WO2025166261A1 - Method and system for calculating insulin dosing parameters in computer aided dosing - Google Patents

Abstract

A computer implemented method for controlling insulin dosing for a subject includes saving field collected glucose and insulin data for the subject. The method implements a virtual personal model, or digital twin, of the subject by using the field collected data to save electronic field collected traces of blood glucose levels and electronic field collected traces of insulin levels for the subject. A replay phase of the virtual personal model emulates test insulin therapies to generate virtual insulin doses that correspond to the field collected data. The method converges to a result by comparing field collected glucose traces and regenerated glucose traces. This method recommends a basal rate profile for the subject using saved replay data by calculating inversions of the virtual personal model by deconvolution.

Description

METHOD AND SYSTEM FOR CALCULATING INSULIN DOSING PARAMETERS IN COMPUTER AIDED DOSING CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to and the benefit of U.S. provisional patent application No.63/548,663, filed on February 1, 2024, and titled Method for Optimizing Insulin Dosing Parameters in Computer Aided Dosing System, the disclosure of which is hereby incorporated by reference herein in its entirety. STATEMENT OF GOVERNMENT RIGHTS [0002] This invention was made with government support under Grant Nos. DK085623, DK127551 and DK051562, awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND [0003] Insulin therapies for subjects suffering diabetes and other illnesses are affected by various insulin dosing parameters, generally pre-programmed for each individual and/or tracked over time. These parameters mainly consist of a programmed basal dose (basal insulin injection) or basal rate (BR, used in insulin pump therapy), the insulin-to-carbohydrate ratio (CR), and the correction factor (CF), which are used to calculate the insulin dose at basal conditions, at pre- mealtimes to compensate for postprandial glucose excursions, and at hyperglycemic events to lower high glucose values. However, these parameters differ greatly between individuals and within the same person, and using suboptimal values in insulin therapies may result in insulin over- or under-delivery, compromising glucose control. [0004] Improvements in dosing insulin are necessary to achieve greater success in medical treatment. Advances in computer implemented dosing technologies are a particular area of improvement discussed herein. This disclosure includes, but is not limited to, algorithmic approaches to insulin dosing that utilize mathematical approaches, including convolutional networks, and associated deconvolution, to maximize efficiency and optimize insulin treatments on an individual basis. [0005] In some embodiments, artificial intelligence and machine learning techniques may be used in optional embodiments of this disclosure. Machine Learning (ML) and Artificial Intelligence (AI) systems are in widespread use in customer service, marketing, and other industries, including medicine and science. Machine learning is considered a subset of more general artificial intelligence operations, and AI endeavors may utilize numerous instances of machine learning to make decisions, predict outputs, and perform human-like intelligent operations. Machine learning protocols typically involve programming a model that instantiates an appropriate algorithm for a given computing environment and training the model on a particular data set or domain with known historical results. The results are generally known outputs of many combinations of parameter values that the algorithm accesses during training. The model uses numerous statistical and mathematical operations to learn how to make logical decisions and generate new outputs based on the historical training data. Machine learning (ML) includes, but is not limited to, a number of models such as neural networks, deep learning algorithms, support vector machines, data clustering, regression models, and Monte Carlo simulations. Other models may utilize linear regression, logistic regression, support vector machines, K-means clustering, classification models such as a binary classifier or a multi-class classifier, clustering models, anomaly detection, other supervised learning models, and even combinations of one or more machine language model types. Most of these take vectors of data as inputs. [0006] The term “artificial intelligence,” therefore, includes any technique that enables one or more computing devices or comping systems (i.e., a machine) to mimic human intelligence. Artificial intelligence (AI) includes, but is not limited to, knowledge bases, machine learning, representation learning, and deep learning. The term “machine learning” is generally a subset of AI that enables a machine to acquire knowledge by extracting patterns from raw data. [0007] The term “representation learning” may be used as a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, or classification from raw data. Representation learning techniques include, but are not limited to, autoencoders. [0008] The term “deep learning” may also be considered a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, classification, etc. using layers of processing. Deep learning techniques include, but are not limited to, artificial neural network or multilayer perceptron (MLP). [0009] Machine learning models include supervised, semi-supervised, and unsupervised learning models. In a supervised learning model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with a labeled data set (or dataset). In an unsupervised learning model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with an unlabeled data set. In a semi-supervised model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with both labeled and unlabeled data. [0010] Some machine learning models are designed for a specific data set or domain and are highly expert at handling the nuances within that narrow domain. It is with respect to these and other considerations that the various aspects of the present disclosure as described below are presented. [0011] This disclosure combines algorithms deciphered by artificial intelligence and machine learning with currently know systems and models that gather data from a patient on a real time basis. Accordingly this disclosure can utilize sensors and medical equipment that measure glucose and insulin and other biometric data in a patient’s body and then use that data to improve insulin dosing. SUMMARY [0012] A computer implemented method for controlling insulin dosing for a subject includes using a computer having a processor and computer memory storing software that executes steps. The steps may include saving field collected data for the subject in the computer memory, wherein the field collected data comprises continuous glucose monitoring data and insulin data over a collection period of time; and implementing a virtual personal model of the subject by using the field collected data to save electronic field collected traces of blood glucose levels and electronic field collected traces of insulin levels for the subject over a designated period of time. The virtual personal model calculates an output plasma glucose concentration (G) from inputs comprising subcutaneous insulin infusion data (uins) and meal carbohydrate mass data (umeal), and a net effect signal of unmodeled factors (w). The steps of the method further include implementing a replay phase of the virtual personal model for a selected time period, and the replay phase may include emulating test insulin therapies to generate virtual insulin doses that correspond to the subcutaneous insulin infusion data from the field collected data. The method uses the virtual personal model to reproduce the field collected glucose traces and field collected insulin traces and output regenerated glucose traces and regenerated insulin traces, wherein the replay phase uses the net effect signal and the meal carbohydrate mass data as feedback data returning into the virtual personal model. The method converges to a result by comparing field collected glucose traces and regenerated glucose traces and saving replay data for the selected time period as a valid time period for comparisons in which a difference between the field collected glucose traces and the regenerated glucose traces is within a threshold. This method recommends a basal rate profile for the subject using saved replay data by calculating inversions of the virtual personal model by deconvolution. The model inversions include estimating a net effect signal needed to achieve a continuous glucose monitoring that matches the regenerated glucose traces; and determining an insulin input to the virtual personal model that maintains a blood glucose profile at a selected level. [0013] In some embodiments, the method may utilize meal intake data that has mass of carbohydrates data and wherein the insulin data comprises insulin basal values and/or insulin bolus values. [0014] In some embodiments, the method may include standardizing the field collected data by at least one of sorting, time stamping, synchronizing, removing duplicates, identifying gaps, or filling gaps in the field collected data. [0015] In some embodiments, the method may include standardizing the data by parsing the data into selected time periods. [0016] In some embodiments, the method may include parsing the data into selected time periods of extended days that include head hours from the previous day and tail hours from the next day. [0017] In some embodiments, the method may include validating the field collected data by determining that within the selected time period, continuous glucose monitoring gaps are less than a preferred number of hours, at least a preferred percentage of continuous glucose measurements are present, and at least a set number of insulin bolus doses occurred. [0018] In some embodiments, the method may include reconstructing missing meal intake data. [0019] In some embodiments, the method may include reconstructing missing meal intake data by utilizing a logistic regression-based detection algorithm having features, coefficients, and thresholds calculated from in-patient clinical trial data. [0020] In some embodiments, the method may include reconstructing missing meal intake data with the algorithm by calculating the net effect signal with the in-patient clinical trial data, wherein the net effect signal for the in-patient clinical trial data determines the rate of appearance of unknown meal disturbances. [0021] In some embodiments, the method may include a secondary detector trained to detect hypoglycemic treatments by using logistic regression to fit a second order polynomial to extract prevailing value, slope, and curvature, and a minimum continuous glucose monitoring reached. [0022] In some embodiments, the method may include, for multiple daily injection therapy, calculating an artificial meal input by analyzing a total daily intake of insulin and basal glucose. [0023] In some embodiments, the method may include implementing a virtual personal model of the subject further comprises accounting for circadian rhythms, physical activity, illness, and/or menstrual cycles in the net effect signal. [0024] In some embodiments, the method may include, for multiple daily injection therapy, implementing a virtual personal model that is characterized with an insulin basal dose input. [0025] In some embodiments, the method may include implementing a virtual personal model of the subject further includes linearizing the virtual personal model; solving requisite linear differential equations organized in a continuous state model; and discretizing the continuous state model into a discrete state-space model. [0026] In some embodiments, the method may include implementing a virtual personal model of the subject further comprises identifying selected parameters for the subject that minimize the difference between the field collected glucose traces and the regenerated glucose traces is within a threshold. [0027] In some embodiments, the method may include selecting the parameters by minimizing the difference between the field collected glucose traces and the regenerated glucose traces. [0028] In some embodiments, the method may include selecting the parameters by estimating an insulin sensitivity parameter, an insulin time constant, and a set of carbohydrate absorption parameters. [0029] In some embodiments, the method may include recommending a basal rate profile for the subject by