpose of clarifying the description of this application and do not constitute an admission that any such reference is “prior art” to any aspect of this application described herein. In notation, “[n]” corresponds to the nth reference in the list. All references cited and discussed in this specification are incorporated herein by reference to the same extent as if each reference were individually incorporated by reference.

[0062] As used herein, the term “approximately” means about, within a range, roughly, or near. When the term “approximately” is used with a numerical range, it modifies the range by extending the boundaries above and below the listed numerical values. Typically, the term “approximately” is used herein to modify numerical values ​​above and below the stated value by 10% of the variance. In one aspect, the term “approximately” means plus or minus 10% of the numerical value of the number it is used with. Thus, approximately 50% means within the range of 45%–55%. The numerical ranges listed by endpoints in this document include all numbers and fractions contained within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, 4.24, and 5). Similarly, the numerical ranges listed by endpoints in this document include subranges contained within that range (e.g., 1 to 5 includes 1-1.5, 1.5-2, 2-2.75, 2.75-3, 3-3.90, 3.90-4, 4-4.24, 4.24-5, 2-5, 3-5, 1-4, and 2-4). It should also be understood that all numbers and their fractions are considered to be modified by the term “about”.

[0063] Based on the aforementioned goals and benefits of achieving and maintaining optimal TIR, we, the inventors at the University of Virginia (UVA), propose a CLC system called Reactive Optimal Carbohydrate Kinetic Estimation AP or RocketAP (hereinafter “RocketAP”). In doing so, it is conceivable that RocketAP may include model predictive control (MPC) implemented in the form of a Diabetes Assistant (DiA) 20, provided by, for example, a smartphone or other receiving and / or computing platform, configured to implement an insulin infusion pump 22 (e.g., Tandem t:slim X2). TM ) and Continuous Glucose Monitor (CGM) 24 (e.g., Dexcom G6) TM )(See Figure 1 The communication between the DiA and the Controller (DiA) is also considered. Therefore, the DiA can define a general control paradigm, referred to herein as the "controller," whose task is to continuously predict future blood glucose levels and calculate the optimal insulin dose to maintain an individual's target blood glucose level. Therefore, in the reference... Figure 1 The controller may include and be defined by at least those components and functional flows shown in gray, and specifically includes one or more modules providing MPC, a Kalman filter, a mealtime dose initiation system (BPS), and a unified safety system (USS) consisting of a safety system module (SSM) and a hyperglycemic mitigation system (HMS). Furthermore, the controller may include an onboard insulin monitor (IOBSUP) and a power brake (PB) module, implemented as part of the USS. Each aspect of the controller is discussed in detail below. The controller can be configured to (a) be fully automatic to present full CLC (FCLC) by automatically suppressing the glycemic effect caused by unnotified ingested carbohydrates, and / or (b) operate as a hybrid CLC system that can provide mealtime doses in response to eating notifications. Figure 1 middle, y m y′ m IOB and IOB represent the current CGM measurement, its time derivative, and onboard insulin, respectively. and Let u represent the Kalman estimates of glucose state and disorder at time k, respectively. bolus u mpc , and u total Let's discuss that below.

[0064] Specifically, the controller can be configured based on Garcia Tirado et al. 34A variant of the proposed model is used to predict blood glucose levels and calculate insulin dosage. This model omits its oral submodel and treats subcutaneous insulin infusion as a triangular submodel. The predictive model implemented by the controller can be given as follows (according to its equations 1-6), relative to the parameters and overall values ​​shown in Table 1 below:

[0065]

[0066] and IR a (t)=k1I sc1 (t)+k2I sc2 (t) (6)

[0067] Where G (mg / dL) is the plasma glucose concentration, X (L / min) is the proportion of insulin in the distal compartment, and I... sc1 (mU) and I sc2 (mU) represents the amount of non-monomer and monomeric insulin in the subcutaneous space, I represents the amount of plasma insulin (mU), d (mg / dL) represents the dysregulation input into glucose kinetics, and u (mU / min) represents the exogenous insulin input.

[0068] Table 1

[0069]

[0070] Equations (1) to (5) Linearization, The subject-specific basal insulin rate, expressed as a percentage of sampling time t. s = 5-minute discretization to embed a linear MPC framework.

[0071] x k+1 =Ax k +B I u k +B d d k (7)

[0072] y k =Cx k (8)

[0073] in It is a state vector, A, B I B d C is the matrix of the discrete-time linear system with the corresponding dimensions (equations (7)-(8)). These are relative to u op and G op Deviations in insulin injection and glucose measurement.

[0074] To estimate the current state and disturbance d, such as unnotified eating, we assume that d is a disturbance that includes unmodeled phenomena that directly affect glucose kinetics. 37 Therefore, we extend equation (7) to d k+1 =d k , and thus

[0075]

[0076] in and

[0077]

[0078] Modeling disorder as a constant dynamic allows for... Figure 1 A state estimator implemented using a Kalman filter is used to correct disturbances entering the active dynamics.

[0079] To make the model fit specific subjects, and Figure 1 The sum shown includes y m y′ m (i.e., the current CGM measurement and its time derivative), IOB, and The quantitative contextualization of (i.e., the state and disordered Kalman estimates at time k) in consideration of θ ide =[S g k b k a S I [p2] The model in equation (9) was individualized using the set of parameters to be discovered from CGM and insulin records from a 14-day data collection period. The remaining parameters were set to population values ​​because they were considered unidentifiable. Model individualization was performed in a three-step process, in which (i) data gaps and compression artifacts were removed from the data, (ii) disturbance characteristics d for the entire data collection period were estimated using a Kalman filter and equation (9) with population values, and (iii) the available data were identified and validated after the disturbance estimated in (ii) was used to divide the available data into identification and validation datasets. The predictive power of each model was evaluated using the root mean square error (RMSE) metric.

[0080]

[0081] Where N is the number of data points. It's CGM data. The output prediction is made using the identified model. Equation (9) does not explicitly treat feeding as an input. Instead, in the FCLC mode of the controller proposed in this paper, the controller is notified once feeding begins to affect the CGM value.

[0082] 1) MPC

[0083] Based on individualization and any detected disturbances, the MPC can instruct the controller to deliver basal insulin every five (5) minutes (i.e., a mealtime dose), and adjust this delivery by using the aggressiveness or amount of this administration as a function of the rate of glucose change. In this way, the MPC is optimized relative to a predetermined, fixed target glucose level (e.g., 120 mg / dL), as described below, making it possible to substantially avoid hypoglycemic and hyperglycemic events. The MPC can be implemented according to the following... 39,40 :

[0084]

[0085] stmodel(9)-(9)(10) (12b)

[0086] u min ≤u j ≤u max (12c)

[0087] Δu min ≤Δu j ≤Δu max (12d)

[0088] y min -y j ≤η j (12e)

[0089] η j ≥0 (12f)

[0090] 12b, 12e, and 12f are 12c and 12d are N p and N c They serve as the prediction layer and the control layer, respectively. Indicates control strategy and Strategies for slack variables. Achieved low blood sugar constraint factor y j ≥y min The softening process produces equations (12e)-(12f). Equations (12c) and (12d) ensure the control input and the difference Δu j =u j -u j-1 Located in the interval [u min ,u max ] and [Δu min ,Δu max ] Cost Φ in (12a) mpc It varies as a function of IOB and the rate of insulin change. IOB is widely used in constructing APS to prevent insulin accumulation in the closed-loop system due to delayed insulin action. 41 Each time a certain amount of insulin is injected, it accumulates into a decay curve reflecting the circulating insulin level.

[0091] The cost function in (12a) can be defined as follows:

[0092]

[0093] by The glucose target error at step j; r k Defined as an asymmetric time-varying exponential reference signal 31、34、42、43 κ is used as a constant to penalize predictions for hypoglycemia. Q z (IOB) will predict the model With controller reference The differences between the evolutions are weighted as a function of IOB and given by the following formula.

