Patent Application: US-201214241383-A

Abstract:
an adaptive advisory control interactive process involving algorithm - based assessment and communication of physiologic and behavioral parameters and patterns assists patients with diabetes with the optimization of their glycemic control . the method and system may uses all available sources of information about the patient ; eo data and cmg ), insulin data , and patient self reporting data to : retroactively assess the risk of hypoglycemia , retroactively assess risk - based reduction of insulin delivery , and then report to the patient how a risk - based insulin reduction system would have acted consistently to prevent hypoglycemia .

Description:
some exemplary elements of the aa system 10 are illustrated in fig4 . all four primary functions of the system provide , among other things , long - term historical trends in the patient &# 39 ; s physiological responses to carbohydrate intake and insulin , as well as to the patient &# 39 ; s self treatment , eating , and exercise behaviors . the “ retroactive ” advisory components are designed to illustrate to the patient how a safety - supervised and / or optimal insulin regiment would have differed from what the patient actually did , providing the evidence needed by the patient to change his / her self treatment behaviors . the “ on demand ” component , which relies on real - time bg and insulin data in addition to the historical record , can advise the patient on correction insulin amounts , acting in a sense as an adaptive bolus calculator , i . e ., adapted to the patient &# 39 ; s physiology , anticipated future behaviors , and real - time metabolic state . it is worth noting that the aa system above could easily be used in conjunction with a real time safety supervision system , in which cgm and insulin data inform model - based reductions to insulin delivery ( e . g . attenuation of basal rate ) in real time . the use of such a safety supervision system is entirely optional . the subsections that follow provide a detailed description of the four main system components : ( i ) retroactive risk - based safety , ( ii ) “ net effect ”- based patient adaptive model , ( iii ) retroactive assessment of optimal insulin delivery , and ( iv ) on demand adaptive correction insulin advice . it should be appreciated that the modules , systems , sub - systems and devices associated with the invention may be integrally or separately formed in a variety of forms , and be in communication wirelessly or by - wire ( or a combination of both ) utilizing technology and approaches as would be available to one skilled in the art . some non - limiting examples of device , module , network and system interfaces and communications may be referred to by all of the references , applications and publications disclosed herein ( and are hereby incorporated by reference ). moreover , an example of possible interface and communication between the various systems , devices and networks is disclosed in ( but not limited thereto ) international patent application serial no . pct / us2008 / 082063 , magni , et al ., entitled “ model predictive control based method for closed - loop control of insulin delivery in diabetes using continuous glucose sensing ,” filed oct . 31 , 2008 ; and u . s . patent application ser . no . 12 / 740 , 275 , magni , et al ., entitled “ predictive control based system and method for control of insulin delivery in diabetes using glucose sensing ,” filed apr . 28 , 2010 — in particular see fig1 - 4 and 6 - 10 of magni et al . ( of which both of the disclosures are hereby incorporated by reference herein in their entirety ). the parts of the system devoted to retroactive risk - based safety assessment are illustrated in fig5 , resulting in two main outputs , both of which can be displayed to the patient for enhanced understanding of his / her risk of hypoglycemia as follows : this part of the retroactive risk - based safety subsystem analyzes the historical record and uses kernel density estimates of the patient &# 39 ; s bg time series to compute a statistic , r hypo ( record ), for the risk of hypoglycemia based on the absolute bg levels , bg variability , and insulin delivery that is highly correlated to the posterior ( conditional ) probability of hypoglycemia , p ( e hypo | record ), where e hypo denotes the event of hypoglycemia in the next day and record refers to the patients historical bg 22 , insulin 32 , and activities record 52 . by explicitly informing the patient of the posterior probability of hypoglycemia 26 over the next treatment day , the patient gets actionable prior warning of the possibility of hypoglycemia . the patient could use this information to lower his / her own internal thresholds for deciding on reduced temporary basal rates before meals and / or following exercise . this “ posterior assessment ” of the risk of hypoglycemia is intended to complement existing methods for computing “ bg profiles ” that highlight hypoglycemia “ risk zones ” throughout the treatment day ( as in fig2 ). this invention does not claim the notion of a “ bg profile ”, but rather it claims the method of computing the posterior probability of hypoglycemia given the patient &# 39 ; s historical record ( 22 , 32 , 52 ). it should be appreciated that the absolute bg levels and bg variability may be data derived from a patient &# 39 ; s cgm device ( or records or data storage of glucose readings ) and the absolute insulin delivery may be data obtained from the patient &# 39 ; s insulin pump device ( or records or data storage of insulin delivery ) from multiple daily injections . for instance , in various embodiments as disclosed throughout , the manifestation of the aa system is based on cgm and insulin pump data or manual injection of insulin data . however , in alternative embodiments , the components of the aa system can be realized without cgm or an insulin pump , though the time scale for making the computations would have to change considerable . for example , “ net effect ” curves based on smbg and insulin pump data could be computed , though such a methodology would need extensively more such “ net effect ” curves to obtain an accurate representation of patient behavior . as a further example , an smbg device may be utilized with a manual insulin injection device , such as an insulin pen , needle or similar type of devices . this part of the retroactive risk - based safety subsystem analyzes the historical record and retroactively computes a risk based insulin attenuation factor to the patient &# 39 ; s record of insulin delivery . in one embodiment of the method , the risk - based attenuation factor ( alternatively insulin constraint ) would be computed as in [ 30 ]: where r ( t , τ ) is a measure of the risk of hypoglycemia between time t and t + τ based on the historical record of bg and insulin data up to time t , based on the bg symmetrization of function in [ 34 ] and k patient is a patient - specific “ aggressiveness ” factor . other methods of computing attenuation factors exist , including methods based on assessing the patient &# 39 ; s active insulin up to time t and adjusting the measured value of bg at time t , based on the patient &# 39 ; s correction factor . an exemplary key step of an embodiment of the invention ( but not limited thereto ) is that the system ( and related method ) looks for consistency in the retroactively computed attenuation factors . specifically , the system computes kernel density estimates of φ ( r ( t , τ )) in 24 one - hour bins representing the patient &# 39 ; s treatment day , and then presents to the patient the median level attenuation that would have been applied in each hour - long segment . again , the patient could use this information to lower his / her own internal thresholds for deciding on reduced temporary basal rates before meals and / or following exercise in the future . the parts of the system devoted to the “ net effect ”- based patient adaptive model are illustrated in fig6 . the model that the aa system produces may include ( but not limited thereto ) two main components : ( i ) a dynamic model of the patient &# 39 ; s metabolic system and ( ii ) a corresponding , inferred history of behavioral “ net effect ” curves that explain the glucose variability in the historical record through the dynamic model . in one aspect , the “ net effect ”- based patient adaptive model is , but not limited thereto , a formal mathematical representation of meal profiles such as those presented in fig1 , but also taking into account the influence of other system perturbations , such as physical activity , and sleep / awake periods ( fig3 ). the metabolic model , descriptive of the patient &# 39 ; s individual physiology , provides a mathematical representation of the dynamic relationship between oral carbs d ( g / min ), physical activity e ( cal / min ), subcutaneous insulin u ( u / hr ), and the patient &# 39 ; s metabolic state vector x whose elements include glucose and insulin concentrations ( mg / dl ) in various compartments of the body and carbohydrate mass ( mg ) in the gut . abstractly , this relationship can be described as a set of discrete - time nonlinear difference equations : χ ( k + 1 )= f ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) bg model ( k )= g ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) where f and g are nonlinear system equations and θ ( k ) is a vector of parameter values that are characteristic of the patient , such as body weight , volumes of distribution in various compartments , various time constant that describe the rates of absorption and clearance between various compartments , some of which are