WO2025090855A1

WO2025090855A1 - Targeted basal insulin titration using sparse and dense glucose measurements

Info

Publication number: WO2025090855A1
Application number: PCT/US2024/052941
Authority: WO
Inventors: Marc D. Breton; Anas El FATHI
Original assignee: University of Virginia Patent Foundation
Current assignee: UVA Licensing and Ventures Group
Priority date: 2023-10-26
Filing date: 2024-10-25
Publication date: 2025-05-01
Anticipated expiration: 2026-04-26

Abstract

Provided are a method, system, and computer-readable medium for personalizing titration measurement for basal insulin dosing. The personalization can be achieved using spare self-monitoring blood glucose (SMBG) and/or dense continuous glucose monitoring (CGM) data to predict a fasting blood glucose (FBG) envelope accounting for online estimation of titration noise representative of variability in daily fasting measurement. As a result, basal insulin dosing directed to causing the FBG envelope to be maintained for an optimal glycemic range can then be obtained.

Description

TARGETED BASAL INSULIN TITRATION USING SPARSE AND DENSE GLUCOSE MEASUREMENTS CROSS-REFERENCE TO RELATED APPLICATION This international application claims priority to and the benefit of U.S. Provisional Application No.63/545,901, filed October 26, 2023, the entire contents of which is incorporated by reference herein. FIELD OF THE DISCLOSURE Disclosed embodiments relate to personalizing titration measurement for basal insulin dosing, and more specifically, to such measurement based on spare self-monitoring blood glucose (SMBG) and/or dense continuous glucose monitoring (CGM) data via online estimation of titration noise. BACKGROUND Citations throughout are to those documents referred to as References and listed at the conclusion of this section. Around a third of Type 2 diabetes (T2D) patients begin treatment according to basal insulin infusion/injection.¹ The basal insulin dose is usually given at fixed intervals and is titrated to achieve a tight glycemic target (e.g., A1c level < 7.0%) without presentation for undue hypoglycemic risk (i.e., blood glucose (BG) level < 70 mg/dL). Dose titration commonly follows an algorithm that relies on self-monitoring blood glucose (SMBG) measurements obtained during fasting. With recent technological advances on continuous glucose monitoring (CGM) systems, CGM-based titration has become possible, though guidelines have yet to be established. Alongside, basal insulin is still not optimally titrated even in clinical practice,² whereas such lack of optimization may be attributed to the large variability in insulin sensitivity between people, the day-to-day variabilities in SMBG measurements, and a complexity of analyzing and interpreting CGM data. REFERENCES 1. Frankenfeld CL, Leslie TF, Makara MA. Diabetes, obesity, and recommended fruit and vegetable consumption in relation to food environment sub-types: a cross-sectional analysis of Behavioral Risk Factor Surveillance System, United States Census, and food establishment data. BMC Public Health.2015 May 14;15(1):491. 2. Berard L, Bonnemaire M, Mical M, Edelman S. Insights into optimal basal insulin titration in type 2 diabetes: Results of a quantitative survey. Diabetes Obes Metab.2018 Feb;20(2):301–8. 3. Fathi AE, Palisaitis E, Boulet B, Legault L, Haidar A. An unannounced meal detection module for artificial pancreas control systems.2019 American Control Conference (ACC). IEEE; 2019. p.4130–5. 4. Lobo B, Farhy L, Shafiei M, Kovatchev B. A Data-Driven Approach to Classifying Daily Continuous Glucose Monitoring (CGM) Time Series. IEEE Trans Biomed Eng.2022 Feb;69(2):654–65. SUMMARY It is to be understood that both the following summary and the detailed description are exemplary and explanatory and are intended to provide further explanation of the present embodiments as claimed. Neither the summary nor the description that follows is intended to define or limit the scope of the present embodiments to the particular features mentioned in the summary or in the description. Embodiments herein relate to personalizing titration measurement for basal insulin dosing, and more specifically, to such measurement based on spare self-monitoring blood glucose (SMBG) and/or dense continuous glucose monitoring (CGM) data via online estimation of titration noise. Such embodiments may be implemented in an electronic device and used as a decision support system to recommend new basal insulin doses so as to achieve a basal insulin recommender (BIR), according to embodiments as are discussed hereinbelow. In particular, such embodiments may include a processor-implemented method for controlling basal insulin infusion to a subject to meet a predetermined glycemia, the method entailing receiving recorded fasting blood glucose (FBG) measurements corresponding to at least a first basal insulin infusion to the subject; providing, based on the recorded FBG measurements of the subject, a range of future FBG measurements of the subject determined for (a) an estimation of titration noise for at least a second basal insulin infusion to the subject and (b) one or more changes in basal insulin dosage for the at least a second basal insulin infusion to the subject; and generating, using (a) and (b), one or more recommended basal insulin doses, for the at least a second basal insulin infusion to the subject, that align the range of future FBG measurements of the subject to the predetermined glycemia. Respective embodiments may further include a relative system and a machine-readable medium commensurate with the embodied method above. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate exemplary embodiments and, together with the description, further serve to enable a person skilled in the pertinent art to make and use these embodiments and others that will be apparent to those skilled in the art. Embodiments herein will be more particularly described in conjunction with the following drawings wherein: Figure 1 illustrates a workflow of basal insulin titration, according to embodiments herein; Figure 2 illustrates prediction for an envelope of future fasting blood glucose (FBG), according to embodiments herein; Figure 3 illustrates results of an in silico experiment comparing standard titration to that which can be achieved according to embodiments herein; Figure 4 illustrates relative absorption profiles for various insulin fitted to an average absorption profile, according to embodiments herein; FIG.5 illustrates a high level block diagram of an exemplary environment in which a basal insulin recommender (BIR), according to embodiments herein, may be implemented; FIG.6 illustrates an exemplary computing device which may implement one or more aspects of the BIR, according to embodiments herein; FIG.7 illustrates a network system which may implement and/or be used in the implementation of the BIR, according to embodiments herein; FIG.8 illustrates a block diagram which may implement and/or be used in the implementation of the BIR in association with a connection to the Internet, according to embodiments herein; FIG.9 illustrates a system which may implement and/or be used in the implementation of the BIR in accordance with one or more of a clinical setting and a connection to the Internet, according to embodiments herein; and FIG.10 illustrates an exemplary architecture embodying operational aspects that can effect the BIR, according to embodiments herein. DETAILED DESCRIPTION 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. The embodiment(s) described, and references in the specification to "one embodiment", "an embodiment", "an example embodiment", etc., indicate that the embodiment(s) described may include a particular feature, structure, or characteristic. Such phrases are not necessarily referring to the same embodiment. The skilled artisan will appreciate 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. In the several figures, like reference numerals may be used for like elements having like functions even in different drawings. The embodiments described, and their detailed construction and elements, are merely provided to assist in a comprehensive understanding of the present embodiments. Thus, it is apparent that the present embodiments can be carried out in a variety of ways, and does not require any of the specific features described herein. Also, well-known functions or constructions are not described in detail since they would obscure the present embodiments with unnecessary detail. 