Method and system for closed-loop control to improve glycemic response after causes of unreported glycemic fluctuations

Abstract

A method, system, and computer-readable medium are provided for a dual-mode closed-loop control (CLC) system that integrates (i) an adaptive, personalized model predictive control (MPC) control law that adjusts the control intensity of insulin infusion depending on recent past control actions, blood glucose measurements, and their derivative(s); (ii) an automatic bolus priming system (BPS) that commands additional insulin infusion upon detection of a valid metabolic state (e.g., an unannounced meal); and (iii) a hyperglycemia mitigation system (HMS) to avoid epidemics of hyperglycemia.

Description

[Technical Field]

[0001] This international application claims priority to and the benefit of U.S. Provisional Application No. 63 / 091,646, filed October 14, 2020, the entire contents of which are incorporated herein by reference.

[0002] The disclosed embodiments relate to providing improved glycemic control for individuals with type 1 diabetes mellitus (T1DM; herein T1D), and more particularly to such improvement that may be implemented in accordance with closed-loop control (CLC) such that prevention of glycemic disturbances due to missing meal notifications can be fully automated. [Background technology]

[0003] T1D is a lifelong, chronic metabolic disease that imposes a significant economic, physical, social and emotional burden on both those living with the condition and their caregivers. 1,2 This autoimmune condition causes absolute insulin deficiency and a lifelong need for exogenous insulin to regulate blood glucose levels. 3 Intensive insulin therapy (IIT) has been shown to be effective in reducing mean blood glucose, usually assessed by hemoglobin A1c (HbA1c), the likelihood of further chronic complications, and comorbidities. 4,5 However, IIT often leads to prolonged periods of hypoglycemia (low blood sugar levels), which can be associated with serious complications and even death. 6 Conversely, systematic exposure to hyperglycemia (high blood sugar levels) has serious short- and long-term consequences for both health and life expectancy. 7 CLC, sometimes implemented in an artificial pancreas (AP), typically includes an insulin infusion pump, a continuous glucose monitor (CGM), and a control algorithm between them, providing a convenient approach to automatically titrate insulin doses, which increases the length of time at euglycemia (normal blood glucose levels) while significantly reducing the physical and mental burden typically associated with T1D. 8 .

[0004] Over the past decade, there has been a dramatic increase in both in silico and in vivo studies involving different types of APs. 9 This increase can be attributed to advances in computer simulation, CGM, insulin pumps, and mobile platforms. 10 This time, in this field, clinical bench 11 From the United States, specifically the Medtronic Minimed 670G 12 and Tandem Control-IQ 13,14 Two commercial hybrid APs are seeing rapid transition into clinical practice. APs are characterized as "hybrid" if they automatically adjust insulin pump infusions but are not designed to fully replace carbohydrate coverage (i.e., the amount of insulin taken with a meal to offset the glycemic effect of ingested carbohydrates). Given the mismatch between meal absorption and subcutaneous (sc) insulin time constants, mealtime insulin boluses must be manually requested 10–15 min before the start of the meal and must be proportional to an a priori estimate of meal size (often given by meal size or carbohydrate content). In contrast, fully automated APs extend the functionality of hybrid APSs by automatically blocking large disturbances in blood glucose levels due to meals and physical activity.

[0005] In these respects, diet remains a barrier to AP due to its significant impact on blood glucose homeostasis. 15 Not only different carbohydrate content but also the overall macronutrient composition of a meal can lead to diverse glycemic responses, even in optimal preprandial conditions. Excellent postprandial results have been reported with hybrid AP when there is little or no uncertainty in the parameters used to calculate the insulin bolus during a meal (e.g., carbohydrate content, insulin-to-carbohydrate ratio (CR), and correction factor (CF)). 13,16,17However, relatively high uncertainty in these parameters appears to be the rule rather than the exception, posing a risk to CLC performance and overall glycemic control. 18 Previous contributions considering unannounced food intake with fully automated designs can be found from an engineering perspective (in silico)

[19] –

[24] and a clinical perspective (in vivo)

[25] –

[30] . In silico studies reported mean percent time in range (TIR) ​​[70–180] mg / dL ranging from 70.4 to 90.0% and percent time <70 mg / dL ranging from 0.0 to 4.04%, while clinical studies reported the same criteria ranging from 63.6 to 84.7% and 0.1 to 2.9%, respectively.

[0006] However, while APs implementing CLC were very good at controlling glycemia in the absence of glycemic fluctuations during meals (e.g., overnight), they had difficulty preventing prolonged hyperglycemia after unannounced, unbolused carbohydrate ingestion. 55 This is partly because CLC systems experience an inherent delay in the CGM sensing both the rise in blood glucose levels during a meal and the onset of action after insulin is infused, and because CLC must also avoid hypoglycemia due to excessive insulin administration. Due to these considerations, all CLC systems currently on the market are in effect hybrid closed-loop (HCL) systems, requiring the user to input the amount of carbohydrates ingested and receive CR during meals to avoid significant hyperglycemia. 56~60 .

[0007] Unfortunately, skipping a carbohydrate bolus is common among people with T1D, affecting 65% of adolescents at least once a week. 52 , 38% lost at least 15% of the bolus 53 Adolescents who missed boluses four times a week experienced a 1% increase in HbA1c. 52 , which may explain why many adolescents do not meet recommended HbA1c levels. 54 .

[0008] It would therefore be desirable to provide an optimization of the CLC that automates bolus administration as needed, in a manner that automatically adjusts insulin delivery to prevent or attenuate glycemic disturbances that tend toward hyperglycemia while reducing the likelihood of hypoglycemia.

[0009] In this regard, results from embodiments herein demonstrate the effectiveness of the established hybrid CLC controller, USS Virginia 32 The results were compared with those obtained according to the method described above.

[0010] In particular, such analyses and results are based on an adult cohort of 100 subjects in the FDA-approved UVA / Padova simulator associated with various metabolic responses and population characteristics. 33 . Summary of the Invention

[0011] It is to be understood that the following summary and detailed description are exemplary and explanatory and are intended to provide further explanation of the presently claimed embodiments. Neither the following summary nor the description is intended to define or limit the scope of the present embodiments to the specific features set forth in the summary or description. Rather, the scope of the present embodiments is defined by the appended claims.

[0012] An embodiment may include a processor-implemented method for regulating blood glucose in a subject with type 1 diabetes (T1D) in an artificial pancreas (AP), the method including: predicting a blood glucose level for the subject based on the subject's continuous blood glucose monitor (CGM) measurements; determining a schedule of basal insulin administration according to the predicted blood glucose level; modifying the schedule according to detecting, from the CGM measurements, a predetermined value of one or more CGM measurements and an increasing rate of change of the CGM measurements, defining a modified schedule according to the modifications; and providing the schedule or modified schedule to the subject. Embodiments may further include calculating a probability that a blood glucose disturbance not notified to the AP occurred within a predetermined period of time, and supplementing the delivery of the schedule or modified schedule with automatic delivery of a first bolus of insulin in response to the calculated probability.

[0013] The schedule and modified schedule may each minimize a cost function that includes (a) a term that corrects the subject's blood glucose level(s) to a predetermined target level, (b) a term that penalizes predicted blood glucose values ​​that trend toward hypoglycemia, and (c) a term that weights the difference between predictions of two successive basal insulin doses.

[0014] A glycemic disturbance may be defined by at least one cause of glycemic fluctuation that is not explained by the predicted blood glucose levels on which the schedule and modified schedule are based, and the calculated probability may be based on CGM measurements over a predetermined period of time.

[0015] The calculated probability may be calculated for each successive interval of CGM measurements, each interval falling within a predetermined period of time.

[0016] The first bolus may be defined by a predetermined percentage of the subject's total daily insulin (TDI).

[0017] As the calculated probability increases, the predetermined percentage may increase.

[0018] For a series of first boluses, each subsequent bolus may be reduced by an amount of insulin on board (IOB) equal to the sum of each of the preceding first boluses.

[0019] Based on predicted blood glucose levels indicative of hypoglycemia, embodiments may include automatically reducing basal insulin dosage to a fraction of its average.

[0020] Further aspects may include automatically supplementing the delivery of the schedule or modified schedule with delivery of a second bolus of insulin (a) in response to a current estimated blood glucose level and a predicted blood glucose level indicative of hyperglycemia, and (b) a predetermined time after delivery of the first bolus.

[0021] The delivery of a second bolus may be prohibited within two hours after the delivery of the first bolus.

[0022] The frequency of delivery of the second bolus may be limited to once per hour.

[0023] Further embodiments may include pausing the automatic delivery of the first bolus in response to a meal notification to the AP and supplementing the delivery of the schedule or modified schedule with the delivery of a third bolus calculated as up to half the bolus based on the subject's insulin-to-carbohydrate ratio (CR) and correction factor (CF).

[0024] Each embodiment may further include associated systems and computer-readable media corresponding to the embodied methods described above.

[0025] In particular embodiments, the disclosed embodiments may include one or more of the features described herein.