prone to varying as a function of time k . this nonlinear representation can be linearized around any desired operating point ( e . g . steady state glucose concentration ) to yield a linear dynamic model : x ( k + 1 )= ax ( k )+ b u u δ ( k )+ b d d ( k )+ b e e ( k ) where x is the vector of metabolic state differentials ( away from the operating point ), u δ ( u / hr ) is the deviation in insulin delivery from the patient &# 39 ; s steady state ( basal ) insulin delivery rate , a , b u , b d , b e are the state space matrices of the linear model , and y ( k ) represents bg deviation away from the desired operating point . ( note that the dependence on θ ( k ) is embedded within the state space matrices a , b u , b d , b e .) it should be appreciated that alternatively , the dynamic relationships can be described as a set of continuous - time nonlinear differential equations : { dot over ( χ )}( t )= f ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )) bg model ( t )= g ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )). some of the novel elements of the “ net effect ”- based patient adaptive model are , but not limited thereto , described below . this element of the “ net effect ”- based patient adaptive model produces a “ history ” of virtual system inputs ( a . k . a . “ net effect ”) that reconciles the patient &# 39 ; s historical record of bg 22 and insulin delivery 32 . to be more specific , given the record of the patient &# 39 ; s bg concentration and insulin delivery , { bg ( k )} kεday and { u ( k )} kεday the net effect that reconciles the historical information is the vector of virtual carbohydrate inputs {( d n . e . ( k )} kεday that minimizes the error function : dist ({ bg ( k )} kεday ,{ bg model ( k )} kεday |{ u ( k )} kεday ,{ d n . e . ( k )} kεday ), where dist measures the distance between two vectors of bg concentration ( in this case actual bg versus model - predicted bg ) given the fixed record of insulin delivery { u ( k )} kεday and the candidate net effect vector { d n . e . ( k )} kεday . note that the resulting optimal net effect vector ( aka . net effect curve 38 ) { d n . e . ( k )} kεday optimal reconciles the bg and insulin data collected by the patient through a virtual carbohydrate signal , which captures all external influences on the patient as a single external disturbance signal measured in ( mg / min ). when the net effect curve 38 is positive this may correspond to the patient actually eating , or it may correspond a period of the day in which the patient is experiencing enhanced insulin sensitivity . when the net effect curve 38 is negative then this may correspond to the patient engaging in intense physical activity or exercise . note also that the computed net effect curve 38 is implicitly a function of the patient &# 39 ; s physiological model , parameterized by θ ( k ). thus a poorly adapted physiological model is likely to produce unusual - looking net effect curves 38 , and the side - effect of a well - adapted physiological model is a set of net effect curves that correspond well to the patients record or recollection of daily activities , meal and exercise behaviors , and self treatment . different types of distance measures are possible for assessing the patients “ net effect ,” including weighted l 1 , l 2 , and l ∞ norms . the combination of the l 2 norm with the linearized version of the patient physiological model makes it particularly easy to compute daily net effect . it is common practice to use techniques of “ system identification ” to recursively update the parameters of dynamic model . in the context of model - based treatment of diabetes , such techniques allow for the estimation of the patients physiological model parameters { θ ( k )} kεday including daily variability due to the patients circadian rhythm . many techniques have been employed including linear least - squares fitting of the data , parametric and non - parametric system identification , adaptive recursive estimation . all of these techniques work to ensure endogenous consistency of the model with the data , generally taking “ exact knowledge ” of patient - inputs ( meals and exercise ) for granted . of course , prior knowledge of the precise content and timing of meals and exercise is only possible within a clinical environment . and , frequently requiring the patient to undergo such testing in order to track long time - scale variability , is not economically feasible . an aspect of an embodiment of the present invention addresses , among other things , the latter concerns by integrating the notion of net effect into the long - term adaptation of the patient &# 39 ; s physiological model parameters . as mentioned above , the side - effect of a well - adapted physiological model is a set of “ net effect ” curves 38 that correspond well to the patients record