predicting the regenerated glucose trace in response to a basal does of insulin in a fasting state. [0030] In some embodiments, the method may include using the virtual personal model to calculate values of prandial dosing parameters by evaluating combinations of the insulin-to- carbohydrate ration (CR) and the correction factor (CF) and resulting effects on glycemia of the virtual personal model of the subject. [0031] In some embodiments, the method may include using the virtual personal model to calculate values of automated insulin delivery (AID) dosing parameters by evaluating combinations of the insulin-to-carbohydrate ration (CR), the correction factor (CF), the basal insulin rate (BR) and other parameters used with a closed loop AID system and resulting effects on glycemia of the virtual personal model of the subject. [0032] In some embodiments, the method may include reducing the number of profile segments for each therapy. BRIEF DESCRIPTION OF THE DRAWINGS [0033] Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. [0034] FIG.1A is a flow chart of an example method of utilizing computer aided dosing for insulin according to this disclosure. [0035] FIG.1B is a flow chart of a replay phase of a virtual personal model of computer aided insulin therapy according to this disclosure, utilizing a net effect signal and meal carbohydrate mass as feedback parameters. [0036] FIG.2 is a high level functional block diagram of an embodiment of the present disclosure, or an aspect of an embodiment of the present disclosure. [0037] FIG.3A is a computer architecture diagram showing a computing system capable of implementing aspects of the present disclosure in accordance with one or more embodiments. [0038] FIG.3B is a computer architecture diagram showing a networking environment that allows for data communication with a computing system capable of implementing aspects of the present disclosure in accordance with one or more embodiments. [0039] FIG.4 is a block diagram that illustrates a system 130 including a computer system 140 and the associated Internet 11 connection upon which an embodiment may be implemented. [0040] FIG.5 illustrates a system in which one or more embodiments of the disclosure can be implemented using a network, or portions of a network or computers. Although the present disclosure glucose monitor, artificial pancreas or insulin device (or other interventional or diagnostic device) may be practiced without a network. [0041] FIG.6 illustrates an embodiment that includes, but is not limited thereto, a system, method, and computer readable medium that a) provides optimizing insulin dosing parameters in computer aided dosing system according to this disclosure. DETAILED DESCRIPTION [0042] In some aspects, the disclosed technology relates to systems, methods, and computer- readable medium improving insulin therapy dosing. Although example embodiments of the disclosed technology are explained in detail herein, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the disclosed technology 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 disclosed technology is capable of other embodiments and of being practiced or carried out in various ways. [0043] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. 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. [0044] By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named. [0045] 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 disclosed technology. 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. [0046] As discussed herein, a “subject” (or “patient”) may be any applicable human, animal, or other organism, living or dead, or other biological or molecular structure or chemical environment, and may relate to particular components of the subject, for instance specific organs, tissues, or fluids of a subject, may be in a particular location of the subject, referred to herein as an “area of interest” or a “region of interest.” [0047] A detailed description of aspects of the disclosed technology, in accordance with various example embodiments, will now be provided with reference to the accompanying drawings. The drawings form a part hereof and show, by way of illustration, specific embodiments and examples. In referring to the drawings, like numerals represent like elements throughout the several figures. [0048] An aspect of an embodiment of the present disclosure provides, among other things, a system, method and computer readable medium for optimizing insulin dosing parameters in computer aided dosing system. [0049] An aspect of an embodiment of the present disclosure provides, among other things, a system, method and computer readable medium for providing optimization methods aimed to periodically adapt therapy parameters that are presented for treatment under multiple daily injections (MDI), sensor augmented pump (SAP), and automatic insulin delivery (AID) systems. [0050] Figure 2 is a high level functional block diagram of an embodiment of the present disclosure, or an aspect of an embodiment of the present disclosure. [0051] As shown in Figure 2, a processor or controller 102 communicates with the glucose monitor or device 101, and optionally the insulin device 100. The glucose monitor or device 101 communicates with the subject 103 to monitor glucose levels of the subject 103. The processor or controller 102 is configured to perform the required calculations. Optionally, the insulin device 100 communicates with the subject 103 to deliver insulin to the subject 103. The processor or controller 102 is configured to perform the required calculations. The glucose monitor 101 and the insulin device 100 may be implemented as a separate device or as a single device. The processor 102 can be implemented locally in the glucose monitor 101, the insulin device 100, or a standalone device (or in any combination of two or more of the glucose monitor, insulin device, or a stand along device). The processor 102 or a portion of the system can be located remotely such that the device is operated as a telemedicine device. Figure 2 also illustrates sensors and detectors that can be used to gather field data measurements for a subject, in real time or from samples, from the patient’s blood. These kinds of sensors and detectors may be stand alone equipment or incorporated into an insulin delivery device or pump. [0052] Referring to Figure 3A, in its most basic configuration, computing device 144 typically includes at least one processing unit 150 and memory 146. Depending on the exact configuration and type of computing device, memory 146 can be volatile (such as RAM), non- volatile (such as ROM, flash memory, etc.) or some combination of the two. [0053] Additionally, 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 is the figure by removable storage 152 and non- removable storage 148. Computer storage media includes 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 are all examples of computer storage media. Computer storage media includes, but is not 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, the device. [0054] The device 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 carry information in a communication media. Communication media typically embodies 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” means 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 includes 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 includes both storage media and communication media. [0055] In addition to a stand-alone computing machine, embodiments of the disclosure can also be implemented on a network system comprising a plurality of computing devices that are in communication with a networking means, such as a network with an infrastructure or an ad hoc network. The network connection can be wired connections or wireless connections. As a way of example, Figure 3B illustrates a network system in which embodiments of the disclosure can be implemented. In this example, the network system comprises computer 156 (e.g. a network server), network connection means 158 (e.g. wired and/or wireless connections), computer terminal 160, and PDA (e.g. a smart-phone) 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 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 be glucose monitor device. In an embodiment, it should be appreciated that the module listed as 156 may be a glucose monitor device, artificial pancreas, and/or an insulin device (or other interventional or diagnostic device). Any of the components shown or discussed with Figure 3B may be multiple in number. The embodiments of the disclosure can be implemented in anyone of the devices of the system. For example, execution of the instructions or other desired processing can be performed on the same computing device that is anyone of 156, 160, and 162. Alternatively, an embodiment of the disclosure can be performed on different computing devices of the network system. For example, certain desired or required processing or execution can be performed on one of the computing devices of the network (e.g. server 156 and/or glucose monitor device), 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 can be performed at one computing device (e.g. server 156 and/or insulin device, artificial pancreas, or glucose monitor device (or other interventional or diagnostic device)); and the other processing or execution of the instructions can be performed at different computing devices that may or may not be networked. For example, the certain processing can be performed at terminal 160, while the other processing or instructions are passed to device 162 where the instructions are executed. This scenario may be of particular value especially when the PDA 162 device, for example, accesses to the network through computer terminal 160 (or an access point in an ad hoc network). For another example, software to be protected can be executed, encoded or processed with one or more embodiments of the disclosure. The processed, encoded or executed software can then be distributed to customers. The distribution can be in a form of storage media (e.g. disk) or electronic copy. [0056] Figure 4 is a block diagram that illustrates a system 130 including a computer system 140 and the associated Internet 11 connection upon which an embodiment may be implemented. Such configuration is typically used for computers (hosts) connected to the Internet 11 and executing a server or a client (or a combination) software. 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 4. The 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 glucose monitor device, an artificial pancreas, an insulin delivery device (or other interventional or diagnostic 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 4 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 are not germane to the present disclosure. 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 4 may, for example, be an Apple Macintosh computer or Power Book, or an IBM compatible PC. Computer system 140 includes 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 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 to be executed by processor 138. [0057] 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 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. 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 DVD) for reading from and writing to a removable optical disk, is 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 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 includes an Operating System (OS) stored in a non-volatile storage for managing the computer resources and provides 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 operating systems are Microsoft Windows, Mac OS X, and Linux. [0058] The term "processor" is meant to include any integrated circuit or other electronic device (or collection of 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., silicon "die"), or 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. [0059] 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 allows a user to view, enter, and/or edit information that is relevant 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 processor 138. Another type of user input device is 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. 