[0026] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate exemplary embodiments and, together with the description, further serve to enable one of ordinary skill in the art to make and use these and other embodiments that will be apparent to those skilled in the art. The embodiments herein are more particularly described in conjunction with the following drawings: [Brief explanation of the drawings]

[0027] [Figure 1] FIG. 1 illustrates a closed-loop control (CLC) system implementing model predictive control (MPC) according to embodiments herein. [Figure 2A] FIG. 2 shows the tuning / detuning plane of the MPC of FIG. 1. [Figure 2B] 2B illustrates the MPC tuning / detuning rules of FIG. 1 and the tuning / detuning plane of FIG. 2A according to embodiments herein. [Figure 3] FIG. 1 shows a relative comparison of the amount of hypoglycemic events induced by total daily insulin (TDI) versus the probability of glycemic disturbance. [Figure 4A] 1 is a histogram of root mean square error (RMSE) for the identification and validation datasets across study subjects associated with the in silico study of CLC described herein. [Figure 4B] 1 is a histogram of root mean square error (RMSE) for the identification and validation datasets across study subjects associated with the in silico study of CLC described herein. [Figure 5] FIG. 1 shows an example of the performance of the Bolus Priming System (BPS) provided by the MPC described herein for a representative subject in an in silico study conducted. [Figure 6] FIG. 1 shows the timing distribution of continuous infusion of unannounced meals across the cohorts of the in silico study. [Figure 7] FIG. 1 shows the change in probability of glycemic disturbance upon administration of different meals to the entire cohort of the in silico study. [Figure 8] FIG. 1 shows the timeline of in silico studies. [Figure 9A] FIG. 1 shows blood glucose measurements across the cohort of the in silico study versus the insulin provided. [Figure 9B] FIG. 1 shows blood glucose measurements across the cohort of the in silico study versus the insulin provided. [Figure 10A] FIG. 10 shows error plots demonstrating time in range (TIR) ​​and time out of range over 6 hours after various meal sizes for an MPC according to embodiments herein versus a legacy control. [Figure 10B] FIG. 10 shows error plots demonstrating out-of-range time over 6 hours after various meal sizes for MPC according to embodiments herein versus legacy control. [Figure 11] FIG. 2 illustrates an exemplary configuration of a CLC according to an embodiment of the present specification. [Figure 12A] FIG. 1 illustrates an exemplary computing device that may implement one or more portions of the CLC of embodiments herein. [Figure 12B] FIG. 1 illustrates a network system that may implement and / or be used in implementing one or more portions of the CLC of the embodiments herein. [Figure 13] FIG. 1 is a block diagram that may implement and / or be used in implementing one or more portions of the CLC herein in connection with a connection to the Internet. [Figure 14] FIG. 1 illustrates a system that may implement and / or be used in implementing one or more portions of the CLC herein, according to one or more of a clinical set and a connection to the Internet. [Figure 15] FIG. 1 illustrates an exemplary architecture embodying one or more portions of the CLC of the present specification. DETAILED DESCRIPTION OF THE INVENTION

[0028] The present disclosure will now be described in terms of various exemplary embodiments. This specification discloses one or more embodiments that incorporate features of the present embodiments. References to a described embodiment(s), and to "one embodiment," "an embodiment," "an exemplary embodiment," etc. in the specification indicate that the described embodiment(s) may include a particular feature, structure, or characteristic. Such phrases do not necessarily refer to the same embodiment. Those skilled in the art will understand that a particular feature, structure, or characteristic described in connection with one embodiment is not necessarily limited to that embodiment, but typically has relevance and applicability to one or more other embodiments.

[0029] In some figures, similar reference numerals may be used for similar elements having similar functions, even in different drawings. The described embodiments and their detailed configurations and elements are provided merely to facilitate a comprehensive understanding of the present embodiments. Therefore, it is apparent that the present embodiments may be implemented in various ways and do not require any of the specific features described herein. Also, known functions or configurations have not been described in detail to avoid obscuring the present embodiments in unnecessary detail.

[0030] The description is not to be taken in a limiting sense, but is made merely for the purpose of illustrating the general principles of the present embodiments, since the scope of the present embodiments is best defined by the appended claims.

[0031] It should also be noted that in some alternative implementations, blocks in flowcharts, communications in sequence diagrams, states in state diagrams, etc. may occur out of the order shown. That is, the order of the illustrated blocks / communications / states is not intended to be limiting. Rather, the illustrated blocks / communications / states may be rearranged in any suitable order, and some of the blocks / communications / states may occur simultaneously.

[0032] All definitions and uses herein should be understood to control for dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0033] The indefinite articles "a" and "an," as used in the specification and claims, unless expressly indicated otherwise, should be understood to mean "at least one."

[0034] The term "and / or," as used in the specification and claims, should be understood to mean "either or both" of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with "and / or" should be construed in the same manner, i.e., "one or more" of the elements so conjoined. Other elements other than the elements specifically identified by the "and / or" phrase may optionally be present, whether related to the elements specifically identified or not. Thus, as a non-limiting example, a reference to "A and / or B," when used in conjunction with open-ended language such as "comprising," may, in one embodiment, refer only to A (optionally including elements other than B); in another embodiment, refer only to B (optionally including elements other than A); in yet another embodiment, refer to both A and B (optionally including other elements); and so forth.

[0035] As used herein and in the 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" shall be construed to be inclusive, i.e., to include not only at least one but also a plurality of, and optionally, additional unlisted items, of a number or list of elements. Only terms clearly indicated otherwise, such as "only one of," or "exactly one of," or, when used in the claims, "consisting of," shall refer to the inclusion of exactly one element of a number or list of elements. In general, the term "or" as used herein shall only be construed as indicating exclusive alternatives (i.e., "one or the other, but not both") when preceded by terms of exclusivity, such as "either," "one," "only one," or "exactly one" of "essentially consisting of," and when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0036] As used in this specification and claims, the phrase "at least one," in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in that list of elements, but need not include at least one of each and every element specifically listed in the list of elements, and does not exclude any combination of elements in the list of elements. This definition also allows for the optional presence of elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, whether related or unrelated to the specifically identified elements. Thus, as a non-limiting example, "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") can refer in one embodiment to at least one that includes two or more As, optionally with no B present (and optionally including elements other than B); in another embodiment to at least one that includes two or more Bs, optionally with no A present (and further optionally including elements other than A); in yet another embodiment to at least one that optionally includes two or more As and optionally includes two or more Bs (and optionally includes other elements); etc.

[0037] As in the foregoing specification, in the claims, all transitional phrases such as "comprise," "include," "carry," "have," "contain," "involve," "hold," "consist," and the like, are to be understood to mean open-ended, i.e., inclusive without limitation. Only the transitional phrases "consisting of" and "consisting essentially of" are closed or conditionally closed transitional phrases, respectively, as set forth in the U.S. Patent Office Manual of Patent Examining Procedures, Section 2111.03.

[0038] Although terms such as first, second, etc. may be used herein to describe various elements, it should be understood that these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of the exemplary embodiments. The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" should not necessarily be construed as preferred or advantageous over other embodiments. Additionally, all embodiments described herein should be considered exemplary unless otherwise stated.

[0039] It should be appreciated that any of the components or modules referred to in connection with any of the embodiments discussed herein may be formed integrally or separately from one another. Furthermore, redundant functions or structures of components or modules may be implemented. Furthermore, the various components may communicate locally and / or remotely with any user / clinician / patient or machine / system / computer / processor. Furthermore, the various components may communicate wirelessly and / or via hardwire, or other desired available communication means, systems, and hardware. Furthermore, the various components and modules may be replaced with other modules or components providing similar functionality.

[0040] It should be appreciated that the devices and associated components discussed herein can take on all shapes along a continuous geometric spectrum of x, y, and z plane manipulation to accommodate and meet anatomical, environmental, and structural demands and operational requirements. Furthermore, the location and arrangement of the various components can be altered as desired or needed.

[0041] It should be recognized that the various sizes, dimensions, contours, stiffness, shape, flexibility, and materials of any of the components or portions of components in the various embodiments discussed throughout may be modified and utilized as desired or needed.

[0042] Although some dimensions are shown in the foregoing figures, it should be recognized that the device may be constructed with a variety of sizes, dimensions, contours, stiffness, shapes, flexibility, and materials as they relate to components or portions of components of the device, and may be modified and utilized as desired or needed.

[0043] While exemplary embodiments of the present disclosure have been described in detail in some instances herein, it should be understood that other embodiments are contemplated. Accordingly, the present disclosure is not intended to be limited in scope to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or carried out in various ways.

[0044] Ranges may be expressed herein as from "about" or "approximately" one particular value and / or to "about" or "approximately" another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and / or to the other particular value.

[0045] In describing exemplary embodiments, technical terms are used for the sake of clarity. Each term is intended to have the broadest meaning as understood by one of ordinary skill in the art and is intended to include all technical equivalents that operate in a similar manner to accomplish a similar purpose. It should also be understood that the reference to one or more steps of a method does not exclude the presence of additional or intervening method steps between those explicitly identified steps. Method steps may be performed in a different order than described herein without departing from the scope of the present disclosure. Similarly, it should also be understood that the reference to one or more components in a device or system does not exclude the presence of additional or intervening components between the explicitly identified components.

[0046] Several references, which may include various patents, patent applications, and publications, are cited in the reference list and discussed in the disclosure provided herein. Citation and / or discussion of such references is provided merely to clarify the explanation of the present disclosure and is not an admission that such references are "prior art" to any aspect of the present disclosure described herein. For purposes of notation, "[n]" corresponds to the nth reference in the list. All references cited and discussed herein are incorporated by reference in their entirety and to the same extent as if each reference was individually incorporated by reference.

[0047] As used herein, the term "about" means approximately, within the region, roughly, or around. When used in conjunction with a numerical range, the term "about" modifies that range by extending the boundaries above and below the stated numerical values. In general, the term "about" is used herein to modify a numerical value above and below the stated value by a variance of 10%. In one embodiment, the term "about" means plus or minus 10% of the numerical value of the number with which it is used. Thus, about 50% means a range of 45% to 55%. Numerical ranges recited herein by endpoints include all numbers and fractions subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, 4.24, and 5). Similarly, numerical ranges recited herein by endpoints include subranges subsumed within that range (e.g., 1 to 5 includes 1 to 1.5, 1.5 to 2, 2 to 2.75, 2.75 to 3, 3 to 3.90, 3.90 to 4, 4 to 4.24, 4.24 to 5, 2 to 5, 3 to 5, 1 to 4, and 2 to 4). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term "about."