[0094]

[0095] Using Q0 = 10 as Q z The nominal weight of (IOB), TDI as the user-specific total daily insulin, IOB min =TDI / 40, α=30 and β=1000 are adjustable parameters. The slope of the detuning rule is defined as...

[0096]

[0097] Therefore, the corresponding mealtime dose can be determined by minimizing the cost function. κ serves as a term to correct individual glucose concentration to the target value; Q serves as a term to penalize low glucose values; z (IOB) is used as a regularization term to weight the difference between two (2) consecutive micromeal doses. This minimization can be based on, i.e., satisfying, optionally predetermined two (2) hour dose windows, thereby determining the basal insulin infusion plan for a given subject. Figure 1 u in mpc In other words, MPC may require deviations from a fixed base rate to produce the optimal dose for that window. Minimization calculations can be performed sequentially at five (5) minute intervals and at one of those moments before the next iteration.

[0098] Optimization of the λ1 tuning / detuning strategy allows for a more aggressive controller response at high CGM change rates and high blood glucose (BG) levels, as shown in equation (15) below, where y m and y' m Let λ represent the first derivative of the CGM trajectory at the current CGM value and the current sampling time, respectively. 1,nom =5 / u b st represents the nominal value of λ1. + =5 and st - =-5 represents the positive slope threshold and the negative slope threshold, respectively, and m1=-0.8 and m2=0.8 represent the tuning / detuning slope. In this way, λ1 represents the degree of inertial deviation from the rate of change of CGM level and high BG level (from the planned baseline infusion). Referring to Figure 2, the design of λ1 at "A" and the tuning / detuning Q are provided. z A graphical overview of (IOB) (and its predecessor format relative to "B") 31 (design and mistuning), where st ± and m 1,2 It was found heuristically based on physiological knowledge, and α, IOB min and λ 1,nom It was found through a grid search to obtain all UVA / Padova simulators. 44 The optimal controller performance for the adult group in terms of TIR percentage, <70 mg / dL time percentage, and >180 mg / dL time percentage. As described above, IOB is defined. min Allowing for more aggressive controller action during an extended period following the onset of disorder d, such increased controller action could result in a modified plan for the subject's basal insulin infusion (i.e., Figure 1 middleu mpc (Modifications).

[0099]

[0100] st0.2·λ 1,nom ≤λ1(y m ,y m′ )≤λ 1,nom ,

[0101] f1(y′ m )=m1·λ 1,nom ·(y m′ -st - )+λ 1,nom

[0102] f2(y′ m )=m2·λ 1,nom ·(y m′-st + )+λ 1,nom

[0103] Finally, referencing trajectories such as those by Garcia Tirado et al. 31 As defined, where

[0104]

[0105] and y sp Indicates the target or setpoint, and This represents the time constant used to adjust how the controller approaches the setpoint. The remaining parameter values ​​of the controller are summarized in Table 2 below.

[0106] Table 2

[0107]

[0108] 2) BPS

[0109] As stated above, the amount and composition of unnotified carbohydrate intake disrupt efforts to maintain normal blood glucose levels, posing a significant challenge to diabetes care and treatment. This is particularly true because conventional CLC systems experience an inherent delay in the perception of pre-meal blood glucose elevation by the CGM and initiation of post-infusion insulin action. Therefore, we have recognized that such CLC systems need to counteract the effects of unnotified eating before providing corrective measures through micromeal-time dosing according to the aforementioned plan or a modified plan of basal infusion. Without such countermeasures, patients with type 1 diabetes are prone to significant levels of hyperglycemia.

[0110] Therefore, we present in this paper a mealtime dose initiation system, or BPS for short, as a module configured to work in conjunction with the aforementioned MPC to induce a timely infusion of a relatively large amount of insulin, calculated as a fraction of the total daily insulin (TDI). More specifically, these fractions can be progressively increased with a calculated estimate of the probability of large glycemic disturbances occurring and including and / or describing at least one source of glycemic fluctuations that cannot be explained by the predetermined glycemic values ​​on which the planned and modified plans are based. Such sources may include unannounced meals with a significant carbohydrate composition (see the discussion of carbohydrate amounts below). In these respects, the term "unaccounted for" can substantially mean not adequately explained, only partially explained, or unexplained. Thus, the first mealtime dose (BPS) is defined in this paper as... Figure 1 u in bolus Furthermore, infusions caused by BPS can immediately resolve the problem that could otherwise lead to hyperglycemic events.

[0111] Specifically, the BPS can be configured to operate every five (5) minutes to check (i.e., retrospectively) the probability of eating disorder d occurring in the previous 30 minutes. For this purpose, a second-order polynomial is fitted to the last 30 minutes of CGM data to generate the following equation:

[0112] y p (i)=p1i 2 +p2i+p3

[0113] Where y p (i) represents the glucose values ​​for i = 1, ..., 6, and represents the sequence of CGM data over the past 30 minutes. The coefficients p1, p2, and p3 of this equation can be used as features in a logistic regression classification algorithm.

[0114] Output, y log It can be defined as

[0115]

[0116] β0, β1, β2, and β3 were discovered using a simulated dataset where post-meal time periods were labeled, and the algorithm was trained for detection. For this equation, we use their respective standard deviations σ. 1,2,3 and average value μ 1,2,3 The characteristics were normalized; these were found in data collected from real subjects under routine care throughout the pre-enrollment data collection period. 45 The values ​​of each coefficient and normalization parameter are listed in Table 3 below (parameter values ​​are in mg / dL). The disorder probability π at each iteration... k The following equation can be used to derive the result, where

[0117]

[0118] Table 3

[0119]

[0120] This probability can then be used to determine whether a mealtime dose is needed, and if so, how much insulin should be administered. The BPS considers a predetermined plan describing the percentage P of the total insulin dose (TDI) to be given to the individual at each probability threshold. TDI The plan is shown below, and the probability determination is shown as it increases from 0.3 to 0.9.

[0121]

[0122] Before administering the BPS mealtime dose, subtract the IOB amount from the previous BPS dose. This mealtime dose is calculated as follows:

[0123]

[0124] J BPS P represents the amount of insulin delivered by the BPS. TDI (%) is the percentage of patients with TDI based on the above dosing regimen, IOB BPS This is the amount of insulin ingested from the previous BPS dose related to food intake. IOB BPS You can use a 6-hour IOB curve to obtain it. 46 J BPS The dose can be saturated at 0 to prevent the system from commanding a negative insulin dose. In these ways, BPS provides at least two (2) safeguards against hypoglycemia. First, the dose is adjusted based on the previously injected initiating dose. Second, a threshold, the BPS_threshold, is set to allow the initiating dose only if the glucose concentration is greater than such a threshold. The BPS_threshold can be determined as follows:

[0125] t_prev_hypo: = number of minutes after the last BG ≤ 70 mg / dL

[0126]

[0127] The disorder probability threshold and its corresponding insulin dose were determined using a previously employed method that uses regularized deconvolution to “replay” past real data to address unknown inputs in the glucose-insulin model. 36,47 This method identifies sources of undescribed glycemic variability in insulin and meal records, allowing for simulation (simulation replay) of the effects of changes in insulin dosage on glycemic outcomes. Using data from a previous study (ClinicalTrials.govNCT03859401) collection period, we were able to determine initial numerical correspondences between different probability thresholds [0.1–1.0] and different mealtime insulin doses, i.e., percentages of TDI [3%–9%].

[0128] After each automated mealtime dose (calculated based on the primary CGM value for each period of collected data), we used the above technique to simulate glucose levels over the next two (2) hours and determine whether the mealtime dose would result in a hypoglycemic event (CGM < 70 mg / dL). We empirically set the maximum number of hypoglycemic events to once per day.

[0129] Figure 3The results of a replay experiment evaluating BPS are shown. It is clear that low probability thresholds and high TDI amounts produced the most additional hypoglycemia. As the probability threshold increased and the TDI percentage decreased, the observed hypoglycemia decreased. The results of this study lead us to determine that at probability thresholds of 0.3, 0.5, 0.7, 0.8, and 0.9, TDIs of 3%, 4%, 5%, 6%, and 9% should be delivered, respectively.