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), that allows the device to specify positions in a plane. [0060] The computer system 140 may be used for implementing the methods and techniques described herein. According to one embodiment, those methods and techniques are 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 causes 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 of the disclosure are not limited to any specific combination of hardware circuitry and software. [0061] 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 is 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 includes 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 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. [0062] 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 can 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 can 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 can receive the data carried in the infra-red 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. [0063] Computer system 140 also includes a communication interface 141 coupled to bus 137. Communication interface 141 provides a two-way data communication coupling to a network link 139 that is 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 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. [0064] Wireless links 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. 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 to data equipment operated by an Internet Service Provider (ISP) 142. ISP 142 in turn provides data communication services through the world wide packet data communication network Internet 11. Local network 111 and Internet 11 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. [0065] 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. [0066] The concept of a) optimizing insulin dosing parameters in computer aided dosing system and/or b) providing optimization methods aimed to periodically adapt therapy parameters that are presented for treatment under multiple daily injections (MDI), sensor augmented pump (SAP), and automatic insulin delivery (AID) systems has been developed by the present inventor. As seen from the algorithm and methodology requirements discussed herein, the procedure is readily applicable into devices for a) optimizing insulin dosing parameters in computer aided dosing system and/or b) providing optimization methods aimed to periodically adapt therapy parameters that are presented for treatment under multiple daily injections (MDI), sensor augmented pump (SAP), and automatic insulin delivery (AID) systems, and may be implemented and utilized with the related processors, networks, computer systems, internet, and components and functions according to the schemes disclosed herein. [0067] Figure 5 illustrates a system in which one or more embodiments of the disclosure can be implemented using a network, or portions of a network or computers. Although the present disclosure glucose monitor, artificial pancreas or insulin device (or other interventional or diagnostic device) may be practiced without a network. [0068] Figure 5 diagrammatically illustrates an exemplary system in which examples of the disclosure can be implemented. In an embodiment the glucose monitor, artificial pancreas or insulin device (or other interventional or diagnostic device) may be implemented by the subject (or patient) locally at home or other desired location. However, in an alternative embodiment it may be implemented in a clinic setting or assistance setting. For instance, referring to Figure 5, a clinic setup l58 provides 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 glucose monitoring device 10 can be used to monitor and/or test the glucose levels of the patient—as a standalone device. It should be appreciated that while only glucose monitor device 10 is shown in the figure, the system of the disclosure and any component thereof may be used in the manner depicted by Figure 5. The system or component 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 glucose monitor device 10 (or other related devices or systems such as a controller, and/or an artificial pancreas, an insulin pump (or other interventional or diagnostic device), 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 monitor and/or test can be short term (e.g. clinical visit) or long term (e.g. clinical stay or family). The glucose monitoring device outputs can 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 glucose monitoring device output can be delivered to computer terminal 168 for instant or future analyses. The delivery can be through cable or wireless or any other suitable medium. The glucose monitoring device output from the patient can also be delivered to a portable device, such as PDA 166. The glucose monitoring device outputs with improved accuracy can 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 can be wired or wireless. [0069] In addition to the glucose monitoring device outputs, errors, parameters for accuracy improvements, and any accuracy related information can be delivered, such as to computer 168, and / or glucose monitoring center 172 for performing error analyses. This can provide a centralized accuracy monitoring, modeling and/or accuracy enhancement for glucose centers (or other interventional or diagnostic centers), due to the importance of the glucose sensors (or other interventional or diagnostic sensors or devices). [0070] Examples of the disclosure can also be implemented in a standalone computing device associated with the target glucose monitoring device, artificial pancreas, and/or insulin device (or other interventional or diagnostic device). An exemplary computing device (or portions thereof) in which examples of the disclosure can be implemented is schematically illustrated in Figure 3A. [0071] FIG.6 is a block diagram illustrating an example of a machine upon which one or more aspects of embodiments of the present disclosure can be implemented. [0072] Referring to FIG.6, an aspect of an embodiment of the present disclosure includes, but not limited thereto, a system, method, and computer readable medium that a) provides optimizing insulin dosing parameters in computer aided dosing system and/or b) provides for optimization methods aimed to periodically adapt therapy parameters that are presented for treatment under multiple daily injections (MDI), sensor augmented pump (SAP), and automatic insulin delivery (AID) systems, which illustrates a block diagram of an example machine 400 upon which one or more embodiments (e.g., discussed methodologies) can be implemented (e.g., run). [0073] Examples of machine 400 can include logic, one or more components, circuits (e.g., modules), or mechanisms. Circuits are tangible entities configured to perform certain operations. In an example, circuits can 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) can be configured 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 can 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, causes the circuit to perform the certain operations. [0074] In an example, a circuit can be implemented mechanically or electronically. For example, a circuit can comprise dedicated circuitry or logic that is specifically configured to perform one or more techniques such as discussed above, such as including a special-purpose processor, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). In an example, a circuit can comprise programmable logic (e.g., circuitry, as encompassed within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform the 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) can be driven by cost and time considerations. [0075] Accordingly, the term “circuit” is understood to encompass a tangible entity, be that an entity that is 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 can be configured as respective different circuits at different times. Software can 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. [0076] In an example, circuits can provide information to, and receive information from, other circuits. In this example, the circuits can be regarded as being communicatively coupled to one or more other circuits. Where multiple of such circuits exist contemporaneously, communications can 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 can 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 can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further circuit can then, at a later time, access the memory device to retrieve and process the stored output. In an example, circuits can be configured to initiate or receive communications with input or output devices and can operate on a resource (e.g., a collection of information). [0077] The various operations of method examples described herein can be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented circuits that operate to perform one or more operations or functions. In an example, the circuits referred to herein can comprise processor-implemented circuits. [0078] Similarly, the methods described herein can be at least partially processor- implemented. For example, at least some of the operations of a method can be performed by one or processors or processor-implemented circuits. The performance of certain of the operations can 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 can 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 can be distributed across a number of locations. [0079] The one or more processors can 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 can 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).) [0080] Example embodiments (e.g., apparatus, systems, or methods) can be implemented in digital electronic circuitry, in computer hardware, in firmware, in software, or in any combination thereof. Example embodiments can 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). [0081] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it 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 or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. [0082] In an example, operations can 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 can 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)). [0083] The computing system can include clients and servers. A client and server are generally 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 require consideration. Specifically, it will be appreciated that the choice of 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 can be a design choice. Below are set out hardware (e.g., machine 400) and software architectures that can be deployed in example embodiments. [0084] In an example, the machine 400 can operate as a standalone device or the machine 400 can be connected (e.g., networked) to other machines. [0085] In a networked deployment, the machine 400 can operate in the capacity of either a server or a client machine in server-client network environments. In an example, machine 400 can act as a peer machine in peer-to-peer (or other distributed) network environments. The machine 400 can 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 methodologies discussed herein. [0086] Example machine (e.g., computer system) 400 can 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 can communicate with each other via a bus 408. The machine 400 can 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 can be a touch screen display. The machine 400 can 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. [0087] The storage device 416 can 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 can 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 can constitute machine readable media. [0088] While the machine readable medium 422 is illustrated as a single medium, the term "machine readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that configured to store the one or more instructions 424. The term “machine readable medium” can also be taken to include any tangible medium that is 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 methodologies of the present disclosure or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine readable medium” can accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine readable media can 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. [0089] The instructions 424 can 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 can 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” shall be taken to include any intangible medium that is 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. [0090] 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. [0091] It should be appreciated that any element, part, section, subsection, or component described with reference to any specific embodiment above may be incorporated with, integrated into, or otherwise adapted for use with any other embodiment described herein unless specifically noted otherwise or if it should render the embodiment device non-functional. Likewise, any step described with reference to a particular method or process may be integrated, incorporated, or otherwise combined with other methods or processes described herein unless specifically stated otherwise or if it should render the embodiment method nonfunctional. Furthermore, multiple embodiment devices or embodiment methods may be combined, incorporated, or otherwise integrated into one another to construct or develop further embodiments of the disclosure described herein. [0092] It should be appreciated that any of the components or modules referred to with regards to any of the present disclosure 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. [0093] 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. [0094] 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. [0095] 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. [0096] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. 