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 are best defined by the appended claims. It should also be noted that in some alternative implementations, the blocks in a flowchart, the communications in a sequence-diagram, the states in a state-diagram, etc., may occur out of the orders illustrated in the figures. That is, the illustrated orders of the blocks/communications/states are not intended to be limiting. Rather, the illustrated blocks/communications/states may be reordered into any suitable order, and some of the blocks/communications/states could occur simultaneously. All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms. The indefinite articles "a" and "an," as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean "at least one." The phrase "and/or," as used herein in the specification and in the 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 fashion, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the "and/or" clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to "A and/or B", when used in conjunction with open-ended language such as "comprising" can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc. As used herein in the specification 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 interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as "only one of or "exactly one of," or, when used in the claims, "consisting of," will 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 interpreted as indicating exclusive alternatives (i.e. "one or the other but not both") when preceded by terms of exclusivity, such as "either," "one of," "only one of," or "exactly one of` "Consisting essentially of," when used in the claims, shall have its ordinary meaning as used in the field of patent law. As used herein in the specification and in the 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 the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified. 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, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc. In the claims, as well as in the specification above, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "composed of," and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedure, Section 2111.03. It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, 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 example embodiments. The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Additionally, all embodiments described herein should be considered exemplary unless otherwise stated. It should be appreciated that any of the components or modules referred to with regards to any of the embodiments discussed herein, may be integrally or separately formed with one another. Further, redundant functions or structures of the components or modules may be implemented. Moreover, the various components may be communicated locally and/or remotely with any user/clinician/patient or machine/system/computer/processor. Moreover, the various components may be in communication via wireless and/or hardwire or other desirable and available communication means, systems and hardware. Moreover, various components and modules may be substituted with other modules or components that provide similar functions. It should be appreciated that the device and related components discussed herein may take on all shapes along the entire continual geometric spectrum of manipulation of x, y and z planes to provide and meet the anatomical, environmental, and structural demands and operational requirements. Moreover, locations and alignments of the various components may vary as desired or required. It should be appreciated that various sizes, dimensions, contours, rigidity, shapes, flexibility and materials of any of the components or portions of components in the various embodiments discussed throughout may be varied and utilized as desired or required. It should be appreciated that while some dimensions are provided on the aforementioned figures, the device may constitute various sizes, dimensions, contours, rigidity, shapes, flexibility and materials as it pertains to the components or portions of components of the device, and therefore may be varied and utilized as desired or required. Although example embodiments of the present disclosure are explained in some instances in detail herein, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the present disclosure be limited in its scope to the details of construction and 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. 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. In describing example embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. It is also to be understood that the mention of one or more steps of a method does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Steps of a method may be performed in a different order than those described herein without departing from the scope of the present disclosure. Similarly, it is also to be understood that the mention of one or more components in a device or system does not preclude the presence of additional components or intervening components between those components expressly identified. Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and/or discussion of such references is provided merely to clarify the description of the present disclosure and is not an admission that any such reference is “prior art” to any aspects of the present disclosure described herein. In terms of notation, “[n]” corresponds to the n^th reference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entireties and to the same extent as if each reference was individually incorporated by reference. The term “about,” as used herein, means approximately, in the region of, roughly, or around. When the term “about” is used in conjunction with a numerical range, it modifies that range by extending the boundaries above and below the numerical values set forth. 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 aspect, the term “about” means plus or minus 10% of the numerical value of the number with which it is being used. Therefore, about 50% means in the range of 45%-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 1-1.5, 1.5-2, 2-2.75, 2.75-3, 3-3.90, 3.90-4, 4-4.24, 4.24-5, 2-5, 3-5, 1- 4, and 2-4). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term “about.” Embodiments herein provide a model-based approach where up to T=26 weeks of historical data (e.g., insulin, SMBG, and CGM data) is fitted to construct a titration fasting model providing a basal insulin recommender (BIR) demonstrated to achieve glycemic control within an optimal range (OR), i.e., A1c < 7.0% and BG of 70 mg/dL or greater. To do so, the BIR can predict the envelope, i.e., range, of future fasting blood glucose (FBG) by estimating user titration noise, i.e., a measure describing variability in daily fasting measurement. Resultingly, the predicted envelope, as determined according to the estimation, can inform need for an optimal change in basal insulin dosing, relative to current delivery, that causes the FBG envelope to be aligned with, i.e., maintained in satisfaction of, the OR. The BIR has three components, which, as discussed in more detail below, include (i) a generic insulin absorption model that can be customized to the type of insulin used; (ii) a FBG model providing an adaptive sensitivity enabling user personalization; and (iii) a titration noise model describing the distribution of fasting measurements due to metabolic variabilities and sensor accuracy. In referring to Figure 1, there is illustrated a workflow accomplished with respect to the BIR, in which, at “a,” a new insulin dose can be administered to a user, whereby the new insulin dose can correspond to at least a first basal insulin infusion and be in accord with an amount of insulin prescribed by the user’s standard treatment regime, and alternatively, dosing according to an iteration of operation of the BIR as is discussed herein. That is, such an administered dose can serve as a datum for recommendation according to the BIR. During absorption of that dose, the BIR is active to: at “b,” receive subsequently recorded fasting measurements, whether by recording operations for SMBG or CGM; at “c,” adapt operations for the titration model, according to operation of the generic insulin absorption and FBG modeling and by accounting for previous insulin dosing and fasting BG measurements, to provide a range of future FBG measurements of the user; and at “d,” optimize, using the adapted titration model, the administered insulin dosing to effect the aforementioned predicted FBG envelope meeting the OR, i.e., to maintain BG within the OR. As will be understood from the discussion herein, the optimized dosing can be the same as the initially administered dose at “a,” a reduction in such a dose, or an increase in dosage. In these ways, the above-discussed modeling comprising the BIR can dependably achieve glucose levels satisfying the OR. Fasting BG measurements Herein, fasting BG measurements can be a product of dense CGM trace that is analyzed for fasting periods to extract an average fasting measurement per day. Fasting extraction from CGM data can be performed by removing meal related traces from CGM³ and by matching CGM profile to known fasting motifs.⁴ The extracted CGM daily measurement(s) is/are combined with sparse SMBG measurements, whereby the combined measurements may be referred to as G^୩ ୫ , relative to modeling discussed hereinbelow. Titration Fasting Model as a BIR Generic Insulin Absorption Model Different formulations of basal insulin are available, where these formulations are particularized by their pharmacokinetics (i.e., rate of absorption from injection site to the plasma). Accordingly, insulin plasma (mU/L) concentration following a dose injection u (U) may, according to embodiments herein, be given as follows: I