[0048] In accordance with the above goals and advantages of achieving and maintaining optimal TIR, inventors at the University of Virginia (UVA) present a CLC system called the Reactive Optical Carbohydrates Kinetics Estimation AP, or ROCKET AP (hereinafter "Rocket AP"). In doing so, the Rocket AP is capable of, for example, controlling an insulin infusion pump 22 (e.g., Tandem t:slim X2 TM ) and a continuous glucose monitor (CGM) 24 (e.g., Dexcom G6 TMIt is contemplated that the Diabetes Assistant (DiA) may include a model predictive control (MPC) implemented in DiA format 20 provided by a smartphone or other receiving and / or computing platform configured to enable communication between the Diabetes Assistant (DiA) (see FIG. 1 ) and the Diabetes Assistant (DiA). Accordingly, the DiA may define a general control paradigm, which may be referred to herein as a “controller” tasked with continuously predicting future blood glucose values ​​and calculating optimal insulin doses to maintain an individual’s target blood glucose level. Accordingly, as illustrated with reference to FIG. 1 , the controller may include and be defined by one or more modules providing at least the components and functional flow depicted in the shaded portions thereof, specifically the MPC, the Kalman filter, the bolus priming system (BPS), and the integrated safety system (USS), which is comprised of a safety system module (SSM) and a hyperglycemia mitigation system (HMS). Additionally, the controller may include and implement an insulin onboard supervisor (IOBSUP) and a power brake (PB) module as part of the USS. Each aspect of the controller is discussed in detail in the following discussion. The controller can be configured to either (a) be fully automated to provide a full CLC (FCLC) by automatically blocking the glycemic effect caused by carbohydrates in an unannounced meal, and / or (b) operate as a hybrid CLC system in which a meal bolus is provided in response to a meal annunciation. m ,y' m , and IOB represent the current CGM measurement, its time derivative, and insulin on board, respectively.

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[0049] In particular, the controller omits the oral submodel and considers subcutaneous insulin delivery infusion as a triangular submodel. Gracia-Tirado et al. 34 It can be configured to predict blood glucose levels and calculate insulin doses based on variations of the model presented by [Table 1]. As shown in Table 1 below, in relation to the parameters and population values, the predictive model implemented by the controller can be given as follows (according to Equations 1-6):

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[0050] x k+1 =Ax k +B I u k +B d d k (7) y k =Cx k (8) where:

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[0051] To estimate the current state and a disturbance d, e.g., an unannounced meal, we assume that d is a disturbance that includes unmodeled phenomena that directly affect blood glucose dynamics. 37 For this purpose, we can rewrite equation (7) as follows: k+1 =d k Increment by

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[0052] where:

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[0053] Modeling the disturbances as constant dynamics allows the state estimator, embodied by the Kalman filter in Figure 1, to correct for disturbances that enter the main dynamics.

[0054] The model was matched to a specific subject, as illustrated in Figure 1. m ,y' m ,(i.e., current CGM measurement and its time derivative),IOB,

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[0055] 1) MPC Based on the individualization and any detected disturbances d, the MPC commands the controller to provide a basal insulin dose (i.e., a microbolus) every 5 minutes, and does so by adjusting the aggressiveness or amount of such dose as a function of the rate of blood glucose change. In this way, the MPC is optimized for a predetermined fixed target blood glucose level, e.g., 120 mg / dL, as discussed below, and can substantially avoid hypoglycemic and hyperglycemic events. The MPC may be embodied according to the following: 39,40 .

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[0056] The cost function in (12a) is

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[0057] Therefore, each microbolus is a term that corrects the individual's blood glucose concentration to the target value.

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[0058] Optimizing the λ1 tuning / detuning strategy allows for a more aggressive controller response when the CGM change rate is high and blood glucose (BG) levels are high, as shown in equation (15) below, where y m and y' m denote the current CGM value and the first derivative of the CGN trace at the current sampling time, respectively, and λ 1,nom =5 / u b represents the nominal value of λ1, and st + =5 and st - = -5 represent the positive and negative gradient thresholds, respectively, and m1 = -0.8 and m2 = 0.8 represent the tuning / detuning gradients. Thus, λ1 represents the momentum order of deviation (from the scheduled basal infusion) depending on the rate of change of CGM level and high BG level. As shown with reference to Figures 2A and 2B, λ1 and tuning / detuning Q in Figure 2A z The design of the IOB (Instruction on Board) (preceded format in Figure 2B) 31 A graphical overview (compared to that of st ± and m 1,2 is heuristically discovered based on physiological knowledge, and min , and λ 1,nom was found by grid search and the UVA / Padova simulator 44 The best controller performance was obtained in terms of percentage of TIR, time < 70 mg / dL, and time > 180 mg / dL for all adult cohorts. min Defining u allows for more aggressive controller action for an extended period immediately following the onset of disturbance d, and the increased controller action corresponds to a modified schedule of basal insulin infusions for the subject (i.e., u in Figure 1). mpc (changes in

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[0059] Finally, the reference trajectory is determined by Gracia-Tirado et al. 31 was defined as follows:

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[0060] 2) BPS As discussed above, disruptions to efforts to maintain euglycemia caused by carbohydrate content and the composition of uninformed meals pose significant challenges in diabetes care and treatment. This is particularly true because conventional CLC systems have an inherent delay in CGM sensing of elevated blood glucose levels during a meal and in the initiation of insulin action following an infusion. Therefore, it has been recognized that such CLC systems, according to the aforementioned schedule or modified schedule for basal infusion, must counteract the effects of such uninformed meals before providing corrective action via a microbolus. Without such measures, individuals with T1D are prone to experiencing significant levels of hyperglycemia.

[0061] Accordingly, a bolus priming system, or BPS, is presented herein as a module configured to, in conjunction with the MPC discussed above, cause the accurate and automatic injection of relatively large amounts of insulin calculated as a percentage of total daily insulin (TDI). More specifically, such percentage may increase over time according to a calculated estimate of the probability that a major glycemic disturbance has occurred, which comprises and / or explains at least one cause of glycemic fluctuations that is unexplained by the predicted blood glucose levels on which the schedule and modified schedule are based. Such a cause may comprise an unannounced meal with a significant carbohydrate composition (see discussion below regarding carbohydrate amounts). In this context, the term "unexplained" may mean substantially not fully explained, only partially explained, or unexplained. Thus, the first bolus (U in FIG. 1) of the present invention may be a bolus priming system (BPS) that is configured to cause a significant glycemic disturbance that is unexplained by the predicted blood glucose levels on which the schedule and modified schedule are based. bolus ) and the infusion generated by the BPS can immediately address what would otherwise result in a hyperglycemic event.

[0062] In particular, the BPS can be configured to operate at every 5-minute interval to retrospectively examine the probability that a disturbance, d, such as a meal, occurred within the past 30 minutes. To do so, a second-order polynomial is fitted to the last 30 minutes of CGM data, producing the following equation: y p (i)=p1i 2 +p2i+p3 where y p (i) represents the blood glucose level at i = 1,...,6, which represents the sequence of CGM data for the past 30 minutes. The coefficients p1, p2, and p3 in this equation can be used as features in a logistic regression classification algorithm. The output y log teeth,

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[0063] [Table 3] This probability can be used to determine whether a bolus is needed and, if so, how much insulin should be administered. The BPS is calculated based on the individual's TDI, P TDIA predetermined schedule is contemplated that describes what percentage of should be administered at each probability threshold. The schedule is provided below, showing increasing probability determinations from 0.3 to 0.9.

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[0064] Here, J BPS represents the amount of insulin provided by the BPS, and P TDI (%) is the percentage of the patient's TDI required based on the above dosing schedule, and IOB BPS is the amount of insulin onboard since the previous meal-related BPS administration. BPS can be found using the 6-hour IOB curve 46 J BPS The dose can be saturated at 0 to prevent the system from commanding a negative insulin dose. In this way, the BPS provides at least two safeguards that tend to avoid hypoglycemia. First, J BPS The dose is adjusted based on the previously injected priming dose. Second, a threshold, BPS_threshold, is set to allow a priming dose only if the blood glucose concentration is greater than the threshold. BPS_threshold can be determined by: t_prev_hypo:=last BG ≦ 70 mg / dL

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[0065] Both the disturbance probability threshold and the corresponding insulin dose were determined using a previously applied method to "play back" past actual data using regularized deconvolution to resolve unknown inputs to the blood glucose-insulin model. 36,47 The method determines sources of glycemic excursions not accounted for by insulin and food records and allows for the simulation of insulin dose changes and the resulting impact on blood glucose levels (simulation replay). Using data from a collection period from a previous study (ClinicalTrials.gov NCT03859401), we were able to determine the initial numerical correspondence between different probability thresholds [0.1-1.0] and different insulin boluses as percentages of TDI [3%-9%].

[0066] After each automated bolus (calculated based on typical CGM values ​​at each epoch of collected data), the blood glucose levels for the next 2 hours were simulated using the techniques described above to determine whether the bolus may have caused a hypoglycemic event (CGM < 70 mg / dL). The maximum number of hypoglycemic events was empirically set at one event per day.