or recollection of daily activities , meal and exercise behaviors , and self treatment . specifically , our system ( and method and computer readable medium ) may use a recursive procedure for updating the patients physiological parameters based on both ( i ) the ability of the model to predict future bg based on known inputs and ( ii ) the ability of the model to produce net effect curves 38 that are consistent with the patient &# 39 ; s record of eating , exercise , and self - treatment behaviors . mathematically , the net - effect based model updater , takes the form where u is the recursive parameter update function , which could be gradient - based , bg res is a vector of bg model prediction errors ( residuals ) and ne res is a vector of errors between the computed net effect curve and the patient &# 39 ; s record of actual ( verified ) behavioral inputs . in practice , it is justified to adjust the model on multiple time scales . for example , parameter updates can be computed daily based on bg residuals : and updates based on net effect mismatch can be computed on a longer time scale , say every week or month : the parts of the system devoted to retroactive assessment of optimal insulin delivery are illustrated in fig7 . one of the key elements of the retroactive assessment of optimal insulin delivery subsystem , but not limited thereto , are ( i ) the retrospective optimal control analyzer 42 and ( ii ) the retro - optimal basal rate extractor 44 , both of which make use of the “ net effect ”- based patient adaptive model , as described in the following paragraphs . this element of the retroactive assessment of optimal insulin delivery subsystem serves to retroactively compute what the patient &# 39 ; s optimal rate of insulin delivery would have been over a predetermined period of historical time given that the disturbances to the system are exactly the historical of net effect curves 38 computed for the patient over that interval of time . thus , for each “ history ” of net effect curves there is a corresponding “ history ” of insulin delivery rates that account for meals , exercise , and corrections for each day in the considered interval of time . for example , associated with any day in the historical record , we have i . e ., there is a mapping between the net effect curve 38 for a given day and the model - based response of an optimal controller 42 . these vectors of optimal responses can be collected and analyzed , and can be directly presented to the patient for a day - by - day review of insulin treatment . a specific form of this analysis takes shape in the retro - optimal basal rate extract 46 described below . it may be noted that the retrospective optimal control analyzer 42 uses both components of the “ net effect ”- based patient adaptive model , i . e . both the “ history ” of net effect curves computed for the patient and the adapted patient physiological model . a beneficial feature of this architecture is that , but not limited thereto , errors in the patient model ( i . e . θ misadapted to the patient ) do not have a large effect on the retrospective optimal control analysis . the reason for this is that , while 0 may be off , the net effect curves computed for the patient reconcile the actual insulin and bg data for the patient through the model . as long as θ is close (“ in the ballpark ”), the optimal control responses will still be patient - adapted . different types of optimal control methodologies ( from the prior art , for example ) could be employed to compute the optimal control responses { u opt ( k )} kεday , including deterministic and stochastic model predictive control algorithms [ 20 , 27 , 38 , 45 ]. the open - loop feedback control ( olfc ) scheme of [ 47 ] is particularly well - suited for the various embodiments of the invention . a novel aspect of an aspect of an embodiment of the present invention , but not limited thereto , is the concept , method , and system based on ( i ) feeding the patient &# 39 ; s history of net effect curves 38 into various types of optimal control algorithms and ( ii ) retroactively analyzing the optimal responses , and informing the patient of through comparative analysis . this element of the retroactive assessment of optimal insulin delivery subsystem serves to ( i ) take the “ history 43 ” of optimal control responses computed by the retrospective optimal control analyzer 42 and ( ii ) extract features from the optimal responses that correspond to important but random events ( i . e . subtract discrete amounts of insulin associated with meals or account for discrete insulin deficits associated with temporary basal rates around exercise ). the remaining schedule of insulin delivery corresponds to a representation of the patient &# 39 ; s “ optimal ” basal pattern each day in the historical record . next , the retro - optimal