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. [0097] By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, or method steps, even if the other such compounds, material, particles, or method steps have the same function as what is named. [0098] 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. [0099] 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. [00100] It should be appreciated that as discussed herein, a subject may be a human or any animal. It should be appreciated that an animal may be a variety of any applicable type, including, but not limited thereto, mammal, veterinarian animal, livestock animal or pet type animal, etc. As an example, the animal may be a laboratory animal specifically selected to have certain characteristics similar to human (e.g. rat, dog, pig, monkey), etc. It should be appreciated that the subject may be any applicable human patient, for example. [00101] 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.” [00102] Additional descriptions of aspects of the present disclosure will now be provided with reference to the accompanying drawings. The drawings form a part hereof and show, by way of illustration, specific embodiments or examples. [00103] Insulin therapies are affected by various insulin dosing parameters, generally pre- programmed for each individual and/or tracked over time. These parameters mainly consist of a programmed basal dose (basal insulin injection) or basal rate (BR, used in insulin pump therapy), the insulin-to-carbohydrate ratio (CR), and the correction factor (CF), which are used to calculate the insulin dose at basal conditions, at pre-mealtimes to compensate for postprandial glucose excursions, and at hyperglycemic events to lower high glucose values. However, these parameters differ greatly between individuals and within the same person, and using suboptimal values in insulin therapies may result in insulin over- or under-delivery, compromising glucose control. [00104] Three optimization methods aiming to periodically adapt therapy parameters are presented for treatment under multiple daily injections (MDI), sensor augmented pump (SAP), and automatic insulin delivery (AID) systems. All methods are built on a replay simulation methodology that allows to test “what-if” scenarios. This is a data-driven tool based on regularized deconvolution and a subject-specific model of blood glucose (BG) and insulin dynamics, from which a signal that represents the unmodeled phenomena, referred to as net effect (NE), is extracted. Once the net effect signal is obtained, this is fed back to the model with the insulin/meal record to reproduce the data with all its included variability. [00105] The optimization method consists of four phases, three of which are common to all insulin therapies, while the fourth is different depending on the insulin therapy under consideration. The phases of the proposed method are described below. [00106] An initial data preprocessing is done to parse data and standardize its format given a dataset containing continuous glucose monitoring (CGM), insulin, meal, and therapy profiles. Standardization consists of sample sorting, timestamp synchronization, duplicate removal and gap localization and filling. In detail, for each data stream (e.g., CGM), the following steps are taken: [00107] The available data is sorted by datetime. [00108] Any duplicate is removed, keeping the first occurrence in the data stream. [00109] The method may include filling gaps in the biomedical data being used. Gaps filling may include: [00110] for CGM, gaps shorten than 1h are interpolated. [00111] Meals records and reconstructed meals (see section 1.1) are collated in a single data stream. [00112] The field data is also subject to Data Regularization: [00113] Available datetimes are rounded to the closest 5min interval (e.g.12:03 becomes 12:05). [00114] CGM values with the same rounded datetime are averaged and the average is associated as the unique value corresponding to this 5min interval. [00115] Insulin values with the same rounded datetime are added and the sum is associated as the unique value corresponding to this time interval. [00116] In case of recorded basal rates (vs micro bolus), the last available recorded rate is used to fill the 5 minute interval value with the U/5min value. If boluses are also present in the interval they are added to the basal value. [00117] Meals values with the same rounded datetime are added and the sum is associated as the unique value corresponding to this time interval. [00118] Subsequently, preprocessed data are parsed into “extended days”: each extended day is obtained from a 24-hour calendar day by adding a 6-hour head and a 2-hour tail from the previous and next day, respectively. Finally, each day of data is classified as valid or not based on a data quality assessment that verifies if CGM gaps do not exceed 3 hours, there is at least 70% of CGM records available, and there are at least two bolusing events. [00119] Data quality can often be a concern in long term, outpatient, clinical trials or a fortiori outside of organized research. Recording meals accurately with regards to timing and amount of carbohydrates is burdensome and people with diabetes often forget to record meals or take note of them long after the fact. To ensure robustness of the proposed optimization method against poor data quality, algorithmic meal detections can mitigate the absence of recorded meals. To do so, we have developed a meal detection algorithm that uses device generated inputs (i.e., insulin and CGM records) to reconstruct and amend the meal record of the user. This logistic regression-based detection algorithm calculates the value of numerous features for each 5-minute interval of the day and based on the combination of those features and their respective coefficients determines if a meal is likely to have occurred at that time. [00120] The predictor variables and their respective coefficients were chosen by training the algorithm on verified meal records collected during an inpatient clinical study. The output of the module is a meal record that includes all acknowledged meals as well as detected meals that were unreported. Meal sizes are assumed to be the average meal size if there is no meal that is reported within a window of time surrounding the detection. For detection with associated meals the meal size is the sum of the meals within close proximity of the detections. [00121] To determine the appropriate features, coefficients, and threshold for the detection algorithm data where meals were known was used to train the model. For this purpose, and without limiting the disclosure inpatient clinical trial data from the Glucose Variability Study conducted at UVA was used. This dataset was selected, because the meal record for each of the patients was recorded by a part of the study team. This in turn ensures that meals were of the amount that was recorded and occurred at that time. As was stated before, the features selected were found from CGM data or were based on the net effect. The net effect has been used in the past to estimate meal disturbances and was used in this context to determine the rate of appearance of unknown meal disturbances. After feature selection and training, the logistic regression meal detector was able to detect meals with a 78.5% true positive rate while only incurring a 0.7% false positive rate. It is important to note that the true positive rate was found by determining if a meal had occurred within 30 mins of a meal and in the final version of the algorithm this rate is higher, because undetected meals are added to the meal record. [00122] A secondary detector is specifically trained to detect hypoglycemic treatments. Also based on logistic regression, it only uses the characteristics of 30min of CGM data, fitting a second order polynomial to extract prevailing value, slope, and curvature, and the minimum CGM reached. [00123] Data is loaded in from an online database and serves as an input in vector form. This includes a vector that has the times of each CGM reading, a vector of recorded meal amounts, vector of bolus amounts, and a vector containing all insulin deliveries. [00124] Features are calculated using the data vectors. The vector of net effect values is found using the calculations described by Patek et al [2]. The features used in the logistic regression are reported in Table 1. Table 1. Features used in logistic regression model for meal reconstruction. Description Formula ^_^^^ Maximum product of the first and second ated at 0 (±30 mins) _{^ ^ ^^^ ^^^^} derivative of NE satur _{^^^ ^ = max^max $0, ^^ ^ ^^^ %^ &'( ) ^ ^ 6} [00125 ng the following formula: [00126] l=β_0+β_1 x_1+⋯+β_12 x_12 (1) [00127] The value of each of the predictor coefficients is given in Table 2. Table 2 - Coefficients and threshold for logistic regression formula. C^{onstant Value 3# 0.0099} 0 [00128] A meal is said to be detected at time, t, if the output of the logistic regression is above the threshold, 4. Defined by the following formula: _{[00129] ^5^56^/') ^^^ = 71 /& 8^^^ > 4 ^2^} fit on the CGM values from 15 minutes before until 15 minutes after that interval. _{[00131] :^;^ = :# + :^^^^^;^ + :^^^^^;^^ ^3^} the minimum CGM value during that period, ^^^_=^>, are used as features for the logistic regression formula output: _{[00133] ?^^^ = 3#:# + 3^:^ + 3^:^ + 3^^^^=^> + 3^ ^4^} [00134] This output is then transformed into a probability, A_BCDE. _{[00135] ABCDE^^^ = ^ ^^FGH^I^ ^5^} treatment having occurred is above a threshold, 4_BCDE, a detection is triggered. _{[00137] ℎ?:' 1 /& ABCDE^^^ > 4BCDE ^LFM^^^^ = N ^6^ 0 '^ℎ5(O/P5} determined by identifying consecutive 5-minute intervals where ℎ?:'_^LFM^ is equal to 1. The timing of the event is chosen to be the 5-minute interval that is 25% of the way through the window of time where the detection value is nonzero. At these times, a carbohydrate amount of 20 grams is added to the record. As a final step, hypoglycemia treatment detections are only added to the record if they occurred more than 30 minutes before or 90 minutes after a recorded or previously detected meal. Table 3. Coefficients and threshold for hypoglycemia treatment detector Variable Description Value N m 4 ^{1 6} 8 ₀ 6 [00 39] Some peop e us ng erapy do no carbo ydra e coun bu on y o ow a fixed dose regiment. Since our model-based approach (see below) uses timing and amount of carbohydrates in the consumed meals, this input is artificially reconstructed following insulin dosing rules: _{[00140] \=FM]^;^ = ^_^ `^## abc `\dE]ef^;^ − G(k)^gh ^!## ijkl , 0l ; ∈ [1, )] ^7^} to (6), the input U_tuvw can be nonzero only if the input U_rxwyz is nonzero, thus including only instances of bolused carbohydrate intakes. [00142] For each extended day, the Subcutaneous (SC) Oral Glucose Minimal Model (SOGMM) is individualized along with the net effect signal using the collected data. The mathematical model is described by the following equations: &_{^ ^ ^{ = −| ^^ ^ ;^^^ + ;^^^ } − ^d − |c ^ ~ + + ^} [00143] proportion of active insulin in the remote compartment (mU/L), k_f^^ and k_f^^ are the insulin masses in the first and second compartments of the interstitial transport model (mU), k is the insulin mass in plasma (mU), and ^_^ and ^_^ are the glucose masses in the first and second compartments of the oral glucose transport model (mg). The model output is ^, and inputs are ^_^>f and ^_=FM] corresponding to the SC insulin infusion (mU/min) and meals (mg/min), respectively. The signal _{^ = ^^^^^^^^^^^^ M ^^^^ is the plasma glucose rate of appearance (mg/dL/min), and ^ is the NE signal} unmodeled phenomena, for instance, due to circadian rhythms, physical activity, illness, or menstrual cycle. [00144] Model parameters along with their corresponding population (default) values are listed in Table 4. Table 4. SOGMM parameters