[0130] Therefore, it can be understood from the above that, according to Figure 1 The controller can be configured to operate in either a first mode or an FCLC mode, allowing glycemic disturbances caused by unannounced food / carbohydrate intake to be automatically suppressed, i.e., without user intervention. As will be understood further, suppression may be achieved based on a retrospective review of past CGM data to determine possible food intake, thereby replacing alternative food notifications.

[0131] Furthermore, although according to Figure 1 The controller can be configured to operate in a first mode, as previously described, in a second mode, or in hybrid CLC (HCLC), but a mode that enables standard meal notification is also conceivable. In this case, a meal notification is given, and the meal dose (referred to herein as the third meal dose) is calculated starting from the subject's CR and CF. In this case, the controller recognizes the notification and thus suspends any upcoming or concurrent BPS meal doses.

[0132] 3) USS (SSM and HMS)

[0133] Continue to refer to Figure 1 Its USS includes a Safety System Module (SSM) and a Hyperglycemia Mitigation System (HMS) module to combat the possibility of hypoglycemia and hyperglycemia events, respectively.

[0134] Specifically, SSM assesses u total ( Figure 1 This involves monitoring hypoglycemia-related risks associated with basal regulation of the controller through short-term (30-minute) blood glucose prediction and risk space transformation. 48 If a risk of hypoglycemia is predicted, the module saturates insulin commands to a fraction of the user's average basal rate; otherwise, the command is allowed to proceed.

[0135] At this point, the SSM can implement an onboard insulin monitor (IOBSUP) module, which estimates the IOB for each of the five (5) minute consecutive intervals. With this estimate, the IOBSUP then distributes it to all other modules to reduce any risk of insulin buildup. The IOB is calculated based on the four (4) hour action curve derived by Swan et al. (DiabetesCare 2009), which assumes that insulin is depleted from the individual system four (4) hours after injection, relative to the calculations below.

[0136] J (as a vector of past insulin injections in 5-minute increments) is corrected for basal insulin infusion (basal_hist):

[0137]

[0138] Then IOB is calculated as IOB = J_diffx_effect curve.

[0139] The SSM can also command the controller to operate in HYPOSAFE mode to prevent significant hypoglycemic events by limiting insulin injections to less than or equal to baseline levels for one (1) hour after hypoglycemia is detected and notified. The logic of this mode is as follows: as a result of the controller's decision, it enables...

[0140] Consider adding the diff_rate as a differential base rate (signed) to the base level as a controller decision result.

[0141] Read last_hypo_time from the database

[0142] If the current time - the last time below the threshold is ≤ 60 minutes

[0143] If diff_rate ≥ 0

[0144] Injection_Send_to_Pump = u basal

[0145] otherwise

[0146] Injection_Send_to_Pump = u basal +diff_rate

[0147] Finish

[0148] Finish

[0149] In other cases, diff_rate is adjusted with a minus sign that tends to keep insulin levels below baseline.

[0150] The SSM can further implement a Meal Informed Power Brake (MIPB) module, which is always active to veto any insulin delivery request from any other module. Specifically, the MIPB module calculates an estimate of the patient's metabolic state every 5 minutes using the aforementioned Kalman filter; it uses the metabolic state (a combination of the feedforward model state and the Kalman filter-estimated state) to make 10-minute, 30-minute, and 60-minute blood glucose predictions based on two different assumptions: (i) no insulin injection for the 10-minute prediction, and (ii) a predicted basal rate for the 30- and 60-minute predictions. The 10-minute and 60-minute predictions are propagated to other modules, while the 30-minute prediction is used to calculate the predicted blood glucose risk. Based on this predicted risk, the MIPB then suppresses the basal rate to generate an insulin constraint factor, which is then compared to any received injection request, and the minimum of the constraint factor or request is then sent to the pump for injection. The braking action can be understood relative to the following set of parameters and their implementation at the controller.

[0151]

[0152] Create a vector of estimated states:

[0153]

[0154] Create output variable G est :

[0155]

[0156] Among them G sp This is the basic glucose concentration.

[0157] Using TMM-ΔX and a new state vector (for prediction)

[0158]

[0159] We calculate G pred,30 and G pred,light :

[0160]

[0161] And Ξ2=A p B I,p +B i,p

[0162] Where ΔX=X cl -X ol A p and B I,p It is a linearized TMM-ΔX state and input (insulin) matrix.

[0163] Calculate braking action

[0164]

[0165] Finally, calculate the safety system constraint factors:

[0166] Cons = BrakeAction·u I

[0167] The final dose is calculated as follows:

[0168] If (BrakeAction < 1)

[0169]

[0170] otherwise

[0171]

[0172] Finish

[0173] In this way, the SSM can accept or reject all or part of the requested injection dose. Furthermore, the SSM can request manual confirmation of the requested injection dose from the controller user through the DiA interface. Through the interface, the outputs of MIPB and IOBSUP are combined to determine the hypoglycemia and hyperglycemia red light system, informing the patient of hypoglycemia risk status (i.e., green, no perceived risk; yellow, predicted risk of insulin suppression; red, predicted impending hypoglycemia requiring external intervention); and hyperglycemia risk status (i.e., green, no perceived risk; yellow, predicted risk of increased basal rate; red, perceived hyperglycemia requiring external intervention).

[0174] On the other hand, the HMS module monitors BG level estimates and automatically commands one or more insulin corrective mealtime doses, referred to herein as the second mealtime dose, to counteract generalized hyperglycemia. Command frequency is saturated to allow commands to occur at most once per hour, and any corrective mealtime dose issued by the HMS is optionally blocked for two (2) hours from the first (i.e., BPS) mealtime dose within a predetermined timeframe. That is, no HMS mealtime dose is issued within a two (2) hour window of the BPS mealtime dose issuance. Correction is considered every five (5) minutes if G... k If the CGM trend (calculated as the slope coefficient of the CGM sum-of-squares regression) is flat or increases (defined as >-1 mg / dL·min), a correction is issued. The HMS-related corrected mealtime dose can be calculated according to equation (18) below to correct glucose levels to an optional 110 mg / dL, where

[0175] HMScorr =HMS ratio HMS ini (18)

[0176] in

[0177]

[0178] by This is the current absolute CGM value. HMS ratio It is the initial calculation correction of attenuation HMS ini Items.

[0179] Computer simulation research

[0180] Results were obtained using the entire adult cohort of 100 virtual subjects in the FDA-approved UVA / Padova simulator; population statistics are summarized in Table 4 below. 33 The dawn phenomenon and individual and interpersonal changes in insulin sensitivity were included in the experimental setup. Figure 1 The controller underwent a full set of experiments, including not only upcoming clinical scenarios (discussed below) but also robustness tests for changes in meal portions. Specifically, the following results were obtained, including (A) model individualization, (B) BPS performance, and (C) overall controller performance in nominal clinical scenarios, including changes in carbohydrate content for notified and unnotified meals and unnotified dinners. USS Virginia was used as the baseline controller. 32 .

[0181] Table 4

[0182]

[0183] A) Model personalization

[0184] To obtain a subject-specific controller design, subjects underwent a 14-day data collection period prior to enrollment. Data collection included different food intakes, amounts, and times of day. Daily datasets were randomly assigned to either identification (5 days) or validation (9 days).

[0185] Figure 4A and 4B Histograms of daily identification and validation RMSEs are shown, comparing the identified model with a population value for all virtual subjects in the adult cohort. Table 5 below shows other statistics that help assess the personalized benefits of the model.

[0186] Table 5

[0187]

[0188] B) BPS performance

[0189] Figure 5 The performance of BPS in representative subjects in the simulator is shown. After being given 80g of carbohydrates at 6 pm, Figure 1 The controller automatically commanded multiple safe mealtime doses (totaling 2.47U, compared to 6.33U for USS Virginia). In general, the controller resulted in an infusion of 2.47U without meal notification, while infusions of 3.16U and 6.33U occurred with meal notification (for USS Virgin). Specifically, in Figure 5 As can be seen, the RocketAP controller accurately predicted the disturbance caused by eating at 6 p.m. relative to the CGM value shown. Figure 6 The time distribution of sequential injections of unnotified 80g carbohydrate intake is shown for the entire adult group.