where δ(t) is a dirac delta function, F represents the bioavailability of insulin_, k_{BW (}kg) is the body weight, V୧ is the insulin volume distribution, kୡ୪ is the clearance and k1, k2 are time constants; k₁ is directly related to the insulin half-life, as and when the basal insulin is injected at fixed intervals T_୧୬୨, k_ଶ can be linked to the tim

e-to-peak absorption (t_୫ୟ^) as follows: t

Examples of values for these parameters for different formulations of basal insulin are presented in Table 1 below, which presents parameter values for a generic insulin absorption model that can be tailored to a specific drug or used with an average profile. Figure 4 presents the respectively corresponding absorption profiles for the different types of insulin therein as fitted by the discussed absorption modeling to generic absorption. Table 1

Fasting Blood Glucose Model FBG resulting from multiple insulin injections (uⁱ at time tⁱ) can be described using a linear model (A) or a non-linear model (B) as follows: (

where G_^ (mg/dL) is the fasting blood glucose at zero insulin characterizing the individual’s non- insulin resistance and which can be affected by other formulations (e.g., metformin, GLP-1); and S_୧ (mg/dL per mU/L) is the insulin sensitivity, in which G_^ and S_୧ are unique for each individual and can vary over time. Titration Noise Model Titration noise n^୩ of a fasting blood glucose (FBG) measurement at time ^୩ may be

described by the CGM sensor noise and the user’s metabolic variabilities (e.g., body weight, cholesterol level, blood pressure level, etc.), and can be related to “average” fasting blood _{glucose G as follows:}

relative to the assumption that such titration noise follows a gaussian distribution with zero mean, and is only characterized by its standard deviation

Online Adaptation of Titration Fasting Model as the BIR U_{sing the maximum-a-posteriori principle, the optimal set of parameters}

that explains the data (i.e., FBG measurements considering titration noise) being plausible (as drawn from a physiological a priori distribution) is expressed as follows:

, where Ρ is the domain of physiologically plausible solutions. This optimization is solved in two steps, in which, first, the closed form solution of the unconstrained problem is used:

^ _{0 0}ù ,_{where Xା indicates the Moore–Penrose inverse and M ൌ ^ ⋱ 0}ú _{, Y ൌ} _^ú

₀ ^{^మ} బû

^

A literal derivation of the above may be provided as:

_{Then, this solution is projected into using the formula:}

Basal Insulin Optimization T_{he envelope, i.e., range, of possible future fasting blood glucose} resulting from a change in insulin dose δu (Figure 2) can be expressed as follows:

_{, where σ and α = 1.65 is chosen for a confidence interval of 5-95%.} That is,

once having obtained the projected solution (i.e., the predicted FBG envelope (s^, the BIR may be considered to have been adapted (see “c” in Figure 1) and readied for determination of an optimal basal insulin dosing that maintains the OR of glucose levels for a user of the BIR. Since the predicted fasting plasma glucose may now be considered to be a function of the incremental change in the basal insulin dose, it is possible to search for the optimal insulin change that causes the predicted FBG envelope to be satisfied (i.e., be maintained for the period of the prediction) with respect to the OR. As such, provided below is the following applicable optimization function: J

, where γ_୧ are gain parameters, GT_U and GT_L are the upper and lower glucose target (as specified by accepted glycemic regulation). The optimization problem (i.e., the optimal basal insulin dosing to keep the predicted FBG envelope in satisfaction of the OR) may be solved with a grid search on eligible insulin _{changes du between being the minimum and}

maximum insulin change, respectively, and Δu being the incremental change from the latest basal dose. Here, u are determined as a function of the number of available fasting blood glucose meas