[0067] Figure 3 shows the results of the regeneration experiment in which BPS was evaluated. It can be clearly seen that low probability thresholds and high TDI amounts caused the most additional hypoglycemia. As the probability threshold increased and the TDI percentage decreased, fewer hypoglycemias were observed. From the results of this study, it was determined that probability thresholds of 0.3, 0.5, 0.7, 0.8, and 0.9 were required to deliver 3%, 4%, 5%, 6%, and 9% of the TDI, respectively.

[0068] 1 can be configured to operate in a first mode, i.e., FCLC mode, such that glycemic disturbances due to uninformed meal / carbohydrate ingestion can be blocked automatically, i.e., without user intervention. As will be further understood, implementation of that blocking can result in meal intake determinations that may supersede alternative meal notifications based on retrospective examination of past CGM data.

[0069] In addition, while the controller according to FIG. 1 can be configured to operate in a first mode, as described above, operation in a second mode, i.e., hybrid CLC (HCLC) mode, is also contemplated when a standard meal notification and controller-activated bolus is in place. In such a case, a meal is notified and a meal bolus, referred to herein as a third bolus, is calculated as up to 50% of the bolus calculated from the subject's CR and CF, i.e., 1% to 50%. In this case, the controller recognizes the notification and suspends any impending or concurrent BPS bolus.

[0070] 3) USS (SSM and HMS) With continued reference to FIG. 1, the USS includes both a Safety System Module (SSM) and a Hyperglycemic Mitigation System (HMS) module to counter potential hypoglycemic and hyperglycemic events, respectively.

[0071] Specifically, SSM is total (Figure 1) and monitor hypoglycemia-related risk linked to basal controller adjustments by short-term (30-minute) blood glucose prediction and risk space transformation. 48 This module automatically saturates insulin commands to a percentage of the user's average basal rate if a risk of hypoglycemia is predicted, and passes the command through otherwise.

[0072] In this regard, the SSM may implement an Insulin On-Board Supervisor (IOBSUP) module that functions to estimate IOB at each consecutive 5-minute interval. Using this estimate, IOBSUP broadcasts the estimate to all other modules, mitigating the risk of insulin accumulation. For the following calculations, IOB was calculated based on a 4-hour operating curve derived from Swan et al. (Diabetes Care 2009). That is, insulin is considered to be depleted from an individual's system 4 hours after infusion. J (as a vector of the past 5-minute incremental insulin infusion) was corrected for basal insulin infusion (basal_hist).

number

[0073] The SSM can also instruct the controller to operate in HYPOSAFE mode to prevent significant hypoglycemic events by limiting insulin infusion to below the basal level for one hour after hypoglycemia is detected and signaled. The logic underlying this mode is shown below, where diff_rate is considered to be the differential basal rate (signed) that is being added to the basal level as a result of the controller's decision making. Read last_hypo_time from the database. IF current_time-last_hypo_time≦60 minutes IF diff_rate≧0 injection_sent_to_pump=u basal ELSE injection_sent_to_pump=u basal +diff_rate END END Here, in the else condition, diff_rate is adjusted to a negative sign which tends to keep insulin levels below basal.

[0074] The SSM may further implement a meal-informed power brake (MIPB) module, which is always active to prevent any insulin delivery requests by any other module. Specifically, the MIPB module calculates an estimate of the patient's metabolic state every five minutes using the Kalman filter discussed above. The metabolic state (a combination of the feedforward model states and the Kalman filter-estimated states) is used to generate 10-, 30-, and 60-minute blood glucose predictions based on two different assumptions: (i) no insulin delivery for the 10-minute predictions, and (ii) a basal rate delivery for the 30- and 60-minute predictions. The 10- and 60-minute predictions are broadcast to other modules, while the 30-minute prediction is used to calculate a predicted glycemic risk. Based on that predicted risk, the MIPB attenuates the basal rate to generate an insulin constraint, compares the insulin constraint to the received infusion request, and sends the minimum of the constraint or request to the infusion pump. Braking operation can be understood in relation to the following set of parameters and their implementation in the controller: [Table 4] Create a vector of estimated states.

number

number

number

number

number

number

[0075] Finally, the safety system constraints are calculated. Cons=BrakeAction·u I The final dose is calculated as follows:

number

[0076] Meanwhile, the HMS module monitors the estimated BG levels and automatically commands one or more insulin correction boluses, referred to herein as second boluses, to counteract prevailing hyperglycemia. The command frequency is saturated so that commands occur at most once per hour, and correction boluses issued by the HMS are inhibited within a predetermined time and, optionally, within two hours of the first, i.e., BPS bolus. That is, an HMS bolus is not issued within two windows after the issuance of a BPS bolus. Corrections are considered every five minutes, and G K Issued when the CGM trend (calculated as the slope coefficient of the CGM sum of squared regression) is flat or increasing (defined as >-1 mg / dL·min) with a current estimated blood glucose of >180 mg / dL. The HMS-related correction bolus is calculated using the following equation (18): HMS corr =HMS ratio HMS ini (18) The blood glucose level may optionally be calculated to correct to 110 mg / dL according to

number

number

[0077] In silico studies Results were obtained using the entire adult cohort of 100 virtual subjects on the FDA-approved UVA / Padova simulator, and demographics are summarized in Table 4 below. 33The dawn phenomenon and both individual and inter-individual variations in insulin sensitivity were included in the experimental setup. The controller in Figure 1 was tested in a full series of experiments, including stability tests for variation in meal size as well as future clinical scenarios (discussed below). Specifically, the results below include (A) model individualization, (B) BPS performance, and (C) overall controller performance for nominal clinical scenarios involving informed and uninformed meals and variation in the carbohydrate content of the uninformed dinner. USVirginia was used as the baseline controller. 32 . [Table 5]

[0078] A) Model individualization To obtain a subject-specific controller design, subjects underwent a 14-day data collection period prior to dosing. Data collection included various meals throughout the day, with varying meal sizes and times. Daily data sets were randomly split into identification (5 days) or validation (9 days).

[0079] Figures 4A and 4B show histograms of the daily identification and validation RMSE, respectively, contrasting the identified model with the population values ​​of all virtual subjects in the adult cohort. Table 5 below provides additional statistics useful for assessing the benefits of model personalization. [Table 6]

[0080] B) BPS performance Figure 5 shows the performance of the BPS for a representative subject in the simulator. After being given an 80g carbohydrate meal at 6 PM, the controller in Figure 1 automatically commanded multiple safety boluses (2.47U total vs. 6.33U total for the USS Virginia). In total, the controller delivered a 2.47U infusion for the unannounced meal, compared with the 3.16U and 6.33U infusions (for the USS Virginia) that would have been delivered for the announced meal. This can be seen particularly in Figure 5, where the Rocket AP controller accurately predicted the 6 PM meal disturbance to the CGM values ​​shown. Figure 6 shows the timing distribution of the continuous infusion of the unannounced 80g carbohydrate meal for the entire adult cohort.

[0081] In the second scenario, three meals of 50g, 50g, and 80g carbohydrates are administered to the entire adult cohort in the UVA / Padova simulator at 8:00 AM, 12:00 PM, and 6:00 PM, respectively. As shown with reference to Figure 7, the time course of disturbance probability for the three meals is shown, with the median indicated by "E," the range of 25% to 75% indicated by "F," and the range of 5% to 95% indicated by "G."

[0082] C) Clinical trial simulation The simulation protocol was designed to mimic an in vivo clinical trial (NCT04545567, ClinicalTrials.gov). Simulation results evaluated the controller's performance during experimental treatments relative to baseline treatments, assuming fully functional hardware. Participants were randomly assigned to either the Rocket AP or the USS Virginia, as shown in Figure 8, which further illustrates the study timeline. Participants engaged in real-life activities on days 2, 3, 6, and 7, during which they were randomized between two controllers (e.g., randomly assigned to the Rocket AP on days 2 and 3, the USS Virginia 2 on days 3 and 4, or vice versa). During each of the four days of the first and second administrations, participants consumed three structured meals with identical protein, fat, and carbohydrate content at 8:00 AM, 12:00 AM, and 6:00 PM each day during the study session. Carbohydrate contents of 50g, 50g, and 80g were administered for breakfast, lunch, and dinner, respectively. On days 2 and 6, all meals were reported to the controller. On the third and seventh days, breakfast and lunch were announced, but dinner was not.

[0083] Figures 9A and 9B show relative comparisons of Rocket AP® to USS Virginia (VA) for the median and 5th-95th percentile range of CGM for the entire adult cohort for the "all meals informed" and "dinner without informed" administration days, respectively (where the envelope represents the range, the solid line represents the median, and insulin is basal insulin). Tables 6 and 7 below show CGM-related criteria for both administration days, according to international standards. 44 In particular, the percent TIR, i.e., the percent time spent in euglycemia [70-180] mg / dL, the percent time in hypoglycemia (<70 mg / dL), and the percent time in hyperglycemia (>180 mg / dL), were examined. The hypoglycemic index (LBGI) and hyperglycemic index (HBGI) were calculated according to BPKovatchev. 51 . [Table 7] [Table 8] As can be seen from Figures 9A and 9B and the table above, the Rocket AP outperformed the USS Virginia after the no-report period (dinner) in terms of percent TIR and percent time >180 mg / dL.

[0084] Finally, we investigated the performance of both the Rocket AP and USS Virginia after various hypothetical unannounced meals containing various carbohydrate loads ranging from 5 g to 80 g of carbohydrate. Both systems demonstrated stability for meal intakes of less than 30 g of carbohydrate (time in percent range 100 [100-100]%) at 6 hours post-meal. Referring to Figures 10A and 10B, these show median and IQR error plots of percent TIR and percent time >180 mg / dL for the Rocket AP® and USS Virginia (VA), respectively, for carbohydrate values ​​ranging from 35 g to 80 g at 6 hours post-meal. The percent time <70 mg / dL was 0 [0-0]% for both controllers. Thus, as expected, percent TIR decreased with increasing carbohydrate content, and the percentage loss translated into percent time >180 mg / dL.