odel collecting data points are also collected before and after this period. For example, if the primary period is one day, data is gathered 6 hours before its start and 2 hours into the following day. The number parameters to estimate for the pre-specified period and the length of net effect signal are proportional to the length of the pre-specified time period. For example, when the time-period encapsulates multiple days, additional daily parameters are estimated. [00146] To solve the differential equations of the SOGMM model described above (_{Equations (8) and (9)), we first linearize it around the steady state (^ = ^d, k = kd, and ~ = 0).} This results on changing the first equation to the following: [_{00147] ^{ = −|}^^ − ^d^ − | ^^^^^^^^^^^^ c ^d ~ + ^^^^ + ^ ^10^} be organized in the form of a continuous state space model. The state is a collection of all relevant quantities describing the differential equations, e.g., a basic state has the form ^ = ^{[^ ~ k k} _f^^ ^k _f^^ ^{^} _^ ^{^} _^ ^]a (or ^ = [^{^ ~ k k}f^^ ^kf^^ ^k^^ ^k^^ ^{^}^ ^{^}^^]a in the case of basal insulin injections). The continuous state- space representation can then be written as follows: ^_{^^ ^^ ^^^^ ^^ ^ ^ ^ ^ ^^^^^ ^^^^^^^^^ ^^ ^^ ^ ^ ^ ^ [00149] The continuous state-space model is then discretized using a sampling time if = 5} min and assuming a zero-order hold on the model inputs. [00150] The final discrete state-space model can be summarized as follows: ^_{^^ + S^ = ^ ^^^^ + ^^^^^^ ^^^^^^^^^ + ^^[^ ^ ^^[^ ^^^^ + ^¡¢^^ ^¡¢^^^^^ + ^£YZ[ ^£YZ[¤¥^^^ + ^¦¦^^^ §^^^ = ¨ ^^^^ ^11^} ^_{^[^ ^^^^ as this disclosure assume that the insulin absorption parameters (;M^, ;M^, ;^) do not} change during a single extended day. However, different meals ^_¡¢^^^^^ (and by extension hypoglycemia treatments ^_£YZ[¤¥^^^) are modeled as with different c_{arbohydrate absorption parameters ^&=, ;= = = ^ , ;^ , ;^ ^=∈{=FM]f ∪ BCDEa«}.} [00152] For each extended the model by identifying selected parameters, following a Bayesian-based optimization procedure to minimize the difference between glucose measurements and predictions, and penalize deviations of model parameters from their population values. [_{00153] Using the model of section 2.1.2 with patient data ­ = {^, \=FM] , \dMfM], \dE]ef}} this disclosure for example estimate one insulin sensitivity (|_^) and one insulin time constant (e.g. ;_M^) per extended day. Critically, this disclosure also estimates one set of c_{arbohydrate absorption parameters ^&, ;^^, ;^^, ;^^^^ for each meal to account for inter-meal} absorption differences (e.g., from the type and ratio of macronutrients in the consumed meal) and errors in the reconstructed meal input. Population parameters are used for the remaining parameters. [_{00154] The list of parameters is denoted by ® = .&=, ;= = = ^ ^ , ;^ , ;^ , |^ 0=∈=FM]f & ^∈^MCf.} Parameters are estimated following a maximum-a- probability of observing ® conditioned on the data ­ is maximized. Note that in this step the residual metabolic signal (^) is assumed equal to zero. The parameter vector is thus obtained as: _{®° = _(± ^ ²_^ ³^®|^, \dMfM] , \dE]ef, ^ = 0^} [00155] = _{(± ®, , \d ef, ^ = 0^³^®^ ^12^ ³^G| θ, Urvzvw, Urxwyz, ω = 0^ is the likelihood of the measurements G calculated by assuming} they are independently and identically distributed under a normal distribution with a constant _{coefficient of variation and mean ·^®, \dMfM], \dE]ef, ^ = 0^. [00157] ?^M^M = ¸ ^¹^0^ + ℬ^ + »^ ^13^} [00158] With ¸, ℬ, and » being the corresponding expanded matrices of model (8). [00159] The initial state ~^°^0^ at time of day t₀ can be estimated or directly determined by _{assuming that basal insulin doses were given each day at the same time t½ ∈ [0, 24h],} considering the other model inputs to be zero. In other words, ~^{°^}0^{^} is the steady state resulting from a train of Dirac basal inputs given at t_B in the absence of any meals and boluses. A closed form for ~^0^ is derived in the appendix as: _{[00160] ~°^0^ = 5^:.À^ ^i − ^ ^0 ^k − 5^:^À^ ^^^^ ^ i ^^\dMfM] ^14^} to an identified model as follow:  _{^^^ + S^ = ^Á ^^^^ + ^Á ^^^^^ ^^^^^^^^^ + ^Á ^[^ ^ ^^[^ ^^^^ + ^ ^Á,¡ ¡¢^^ ^¡¢^^ ^^^ + ^Á £YZ[¤¥ ^£YZ[¤¥^^^ + ^Á ¦ ¦^^^} are used to compute the NE signal ω by regularized deconvolution [1,2]. _{[00163] The estimate of ω, denoted as ωà , is obtained by solving the following quadratic} optimization problem ω_{à = _(±^ Ä/) Å^?^M^M − ¸^# − ℬ^ − ℬÄω^aÆ^^^^?^M^M − ¸^# − ℬ^ − ℬÄω^} [00164] where ?_^M^M are the experimental glucose data; ¸^_# is the effect of the state initial conditions on the measurements; ℬ^ is the model-based prediction of glucose based on all model inputs but the NE; ℬ_Ä is the matrix transforming ω into contribution to the measurements; and Æ_^^^, Æ_LF} are matrices designed to balance data fit (first term of the cost function) and smoothness of the estimated NE signal (second term of the cost function). [00165] The solution of (17a) can be explicitly calculated, to be _{[00166] ωà =.ℬ a Ä Æ^^^ℬ ^^ a Ä + ÈÆLF}0 ℬÄ Æ^^^^?^M^M − ¸^# − ℬ^^. ^17b^} daily glucose and insulin traces. To accomplish this, each estimated NE signal and the known meal records are fed back into the associated individual model. In addition, instead of using the collected insulin data as input for the virtual subject model, the algorithm that emulates the insulin therapy with which the data was generated is used to generate the insulin doses as it is visualized in Error! Reference source not found.B. Afterwards, field-collected and regenerated glucose traces are compared, and if the root-mean square error (RSME) between these two is less than a predefined threshold (e.g., 20mg/dL), that day is added to the set of valid days for the optimization procedure. [00168] Considering that the same replay framework as shown in FIG.1B will be then used for therapy optimization, suitable models to represent behavioral patterns, for instance, personalized hypo-treatment response should be extracted at this point to properly address changes in insulin therapy during the optimization phase (based on the hypoglycemia treatment reconstruction). The glucose absorption sub-models corresponding to the hypo-treatments given to each virtual subject are averaged, and the resulting parameters are used to simulate the virtual subject's response to carbohydrate intake for hypoglycemia rescue added during the re-simulation (for example if a change in insulin doses created a new hypoglycemia). Furthermore, the subject's behavior in treating hypoglycemic events is approximated to determine the amount and timing of carbohydrates. The carbohydrate rescue amount is calculated as the average of carbs from the hypo-treatments provided, and the timing is determined by the average glucose value and glucose derivative when hypo-treatments were ingested, as well as the time between consecutive intakes. [00169] Using the identified model, the glucose trace in response to the basal dose, in the absence of meals and insulin boluses, can be predicted as: [00170] _{~ ^; + 1^ = ^ ^ ^ ^ dMfM] ]^ ^ ^ dMfM] À ~dMfM] ; + ^ \dMfM ; + ^Ê^^;^ · ^;^ = ^^~ ^;^} of basal rate (BR) profile or of long-acting insulin dose that optimize fasting glucose. The suggestion is based upon the glycemic behavior observed during the month preceding the generation of the recommendation, relying on an algorithm which assesses the impact of basal rate or long-acting insulin alone on the glycemic levels. Details about the algorithm used to generate the recommendation are reported below. [00172] The algorithm relies on the “net effect” signal and input estimation by deconvolution to estimate the optimal basal rate that allows to maintain a blood glucose level around 110 mg/dl in absence of meal and bolus disturbances. This disclosure presents two methods to determine the optimal basal insulin dose. [00173] A series of two model inversions by deconvolution are performed to obtain the optimal basal rate profile for each extended day. The first model inversion is performed to estimate the net effect signal needed to explain the CGM data for a certain extended day; regularized deconvolution is used for this purpose allowing to reconstruct a smooth net effect signal that embeds all glucose dynamics not explicitly explained by the model. [00174] Upon estimation of the net effect signal, a second model inversion is performed, again by regularized deconvolution, to determine the optimal insulin input that allows to obtain a BG profile around 110 mg/dl in absence of any meal disturbance. The optimal basal rate profile is computed for each extended day and the median across days is then taken in order to output a single 24-hour basal rate signal. This reconstructed insulin input is then saturated not to deviate more than 10% from the previous BR profile used by the subject. Of note, operators different than the median can be considered when the variability across basal rate profiles recommended for the extended days is above a certain threshold. For example, the 25^th percentile can be chosen instead of the median in the presence of high variability, thereby providing a more conservative recommendation in terms of optimal basal rate (i.e., a lower basal rate). [00175] As described for the case above, also in this case, a model inversion by regularized deconvolution is performed to estimate the net effect signal needed to explain the CGM data for a certain extended day. [00176] Upon estimation of the net effect signal for each extended day, the optimal long- acting insulin dose is obtained by solving via grid search an optimization problem formulated as to penalize theoretical fasting glucose with elevated glycemic risk, with reinforced protection against hypoglycemia in the overnight period by penalizing glucose levels under the desired glycemic target; the optimization problem is reported below: ?