urements since the last insulin dose. Herein, embodiments herein can be implemented, for example, as one or more portions of a smartphone application used as a health decision support system. The smartphone can be connected to a smart insulin pen to sync previous basal insulin doses and to a CGM to sync glucose history. When requested, a new basal insulin dose is proposed to optimize glycemia. As will be understood, the BIR discussed herein may additionally be included among operations of an artificial pancreas (AP) (i.e., coordinated controller, insulin pump, and CGM). Proof of concept: Basal Insulin Titration In this in silico experiment, we compared the proposed titration algorithm using CGM data to a standard 3-SMBG algorithm in a cohort of (n=97) virtual subjects. As demonstrated by reference to Figure 3, a quick convergence to glycemic targets, resulted. Referring to FIGS.5-10, there are illustrated various apparatuses and associated architecture for implementing operability of embodiments herein. Referring to FIG.5, there is shown a high level functional block diagram of an exemplary artificial pancreas (AP) that can be used in conjunction with embodiments herein. Though an exemplary embodiment of an AP is now discussed for purposes of the forthcoming descriptions, one of ordinary skill in the art will appreciate that the aforementioned embodiments may also be made applicable to the smart insulin pen/CGM configuration as is referenced above. As shown, a processor or controller 102 may be configured to implement one or more portions of embodiments discussed above and to communicate with a CGM 101, and optionally with an insulin device 100 enabled to deliver insulin. The glucose monitor or device 101 may communicate with a subject 103 to monitor glucose levels thereof. The processor or controller 102 may be configured to include all necessary hardware and/or software necessary to perform the required instructions to achieve the aforementioned tasks. Optionally, the insulin device 100 may communicate with the subject 103 to deliver insulin thereto. The glucose monitor 101 and the insulin device 100 may be implemented as separate devices or as a single device in combination. The processor 102 may be implemented locally in the glucose monitor 101, the insulin device 100, or as a standalone device (or in any combination of two or more of the glucose monitor, insulin device, or a standalone device). The processor 102 or a portion of the AP may be located remotely, such that the AP may be operated as a telemedicine device. Referring to Figure 6, a computing device 144 may implement one or more portions of embodiments herein and may typically include 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, etc.) or some combination of the two. Additionally, computing device 144 may also have other features and/or functionality. For example, the device could also include additional removable and/or non-removable storage including, but not limited to, magnetic or optical disks or tape, as well as writable electrical storage media. Such additional storage may be represented as removable storage 152 and non- removable storage 148. Computer storage media may include volatile and nonvolatile, 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. The memory, the removable storage and the non-removable storage may comprise examples of computer storage media. Computer storage media may include, but not be limited to, RAM, ROM, EEPROM, flash memory or other memory technology CDROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the device. Any such computer storage media may be part of, or used in conjunction with, one or more components of the AP. The computer device 144 may also contain one or more communications connections 154 that allow the device to communicate with other devices (e.g. other computing devices). The communications connections may carry information in a communication media. Communication media may typically embody 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 includes any information delivery media. The term “modulated data signal” may include a signal that has one or more of its 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, communication medium may include wired media such as a wired network or direct-wired connection, and wireless media such as radio, RF, infrared and other wireless media. As discussed above, the term computer readable media as used herein may include both storage media and communication media. In addition to a stand-alone computing machine, embodiments herein may also be implemented on a network system comprising a plurality of computing devices that may in communication via a network, such as a network with an infrastructure or an ad hoc network. The network connection may include wired connections or wireless connections. For example, Figure 7 illustrates a network system in which embodiments herein may be implemented. In this example, the network system may comprise a computer 156 (e.g., a network server), network connection means 158 (e.g., wired and/or wireless connections), 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, etc. or other handheld devices (or non-portable devices) with combinations of such features). In an embodiment, it should be appreciated that the module listed as 156 may implement a CGM. In an embodiment, it should be appreciated that the module listed as 156 may be a glucose monitor device, an artificial pancreas, and/or an insulin device. Any of the components shown or discussed with Figure 7 may be multiple in number. Embodiments herein may be implemented in anyone of the aforementioned devices. For example, execution of the instructions or other desired processing may be performed on the same computing device that is anyone of 156, 160, and 162. Alternatively, an embodiment may be performed on different computing devices of the network system. For example, certain desired or required processing or execution may be performed on one of the computing devices of the network (e.g. server 156 and/or a CGM), whereas other processing and execution of the instruction can be performed at another computing device (e.g., terminal 160) of the network system, or vice versa. In fact, certain processing or execution may be performed at one computing device (e.g. server 156 and/or insulin device, artificial pancreas, or CGM); and the other processing or execution of the instructions may be performed at different computing devices that may or may not be networked. For example, such certain processing may be performed at terminal 160, while the other processing or instructions may be passed to device 162 where the instructions may be executed. This scenario may be of particular value especially when the PDA 162 device, for example, accesses the network through computer terminal 160 (or an access point in an ad hoc network). For another example, software comprising the instructions may be executed, encoded or processed according to one or more embodiments herein. The processed, encoded or executed instructions may then be distributed to customers in the form of a storage media (e.g. disk) or electronic copy. Figure 8 illustrates a block diagram that of a system 130 including a computer system 140 and the associated Internet 11 connection upon which an embodiment may be implemented. Such configuration may typically used for computers (i.e., hosts) connected to the Internet 11 and executing software on a server or a client (or a combination thereof). A source computer such as laptop, an ultimate destination computer and relay servers, for example, as well as any computer or processor described herein, may use the computer system configuration and the Internet connection shown in Figure 8. The system 140 may take the form of a portable electronic device such as a notebook/laptop computer, a media player (e.g., a MP3 based or video player), a cellular phone, a Personal Digital Assistant (PDA), a CGM, an AP, an insulin delivery device, an image processing device (e.g., a digital camera or video recorder), and/or any other handheld computing devices, or a combination of any of these devices. Note that while Figure 8 illustrates various components of a computer system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such, details of such interconnection are omitted. It will also be appreciated that network computers, handheld computers, cell phones and other data processing systems which have fewer components or perhaps more components may also be used. The computer system of Figure 8 may, for example, be an Apple Macintosh computer or Power Book, or an IBM compatible PC. Computer system 140 may include a bus 137, an interconnect, or other communication mechanism for communicating information, and a processor 138, commonly in the form of an integrated circuit, coupled with bus 137 for processing information and for executing the computer executable instructions. Computer system 140 may also include 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 to be executed by processor 138. Main memory 134 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 138. Computer system 140 may further include 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 processing by processor 138. A storage device 135, such as a magnetic disk or optical disk, a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from