[0085] In vivo studies Regarding the clinical study reported above, which examined 18 study participants who completed the study and were aged 12-20 years, the Rocket AP controller in Figure 1 was found to perform similarly to the in silico study with respect to TIR and time percentiles <70 mg / dL when compared to the commercially available USS Virginia. This was the case for unannounced dinner glycemic results and overall results.

[0086] For the unannounced dinner, the 6-hour TIR (primary outcome) after the unannounced dinner was significantly higher with the Rocket AP Controller RCKT than with the USS Virginia (83% [64-93] vs. 53% [40-71]; p=0.004). See Table 8 below. The time in the tight range (TTR), i.e., 80-140 mg / dL, was also higher (49% [41-59] vs. 27% [22-36]; p=0.002). The mean CGM and time percentile >180 mg / dL were significantly lower with the Rocket AP Controller (141±21 mg / dL vs. 166±26 mg / dL; p=0.001) and (17% [1.3-34] vs. 47% [28-60]; p=0.01), respectively (see Table 1). Glycemic improvement was prolonged for the Rocket AP controller for 12 hours after dinner without reminder. With regard to dinner reminder, the Rocket AP controller further outperformed the USS Virginia for these study participants, providing a point of difference from the in silico study discussed herein. [Table 9]

[0087] Overall control by approximately 46 hours revealed that Rocket AP achieved higher TTR and HR (72.3% ± 7.9 vs. 63.7% ± 13; p = 0.01; and 87% ± 6.6 vs. 80% ± 9.6; p = 0.007, respectively) and lower mean BG and hourly percentiles > 180 mg / dL (122 ± 7.5 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 per USS Virginia showed increased TIR in 15 / 18 participants compared to 17 / 18 participants per Rocket AP. [Table 10]

[0088] As shown in Table 10 below, the Rocket AP Controller achieved significantly tighter control overnight compared to the USS Virginia, not only in TTR (95.3 [90.4-100]% vs. 76.3 [58.5-87.4]%, p<0.001), but also in 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). [Table 11]

[0089] Compared to the in silico studies featured herein, which exemplify the performance of Rocket AP in adult cohorts, the above in vivo results further strengthen the integrity of such performance, as they focused on an adolescent population notoriously poor at reporting food intake. 52~54 .

[0090] In view of the above, it is recognized that a dual-mode CLC system has been disclosed that integrates each of (i) an adaptive personalized MPC control law that adjusts the control intensity of insulin infusion depending on recent past control actions, blood glucose measurements, and their derivative(s); (ii) an automated BPS that safely commands additional insulin infusion upon detection of a valid metabolic state (e.g., unannounced meal); and (iii) an HMS to avoid widespread hyperglycemia.

[0091] As shown with reference to FIG. 11 , a processor or controller 102, e.g., as embodied by DiA, communicates with a blood glucose monitor or device 101 and, optionally, an insulin device 100. The processor or controller 102, e.g., as embodied by DiA, may be configured to include all necessary hardware and / or software necessary to execute any and all instructions, or portions thereof, necessary to accomplish the aforementioned tasks discussed herein, such as, e.g., bolus calculation. The blood glucose monitor or device 101 communicates with a subject 103 to monitor the blood glucose level of the subject 103. The processor or controller 102 is configured to perform the necessary calculations. Optionally, the insulin device 100 communicates with the subject 103 to provide insulin to the subject 103. The processor or controller 102 is configured to perform the necessary calculations. The blood glucose monitor 101 and the insulin device 100 may be implemented as separate devices or as a single device. The processor 102 may be implemented locally in the blood glucose monitor 101, the insulin device 100, or a standalone device (or any combination of two or more of the blood glucose monitor, insulin device, or standalone device). The processor 102 or parts of the system may be located remotely such that the device is operated as a telemedicine device.

[0092] 12A, in its most basic configuration, a computing device 144, optionally implementing DiA, typically includes at least one processing unit 150 and memory 146. Depending on the exact configuration and type of computing device, memory 146 may be volatile (such as RAM), non-volatile (such as ROM, flash memory), or some combination of the two.

[0093] In addition, device 144 may have other features and / or functionality. For example, the device may also include additional removable and / or non-removable storage, including, but not limited to, magnetic or optical disks or tape, and writable electronic storage media. Such additional storage may be in the form of removable storage 152 and non-removable storage 148. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Memory, removable storage, and non-removable storage are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and that can be accessed by the device. Any such computer storage media may be part of or used in combination with the device.

[0094] A device may also contain one or more communications connections 154 that allow the device to communicate with other devices (e.g., other computing devices). Communications connections carry information on a communications medium. Communications media typically embodi computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information-delivery media. The term "modulated data signal" means a signal that has one or more characteristics set or changed in such a manner as to encode, execute, or process information in the signal. By way of example, and not limitation, communications media include wired media such as a wired network or direct-wired connection, and wireless media such as wireless, RF, infrared, and other wireless media. As discussed above, the term computer-readable media as used herein includes both storage media and communications media.

[0095] As shown with reference to FIG. 12B , embodiments herein may be implemented in a network system comprising multiple computing devices in communication with a networking means, such as a network with infrastructure or an ad-hoc network. The network connection may be a wired or wireless connection. In this example, the network system comprises a computer 156 (e.g., a network server), a network connection means 158 (e.g., a 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 cell phone, laptop computer, tablet computer, GPS receiver, mp3 player, handheld video player, pocket projector, or a combination of such functions). It should be appreciated that in embodiments, the module listed as 156 may be a blood glucose monitor device. It should be appreciated that in embodiments, the module listed as 156 may be a blood glucose monitor device, an artificial pancreas, and / or an insulin device (or other interventional or diagnostic device). Any of the components shown in or discussed with FIG. 12B may be present in multiple locations. Embodiments herein may be implemented in any of the devices of the system. For example, execution of instructions or other desired processing may be performed on the same computing device, which may be any of 156, 160, and 162. Alternatively, embodiments may be performed on different computing devices of the network system. For example, certain desired or necessary processing or execution may be performed on one of the computing devices of the network (e.g., server 156 and / or a blood glucose monitor device), while other processing and execution of instructions may be performed on another computing device of the network system (e.g., terminal 160 of the network system), or vice versa.Indeed, certain processes or executions may be performed on one computing device (e.g., server 156 and / or insulin device, AP, or blood glucose monitor device (or other intervention or diagnostic device)), while other processes or executions of instructions may be performed on a different computing device, which may or may not be networked. For example, certain processes may be performed on terminal 160, while other processes or instructions are passed to device 162, where the instructions are executed. This scenario may be particularly valuable when a PDA 162 device accesses a network, for example, via computer terminal 160 (or an access point in an ad-hoc network). As another example, protected software may be executed, encoded, or processed in accordance with one or more embodiments herein. The processed, encoded, or executed software may have been distributed to a customer. Distribution may be in the form of a storage medium (e.g., disk) or an electronic copy.

[0096] Referring to FIG. 13 , a block diagram illustrating a system 130 including a computer system 140 and associated Internet 11 connection is shown, in which embodiments may be implemented. Such a configuration is typically used for computers (hosts) connected to the Internet 11 and running server or client (or combination) software. For example, a source computer such as a laptop, a final destination computer, and an intermediary server, as well as any computer or processor described herein, may use the computer system configuration and Internet connection shown in FIG. 13 . System 140 may be used as a portable electronic device, such as a notebook / laptop computer, a media player (e.g., MP3-based or video player), a cellular phone, a personal digital assistant (PDA), a blood glucose monitoring device, an artificial pancreas, an insulin delivery device (or other interventional or diagnostic device), an imaging device (e.g., a digital camera or video recorder), and / or any other handheld computing device, or any combination of these devices. While FIG. 13 illustrates various components of a computer system, it is not intended to represent any particular architecture or manner of interconnecting the components, and such details are not germane to the embodiments herein. It is also recognized that network computers, handheld computers, cell phones, and other data processing systems having fewer or perhaps more components may also be used. The computer system of Figure 13 may be, for example, an Apple Macintosh computer or Power Book, or an IBM-compatible PC. Computer system 140 includes a bus 137, interconnect, or other communication mechanism for communicating information, and a processor 138, typically in the form of an integrated circuit, coupled to bus 137 for processing information and executing computer-executable instructions. Computer system 140 also includes a main memory 134, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 137 for storing information and instructions executed by processor 138.

[0097] Main memory 134 may also be used for storing temporary variables or other intermediate information during execution of instructions by processor 138. Computer system 140 further includes a read-only memory (ROM) 136 (or other non-volatile memory) or other static storage device coupled to bus 137 for storing static information and instructions for processor 138. Storage devices 135, such as magnetic or optical disks, hard disk drives for reading from and writing to hard disks, magnetic disk drives for reading from and writing to magnetic disks, and / or optical disk drives for reading from and writing to removable optical disks (such as DVDs), are coupled to bus 137 for storing information and instructions. The hard disk drives, magnetic disk drives, and optical disk drives may be connected to the system bus by hard disk drive interfaces, magnetic disk drive interfaces, and optical disk drive interfaces, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the general-purpose computing device. Typically, computer system 140 includes an operating system (OS) stored in non-volatile storage to manage computer resources and provide access to computer resources and interfaces to applications and programs. The operating system typically handles system data and user input and responds by allocating and managing tasks and internal system resources, such as controlling and allocating memory, prioritizing system requests, controlling input / output devices, facilitating networking, and managing files. Non-limiting examples of operating systems are Microsoft Windows®, Mac OS X, and Linux®.