[0190] In the second scenario, the entire adult group using the UVA / Padova simulator was given three meals of 50g, 50g, and 80g carbohydrates at 8:00 AM, 12:00 PM, and 6:00 PM, respectively. (Reference) Figure 7 The figure shows the temporal evolution of the probability of meal disorder, where the median is represented by "E", the range of 25%-75% is represented by "F", and the range of 5%-95% is represented by "G".

[0191] C) Clinical trial simulation

[0192] The simulation protocol was designed to mimic an in vivo clinical trial (NCT04545567, ClinicalTrials.gov). Simulation results evaluated the controller's performance relative to baseline treatment during the experimental treatment, assuming perfect hardware functionality. Participants were randomly assigned to Rocket AP or USS Virginia, as... Figure 8 As shown, this further illustrates the study timeline. Participants engaged in real-life activities on days 2, 3, 6, and 7, while being randomly assigned to two controllers (e.g., Rocket AP on days 2 and 3 and USS Virginia 2 on days 3 and 4, or vice versa, based on randomization). During the first and second enrollment days, participants ate structured meals at 8:00 AM, 12:00 PM, and 6:00 PM daily, with the same protein, fat, and carbohydrate content throughout the study. The carbohydrate content for breakfast, lunch, and dinner was 50g, 50g, and 80g, respectively. On days 2 and 6, all meals were communicated to the controllers. On days 3 and 7, breakfast and lunch were communicated, but dinner was not.

[0193] Figure 9A and9B The relative comparisons of the median and percentile ranges of CGM for the entire adult cohort on the enrollment days of “notified all meals” and “unnotified dinner” are shown separately for Rocket AP(R) and USS Virginia(VA) (where the envelope represents the range, the solid line represents the median, and insulin is basal insulin). Tables 6 and 7 below show the results according to international standards. 44 CGM-related parameters were measured on both enrollment days. Specifically, the percentage of time spent in the TIR (transient intravascular coagulation) state or normal glycemic state [70-180 mg / dL], the percentage of time spent in the hypoglycemic state (<70 mg / dL), and the percentage of time spent in the hyperglycemic state (>180 mg / dL) were examined. (Based on BPKovatchev) 51 Calculate the hypoglycemic index (LBGI) and hyperglycemic index (HBGI).

[0194] Table 6

[0195]

[0196] SD and CV represent the standard deviation and coefficient of variation, respectively. Values ​​for normally distributed samples are shown as mean ± SD, and values ​​for non-normally distributed samples are shown as the median [interquartile range]. Significance levels < 0.05 are indicated in bold. *One-sided paired t-test. Wilcoxon Sign-Rank Test

[0197] Table 7

[0198]

[0199] SD and CV represent the standard deviation and coefficient of variation, respectively. Values ​​for normally distributed samples are shown as mean ± SD, and values ​​for non-normally distributed samples are shown as the median [interquartile range]. Significance levels < 0.05 are indicated in bold. *One-sided paired t-test. Wilcoxon Sign-Rank Test

[0200] from Figure 9A and 9B As can be seen from the table above, Rocket AP outperforms USS Virginia in terms of TIR percentage and time percentage 180 mg / dL after no notification (dinner).

[0201] Finally, we investigated the performance of Rocket AP and USS Virginia after various hypothetical unannounced meals with carbohydrate content ranging from 5g to 80g. Both systems demonstrated robustness to dietary challenges of less than 30g of carbohydrates within six (6) hours postprandial (time percentage range 100% [100-100]%). Figure 10A and 10B Median and interquartile range (IQR) error plots are shown for the percentage of TIR and the percentage of time >180 mg / dL over six (6) hours postprandial for RocketAP(R) and USS Virginia(VA) with carbohydrate contents ranging from 35 g to 80 g. The percentage of time <70 mg / dL was 0 [0-0]% for both controllers. Therefore, as expected, the percentage of TIR decreased with increasing carbohydrate content, thus translating the loss percentage into the percentage of time >180 mg / dL.

[0202] In vivo studies

[0203] Compared to the aforementioned clinical study involving 18 participants aged 12-20 years who completed the study, we found that, compared to commercially available USS Virginia, Figure 1 The RocketAP controller performed similarly to the computer simulation study in terms of TIR and the percentage of time <70 mg / dL. The same was true for unannounced dinner blood glucose results and overall results.

[0204] Regarding unannounced dinners, the Rocket AP controller RCKT showed a significantly higher TIR (primary outcome) over six (6) hours following the unannounced dinner compared to the USS Virginia (83% [64–93] vs. 53% [40–71]; p = 0.004), as shown in Table 8 below. The time in the tight range (TTR) or 80–140 mg / dL was also higher (49% [41–59] vs. 27% [22–36]; p = 0.002). The mean CGM and the percentage of time >180 mg / dL were significantly lower in the Rocket AP controller (141 ± 21 mg / dL vs. 166 ± 26 mg / dL; p = 0.00 and 17% [1.3–34] vs. 47% [28–60]; p = 0.01) (see Table 1). Glycemic improvement was prolonged in the Rocket AP controller over 12 hours following the unannounced dinner. Regarding the notification of dinner, the Rocket AP controller further outperformed the USS Virginia study participants, which differs somewhat from the computer simulation study discussed in this paper.

[0205] Table 8

[0206]

[0207] In the overall control of approximately 46 hours, RocketAP achieved higher TTR and TIR (72.3% ± 7.9 vs. 63.7% ± 13; p = 0.01; and 87% ± 6.6 vs. 80% ± 9.6; p = 0.007), as well as lower mean BG and percentage of time >180 mg / dL (122 ± 7.5 mg / dL vs. 128 ± 15.5 mg / dL, p = 0.05; and 9.4 ± 5.6% vs. 13.4 ± 8.7%, p = 0.03) (see Table 9 below). Baseline control in USS Virginia showed an increase in TIR in 15 / 18 participants compared to 17 / 18 participants per RocketAP.

[0208] Table 9

[0209]

[0210] As shown in Table 10 below, compared with the USS Virginia, the RocketAP controller achieved significantly tighter control over overnight TTR (95.3% [90.4-100]% vs 76.3% [58.5-87.4]%, p<0.001), TIR (99.2% [95.7-100]% vs 92.2% [81.2-96]%, p<0.001) and mean BG (106.4 ± 7.3 mg / dL vs 123 ± 20 mg / dL; p = 0.002).

[0211] Table 10

[0212]

[0213] Compared to the computer simulation studies described in this paper, and exemplifying the performance of RocketAP in an adult group, the above in vivo results further support the completeness of this performance, as the focus was on adolescents, who are known for their lack of notification regarding feeding. 52-54 .

[0214] In light of the foregoing, it should be understood that we have disclosed a dual-mode CLC system that integrates each of the following: (i) an adaptive personalized MPC control law that adjusts the control strength of insulin infusion based on the most recent control action, glucose measurement and its derived derivatives; (ii) an automatic BPS that safely commands additional insulin injections when a feasible metabolic condition (e.g., unannounced eating) is detected; and (iii) a HMS that avoids generalized hyperglycemia.

[0215] Reference Figure 11 For example, a processor or controller 102 implemented via DiA communicates with a glucose monitor or device 101 and optionally with an insulin device 100. As implemented via DiA, the processor or controller 102 may be configured to include all necessary hardware and / or software, or a portion thereof, necessary to execute any and all required instructions. The glucose monitor or device 101 communicates with a subject 103 to monitor the subject's glucose levels. The processor or controller 102 is configured to perform required calculations. Optionally, the insulin device 100 communicates with the subject 103 to deliver insulin to the subject. The processor or controller 102 is configured to perform required calculations. The glucose monitor 101 and the insulin device 100 may be implemented as separate devices or a single device. The processor 102 may be implemented locally in the glucose monitor 101, the insulin device 100, or a standalone device (or in any combination of two or more of the glucose monitor, insulin device, or standalone device). The processor 102 or a portion of the system may be remotely located, enabling the device to operate as a telemedicine device.