basal rate extractor 44 then looks for consistency in the retroactively computed optimal basal rates . specifically , the system computes kernel density estimates of the optimal basal rates in 24 one - hour bins representing the patient &# 39 ; s treatment day , and then presents to the patient the median level of basal insulin 46 that would have been applied in each hour - long segment . the patient could use this information to ( i ) decide upon on reduced temporary basal rates before meals and / or following exercise in the future or ( ii ) adjust his / her long - term basal rate profile . some of the exemplary parts of the system devoted to on demand adaptive correction insulin advice are illustrated in fig8 . an over - arching goal , among other things , of this component of the adaptive advisory system ( and related method ) is to provide in - the - moment correction insulin advice to the patient based on both ( i ) the historical record 22 , 32 , 52 and ( ii ) real - time cgm / smbg measurements and insulin pump data 62 . one of the first steps of this system may be to develop a stochastic model of upcoming behavioral disturbances . with this model it is possible to reason about appropriate correction insulin amounts that anticipate meals and exercise that are forthcoming . some of the key elements , but not limited thereto , of the on demand adaptive correction insulin advice subsystem may be ( i ) the retrospective meal & amp ; exercise detector , 54 ( ii ) the meal & amp ; exercise stochastic modeler 56 , and ( iii ) the on demand correction bolus advisor 58 , described in the following paragraphs . these elements of the subsystem can work in tandem , and there is also independent value in each element individually . this element of the on demand adaptive correction insulin advice subsystem serves to reconcile 55 the current “ history ” of patient “ net effect ” curves 38 with the historical record of patient - acknowledged meals and exercise events to produce a validated ( high - confidence ) record of relevant patient behaviors . the retrospective meal & amp ; exercise detector 54 looks for discrepancies between ( i ) the net effect curves 38 computed from the available bg and insulin data for the patient and ( ii ) the meal and exercise events 55 that are acknowledged 62 by the patient through the systems user interface . when discrepancies arise the retrospective meal & amp ; exercise detector 54 suggests possible resolutions , such as “ perhaps you had a meal between 1 pm and 2 pm that you failed to acknowledge ?” or “ there is an indication to you engaged in intense physical activity between 3 pm and 3 : 30 pm . is this true ?” the responses from the patient are then taken to form the final , validated record of relevant patient activities . internally , the retrospective meal & amp ; exercise detector 54 employs a method of analyzing the net effect curves 36 to produce discrete estimates of meal and exercise events . the method may be based on , among other things , ( i ) identifying significant local extreme of the net effect curves , ( ii ) computing areas under the over time - windows that correspond to meals and exercise , ( iii ) computing most - likely times of meal and exercise events , and then ( iv ) confirming that the resulting estimation of meal and exercise behaviors yield model - predicted bg traces that are close to the actual record . this element of the on demand adaptive correction insulin advice subsystem serves to take the reconciled ( validated ) history of behavioral events 55 above , and then produce a stochastic model 57 that describes the timing and content of meals and exercise . the model 57 essentially describes the patient &# 39 ; s daily behavior as a sequence of non - overlapping meal and exercise regimes . each regime is described in terms of ( i ) an earliest possible time at which the disturbance could “ arrive ” ( e . g . the earliest possible breakfast time ), ( ii ) a latest possible disturbance arrival time ( e . g . the latest possible breakfast time ), and ( iii ) a relative frequency distribution for the times at which the disturbance arrives within the regime that also accounts for the possibility that the disturbance will be “ skipped ” [ 67 ]. one of the key novel aspects here is the method by which meal regimes are determined from the reconciled history of meal and exercise events 55 ( based on clustering analysis ), for estimating the relative frequency distribution of meal timing within the regime , and for characterizing the random variable that describes the size of the meal or exercise disturbance associated with the regime . this element of the on demand adaptive correction insulin advice subsystem serves to continuously monitor the patient &# 39 ; s status and to provide correction insulin advice 59 in the moment the patient asks