[0098] The term "processor" is meant to include any integrated circuit or other electronic device (or collection of devices) capable of performing operations with at least one instruction, including, but not limited to, reduced instruction set core (RISC) processors, CISC microprocessors, microcontroller units (MCUs), CISC-based central processing units (CPUs), and digital signal processors (DSPs). The hardware of such devices may be integrated on a single substrate (e.g., a silicon "die") or distributed across two or more substrates. Furthermore, various functional aspects of a processor may be implemented solely as software or firmware associated with the processor.

[0099] The computer system 140 may be coupled via bus 137 to a display 131, such as a cathode ray tube (CRT), liquid crystal display (LCD), flat screen monitor, touch screen monitor, or similar means for displaying text and graphical data to a user. The display may be connected via a video adapter that supports the display. The display allows a user to view, input, and / or edit information related to the operation of the system. An input device 132, including alphanumeric and other keys, is coupled to bus 137 for communicating information and command selections to the processor 138. Another type of user input device is a cursor control 133, such as a mouse, trackball, or cursor direction keys, for communicating directional information and command selections to the processor 138 and for controlling cursor movement on the display 131. This input device typically has two degrees of freedom in 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.

[0100] A computer system 140 may be used to implement the methods and techniques described herein. According to one embodiment, these methods and techniques are performed by the computer system 140 in response to the processor 138 executing one or more sequences of one or more instructions contained in the main memory 134. Such instructions may be read into the main memory 134 from another computer-readable medium, such as the storage device 135. Execution of the sequences of instructions contained in the main memory 134 causes the processor 138 to perform the process steps described herein. In alternative embodiments, hardwired circuitry may be used in place of or in combination with software instructions to implement the configurations. Thus, embodiments herein are not limited to any specific combination of hardware circuitry and software.

[0101] As used herein, the term "computer-readable medium" (or "machine-readable medium") is an expansive term that refers to any medium or any memory that participates in providing instructions to a processor (such as processor 138) for execution, or any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). Such media can store computer-executable instructions executed by processing elements and / or control logic, and data operated on by processing elements and / or control logic, and can take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 137. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave or infrared data communications, or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer-readable media include, for example, a floppy disk, flexible disk, hard disk, magnetic tape or any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with a pattern of holes, RAM, PROM, and EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described below, or any other medium from which a computer can read.

[0102] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 138 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer may load the instructions into a dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 140 may receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector can receive the data carried in the infrared signal and appropriate circuitry can place the data on bus 137. Bus 137 carries 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 either before or after execution by processor 138.

[0103] Computer system 140 also includes a communication interface 141 coupled to bus 137. The communication interface 141 provides a two-way data communication coupling to a network link 139 that is connected to local network 111. For example, communication interface 141 may be an Integrated Services Digital Network (ISDN) card or a modem to provide a data communication connection 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 a data communication connection to a compatible LAN. For example, Ethernet-based connections based on the IEEE 802.3 standard may be used, such as 10 / 100Base-T, 1000Base-T (Gigabit Ethernet), 10 Gigabit Ethernet (10GE or 10GbE or 10GigE as defined by IEEE Standard 802.3ae-2002), 40 Gigabit Ethernet (40GbE), or 100 Gigabit Ethernet (100GbE as defined by Ethernet Standard IEEE P802.3ba), as described in "Internetworking Technologies Handbook," Publication No. 1-587005-001-3 (6 / 99), Cisco Systems, Inc., Chapter 7: "Ethernet Technologies," pages 7-1 to 7-38, which is incorporated in its entirety for all purposes as if fully set forth herein. In such cases, communications interface 141 typically includes a LAN transceiver or modem, such as the Standard Microsystems, Inc. (SMSC) LAN91C111 10 / 100 Ethernet transceiver described in Standard Microsystems, Inc. (SMSC) Datasheet "LAN91C111 10 / 100 Non-PCI Ethernet Single Chip MAC+PHY" Datasheet Revision 15 (02-20-04), which is incorporated in its entirety for all purposes as if fully set forth herein.

[0104] A wireless link may also be implemented. In any such implementation, communication interface 141 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0105] Network link 139 typically provides data communication through one or more networks to other data devices. For example, network link 139 may provide a connection through local network 111 to a host computer or data equipment operated by an Internet Service Provider (ISP) 142. ISP 142, in turn, provides data communication services through the worldwide packet data communication network Internet 11. Local network 111 and the Internet 11 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and through network link 139 and communication interface 141, which carry the digital data to and from computer system 140, are exemplary forms of carrier waves transporting the information.

[0106] The received code may be executed by processor 138 as it is received, and / or stored in storage device 135, or other non-volatile storage, for later execution. In this manner, computer system 140 may obtain application code in the form of a carrier wave.

[0107] The inventors have proposed a personalized artificial pancreas system concept with automated BPS and enhanced safety. As is evident from the algorithmic and methodological requirements discussed herein, the procedures can be readily applied to devices such as blood glucose devices, insulin devices, AP devices, and other interventional or diagnostic devices, and can be implemented and utilized using associated processors, networks, computer systems, the Internet, and other components and functions in accordance with the schemes disclosed herein.

[0108] As shown with reference to FIG. 14, a system is shown in which one or more embodiments herein may be implemented using a network, or a portion of a network or computer, although the blood glucose monitor, AP, or insulin device (or other intervention or diagnostic device) currently discussed may be implemented without a network.

[0109] FIG. 14 diagrammatically illustrates an exemplary system in which example embodiments herein may be implemented. In embodiments, a blood glucose monitor, AP, or insulin device (or other interventional or diagnostic device) may be implemented locally by a subject (or patient) at home or other desired location. However, in alternative embodiments, it may be implemented in a clinical or support setting. For example, as illustrated with reference to FIG. 14 , a clinical setting 158 provides a location for a physician (e.g., 164) or clinician / assistant to diagnose a patient (e.g., 159) with a blood glucose-related disorder and related diseases and conditions. The blood glucose monitor device 10 may be used as a standalone device to monitor and / or test a patient's blood glucose level. While only the blood glucose monitor device 10 is shown in the figure, it should be appreciated that the system of embodiments herein and any of its components may be used as illustrated in FIG. 14 . The system or components may be attached to or in communication with the patient, as desired or necessary. For example, a system or combination of its components, including blood glucose monitoring device 10 (or a controller, and / or other associated devices or systems, such as an artificial pancreas, an insulin pump (or other interventional or diagnostic device), or any other desired or required device or component), may contact, communicate with, or be attached to a patient via tape or tubing (or other medical equipment or component), or may communicate via a wired or wireless connection. Such monitoring and / or testing may be short-term (e.g., a clinical visit) or long-term (e.g., a clinical stay or at home). The output of the blood glucose monitoring device may be used by a physician (clinician or assistant) for appropriate action, such as injecting insulin or providing food to the patient, or other appropriate action or modeling. Alternatively, the output of the blood glucose monitoring device may be provided to a computer terminal 168 for immediate or future analysis. This provision may be via cable, wireless, or any other suitable medium. The blood glucose monitoring device output from the patient may be provided to a portable device, such as a PDA 166. The output of the blood glucose monitoring device with improved accuracy may be provided to blood glucose monitoring center 172 for processing and / or analysis.Such provisioning may be accomplished in a variety of ways, such as through a network connection 169, which may be wired or wireless.

[0110] In addition to the output of the blood glucose monitoring device, the error, accuracy improvement parameters, and any accuracy related information may be provided to a computer 168 and / or a blood glucose monitoring center 172 for performing error analysis, etc. This may provide centralized accuracy monitoring, modeling, and / or accuracy improvement of blood glucose sensors due to the importance of blood glucose sensors.

[0111] Example embodiments herein may also be implemented in a standalone computing device associated with a targeted blood glucose monitor device, an artificial pancreas, and / or an insulin device (or other interventional or diagnostic device). An exemplary computing device (or portion thereof) on which example embodiments herein may be implemented is shown schematically in FIG. 12A.

[0112] Referring now to FIG. 15, a block diagram illustrating an example machine in which one or more aspects of the embodiments herein may be implemented is shown.

[0113] FIG. 15 illustrates a block diagram of an example machine 400 capable of implementing (eg, executing) one or more embodiments (eg, the discussed methodologies).

[0114] An example of machine 400 may include logic, one or more components, circuits (e.g., modules), or mechanisms. A circuit is a tangible entity configured to perform certain operations. In an example, a circuit may be arranged in a specified manner (e.g., internally or relative to an external entity, such as another circuit). In an example, one or more computer systems (e.g., stand-alone, client, or server computer systems) or one or more hardware processors (processors) may be configured with software (e.g., instructions, application portions, or applications) as circuitry that operates to perform certain operations described herein. In an example, the software may reside (1) in a non-transitory machine-readable medium or (2) in a transmission signal. In an example, the software, when executed by the circuit's underlying hardware, causes the circuit to perform certain operations.

[0115] In examples, a circuit may be implemented mechanically or electronically. For example, a circuit may comprise dedicated circuitry or logic specifically configured to perform one or more techniques as discussed above, such as including a dedicated processor, a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In examples, a circuit may comprise programmable logic (e.g., circuitry such as that contained in a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform particular operations. It is recognized that the decision whether to implement a circuit mechanically (e.g., with dedicated, permanently configured circuitry) or with temporarily configured (e.g., configured by software) circuitry may be driven by cost and time considerations.