[0216] refer to Figure 12A In its most basic configuration, the computing device 144, which optionally implements DiA, typically includes at least one processing unit 150 and a memory 146. Depending on the exact configuration and type of the computing device, the memory 146 may be volatile (e.g., RAM), non-volatile (e.g., ROM, flash memory, etc.), or some combination of both.

[0217] In addition, device 144 may have other features and / or functions. For example, the device may also include additional removable and / or non-removable memory, including but not limited to magnetic disks, optical disks, or magnetic tapes, and writable electrical storage media. Such additional memory is represented by removable memory 152 and non-removable memory 148. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Memory, removable memory, and non-removable memory are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technologies, CDROM, digital versatile disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by the device. Any such computer storage medium may be part of the device or used with the device.

[0218] The device may also include one or more communication connections 154 that allow the device to communicate with other devices (e.g., other computing devices). The communication connection carries information in a communication medium. A communication medium typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal whose one or more characteristics are set or altered in a manner that encodes, executes, or processes information in the signal. By way of example and not limitation, a communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as radio, RF, infrared, and other wireless media. As mentioned above, the term computer-readable medium as used herein includes storage media and communication media.

[0219] Reference Figure 12B The embodiments described herein can also be implemented on a network system comprising multiple computing devices communicating with a networked device, such as a network with infrastructure or an ad hoc network. The network connection can be wired or wireless. In this example, the network system includes a computer 156 (e.g., a web server), a network connection device 158 (e.g., wired and / or wireless connection), a computer terminal 160, and a PDA (e.g., a smartphone) 162 (or other handheld or portable device, such as a mobile phone, laptop, tablet, GPS receiver, MP3 player, handheld video player, pocket projector, etc., or a combination of these functionalities). In one embodiment, it should be understood that the module listed as 156 can be a glucose monitoring device. In one embodiment, it should be understood that the module listed as 156 can be a glucose monitoring device, an artificial pancreas and / or insulin device (or other interventional or diagnostic device). Figure 12BAny components shown or discussed together may be multiple in number. The implementation schemes described herein can be implemented in any device within the system. For example, the execution of instructions or other desired processing can be performed on the same computing device of any of 156, 160, and 162. Alternatively, implementation schemes can be performed on different computing devices within the network system. For example, certain desired or required processing or execution can be performed on one of the network's computing devices (e.g., server 156 and / or glucose monitoring device), while other processing and execution of instructions can be performed on another computing device within the network system (e.g., terminal 160), and vice versa. In fact, certain processing or execution can be performed on one computing device (e.g., server 156 and / or insulin device, AP, or glucose monitoring device (or other interventional or diagnostic device)); and other processing or execution of instructions can be performed on different computing devices, whether networked or not. For example, some processing can be performed on terminal 160, while other processing or instructions are passed to device 162, which executes the instructions. This situation can be particularly valuable, especially when the PDA 162 device is connected to the network, for example, via computer terminal 160 (or an access point in an ad hoc network). As another example, one or more embodiments described herein can be used to execute, encode, or process the software to be protected. The processed, encoded, or executed software can then be distributed to users. Distribution can take the form of storage media (e.g., disks) or electronic copies.

[0220] Reference Figure 13 A block diagram illustrating system 130 is shown, which includes computer system 140 and an associated Internet 11 connection upon which implementation schemes can be based. This configuration is typically used for a computer (host) connected to the Internet 11 and executing server or client (or combination) software. Source computers, such as laptops, ultimate destination computers, and relay servers, as well as any computer or processor described herein, can be used. Figure 13 The computer system configuration and internet connection are shown. System 140 can be used as a portable electronic device, such as a laptop computer / notebook computer, media player (e.g., MP3-based or video player), mobile phone, personal digital assistant (PDA), glucose monitoring device, artificial pancreas, insulin delivery device (or other interventional or diagnostic device), image processing device (e.g., digital camera or video recorder), and / or any other handheld computing device, or any combination of these devices. Note that, although Figure 13Various components of a computer system are shown, but they are not intended to represent any particular architecture or manner of interconnecting the components; such details are not closely related to the implementation described herein. It will also be appreciated that network computers, handheld computers, mobile phones, and other data processing systems with fewer or possibly more components can also be used. For example, Figure 13 The computer system may be an Apple Macintosh computer or a PowerBook, or an IBM-compatible PC. Computer system 140 includes a bus 137, interconnects or other communication mechanisms for communicating information, and a processor 138, typically in the form of an integrated circuit, coupled to the bus 137, for processing information and executing computer-executable instructions. Computer system 140 also includes main memory 134, such as random access memory (RAM) or other dynamic storage devices, coupled to the bus 137, for storing information and instructions to be executed by the processor 138.

[0221] Main memory 134 can also be used to store temporary variables or other intermediate information during the execution of instructions executed by processor 138. Computer system 140 also includes read-only memory (ROM) 136 (or other non-volatile memory) or other static storage devices coupled to bus 137 for storing static information and instructions of processor 138. Storage devices 135, such as disks or optical discs, hard disk drives for reading and writing to hard disks, disk drives for reading and writing to hard disk disks, and / or optical disc drives (such as DVDs) for reading and writing to removable optical discs are coupled to bus 137 for storing information and instructions. Hard disk drives, disk drives, and optical disc drives can be connected to the system bus via hard disk drive interfaces, disk drive interfaces, and optical disc drive interfaces, respectively. Drives and their associated computer-readable media provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for general-purpose computing devices. Typically, computer system 140 includes an operating system (OS) stored in non-volatile memory for managing computer resources and providing applications and programs with access to computer resources and interfaces. Operating systems typically handle system data and user input, responding by allocating and managing tasks and internal system resources. This includes controlling and allocating memory, prioritizing system requests, controlling input and output devices, facilitating networking, and managing files. Non-limiting examples of operating systems include Microsoft Windows, macOS X, and Linux.

[0222] The term "processor" refers to any integrated circuit or other electronic device (or set of devices) capable of performing operations on at least one instruction, including but not limited to Reduced Instruction Set Computing (RISC) processors, CISC microprocessors, microcontroller units (MCUs), CISC-based central processing units (CPUs), and digital signal processors (DSPs). The hardware of such a device can be integrated onto a single substrate (e.g., a silicon "die") or distributed across two or more substrates. Furthermore, various functional aspects of a processor can be implemented solely as software or firmware associated with the processor.

[0223] Computer system 140 can be coupled to display 131 via bus 137, such as a cathode ray tube (CRT), liquid crystal display (LCD), flat panel display, touch screen display, or similar device for displaying text and graphic data to a user. The display can be connected via a video adapter to support it. The display allows the user to view, input, and / or edit information related to system operation. Input device 132, including alphanumeric and other keys, is connected to bus 137 for transmitting information and command selections to processor 138. Another type of user input device is cursor control 133, such as a mouse, trackball, or arrow keys, for transmitting directional information and command selections to processor 138 and for controlling cursor movement on display 131. This input device typically has two degrees of freedom on two axes, a first axis (e.g., x) and a second axis (e.g., y), allowing the device to specify a position in a plane.

[0224] Computer system 140 can be used to implement the methods and techniques described herein. According to one embodiment, these methods and techniques are executed by computer system 140 in response to processor 138 executing one or more queues of instructions contained in main memory 134. Such instructions may be read into main memory 134 from another computer-readable medium, such as storage device 135. Execution of the instruction queues contained in main memory 134 causes processor 138 to perform the processing steps described herein. In alternative embodiments, hardwired circuitry may be used instead of or in combination with software instructions to implement this arrangement. Therefore, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0225] As used herein, the term "computer-readable medium" (or "machine-readable medium") is a broad term that refers to any medium or memory involved in providing instructions to a processor (such as processor 138) for execution, or any mechanism for storing or transmitting information in a machine-readable form. Such a medium may store computer-executable instructions to be executed by processing elements and / or control logic, as well as data manipulated by processing elements or control logic, and may take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Transmission media include coaxial cables, copper wires, and optical fibers, including wires constituting bus 137. Transmission media may also take the form of sound waves or light waves, such as sound waves or light waves generated during radio wave and infrared data communication, or other forms of propagating signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes or any other magnetic media, CD-ROMs, any other optical media, punched cards, paper tapes, any other physical media with a perforated pattern, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges, carrier waves as described below, or any other media that a computer can read.