for it , based on ( i ) the stochastic model 57 above for upcoming behavioral disturbances and ( ii ) the current physiological model for the patient ( i . e ., dynamic model of the patient &# 39 ; s metabolic system ) that allows for the prediction of the impact of various alternative correction insulin amounts . the concept of this user - prompted advisory mode correction system is illustrated in fig9 . fig9 graphically illustrates an example of the on demand adaptive correction insulin system . the system and method assumes that ( i ) the patient is in charge of computing insulin boluses at mealtimes using conventional methods and ( ii ) the patient uses our advisory system to address unplanned hyperglycemia , such as at time t shown in the figure . when the patient activates the advisory system , he / she has the option to provide information regarding the timing and content of the next meal , and the system proceeds to update the stochastic model 56 of meal and exercise timing ( referred to as the meal behavioral profile and illustrated as a shaded probability distribution in the figure ). next , the system computes an insulin recommendation that is optimal with respect the patient &# 39 ; s future ( random ) metabolic trajectory . specifically , the advised bolus is computed as the optimal solution to an indefinite - horizon linear quadratic problem defined by the uncertain time at which the patient will next eat . one of the key benefits of the proposed method , but not limited thereto , is that it is minimally invasive and only provides advice in response to the user &# 39 ; s interaction with the system . with the patient being ultimately “ in charge ,” he / she can easily override the system in case of un - modeled metabolic disturbances , e . g . intense physical activity . another benefit of the system , among other things , is that it allows the patient to implement a “ conventional ” bolusing strategy at mealtimes , including the option to implement an extended meal bolus to account for meals with high fat content . the framework that we present here computes correction bolus insulin recommendations based on a model of the patient &# 39 ; s metabolism , and the framework can adapt to either a “ population average ” model or patient - specific metabolic models . in addition , recommended insulin boluses are computed with respect to a model of the patient &# 39 ; s individual eating behavior . in particular , the system is constantly aware of the next meal opportunity and is prepared to optimize correction recommendations with respect to an empirical stochastic model for meal timing and size ( including the possibility that the meal will be skipped ). knowing that the patient is responsible for mealtime boluses , the system will avoid making large corrections immediately prior to anticipated meals . finally , the insulin recommendations produced by the system anticipate the patient &# 39 ; s treatment behavior at the time of the next meal , knowing that the patient will compute a mealtime bolus based on his / her insulin to carbohydrate ratio ( cr ) and correction factor ( cf ). fig1 - 15 present screenshots of one possible implementation of the aa system on a personal computer . similar implementations are possible on a tablet , portable computers ( e . g ., laptops or notebooks ), via internet applications or network applications , cellular phones , or on a smart phone such as pdas ( with appropriately reduced text and graphs if desired or required ). specifically : fig1 presents the initialization screen where the system is customized to a particular person ; fig1 provides a screen that presents an opportunity for input of carbohydrate intake ( meals ) and physical activity by time and amount ; fig1 provides a screen that is a representation of the day in review , including glucose trace and superimposed behaviorally - driven events ; fig1 and 14 provide screens that present daily profiles at a different level of detail ( simple in fig1 and with added probability plots in fig1 ); fig1 provides a screen that presents an advisory screen including identified periods of risk for hyper - and hypoglycemia during a typical day off work ( shaded red in upper screen panel ), and system advice to reduce insulin dose to avoid hypoglycemia ( dotted line in lower screen panel ). fig1 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 fig1 . 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 ), 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 fig1 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 invention . 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 fig1 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 . 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 . 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 . 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 . 