[0116] Thus, the term "circuit" is understood to encompass a tangible entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., transiently) configured (e.g., programmed) to operate in a specified manner or perform specified operations. In an example, given multiple temporarily configured circuits, each of the circuits need not be configured or instantiated at any given time. For example, if a circuit comprises a general-purpose processor configured via software, the general-purpose processor may be configured as each different circuit at different times. Thus, the software may, for example, configure the processor to configure a particular circuit at one time and a different circuit at another time.

[0117] In an example, a circuit can provide information to and receive information from other circuits. In this example, a circuit can be considered to be communicatively coupled to one or more other circuits. When multiple such circuits exist simultaneously, communication can be achieved by signal transmission connecting the circuits (e.g., via appropriate circuits and buses). In embodiments in which multiple circuits are configured or instantiated at different times, communication between such circuits can be achieved, for example, through the storage and retrieval of information in a memory structure to which the multiple circuits have access. For example, one circuit can perform an operation and store the output of that operation in a communicatively connected memory device. Another circuit can then later access the memory device to retrieve and process the stored output. In an example, a circuit can be configured to initiate or receive communication with an input or output device and can perform an operation on a resource (e.g., a set of information).

[0118] Various operations of the example methods described herein may 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 associated operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented circuitry that operates to perform one or more operations or functions. In examples, circuitry referred to herein may comprise processor-implemented circuitry.

[0119] Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented circuitry. Performance of a particular operation may reside on a single machine, but may also be distributed among one or more processors deployed across multiple machines. In examples, one or more processors may be located in a single location (e.g., in a home environment, an office environment, or as a server farm), while in other examples, the processors may be distributed across several locations.

[0120] The one or more processors may also operate to support performance of associated operations in a "cloud computing" environment or as "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (such as an example of a machine that includes a processor), and the operations may be accessible over a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application program interfaces (APIs)).

[0121] Exemplary embodiments (e.g., devices, systems, or methods) may be implemented in digital electronic circuitry, computer hardware, firmware, software, or any combination thereof. Exemplary embodiments may be implemented using a computer program product (e.g., a computer program tangibly embodied in an information carrier or machine-readable medium for execution by or to control the operation of a data processing apparatus such as a programmable processor, a computer, or multiple computers).

[0122] The computer program can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a software module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer, on multiple computers at a single site, or distributed across multiple sites and interconnected by a communication network.

[0123] In examples, operations may be performed by one or more programmable processors that execute computer programs and perform functions by manipulating input data to generate output. Example method operations may be performed, and example apparatus may be implemented, by special purpose logic circuitry (e.g., a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC)).

[0124] A computing system may include clients and servers. Clients and servers are typically remote from each other and typically interact through a communications network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. It is recognized that in embodiments deploying a programmable computing system, both hardware and software architectures need to be considered. In particular, it is recognized that the selection of whether to implement a particular function in permanently configured hardware (e.g., ASICs), temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. The following is a configured hardware (e.g., machine 400) and software architecture that may be deployed in an exemplary embodiment.

[0125] In examples, machine 400 can operate as a standalone device, or machine 400 can be connected (eg, networked) to other machines.

[0126] In a network deployment, machine 400 can operate in the capacity of either a server or a client machine in a server-client network environment. In an 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), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a network router, switch, or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken (e.g., performed) by machine 400. Furthermore, while only a single machine 400 is shown, the term "machine" shall also be interpreted to include any collection of machines that individually or collectively execute a set (or sets) of instructions to perform any one or more of the methodologies discussed herein.

[0127] The exemplary 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), a main memory 404, and a static memory 406, some or all of which may communicate with each other via a bus 408. The machine 400 may further include a display unit 410, an alphanumeric input device 412 (e.g., a keyboard), and a user interface (UI) navigation device 414 (e.g., a mouse). In an example, the display unit 410, the input device 412, and the UI navigation device 414 may be touchscreen displays. The machine 400 may additionally 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), a compass, an accelerometer, or other sensors.

[0128] Storage device 416 may include machine-readable medium 422 on which is stored one or more sets of data structures or instructions 424 (e.g., software) that embody or utilize any one or more of the methodologies or functions described herein. Instructions 424 may also reside, completely or at least partially, in main memory 404, static memory 406, or processor 402 during execution thereof by machine 400. In an example, one or any combination of processor 402, main memory 404, static memory 406, or storage device 416 may constitute a machine-readable medium.

[0129] While the machine-readable medium 422 is depicted as a single medium, the term “machine-readable medium” can include a single medium or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) configured to store one or more instructions 424. The term “machine-readable medium” may be interpreted to include any tangible medium capable of storing, encoding, or carrying instructions for execution by a machine, causing a machine to perform any one or more of the methodologies of this disclosure, or capable of storing, encoding, or carrying data structures utilized by or associated with such instructions. Accordingly, the term “machine-readable medium” may be interpreted to include, but is not limited to, solid-state memory, and optical and magnetic media. Specific examples of machine-readable media may include, by way of example, non-volatile memory, including 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.

[0130] The instructions 424 may further be transmitted or received over a communications network 426 using a transmission medium via a network interface device 420 utilizing any one of a number of transport protocols (e.g., Frame Relay, IP, TCP, UDP, HTTP, etc.). Exemplary communications networks may include, among others, local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile telephone networks (e.g., cellular networks), plain old telephone (POTS) networks, and wireless data networks (e.g., the IEEE 802.11 family of standards known as Wi-Fi®, the IEEE 802.16 family of standards known as WiMax®), peer-to-peer (P2P) networks, etc. The term “transmission medium” is intended to encompass any intangible medium capable of storing, encoding, or carrying instructions for execution by a machine, including digital or analog communications signals or other intangible media that facilitate communication of such software.

[0131] As discussed herein, a "subject" may be any applicable human, animal, or other living organism, living or dead, or other biological or molecular structure or chemical environment, and may be a particular location within the subject, referred to herein as an "area of ​​interest" or "region of interest," which may relate to a particular component of the subject, such as a particular tissue or bodily fluid of the subject (e.g., human tissue in a particular area of ​​the body of a living subject).

[0132] As discussed herein, it should be appreciated that a subject may be a human or any animal. It should be appreciated that an animal may be of any of a variety of applicable types, including, but not limited to, a mammal, a veterinarian animal, a livestock or pet-type animal, etc. By way of example, an animal may be a laboratory animal specifically selected to have certain characteristics similar to humans (e.g., rats, dogs, pigs, monkeys), etc. It should be appreciated that a subject may be, for example, any applicable human patient.

[0133] Several references, which may include various patents, patent applications, and publications, are cited in the reference list and discussed in the disclosure provided herein. Citation and / or discussion of such references is provided merely to clarify the explanation of the present disclosure and is not an admission that such references are "prior art" to any aspect of the present disclosure described herein. For purposes of notation, "[n]" corresponds to the nth reference in the list. All references cited and discussed herein are incorporated by reference in their entirety and to the same extent as if each reference were individually incorporated by reference.

[0134] In summary, while the present disclosure has been described with respect to specific embodiments, many modifications, variations, adaptations, substitutions, and equivalents will be apparent to those skilled in the art. The present disclosure is not limited in scope by the specific embodiments described herein. Indeed, various modifications of the embodiments herein, in addition to those described herein, will be apparent to those skilled in the art from the foregoing description and the accompanying drawings. Accordingly, the embodiments herein should be considered limited only by the spirit and scope of the disclosure (and claims), including all modifications and equivalents.

[0135] Still other embodiments will be readily apparent to those skilled in the art upon review of the above detailed description and drawings of certain exemplary embodiments. It should be understood that numerous variations, modifications, and additional embodiments are possible, and therefore, all such variations, modifications, and embodiments are to be considered within the spirit and scope of the present application. For example, regardless of the content of any portion of this application (e.g., title, field, background, summary, abstract, drawings, etc.), unless expressly specified otherwise, any particular operation or element described and illustrated, any particular order of such operations, or any particular interrelationship of such elements need not be included in the claims of any application described herein or claiming priority hereto. Furthermore, any operation may be repeated, any operation may be performed by multiple entities, and / or any element may be duplicated. Furthermore, any operation or element may be excluded, the order of operations may be changed, and / or the interrelationship of elements may be altered. Unless expressly specified otherwise, there is no requirement for specific described or illustrated operations or elements, any particular order or sequence of such operations, any particular sizes, speeds, materials, magnitudes, or frequencies, or any particular interrelationships of such elements. Therefore, the description and drawings should be considered exemplary in nature and not limiting. Furthermore, when any number or range is described herein, that number or range is approximate unless expressly stated otherwise. When any range is described herein, that range includes all values ​​therein and all subranges therein, unless expressly stated otherwise. Any information in any materials incorporated by reference herein (e.g., U.S. / foreign patents, U.S. / foreign patent applications, books, articles, etc.) is incorporated by reference only to the extent that no conflict exists between such information and the other descriptions and drawings set forth herein.In the event of such a conflict, including a conflict that invalidates any claim set forth herein or claiming priority thereto, the information is so incorporated by reference, and any such conflicting information is not incorporated herein by reference.

[0136] Where applicable, citations herein, whether by numerical designation or otherwise, refer to one or more documents listed in the section entitled "References."