[0226] Various forms of computer-readable media may be involved when transferring one or more queues of one or more instructions to processor 138 for execution. For example, the instructions may initially be carried on the disk of a remote computer. The remote computer may load the instructions into its dynamic memory and transmit them over a telephone line using a modem. A modem local to computer system 140 may receive data over the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector may receive the data carried in the infrared signal, and appropriate circuitry may place the data on bus 137. Bus 137 transfers the data to main memory 134, from which processor 138 retrieves and executes the instructions. The instructions received by main memory 134 may optionally be stored on storage device 135 before or after execution by processor 138.

[0227] Computer system 140 also includes a communication interface 141 coupled to bus 137. Communication interface 141 provides bidirectional data communication coupled to network link 139 connected to local network 111. For example, communication interface 141 may be an Integrated Services Digital Network (ISDN) card or a modem to provide data communication connectivity to a corresponding type of telephone line. As another non-limiting example, communication interface 141 may be a Local Area Network (LAN) card to provide data communication connectivity to a compatible LAN. For example, Ethernet connections based on the IEEE 802.3 standard, such as 10 / 100BaseT, 1000BaseT (Gigabit Ethernet), 10 Gigabit Ethernet (standardized as 10GE or 10GbE or 10GigE, conforming to IEEE Std 802.3ae-2002), 40 Gigabit Ethernet (40GbE), or 100 Gigabit Ethernet (100GbE, conforming to Ethernet standard IEEE P802.3ba), as described in Cisco Systems, Inc. Publication number 1-587005-001-3 (6 / 99), "Internetworking Technologies Handbook", Chapter 7: "Ethernet Technologies", pages 7-1 to 7-38, the entire contents of which are contained herein for all purposes as if fully described herein. In this case, the communication interface 141 typically includes a LAN transceiver or modem, such as the Standard Microsystems Corporation (SMSC) LAN91C11110 / 100 Non-PCI Ethernet Single Chip MAC+PHY described in the Standard Microsystems Corporation (SMSC) Data Sheet "LAN91C11110 / 100 Non-PCI Ethernet Single Chip MAC+PHY" Data-Sheet, Rev. 15 (02-20-04) (which is incorporated herein in its entirety for all purposes).

[0228] Wireless links can also be implemented. In any such implementation, communication interface 141 sends and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0229] Network link 139 typically provides data communication to other data devices via one or more networks. For example, network link 139 may provide a connection to a host computer or to a data device operated by an Internet Service Provider (ISP) 142 via a local network 111. ISP 142, in turn, provides data communication services via the global packet data communication network, the Internet 11. Both local network 111 and the Internet 11 use electrical, electromagnetic, or optical signals carrying digital data streams. Signals through various networks and on network link 139, as well as signals through communication interface 141, are example forms of carrier waves that transmit digital data to and from computer system 140.

[0230] The received code can be executed by processor 138 upon receipt and / or stored in storage device 135 or other non-volatile memory for later execution. In this way, computer system 140 can obtain application code in carrier form.

[0231] The inventors propose a concept for a personalized artificial pancreas system with automated BPS and enhanced safety. As can be seen from the algorithms and methods discussed herein, this program is readily applicable to devices such as glucose devices, insulin devices, AP devices, and other interventional or diagnostic devices, and can be implemented and used in conjunction with related processors, networks, computer systems, the Internet, and components and functions according to the scheme disclosed herein.

[0232] Reference Figure 14 The present invention illustrates a system in which one or more embodiments thereof may be implemented using a network or a portion of a network or computer, although the glucose monitor, AP or insulin device (or other interventional or diagnostic device) discussed herein may be implemented without a network.

[0233] Figure 14 An exemplary system in which the embodiments described herein can be implemented is illustrated schematically. In one embodiment, a glucose monitor, AP, or insulin device (or other interventional or diagnostic device) can be implemented locally by the subject (or patient) at home or other desired location. However, in alternative embodiments, it can be implemented in a clinic setting or auxiliary setting. For example, see reference... Figure 14 The clinic setup 158 provides a space for physicians (e.g., 164) or clinicians / assistants to diagnose patients with glucose-related disorders and related symptoms (e.g., 159). The glucose monitoring device 10 can be used as a standalone device to monitor and / or test a patient's glucose levels. It should be understood that although only the glucose monitoring device 10 is shown in the figure, the system and any components of the embodiments described herein can be used in various ways. Figure 14Used as shown. The system or component may be attached to or communicate with the patient as needed or required. For example, a combination of systems or components—including glucose monitoring device 10 (or other related devices or systems, such as controllers and / or an artificial pancreas, an insulin pump (or other interventional or diagnostic devices), or any other desired or required device or component)—may be contacted, communicate with, or be attached to the patient via tape or tubing (or other medical devices or components), or may communicate via wired or wireless connections. Such monitoring and / or testing may be short-term (e.g., clinical visit) or long-term (e.g., clinical hospitalization or home). The output of the glucose monitoring device may be acted upon appropriately by a physician (clinician or assistant), such as injecting insulin or feeding the patient, or other appropriate actions or modeling. Alternatively, the output of the glucose monitoring device may be transmitted to computer terminal 168 for immediate or future analysis. Delivery may be made via wired or wireless or any other suitable medium. The glucose monitoring device output from the patient may also be transmitted to a portable device, such as a PDA 166. The glucose monitoring device output with improved accuracy may be transmitted to glucose monitoring center 172 for processing and / or analysis. Such transmission can be achieved in a variety of ways, such as through wired or wireless network connections 169.

[0234] In addition to the output of the glucose monitoring device, errors, parameters for accuracy improvement, and any accuracy-related information can be transmitted, for example, to a computer 168 and / or a glucose monitoring center 172, for error analysis. Due to the importance of glucose sensors, this allows the glucose center to provide centralized accuracy monitoring, modeling, and / or accuracy enhancement.

[0235] The implementation examples described herein can also be implemented in a standalone computing device associated with a target glucose monitoring device, an artificial pancreas and / or insulin device (or other interventional or diagnostic device). Figure 12A An exemplary computing device (or part thereof) in which examples of embodiments of the present invention may be implemented is illustrated schematically.

[0236] Reference Figure 15 The diagram shows a block diagram of an example machine on which one or more aspects of the embodiments described herein can be implemented.

[0237] Figure 15 A block diagram of an example machine 400 on which one or more implementation schemes (e.g., the methods discussed) can be implemented (e.g., run) is shown.

[0238] Examples of machine 400 may include logic, one or more components, circuits (e.g., modules), or mechanical devices. A circuit is a tangible entity configured to perform certain operations. In one example, a circuit may be arranged in a specified manner (e.g., internally or relative to an external entity such as other circuits). In one example, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware processors (processors) may be configured by software (e.g., instructions, application portions, or applications) to operate to perform certain operations described herein. In one example, the software may (1) reside on a non-transient machine-readable medium or (2) reside in transmitted signals. In one example, when the software is executed by the underlying hardware of the circuit, it causes the circuit to perform certain operations.

[0239] In one example, the circuit can be implemented mechanically or electronically. For instance, the circuit may include dedicated circuitry or logic elements specifically configured to perform one or more techniques as described above, such as dedicated processors, field-programmable gate arrays (FPGAs), or application-specific integrated circuits (ASICs). In one example, the circuit may include programmable logic elements (e.g., circuitry contained within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform certain operations. It should be understood that the decision to implement the circuit mechanically (e.g., in dedicated and permanently configured circuitry) or in temporarily configured circuitry (e.g., by software configuration) may be made based on cost and time considerations.