and writing to a magnetic disk, and/or an optical disk drive (such as a DVD) for reading from and writing to a removable optical disk, may be coupled to bus 137 for storing information and instructions. The hard disk drive, magnetic disk drive, and optical disk drive may be connected to the system bus by a hard disk drive interface, a magnetic disk drive interface, and an optical disk drive interface, respectively. The drives and their associated computer readable media may provide non-volatile storage of computer readable instructions, data structures, program modules and other data for the general purpose computing devices. Typically, computer system 140 may include an Operating System (OS) stored in a non-volatile storage for managing the computer resources and may provide the applications and programs with an access to the computer resources and interfaces. An operating system commonly processes 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 and output devices, facilitating networking and managing files. Non-limiting examples of OSs may include Microsoft Windows, Mac OS X, and Linux. The term "processor" may include any integrated circuit or other electronic device (or collection of such electronic devices) capable of performing an operation on at least one instruction including, without limitation, 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 onto a single substrate (e.g., a silicon "die"), or may be distributed among two or more substrates. Furthermore, various functional aspects of the processor may be implemented solely as software or firmware associated with the processor. Computer system 140 may be coupled via bus 137 to a display 131, such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), a flat screen monitor, a touch screen monitor or similar means for displaying text and graphical data to a user. The display may be connected via a video adapter for supporting the display. The display may allow a user to view, enter, and/or edit information that may be relevant to the operation of the system. An input device 132, including alphanumeric and other keys, may be coupled to bus 137 for communicating information and command selections to processor 138. Another type of user input device may include cursor control 133, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 138, and for controlling cursor movement on display 131. Such an input device may include two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that may allow the device to specify positions in a plane. The computer system 140 may be used for implementing the methods and techniques described herein. According to an embodiment, those methods and techniques may be performed by computer system 140 in response to processor 138 executing one or more sequences of one or more instructions contained in main memory 134. Such instructions may be read into main memory 134 from another computer readable medium, such as storage device 135. Execution of the sequences of instructions contained in main memory 134 may cause processor 138 to perform the process steps described herein. In alternative embodiments, hard- wired circuitry may be used in place of or in combination with software instructions to implement the arrangement. Thus, embodiments herein may not be limited to any specific combination of hardware circuitry and software. The term "computer readable medium" (or "machine readable medium") as used herein is an extensible 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 a medium may store computer-executable instructions to be executed by a processing element and/or control logic, and data which may be manipulated by a processing element and/or control logic, and may take many forms, including but not limited to, non-volatile medium, volatile medium, and transmission medium. Transmission media may include coaxial cables, copper wire and fiber optics, including the wires that comprise bus 137. Transmission media may also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications, or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch-cards, paper-tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read. 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 its 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 infra-red transmitter to convert the data to an infra-red signal. An infra-red detector may receive the data carried in the infra-red signal, and appropriate circuitry may place the data on bus 137. Bus 137 may carry the data to main memory 134, from which processor 138 may retrieve and execute 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. Computer system 140 may also include a communication interface 141 coupled to bus 137. Communication interface 141 may provide a two-way data communication coupling to a network link 139 that may be connected to a 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 connection based on IEEE802.3 standard may be used such as 10/100BaseT, 1000BaseT (gigabit Ethernet), 10 gigabit Ethernet (10 GE or 10 GbE or 10 GigE per IEEE Std 802.3ae-2002 as standard), 40 Gigabit Ethernet (40 GbE), or 100 Gigabit Ethernet (100 GbE as per Ethernet standard IEEE P802.3ba), as described in Cisco Systems, Inc. Publication number 1-587005- 001-3 (6/99), "Internetworking Technologies Handbook", Chapter 7: "Ethernet Technologies", pages 7-1 to 7-38, which is incorporated in its entirety for all purposes as if fully set forth herein. In such a case, the communication interface 141 may typically include a LAN transceiver or a modem, such as Standard Microsystems Corporation (SMSC) LAN91C11110/100 Ethernet transceiver described in the Standard Microsystems Corporation (SMSC) data-sheet "LAN91C11110/100 Non-PCI Ethernet Single Chip MAC+PHY" Data-Sheet, Rev.15 (02-20- 04), which is incorporated in its entirety for all purposes as if fully set forth herein. Wireless links may also be implemented. In any such implementation, communication interface 141 may send and receive electrical, electromagnetic or optical signals that may carry digital data streams representing various types of information. Network link 139 may typically provide 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 to data equipment operated by an Internet Service Provider (ISP) 142. ISP 142, in turn, may provide data communication services through the world wide packet data communication network Internet 11. Local network 111 and Internet 11 may both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 139 and through the communication interface 141, which carry the digital data to and from computer system 140, are exemplary forms of carrier waves transporting the information. A 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. Referring to FIG.9, there is shown an exemplary system by or in which embodiments herein may be implemented. In an embodiment, the CGM, the AP or the insulin device may be implemented by a subject (or patient) locally at home or at another desired location. However, in an alternative embodiment, one or more of the above may be implemented in a clinical setting. For instance, referring to Figure 9, a clinical setup l58 may provide a place for doctors (e.g., 164) or clinician/assistant to diagnose patients (e.g., 159) with diseases related with glucose, and related diseases and conditions. A CGM 10 may be used to monitor and/or test the glucose levels of the patient—as a standalone device. It should be appreciated that while only one CGM 10 is shown in the figure, the system may include other AP components. The system or component, such as the CGM 10, may be affixed to the patient or in communication with the patient as desired or required. For example, the system or combination of components thereof - including a CGM 10 (or other related devices or systems such as a controller, and/or an AP, an insulin pump, or any other desired or required devices or components) - may be in contact, communication or affixed to the patient through tape or tubing (or other medical instruments or components) or may be in communication through wired or wireless connections. Such monitoring and/or testing may be short term (e.g., a clinical visit) or long term (e.g., a clinical stay). The CGM may output results that may be used by the doctor (, clinician or assistant) for appropriate actions, such as insulin injection or food feeding for the patient, or other appropriate actions or modeling. Alternatively, the CGM 10 may output results that may be delivered to computer terminal 168 for instant or future analyses. The delivery may be through cable or wireless or any other suitable medium. The CGM 10 output from the patient may also be delivered to a portable device, such as PDA 166. The CGM 10 output may also be delivered to a glucose monitoring center 172 for processing and/or analyzing. Such delivery can be accomplished in many ways, such as network connection 170, which may be wired or wireless. In addition to the CGM 10 output, errors, parameters for accuracy improvements, and any accuracy related information may be delivered, such as to computer 168, and/or glucose monitoring center 172 for performing error analyses. Doing so may provide centralized monitoring of accuracy, modeling and/or accuracy enhancement for glucose centers, relative to assuring a reliable dependence upon glucose sensors. Embodiments herein may also be implemented in a standalone