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[Table 12] [Table 13] [Table 14] [Table 15]

Claims

1. 1. A processor-implemented method for regulating blood glucose in a subject with type 1 diabetes (T1D) in an artificial pancreas (AP), comprising: the processor predicting a blood glucose level of the subject based on a continuous blood glucose monitor (CGM) measurement of the subject; the processor determining a schedule for basal insulin administration according to the predicted blood glucose level; modifying the schedule according to detection by the processor from the continuous blood glucose monitor (CGM) of a predetermined value among one or more of the continuous blood glucose monitor (CGM) measurements and an increasing rate of change of the continuous blood glucose monitor (CGM) measurements, and defining a modified schedule according to the modification; the processor providing the schedule or the modified schedule to the subject; The processor calculates a probability that a glycemic disturbance that was not notified to the artificial pancreas (AP) occurred during a predetermined period based on the continuous glucose monitor (CGM) measurements, including CGM measurements obtained after providing the schedule or the modified schedule; the processor supplementing the delivery of the schedule or the modified schedule with automatic delivery of a first bolus of insulin in response to the calculated probability; A method comprising:

2. The schedule and the modified schedule each include: (a) correcting one or more blood glucose levels of the subject to a predetermined target level; (b) a term that penalizes predicted blood glucose values ​​that trend toward hypoglycemia; and (c) a term that weights the amount of difference between two successive basal insulin dose predictions; The method of claim 1 , wherein the cost function comprises:

3. 3. The method of claim 2, wherein the glycemic disturbance includes at least one cause of glycemic fluctuation that is not explained by the predicted blood glucose values ​​on which the schedule and the modified schedule are based, and the calculated probability is based on the continuous blood glucose monitor (CGM) measurements for the predetermined period of time.

4. 4. The method of claim 3, wherein the calculated probability is calculated for each successive interval of the continuous blood glucose monitor (CGM) readings, each interval being included in the predetermined period of time.

5. 5. The method of claim 4, wherein the first bolus comprises a predetermined percentage of the subject's total daily insulin (TDI).

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

7. 7. The method of claim 6, wherein for a series of first boluses, each subsequent bolus is reduced by an amount of insulin on board (IOB) equal to the sum of each of the preceding first boluses.

8. The processor automatically reducing the basal insulin dose to a fraction of its average based on the predicted blood glucose value indicating hypoglycemia. The method of claim 7 further comprising:

9. The processor automatically supplements the delivery of the schedule or the modified schedule with the delivery of a second bolus of insulin (a) in response to a current estimated blood glucose level and the predicted blood glucose level indicating hyperglycemia, and (b) a predetermined time after the delivery of the first bolus; The method of claim 8 further comprising:

10. 10. The method of claim 9, wherein the delivery of the second bolus is prohibited within two hours after the delivery of the first bolus.

11. 11. The method of claim 10, wherein the frequency of delivery of the second bolus is limited to once per hour.

12. The processor suspends the automatic delivery of the first bolus in response to a meal notification to the artificial pancreas (AP) and supplements the delivery of the schedule or the modified schedule with the delivery of a third bolus, the third bolus being calculated to be up to half a bolus based on the subject's insulin-to-carbohydrate ratio (CR) and correction factor (CF); The method of claim 11 further comprising:

13. 1. A control system for an artificial pancreas (AP), defining a controller for the AP, comprising: a processor; a processor-readable memory, predicting a blood glucose level of the subject based on a continuous blood glucose monitor (CGM) measurement of the subject; determining a schedule for basal insulin administration according to said predicted values; modifying the schedule in accordance with detecting from the CGM measurements a predetermined value of one or more of the CGM measurements and an increasing rate of change of the CGM measurements, and defining a modified schedule in accordance with the modification; Initiating provision of the schedule or the modified schedule to the subject; calculating a probability that a glycemic disturbance that was not notified to the controller occurred during a predetermined period based on the continuous glucose monitor (CGM) measurements, including CGM measurements obtained after providing the schedule or the modified schedule; and supplementing the delivery of the schedule or the modified schedule with automatic delivery of a first bolus of insulin in response to the calculated probability; a processor-readable memory storing processor-executable instructions for A control system comprising:

14. The schedule and the modified schedule each include: (a) correcting one or more blood glucose levels of the subject to a predetermined target level; (b) a term that penalizes predicted blood glucose values ​​that trend toward hypoglycemia; and (c) a term that weights the amount of difference between two successive basal insulin dose predictions; The system of claim 13 , wherein the cost function comprises:

15. 15. The system of claim 14, wherein the glycemic disturbance includes at least one cause of glycemic fluctuation that is not explained by the predicted blood glucose values ​​on which the schedule and the modified schedule are based, and the calculated probability is based on the continuous blood glucose monitor (CGM) measurements for the predetermined period.

16. 16. The system of claim 15, wherein the calculated probability is calculated for each successive interval of the continuous blood glucose monitor (CGM) readings, each interval falling within the predetermined period of time.

17. 17. The system of claim 16, wherein the first bolus comprises a predetermined percentage of the subject's total daily insulin (TDI).

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

19. 20. The system of claim 18, wherein for a series of first boluses, each subsequent bolus is reduced by an amount of insulin on board (IOB) equal to the sum of each of the preceding first boluses.

20. The processor-executable instructions include: one or more instructions for automatically reducing the basal insulin dose to a fraction of its average based on the predicted blood glucose value indicating hypoglycemia; 20. The system of claim 19, further comprising:

21. The processor-executable instructions include: one or more instructions for automatically supplementing the delivery of the schedule or the modified schedule with the delivery of a second bolus of insulin (a) in response to a current estimated blood glucose level and the predicted blood glucose level indicating hyperglycemia, and (b) a predetermined time after the delivery of the first bolus; 21. The system of claim 20, further comprising:

22. 22. The system of claim 21, wherein the delivery of the second bolus is prohibited within two hours after the delivery of the first bolus.

23. 23. The system of claim 22, wherein the frequency of delivery of the second bolus is limited to once per hour.

24. The processor-executable instructions include: one or more instructions for suspending the automatic delivery of the first bolus in response to a meal notification to the artificial pancreas (AP) and supplementing the delivery of the schedule or the modified schedule with the delivery of a third bolus, the third bolus being calculated as up to half a bolus based on the subject's insulin-to-carbohydrate ratio (CR) and correction factor (CF); 24. The system of claim 23, further comprising:

25. 1. A non-transitory computer-readable medium having stored thereon computer-executable instructions for regulating blood glucose in a subject with type 1 diabetes (T1D) in an artificial pancreas (AP), the non-transitory computer-readable medium comprising: The instructions may include: predicting a blood glucose level of the subject based on a continuous blood glucose monitor (CGM) measurement of the subject; determining a basal insulin administration schedule according to the predicted value; modifying the schedule in accordance with detecting from the continuous blood glucose monitor (CGM) measurements a predetermined value of one or more of the continuous blood glucose monitor (CGM) measurements and an increasing rate of change of the continuous blood glucose monitor (CGM) measurements, and defining a modified schedule in accordance with the modification; Initiating provision of the schedule or the modified schedule to the subject; calculating a probability that a glycemic disturbance that was not notified to the artificial pancreas (AP) occurred during a predetermined period based on the continuous blood glucose monitor (CGM) measurements, including CGM measurements obtained after providing the schedule or the modified schedule; supplementing the delivery of the schedule or the modified schedule with an automatic delivery of a first bolus of insulin in response to the calculated probability. Non-transitory computer-readable medium.

26. The schedule and the modified schedule each include: (a) correcting one or more blood glucose levels of the subject to a predetermined target level; (b) a term that penalizes predicted blood glucose values ​​that trend toward hypoglycemia; and (c) a term that weights the amount of difference between two successive basal insulin dose predictions; 26. The non-transitory computer-readable medium of claim 25, wherein the cost function comprises:

27. 27. The non-transitory computer-readable medium of claim 26, wherein the glycemic disturbance includes at least one cause of glycemic fluctuations that is not explained by the predicted blood glucose values ​​on which the schedule and the modified schedule are based, and the calculated probability is based on the continuous blood glucose monitor (CGM) measurements for the predetermined period of time.

28. 28. The non-transitory computer-readable medium of claim 27, wherein the calculated probability is calculated for each successive interval of the continuous blood glucose monitor (CGM) readings, each interval falling within the predetermined period of time.

29. 30. The non-transitory computer-readable medium of claim 28, wherein the first bolus comprises a predetermined percentage of the subject's total daily insulin (TDI).

30. 30. The non-transitory computer-readable medium of claim 29, wherein the predetermined percentage increases as the calculated probability increases.

31. 31. The non-transitory computer-readable medium of claim 30, wherein for a series of first boluses, each subsequent bolus is reduced by an amount of insulin on board (IOB) equal to each preceding first bolus.

32. The instruction: one or more instructions for automatically reducing the basal insulin dose to a fraction of its average based on the predicted blood glucose value indicating hypoglycemia; 32. The non-transitory computer-readable medium of claim 31, further comprising:

33. The instruction: one or more instructions for automatically supplementing the delivery of the schedule or the modified schedule with delivery of a second bolus of insulin (a) in response to a current blood glucose level and the predicted blood glucose level indicating hyperglycemia, and (b) a predetermined time after the delivery of the first bolus; 33. The non-transitory computer-readable medium of claim 32, further comprising:

34. 34. The non-transitory computer-readable medium of claim 33, wherein the delivery of the second bolus is prohibited within two hours after the delivery of the first bolus.

35. 35. The non-transitory computer-readable medium of claim 34, wherein the frequency of the delivery of the second bolus is limited to once per hour.

36. The instruction: one or more instructions for suspending the automatic delivery of the first bolus in response to a meal notification to the artificial pancreas (AP) and supplementing the delivery of the schedule or the modified schedule with the delivery of a third bolus, the third bolus being calculated as up to half a bolus based on the subject's insulin-to-carbohydrate ratio (CR) and correction factor (CF); 36. The non-transitory computer-readable medium of claim 35, further comprising:

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