[0240] Therefore, the term "circuit" is understood to include tangible entities, referring to entities that are physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., transiently) configured (e.g., programmed) to operate or perform specific operations in a particular manner. In one example, given multiple temporarily configured circuits, each circuit does not need to be configured or instantiated at any given moment in time. For example, in the case where the circuit includes a general-purpose processor configured via software, the general-purpose processor can be configured as different corresponding circuits at different times. The software can configure the processor accordingly, for example, constituting a specific circuit at one moment and different circuits at different times.

[0241] In one example, a circuit can provide information to and receive information from other circuits. In this example, the circuit can be considered communicatively coupled to one or more other circuits. When multiple such circuits exist simultaneously, communication can be achieved through signal transmission connecting the circuits (e.g., via appropriate circuitry and buses). In embodiments where multiple circuits are configured or enumerated at different times, communication between these circuits can be achieved, for example, by storing and retrieving information in a memory structure accessible to the multiple circuits. For example, one circuit can perform an operation and store the output of that operation in a memory device communicatively coupled to it. Then, at a later time, another circuit can access the memory device to retrieve and process the stored output. In one example, a circuit can be configured to initiate or receive communication with input or output devices and can operate on resources (e.g., information sets).

[0242] The various operations of the method examples described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented circuitry for performing one or more operations or functions. In one example, the circuitry referred to herein may include processor-implemented circuitry.

[0243] Similarly, the methods described herein can be implemented at least in part by processors. For example, at least some operations of a method can be performed by one or more processors or circuitry implemented by processors. The performance of certain operations can be distributed among one or more processors, not only within a single machine but also deployed across multiple machines. In one example, one or more processors may be located in a single location (e.g., in a home environment, an office environment, or as a server cluster), while in other examples, the processors may be distributed across multiple locations.

[0244] One or more processors may also support the execution of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations may be performed by a set of computers (as an example of a machine that includes processors), which can be accessed via a network (such as the Internet) and via one or more appropriate interfaces (such as application programming interfaces (APIs)).

[0245] Example implementations (e.g., devices, systems, or methods) may be implemented in digital electronic circuits, computer hardware, firmware, software, or any combination thereof. Example implementations may be implemented using computer program products (e.g., computer programs tangibly embodied in an information carrier or machine-readable medium for execution or control of their operation by a data processing device such as a programmable processor, computer, or multiple computers).

[0246] Computer programs can be written in any programming language, including compiled or interpreted languages, and can be deployed in any way, including as standalone programs or as software modules, subroutines, or other units suitable for use in a computing environment. Computer programs can be deployed to execute on a single computer or multiple computers at a single site, or distributed across multiple sites and interconnected via a communication network.

[0247] In one example, the operation can be executed by one or more programmable processors that execute a computer program to perform a function by manipulating input data and generating output. Examples of the method operation can also be executed by special-purpose logic circuitry (e.g., a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)), and the example device can be implemented as special-purpose logic circuitry.

[0248] Computing systems can include clients and servers. Clients and servers are typically geographically isolated and usually interact via a communication network. The client-server relationship arises from computer programs running on their respective computers and having a client-server relationship with each other. In implementations of programmable computing systems, it will be recognized that both hardware and software architectures need to be considered. Specifically, it will be understood that the choice of implementing certain functions in permanently configured hardware (e.g., ASICs), temporarily configured hardware (e.g., a combination of software and programmable processors), or a combination of permanently and temporarily configured hardware can be a design choice. The following are hardware (e.g., machine 400) and software architectures that can be deployed in example implementations.

[0249] In one example, machine 400 can operate as a standalone device, or machine 400 can be connected (e.g., networked) to other machines.

[0250] In a networked deployment, machine 400 can operate as a server or client machine in a server-client network environment. In one example, machine 400 can act as a peer machine in a peer-to-peer (or other distributed) network environment. Machine 400 can be a personal computer (PC), tablet computer, set-top box (STB), personal digital assistant (PDA), mobile phone, network device, network router, switch, or bridge, or any machine capable of executing instructions (sequence or other) specifying the actions that machine 400 should take (e.g., perform). Furthermore, although only a single machine 400 is shown, the term "machine" should also be considered as including any collection of machines that individually or jointly execute a set (or more) of instructions to perform any one or more methods discussed herein.

[0251] Example machine (e.g., computer system) 400 may include a processor 402 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), main memory 404, and static memory 406, some or all of which may communicate with each other via bus 408. Machine 400 may also include a display unit 410, an alphanumeric input device 412 (e.g., a keyboard), and a user interface (UI) navigation device 411 (e.g., a mouse). In one example, display unit 410, input device 412, and UI navigation device 414 may be a touchscreen display. Machine 400 may also include a storage device (e.g., a drive unit) 416, a signal generation device 418 (e.g., a speaker), a network interface device 420, and one or more sensors 421, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors.

[0252] Storage device 416 may include machine-readable medium 422 on which one or more sets of data structures or instructions 424 (e.g., software) are stored, which are embodied in or used by any one or more of the methods or functions described herein. During execution of instructions 424 by machine 400, instructions 424 may also reside wholly or at least partially within main memory 404, static memory 406, or processor 402. In one example, one or any combination of processor 402, main memory 404, static memory 406, or storage device 416 may constitute the machine-readable medium.

[0253] Although machine-readable medium 422 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store one or more instructions 424. The term "machine-readable medium" can also be considered as any tangible medium capable of storing, encoding, or carrying machine-executable instructions and causing the machine to perform any one or more methods of this application, encoding or carrying data structures used by or associated with such instructions. Therefore, the term "machine-readable medium" can be considered as including, but not limited to, solid-state memory, optical and magnetic media. Specific examples of machine-readable media can include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0254] Instruction 424 can also transmit or receive data via a network interface device 420 using a transmission medium through a communication network 426, employing any of a variety of transmission protocols (e.g., Frame Relay, IP, TCP, UDP, HTTP, etc.). Example communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), conventional telephone (POTS) networks, and wireless data networks (e.g., wireless networks called…). The IEEE 802.11 standard family, known as This includes standards such as the IEEE 802.16 family of standards, peer-to-peer (P2P) networks, etc. The term "transmission medium" should include any intangible medium capable of storing, encoding, or carrying instructions for machine execution, and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

[0255] As described herein, a “subject” can be any applicable human, animal, or other organism, whether alive or dead, or any other biological or molecular structure or chemical environment, and can be related to a specific component of the subject, such as subject-specific tissues or fluids (e.g., human tissue in a specific region of a living subject’s body), which may be located at a specific location on the subject, referred to herein as the “target area” or “target region”.

[0256] It should be understood that, as discussed herein, the subject can be a human or any animal. It should be understood that the animal can be any applicable type of animal in a wide variety of forms, including but not limited to mammals, veterinary animals, livestock, or pets. For example, the animal can be a laboratory animal specifically selected to have certain characteristics similar to humans (e.g., rats, dogs, pigs, monkeys), and it should be understood that the subject can be, for example, any applicable human patient.

[0257] References, including those from various patents, patent applications, and publications, are cited in the reference list and discussed within the disclosure provided herein. The citations and / or discussions of these references are provided solely to clarify the description of this application and do not constitute an admission that any such reference is "prior art" to any aspect of this application described herein. In notation, "[n]" corresponds to the nth reference in the list. All references cited and discussed in this specification are incorporated herein by reference to the same extent as if each reference were individually incorporated by reference.

[0258] In summary, while this application has been described with respect to specific embodiments, many modifications, variations, alterations, substitutions, and equivalents will be apparent to those skilled in the art. The scope of this application is not limited to the specific embodiments described herein. In fact, various modifications to the embodiments described herein will become apparent to those skilled in the art, based on the foregoing description and drawings, in addition to the embodiments described herein. Therefore, the embodiments described herein should be considered to be limited only by the spirit and scope of this application (and the claims), including all modifications and equivalent substitutions.