_{^>f] ^)^ ^ED^ ^ − E>}} ? is the theoretical fasting glu E_>} cose; (/P; is a logarithmic risk function a value to each glucose reading [3]; ×_>^}B^ is the set of overnight glucose values; ^_^ is the glucose target (set to 110 mg/dL); and Ò is the relative weight of the two terms in the objective function (set to 0.15). [00178] Optimal values of prandial dosing parameters (CR and CF) can be computed using the replay framework by evaluating the effect of different combinations of CR and CF profiles on the glycemia of each subject’s virtual representation. To optimize therapy parameters over N days, the problem is formulated to penalize the weighted sum of the risks of hypoglycemia (LBGI) and hyperglycemia (HBGI) and the number of daily hypoglycemic treatments (^_Øa): _{[00179] ×^^^′, ^Ú′^ = ∑àáâHÎ Þ^^ ®^ ∙ Ý^^kÞ + ß^^kÞ + ^Þ,Øa ^20^} ^_{/P;^ = 8'± ^^^.#!^ ^ − 5.381 [00181] Ý^^k = ∑àÎâçèÏéÎ 22.77 ∗ ^/ ^ Þ ^^^ P;^ ∀ ^/P;^ ≤ 0 _)^ ^^^ < 70 [00182] ß^^kÞ = ∑àÎâçèÏéÎ ^^^ 22.77 ∗ ^/P;^ ^ ∀ ^/P;^ > 0 _)^ ^^^ > 180 [00183] and Nzvtëwuz = 288 the number of samples per day considering a sampling time} equal to five minutes. [00184] The modified therapy profiles CR’ and CF’ can be described as deviations from the current subject profiles (^^_> ^ì _E= , ^Ú_> ^ì _E= ) as: _{[00185] ^^í = ^^ì ^# ì >E= ∙ `1 + ^## ÒF«^ l</sub> the extended version in five-minute intervals for the <sub>modulation factors α = {α^, … , αóôõ} and γ = {γ^, … , γóôö}, and ;÷ø and ;÷ù denote the number</sub> of desired segments of the profile. Constraints over α and γ are imposed to limit the allowable change from the current profiles. [00188] Therefore, the optimization problem to be solved is given by: <sub>[00189] mü,iýn J^^^′, ^Ú′^</sub> [00190] Subject to: <sub>[00191] Ò^ ∈ [Ý^, \^] ∀ / = 1, … , ;÷ø [00192] È^ ∈ [Ý^, \^] ∀ / = 1, … , ;÷ù</sub> [00193] With Ý^ and \^ the lower and upper bound for the allowable change per segment. [00194] Finally, an additional safety layer is added when the optimization procedure is run to increase the penalty ®<sub>^</sub> of the risk of hypoglycemia for those participants in which the TBR is <sub>greater than 2%. In this regard, ®^ is initialize as ®^ = 2, and the optimization procedure is</sub> repeated ^<sub>^^FL</sub> times, every time with a higher weight (+0.5 of the previous weight) penalizing the risk of hypoglycemia, until a TBR of less than 2% is reached or the number of iterations ^<sub>^^FL</sub> is equal to five. If surpassing five iterations and the TBR is greater than 2%, no recommendation for the profiles is provided. [00195] Algorithms designed for automated insulin delivery (AID) systems are inherently parametrized to meet individual needs. These parameters may include BR, CR, CF profiles or other signals used in the insulin dosing calculations, such as target or sleep signals that influence the controller’s aggressiveness. In this Section, this disclosure extends upon the optimization problem developed in Section 4.2. for the case of AID systems. As before, data is collected from the last N days and replayed using personalized metabolic models. The virtual representation of the subject is then set in a closed-loop fashion with the AID system algorithm, so that changes in the parameters of that specific control strategy can be observed in resimulated glucose levels. [00196] Considering the insulin-dosing parameters (BR, CR, and CF) and flag variables that adjust the aggressiveness of the controller, the optimization problem to be solved is posed as: [00197] <sub>^</sub> ^<sub>,^,</sub>/<sub>^</sub>)<sub>,^ ×^^^í, ^^í, ^Úí, &8_±^</sub> [00198] Subject to: <sub>[00199] Ò^ , È^, 3^ ∈ [−Ý^, \^] ∀ / = 1, … , ^ … , [00201] With cost function × as defined in Section 4.2, α, β, and γ the modulation profiles</sub> throughout the day for BR, CR, and CF, respectively, m the number of segments per day, Ý^ and \^ the lower and upper bound for the allowable change per segment m, and μ<sub>w</sub> referring to the components of the flag variables needed to define it throughout the day. Note that the flag variables are specific to each control algorithm and, therefore, their number will depend on it. Furthermore, additional constraints can be added to the optimization problem to limit the maximum allowable change between recommendations from one cycle of N days to the next, as well as for the resolution of the changes between the allowed ranges. As example, to limit the maximum allowable change for BR, CR, and CF to 10 percent with respect to the previous insulin-dosing parameters the following constrains can be included: <sub>[00202] ^ ^ t ∑t ^ ^^^ α^ ^ ≤ 1; ^t ∑t ^ ^^^ γ^ ^ ≤ 1; ^t ∑t ^^^ β^ ^ ≤ 1. and CF í í í (^^ ,^^ , ^Ú ) can</sub> be computed as: <sub>[00204] BRí = BRñ ∙ 1 ^# ñ ^xt ` + ^##βuïð l [00205] CRí = CRñ ^# ñ ^xt ∙ `1 + ^## αuïð l l</sub> [00207] with BR<sup>ñ</sup> <sub>^xt</sub> , CR<sup>ñ</sup> <sub>^xt</sub> , and CF<sub>^</sub> <sup>ñ</sup> <sub>xt</sub> the current profiles through the day for BR, CR, <sub>and CF in five-minute intervals, and αñ uïð , γñ uïð and β ñ uïð the extended version of α, β, and γ</sub> through the day in five-minute intervals. [00208] The optimal parameter profiles computed above for BR, CR, and CF consist of 288 samples (daily profile in five-minute intervals) which are unlikely to be practical as pump profiles (288 segments to be entered). Therefore, for both all algorithms described above, an additional step is necessary prior to use in clinical settings: namely to reduce the number of the profile segments based on the envisioned therapy. [00209] In the application to an insulin pump system, the optimal BR, CR, and CF profiles are “leveled” to have a maximum of 7 breakpoints across the 24 hours. This involves three steps following the computation of the optimal profiles. Initially, breakpoints for each profile are identified by comparing consecutive elements, resulting in time and value arrays that characterize the profiles. Secondly, an extended profile is obtained by synchronizing the time points and values of BR, CR, and CF profiles. Lastly, a merging process for adjacent segments is performed on the extended profile. The merging operation involves the following iteration to refine the profiles until comprising a maximum of seven distinct segments: 1. Compute the difference between consecutive time points of the extended time profile and identify the index k<sub>^^^^</sub> of the smallest difference. 2<sub>. For each profile, replace the profile value of the segment k^^^^ − 1 with the average value</sub> between the two segments: ^<sub>('&^Åk ('&^Åk ^^^^ − 1ÇΔiÅk^^^ − 1Ç ^('&^Åk ÇΔiÅk Ç ^ ^ ^^^^ ^^^^ ^^^^ − 1Ç = + ΔiÅk^^^^ − + Δi − + Δi</sub> CF, BR), and Δi denotes the vector of differences between time points. 3. Delete the entry k<sub>^^^^</sub> from ^('&^ and the extended time profile. 4. Repeat process until obtaining maximum seven segments. [00210] The final output consists of the time-value tuple for each profile. [00211] In the multiple injection case, the sequence is “leveled” to have one or two segments (depending on the injection schedule adopted by the user) and transformed in total dose (instead of a rate). [00212] In here, we assume that the basal dose is given at the same time t<sub>½</sub> each day (day duration is T). We assume a line time invariant state space model where the basal dose is considered as a Dirac input, and we ignore all other inputs to the system. At day 1, the state at time t can be written as: <sub>[00213] X^t^ = exp^A^ t^X^0^ + ^ð ^ # exp.A ^t − τ^0 B^Urvzvw^t^dτ [00216] At day n, for t½ + ^n − 1^T ≤ t < t½ + nT, we assume that basal injections have happened at {t½ + kT}ó∈[#,^^^], and the current time is t = t# + nT. [00217] X^t^ = exp^A^ t^X^0^ + ∑^^^ ^ ó^# exp `A .t# + nT − ^t½ + k T^0l B^Urvzvw [00221] X^t^ = exp^A^ t^X^0^ + exp^A^ ^t# − t ^ ^^^ ½^^ exp^A T^ .∑óí^# exp^k′ A^ T ^ 0B^Urvzvw [00223] X^t^ = exp^A^ t^X^0^ + exp.A^ ^t# − t½ + T^0 ^I − exp^n A^T^^ ^I −} exp^A^{^}T^^^{^^}B_^U_{rvzvw [00224] When t = t# + nT is big enough, we have exp^A^ t^~0 and exp^nA^ T^~0.