computing device associated with a glucose monitoring device. An exemplary computing device (or portions thereof) in which embodiments herein may be implemented is schematically illustrated in Figure 6. FIG.10 provides a block diagram illustrating an exemplary machine upon which one or more aspects of embodiments, including methods thereof, herein may be implemented. Machine 400 may include logic, one or more components, and circuits (e.g., modules). Circuits may be tangible entities configured to perform certain operations. In an example, such circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner. In an example, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors (processors) may be configured with or by software (e.g., instructions, an application portion, or an application) as a circuit that operates to perform certain operations as described herein. In an example, the software may reside (1) on a non-transitory machine readable medium or (2) in a transmission signal. In an example, the software, when executed by the underlying hardware of the circuit, may cause the circuit to perform the certain operations. In an example, a circuit may be implemented mechanically or electronically. For example, a circuit may comprise dedicated circuitry or logic that may be specifically configured to perform one or more techniques such as are discussed above, including a special-purpose processor, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). In an example, a circuit may comprise programmable logic (e.g., circuitry, as encompassed within a general-purpose processor or other programmable processor) that may be temporarily configured (e.g., by software) to perform certain operations. It will be appreciated that the decision to implement a circuit mechanically (e.g., in dedicated and permanently configured circuitry), or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. Accordingly, the term “circuit” may be understood to encompass a tangible entity, whether physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform specified operations. In an example, given a plurality of temporarily configured circuits, each of the circuits need not be configured or instantiated at any one instance in time. For example, where the circuits comprise a general-purpose processor configured via software, the general- purpose processor may be configured as respective different circuits at different times. Software may accordingly configure a processor, for example, to constitute a particular circuit at one instance of time and to constitute a different circuit at a different instance of time. In an example, circuits may provide information to, and receive information from, other circuits. In this example, the circuits may be regarded as being communicatively coupled to one or more other circuits. Where multiple of such circuits exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the circuits. In embodiments in which multiple circuits are configured or instantiated at different times, communications between such circuits may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple circuits have access. For example, one circuit may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further circuit may then, at a later time, access the memory device to retrieve and process the stored output. In an example, circuits may be configured to initiate or receive communications with input or output devices and may operate on a collection of information. The various operations of methods described herein may be performed, at least partially, by one or more processors that may temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented circuits that operate to perform one or more operations or functions. In an example, the circuits referred to herein may comprise processor-implemented circuits. 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 processors or processor-implemented circuits. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In an example, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other examples the processors may be distributed across a number of locations. The one or more processors may also operate to support performance of the relevant operations in a "cloud computing" environment or as a "software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs)). Example embodiments (e.g., apparatus, systems, or methods) may be implemented in digital electronic circuitry, in computer hardware, in firmware, in software, or in any combination thereof. Example embodiments may be implemented using a computer program product (e.g., a computer program, tangibly embodied in an information carrier or in a machine readable medium, for execution by, or to control the operation of, data processing apparatus such as a programmable processor, a computer, or multiple computers). A computer program may be written in any form of programming language, including compiled or interpreted languages, and may 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 may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. In an example, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Examples of method operations may also be performed by, and example apparatus can be implemented as, special purpose logic circuitry (e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)). The computing system or systems herein may include clients and servers. A client and server may generally be remote from each other and generally interact through a communication 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. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures may be adapted, as appropriate. Specifically, it will be appreciated that whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in 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 function of efficiency. Below are set out hardware (e.g., machine 400) and software architectures that may be implemented in or as example embodiments. In an example, the machine 400 may operate as a standalone device or the machine 400 may be connected (e.g., networked) to other machines. In a networked deployment, the machine 400 may operate in the capacity of either a server or a client machine in server-client network environments. In an example, machine 400 may act as a peer machine in peer-to-peer (or other distributed) network environments. The machine 400 may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) specifying actions to be taken (e.g., performed) by the machine 400. Further, while only a single machine 400 is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the embodiments discussed herein. Example machine (e.g., computer system) 400 may include a processor 402 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), 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 411 (e.g., a mouse). In an example, the display unit410, input device 412 and UI navigation device 414 may be a touch screen display. The machine 400 may additionally include a storage device (e.g., drive unit) 416, a signal generation device 418 (e.g., a speaker), a network interface device 420, and one or more sensors 421, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The storage device 416 may include a machine readable medium 422 on which is stored one or more sets of data structures or instructions 424 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 424 may also reside, completely or at least partially, within the main memory 404, within static memory 406, or within the processor 402 during execution thereof by the machine 400. In an example, one or any combination of the processor 402, the main memory 404, the static memory 406, or the storage device 416 may constitute machine readable media. While the machine readable medium 422 is illustrated as a single medium, the term "machine readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that may be configured to store the one or more instructions 424. The term “machine readable medium” may also be taken to include any tangible medium that may be capable of storing, encoding, or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the embodiments of the present disclosure or that may be capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine readable medium” may accordingly be understood to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine readable media may include non-volatile memory, including, by way of example, 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. The instructions 424 may further be transmitted or received over a communications network 426 using a transmission medium via the network interface device 420 utilizing any one of a number of transfer protocols (e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., IEEE 802.11 standards family known as Wi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer (P2P) networks, among others. The term “transmission medium” may include any intangible medium that may be capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. Although the present embodiments have been described in detail, those skilled in the art will understand that various changes, substitutions, variations, enhancements, nuances, gradations, lesser forms, alterations, revisions, improvements and knock-offs of the embodiments disclosed herein may be made without departing from the spirit and scope of the embodiments in their broadest form.