[0259] By reading the above detailed description and the accompanying drawings of some exemplary embodiments, those skilled in the art will readily understand other embodiments. It should be understood that many variations, modifications, and additional embodiments are possible, and therefore all such variations, modifications, and embodiments should be considered within the spirit and scope of this application. For example, regardless of the content of any part of this application (e.g., title, field, background, summary of the invention, abstract, drawings, etc.), unless expressly stated otherwise, it is not required that any particular described or illustrated activity or element, any particular order of such activities, or any particular interrelationship of such elements be included in any claim herein or any application claiming priority to this application. Furthermore, any activity may be repeated, any activity may be performed by multiple entities, and / or any element may be reproduced. Furthermore, any activity or element may be excluded, the order of activities may be varied, and / or the interrelationship of elements may be varied. Unless expressly stated otherwise, there is no requirement for any particular described or illustrated activity or element, any particular order or such activity, any particular size, speed, material, dimension, or frequency, or any particular interrelationship of such elements. Therefore, the description and drawings are to be considered illustrative in nature, not restrictive. Furthermore, when describing any number or range, unless otherwise expressly stated, the number or range is an approximation. When any range is described herein, unless otherwise expressly stated, the range includes all values ​​within the range and all its subranges. Any information incorporated herein by reference in any material (e.g., U.S. / foreign patents, U.S. / foreign patent applications, books, articles, etc.) is incorporated herein by reference to the extent that such information does not conflict with other descriptions and figures herein. If such conflict occurs, including conflicts that would invalidate this document or any claim claiming priority herein, any such conflicting information contained in this reference is not incorporated herein by reference.

[0260] Where applicable, all citations in this document, whether by number or otherwise, refer to one or more documents listed in the section entitled "References".

[0261] References

[0262] The devices, systems, apparatuses, modules, compositions, computer program products, non-transient computer-readable media, models, algorithms, and methods of various embodiments disclosed herein may utilize aspects disclosed in the following references, applications, publications, and patents, all of which are incorporated herein by reference and are included in this section without being considered prior art regarding these embodiments:

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Claims

1. A control system for an artificial pancreas (AP) defining a controller for the artificial pancreas and comprising: a processor; a processor-readable memory comprising processor-executable instructions to: predict a subject blood glucose value based on continuous glucose monitor (CGM) measurements of the subject; determine a basal insulin dosing schedule from the predicted value; modify the schedule in accordance with a predetermined value of one or more of the CGM measurements detected from the CGM measurements and a rate of change of the CGM measurements increasing and define a modified schedule from the modification; initiate delivery of the schedule or the modified schedule to the subject; calculate a probability of a blood glucose disturbance occurring within a predetermined time period, the blood glucose disturbance comprising at least one source of blood glucose fluctuation, the blood glucose disturbance not being accounted for by the predictions underlying the schedule or the modified schedule; and in response to the calculated probability, supplement delivery of the schedule or the modified schedule with an automatic delivery of an insulin first meal dose.

2. The system of claim 1, wherein: the schedule and the modified schedule each minimize a cost function comprising (a) correcting a blood glucose level of the subject to a predetermined target level, (b) penalizing predicted blood glucose values tending toward hypoglycemia, and (c) weighting a difference between two consecutive basal insulin dose predictions.

3. The system of claim 2, wherein: the at least one source of blood glucose fluctuation is not accounted for by the predicted blood glucose values underlying the schedule or the modified schedule, and the calculated probability is based on the CGM measurements over the predetermined time period.

4. The system of claim 3, wherein: the calculated probability is calculated at each successive interval of the CGM measurements, each interval comprising within a predetermined period.

5. The system of claim 4, wherein: the insulin first meal dose comprises a predetermined percentage of a total daily insulin (TDI) of the subject, the predetermined percentage being 3%, 4%, 5%, 6%, or 9%.

6. The system of claim 5, wherein: the predetermined percentage increases as the calculated probability increases.

7. The system of claim 6, wherein: for a series of insulin first meal doses, a subsequent one is reduced by an insulin on board (IOB) of a sum of each preceding insulin first meal dose.

8. The system of claim 7, further comprising: one or more instructions to automatically reduce a basal insulin dose to a fraction of its average value based on a predicted blood glucose value indicative of hypoglycemia.

9. The system of claim 8, further comprising: one or more instructions to (a) in response to a current estimated blood glucose value and a predicted blood glucose value indicative of hyperglycemia and (b) subsequent to the insulin first meal dose delivery, cause delivery of the schedule or the modified schedule to be automatically supplemented with a delivery of an insulin second meal dose.

10. The system of claim 9, wherein: the delivery of the insulin second meal dose is blocked within two hours of the insulin first meal dose delivery.

11. The system of claim 10, wherein: ​ The delivery frequency of the insulin second prandial dose is limited to once per hour.

12. The system of claim 11, further comprising: one or more instructions to suspend automatic delivery of the insulin first prandial dose in response to the artificial pancreas being notified of a meal and to supplement the scheduled or modified delivery with an insulin third prandial dose calculated to achieve half of the prandial dose based on the subject’s insulin-carbohydrate ratio (CR) and correction factor (CF).

13. A non-transitory computer-readable medium having stored thereon computer- executable instructions for regulating blood glucose values of a subject having type 1 diabetes (T1D) in an artificial pancreas (AP), the instructions causing a computer to: predict blood glucose values of the subject based on continuous glucose monitor (CGM) measurements of the subject; determine a schedule of basal insulin administration according to the predicted values; modify the schedule according to a predetermined value of one or more of the CGM measurements detected from the CGM measurements and an increasing rate of change of the CGM measurements, and define a modified schedule according to the modification; initiate delivery of the schedule or the modified schedule to the subject; and calculate a probability of a blood glucose disturbance, including at least one source of blood glucose fluctuation, occurring within a predetermined time period, the blood glucose disturbance being unannounced to the artificial pancreas; and cause delivery of the schedule or the modified schedule to be supplemented by automatic delivery of an insulin first prandial dose in response to the calculated probability.

14. The medium of claim 13, wherein: the schedule and the modified schedule each minimize a cost function including (a) correcting blood glucose levels of the subject to a predetermined target level, (b) penalizing predicted blood glucose values that tend to be hypoglycemic, and (c) weighting a difference between two consecutive predicted basal insulin doses.

15. The medium of claim 14, wherein: the at least one source of blood glucose fluctuation is not accounted for by predicted blood glucose values based on which the schedule and modified schedule are based, and the calculated probability is based on the CGM measurements of the predetermined time period.

16. The medium of claim 15, wherein: the calculated probability is calculated at each successive interval of the CGM measurements, each interval including within a predetermined period.

17. The medium of claim 16, wherein: the insulin first prandial dose includes a predetermined percentage of a total daily insulin (TDI) of the subject, the predetermined percentage being 3%, 4%, 5%, 6%, or 9%.

18. The medium of claim 17, wherein: the predetermined percentage increases as the calculated probability increases.

19. The medium of claim 18, wherein: for a series of insulin first prandial doses, a subsequent prandial dose is reduced by an insulin on board (IOB) of each previous insulin first prandial dose.

20. The medium of claim 19, further comprising: one or more instructions to automatically reduce a basal insulin dose to a fraction of its average value based on a predicted blood glucose value indicative of low blood glucose.

21. The medium of claim 20, further comprising: one or more instructions to cause delivery of the schedule or the modified schedule to be automatically supplemented by delivery of an insulin second mealtime bolus in response to a current estimated blood glucose value and a predicted blood glucose value indicative of high blood glucose and after delivery of the insulin first mealtime bolus.

22. The medium of claim 21, wherein: delivery of the insulin second mealtime bolus is blocked within two hours after delivery of the insulin first mealtime bolus.

23. The medium of claim 22, wherein: delivery of the insulin second mealtime bolus is limited to once per hour.

24. The medium of claim 23, further comprising: one or more instructions to suspend automatic delivery of the insulin first mealtime bolus in response to a meal notification to the artificial pancreas and supplement delivery of the schedule or the modified schedule with an insulin third mealtime bolus calculated to achieve half of the mealtime bolus based on a subject's insulin-carbohydrate ratio (CR) and correction factor (CF).

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