} Thus, the above expression simplifies to: _{[00225] X^t#^ = exp `A^ .T − ^t½ − t#^0l ^I − exp^A^T^^^^B^Urvzvw} [00226] Notice that this formula can be generalized for patients with two basal dose injections a day by defining the basal doses time as t_½^ and t_½^ , and the basal doses as U_rvzvw^and U_rvzvw^ . [00227] FIG.1A illustrates an overview of a computer implemented method for controlling insulin dosing for a subject includes using a computer having a processor and computer memory storing software that executes steps. The steps may include saving field collected data 120 for the subject in the computer memory, wherein the field collected data comprises continuous glucose monitoring data and insulin data over a collection period of time; and implementing a virtual personal model 123 of the subject by using the field collected data to save electronic field collected traces of blood glucose levels and electronic field collected traces of insulin levels for the subject over a designated period of time. Optionally, the field collected data may include meal intake data in the form of masses of carbohydrates in the meal. The virtual personal model may also be referred to as a “digital twin” of the subject. The virtual personal model calculates 121, 122, an output plasma glucose concentration (G) from inputs comprising subcutaneous insulin infusion data (uins) and meal carbohydrate mass data (umeal), and a net effect signal of unmodeled factors (w). The steps of the method further include implementing a replay phase 124 of the virtual personal model for a selected time period, and the replay phase may include emulating test insulin therapies to generate virtual insulin doses that correspond to the subcutaneous insulin infusion data from the field collected data. The method uses the virtual personal model to reproduce the field collected glucose traces and field collected insulin traces and output regenerated glucose traces and regenerated insulin traces, wherein the replay phase uses the net effect signal and the meal carbohydrate mass data as feedback data returning into the virtual personal model. The method converges to a result by comparing field collected glucose traces and regenerated glucose traces and saving replay data for the selected time period as a valid time period for comparisons in which a difference between the field collected glucose traces and the regenerated glucose traces is within a threshold. This method recommends 125 a basal rate profile for the subject using saved replay data by calculating inversions of the virtual personal model by deconvolution. The model inversions include estimating a net effect signal needed to achieve a continuous glucose monitoring that matches the regenerated glucose traces; and determining an insulin input to the virtual personal model that maintains a blood glucose profile at a selected level. [00228] In some embodiments, the method may utilize meal intake data that has mass of carbohydrates data and wherein the insulin data comprises insulin basal values and/or insulin bolus values. [00229] In some embodiments, the method may include standardizing the field collected data by at least one of sorting, time stamping, synchronizing, removing duplicates, identifying gaps, or filling gaps in the field collected data. [00230] In some embodiments, the method may include standardizing the data by parsing the data into selected time periods. [00231] In some embodiments, the method may include parsing the data into selected time periods of extended days that include head hours from the previous day and tail hours from the next day. [00232] In some embodiments, the method may include validating the field collected data by determining that within the selected time period, continuous glucose monitoring gaps are less than a preferred number of hours, at least a preferred percentage of continuous glucose measurements are present, and at least a set number of insulin bolus doses occurred. [00233] In some embodiments, the method may include reconstructing missing meal intake data. [00234] In some embodiments, the method may include reconstructing missing meal intake data by utilizing a logistic regression-based detection algorithm having features, coefficients, and thresholds calculated from in-patient clinical trial data. [00235] In some embodiments, the method may include reconstructing missing meal intake data with the algorithm by calculating the net effect signal with the in-patient clinical trial data, wherein the net effect signal for the in-patient clinical trial data determines the rate of appearance of unknown meal disturbances. [00236] In some embodiments, the method may include a secondary detector trained to detect hypoglycemic treatments by using logistic regression to fit a second order polynomial to extract prevailing value, slope, and curvature, and a minimum continuous glucose monitoring reached. [00237] [00238] In some embodiments, the method may include, for multiple daily injection therapy, calculating an artificial meal input by analyzing a total daily intake of insulin and basal glucose. [00239] [00240] In some embodiments, the method may include implementing a virtual personal model of the subject further comprises accounting for circadian rhythms, physical activity, illness, and/or menstrual cycles in the net effect signal. [00241] In some embodiments, the method may include, for multiple daily injection therapy, implementing a virtual personal model that is characterized with an insulin basal dose input. [00242] In some embodiments, the method may include implementing a virtual personal model of the subject further includes linearizing the virtual personal model; solving requisite linear differential equations organized in a continuous state model; and discretizing the continuous state model into a discrete state-space model. [00243] In some embodiments, the method may include implementing a virtual personal model of the subject further comprises identifying selected parameters for the subject that minimize the difference between the field collected glucose traces and the regenerated glucose traces is within a threshold. [00244] In some embodiments, the method may include selecting the parameters by minimizing the difference between the field collected glucose traces and the regenerated glucose traces. [00245] In some embodiments, the method may include selecting the parameters by estimating an insulin sensitivity parameter, an insulin time constant, and a set of carbohydrate absorption parameters. [00246] In some embodiments, the method may include recommending a basal rate profile for the subject by predicting the regenerated glucose trace in response to a basal does of insulin in a fasting state. [00247] In some embodiments, the method may include using the virtual personal model to calculate values of prandial dosing parameters by evaluating combinations of the insulin-to- carbohydrate ration (CR) and the correction factor (CF) and resulting effects on glycemia of the virtual personal model of the subject. [00248] In some embodiments, the method may include using the virtual personal model to calculate values of automated insulin delivery (AID) dosing parameters by evaluating combinations of the insulin-to-carbohydrate ration (CR), the correction factor (CF), the basal insulin rate (BR) and other parameters used with a closed loop AID system and resulting effects on glycemia of the virtual personal model of the subject. [00249] In some embodiments, the method may include reducing the number of profile segments for each therapy. [00250] These and other aspects of the disclosure are further set forth in the claims and the figures herein. [00251] REFERENCES The documents listed below and throughout this document are hereby incorporated by reference in their entirety herein, and which are not admitted to be prior art with respect to the present disclosure by inclusion in this section. [1] J. Hughes, T. Gautier, P. Colmegna, et al., Replay simulations with personalized metabolic model for treatment design and evaluation in type 1 diabetes, Journal of Diabetes Science and Technology 15 (6) (2020) 1326–1336. [2] S. D. Patek, D. Lv, E. A. Ortiz, et al., Empirical representation of blood glucose variability in a compartmental model, in: H. Kirchsteiger, J. B. Jørgensen, E. Renard, L. del Re (Eds.), Prediction Methods for Blood Glucose Concentration: Design, Use and Evaluation, Springer International Publishing, 2016, pp.133–157. [3] Kovatchev BP, Cox DJ, Gonder-Frederick LA, Young-Hyman D, Schlundt DA, Clarke WI. Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. Diabetes care.1998 Nov 1;21(11):1870-5. [00252] The specific configurations, choice of materials and the size and shape of various elements can be varied according to particular design specifications or constraints requiring a system or method constructed according to the principles of the disclosed technology. Such changes are intended to be embraced within the scope of the disclosed technology. The presently disclosed embodiments, therefore, are considered in all respects to be illustrative and not restrictive. The patentable scope of certain embodiments of the disclosed technology is indicated by the appended claims, rather than the foregoing description.