Claims

CLAIMS What is claimed is: 1. A processor-implemented method for controlling basal insulin infusion to a subject to meet a predetermined glycemia, the method comprising: receiving recorded fasting blood glucose (FBG) measurements corresponding to at least a first basal insulin infusion to the subject; providing, based on the recorded FBG measurements of the subject, a range of future FBG measurements of the subject determined for (a) an estimation of titration noise for at least a second basal insulin infusion to the subject and (b) one or more changes in basal insulin dosage for the at least a second basal insulin infusion to the subject; and generating, using (a) and (b), one or more recommended basal insulin doses, for the at least a second basal insulin infusion to the subject, that align the range of future FBG measurements of the subject to the predetermined glycemia.

2. The method of claim 1, wherein: the recorded FBG measurements comprise one or more self-monitoring blood glucose (SMBG) and one or more continuous glucose monitoring (CGM) measurements.

3. The method of claim 2, wherein: the titration noise corresponds to sensor noise associated with the CGM measurements and one or more metabolic variabilities of the subject.

4. The method of claim 1, wherein: the predetermined glycemia comprises A1c < 7.0% and blood glucose (BG) level equal to or greater than 70 mg/dL.

5. The method of claim 4, wherein: the one or more recommended basal insulin doses are based on pharmacokinetics of a predetermined insulin type for the at least a second basal insulin infusion to the subject.

6. The method of claim 5, wherein: the one or more recommended basal insulin doses are generated based at least on an increment in dosing, measured from the at least a first basal insulin infusion to the subject.

7. The method of claim 6, wherein: at least one of the one or more recommended basal insulin doses can be the same as or different from the at least a first basal insulin infusion to the subject.

8. A computing system for controlling basal insulin infusion to a subject to meet a predetermined glycemia, the computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to perform a process comprising: receiving recorded fasting blood glucose (FBG) measurements corresponding to at least a first basal insulin infusion to the subject; providing, based on the recorded FBG measurements of the subject, a range of future FBG measurements of the subject determined for (a) an estimation of titration noise for at least a second basal insulin infusion to the subject and (b) one or more changes in basal insulin dosage for the at least a second basal insulin infusion to the subject; and generating, using (a) and (b), one or more recommended basal insulin doses, for the at least a second basal insulin infusion to the subject, that align the range of future FBG measurements of the subject to the predetermined glycemia.

9. The computing system of claim 8, wherein: the recorded FBG measurements comprise one or more self-monitoring blood glucose (SMBG) and one or more continuous glucose monitoring (CGM) measurements.

10. The computing system of claim 9, wherein: the titration noise corresponds to sensor noise associated with the CGM measurements and one or more metabolic variabilities of the subject.

11. The computing system of claim 8, wherein: the predetermined glycemia comprises A1c < 7.0% and blood glucose (BG) level equal to or greater than 70 mg/dL.

12. The computing system of claim 11, wherein: the one or more recommended basal insulin doses are based on pharmacokinetics of a predetermined insulin type for the at least a second basal insulin infusion to the subject.

13. The computing system of claim 12, wherein: the one or more recommended basal insulin doses are generated based at least on an increment in dosing, measured from the at least a first basal insulin infusion to the subject.

14. The computing system of claim 13, wherein: at least one of the one or more recommended basal insulin doses can be the same as or different from the at least a first basal insulin infusion to the subject.

15. A non-transient machine-readable storage medium having machine-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform a method for controlling basal insulin infusion to a subject to meet a predetermined glycemia, the method comprising: receiving recorded fasting blood glucose (FBG) measurements corresponding to at least a first basal insulin infusion to the subject; providing, based on the recorded FBG measurements of the subject, a range of future FBG measurements of the subject determined for (a) an estimation of titration noise for at least a second basal insulin infusion to the subject and (b) one or more changes in basal insulin dosage for the at least a second basal insulin infusion to the subject; and generating, using (a) and (b), one or more recommended basal insulin doses, for the at least a second basal insulin infusion to the subject, that align the range of future FBG measurements of the subject to the predetermined glycemia.

16. The medium of claim 15, wherein: the recorded FBG measurements comprise one or more self-monitoring blood glucose (SMBG) and one or more continuous glucose monitoring (CGM) measurements.

17. The medium of claim 16, wherein: the titration noise corresponds to sensor noise associated with the CGM measurements and one or more metabolic variabilities of the subject.

18. The medium of claim 15, wherein: the predetermined glycemia comprises A1c < 7.0% and blood glucose (BG) level equal to or greater than 70 mg/dL.

19. The medium of claim 18, wherein: the one or more recommended basal insulin doses are based on pharmacokinetics of a predetermined insulin type for the at least a second basal insulin infusion to the subject.

20. The medium of claim 19, wherein: the one or more recommended basal insulin doses are generated based at least on an increment in dosing, measured from the at least a first basal insulin infusion to the subject.

21. The medium of claim 20, wherein: at least one of the one or more recommended basal insulin doses can be the same as or different from the at least a first basal insulin infusion to the subject.