Claims

CLAIMS 1. A computer implemented method for controlling insulin dosing for a subject, the method comprising: using a computer having a processor and computer memory storing software that executes steps comprising: saving field collected data for the subject in the computer memory, wherein the field collected data comprises continuous glucose monitoring data and insulin data over a collection period of time; implementing a virtual personal model of the subject by using the field collected data to save electronic field collected traces of blood glucose levels and electronic field collected traces of insulin levels for the subject over a designated period of time, wherein the virtual personal model calculates: an output plasma glucose concentration (G) from inputs comprising subcutaneous insulin infusion data (u_ins) and meal carbohydrate mass data (u_meal), and a net effect signal of unmodeled factors (w); implementing a replay phase of the virtual personal model for a selected time period, comprising: emulating test insulin therapies to generate virtual insulin doses that correspond to the subcutaneous insulin infusion data from the field collected data; using the virtual personal model to reproduce the field collected glucose traces and field collected insulin traces and output regenerated glucose traces and regenerated insulin traces, wherein the replay phase uses the net effect signal and the meal carbohydrate mass data as feedback data returning into the virtual personal model; comparing field collected glucose traces and regenerated glucose traces; saving replay data for the selected time period as a valid time period for comparisons in which a difference between the field collected glucose traces and the regenerated glucose traces is within a threshold; and recommending a basal rate profile for the subject using saved replay data by calculating inversions of the virtual personal model by deconvolution, wherein the model inversions comprise: estimating a net effect signal needed to achieve a continuous glucose monitoring that matches the regenerated glucose traces; and determining an insulin input to the virtual personal model that maintains a blood glucose profile at a selected level.

2. The method of Claim 1, wherein the meal intake data comprises mass of carbohydrates data and wherein the insulin data comprises insulin basal values and/or insulin bolus values.

3. The method of Claim 1, further comprising standardizing the field collected data by at least one of sorting, time stamping, synchronizing, removing duplicates, identifying gaps, or filling gaps in the field collected data.

4. The method of Claim 3, further comprising parsing the data into selected time periods.

5. The method of Claim 4, further comprising parsing the data into selected time periods comprising extended days that include head hours from the previous day and tail hours from the next day.

6. The method of Claim 5, further comprising validating the field collected data by determining that within the selected time period, continuous glucose monitoring gaps are less than a preferred number of hours, at least a preferred percentage of continuous glucose measurements are present, and at least a set number of insulin bolus doses occurred.

7. The method of Claim 1, further comprising reconstructing missing meal intake data.

8. The method of Claim 7, wherein reconstructing missing meal intake data further comprises utilizing a logistic regression-based detection algorithm having features, coefficients, and thresholds calculated from in-patient clinical trial data.

9. The method of Claim 8, further comprising reconstructing missing meal intake data with the algorithm by calculating the net effect signal with the in-patient clinical trial data, wherein the net effect signal for the in-patient clinical trial data determines the rate of appearance of unknown meal disturbances.

10. The method of Claim 7, further comprising a secondary detector trained to detect hypoglycemic treatments by using logistic regression to fit a second order polynomial to extract prevailing value, slope, and curvature, and a minimum continuous glucose monitoring reached.

11. The method of Claim 7, further comprising, for multiple daily injection therapy, calculating an artificial meal input by analyzing a total daily intake of insulin and basal glucose.

12. The method of Claim 1, wherein implementing a virtual personal model of the subject further comprises accounting for circadian rhythms, physical activity, illness, and/or menstrual cycles in the net effect signal.

13. The method of Claim 1, wherein, for multiple daily injection therapy, implementing a virtual personal model that is characterized with an insulin basal dose input.

14. The method of Claim 1, wherein implementing a virtual personal model of the subject further comprises: linearizing the virtual personal model; solving requisite linear differential equations organized in a continuous state model; and discretizing the continuous state model into a discrete state-space model.

15. The method of Claim 1, wherein implementing a virtual personal model of the subject further comprises identifying selected parameters for the subject that minimize the difference between the field collected glucose traces and the regenerated glucose traces is within a threshold.

16. The method of Claim 15, wherein selecting the parameters further comprises minimizing the difference between the field collected glucose traces and the regenerated glucose traces.

17. The method of Claim 16, wherein selecting the parameters comprises estimating an insulin sensitivity parameter, an insulin time constant, and a set of carbohydrate absorption parameters.

18. The method of Claim 1, wherein recommending a basal rate profile for the subject comprises predicting the regenerated glucose trace in response to a basal does of insulin in a fasting state.

19. The method of Claim 1, further comprising using the virtual personal model to calculate values of prandial dosing parameters by evaluating combinations of the insulin-to- carbohydrate ration (CR) and the correction factor (CF) and resulting effects on glycemia of the virtual personal model of the subject.

20. The method of Claim 1, further comprising using the virtual personal model to calculate values of automated insulin delivery (AID) dosing parameters by evaluating combinations of the insulin-to-carbohydrate ration (CR), the correction factor (CF), the basal insulin rate (BR) and other parameters used with a closed loop AID system and resulting effects on glycemia of the virtual personal model of the subject.

21. The method of Claim 1, further comprising reducing the number of profile segments for each therapy.