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 invention are not limited to any specific combination of hardware circuitry and software . the term “ computer - readable medium ” ( or “ machine - readable medium ”) as used herein is an extensible term that refers to any medium or any memory , that participates in providing instructions to a processor , ( such as processor 138 ) for execution , or any mechanism for storing or transmitting information in a form readable by a machine ( e . g ., a computer ). such a medium may store computer - executable instructions to be executed by a processing element and / or control logic , and data which 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 . 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 . 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 ( june 1999 ), “ 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 ) lan91c111 10 / 100 ethernet transceiver described in the standard microsystems corporation ( smsc ) data - sheet “ lan91c111 10 / 100 non - pci ethernet single chip mac + phy ” data - sheet , rev . 15 ( feb . 20 , 2004 ), which is incorporated in its entirety for all purposes as if fully set forth herein . wireless links may also be implemented . in any such implementation , communication interface 141 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 . 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 . the concept of retroactively assessing risk of hypoglycemia , retroactively assessing risk - based reduction of insulin delivery , and reporting the same on how to prevent hypoglycemia as well as enjoying other related benefits , may be implemented and utilized with the related processors , networks , computer systems , internet , and components and functions according to the schemes disclosed herein . the following patents , applications and publications as 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 invention by inclusion in this section . 1 . albisser a m , leibel b s , ewart t g , davidovac z , botz c k , zinggg w . an artificial endocrine pancreas . diabetes , 23 : 389 - 396 , 1974 . 2 . bellazzi r , nucci g , cobelli c : the subcutaneous route to insulin - 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monitoring blood glucose ” ( publication no . 2005 / 214892 , sep . 29 , 2005 ). z . u . s . patent application ser . no . 12 / 065 , 257 , kovatchev , et al ., filed aug . 29 , 2008 , entitled “ accuracy of continuous glucose sensors ”, ( publication no . 2008 / 0314395 , dec . 25 , 2008 ). aa . international patent application serial no pct / us2006 / 033724 , kovatchev , et al ., filed aug . 29 , 2006 , entitled “ method for improvising accuracy of continuous glucose sensors and a continuous glucose sensor using the same ”, ( publication no . wo 07027691 , mar . 8 , 2007 ). bb . u . s . patent application ser . no . 12 / 159 , 891 , kovatchev , b ., filed jul . 2 , 2008 , entitled “ method , system and computer program product for evaluation of blood glucose variability in diabetes from self - monitoring data ”, ( publication no . 2009 / 0171589 , jul . 2 , 2009 ). cc . international application no . pct / us2007 / 000370 , kovatchev , b ., filed jan . 5 , 2007 , entitled “ method , system and computer program product for evaluation of blood glucose variability in diabetes from self - monitoring data ”, ( publication no . wo 07081853 , jul . 19 , 2007 ). dd . u . s . patent application ser . no . 11 / 925 , 689 and pct international patent application no . pct / us2007 / 082744 , breton , et al ., both filed oct . 26 , 2007 , entitled “ for method , system and computer program product for real - time detection of sensitivity decline in analyte sensors ”, ( publication nos . 2008 / 0172205 , jul . 17 , 2008 and wo 2008 / 052199 , may 2 , 2008 ). ee . u . s . patent application ser . no . 10 / 069 , 674 , kovatchev , et al ., filed feb . 22 , 2002 , entitled “ method and apparatus for predicting the risk of hypoglycemia ”. ff . international application no . pct / us00 / 22886 , kovatchev , et al ., filed aug . 21 , 2000 , entitled “ method and apparatus for predicting the risk of hypoglycemia ”, ( publication no . wo 01 / 13786 , mar . 1 , 2001 ). gg . u . s . pat . no . 6 , 923 , 763 b1 , kovatchev , et al ., issued aug . 2 , 2005 , entitled “ method and apparatus for predicting the risk of hypoglycemia ”. hh . u . s . patent application publication no . us 2004 / 0254434 a1 , “ glucose measuring module and “ insulin pump combination ”, published dec . 16 , 2004 ., goodnow , et al . ser . no . 10 / 458 , 914 , filed jun . 10 , 2003 . ii . u . s . patent application publication no . us 2009 / 00697456 a1 , estes , et al ., “ operating an infusion pump system ”, published mar . 12 , 2009 . ser . no . 11 / 851 , 194 , sep . 6 , 2007 . jj . fernandez - luque , et al ., ediab : a system for monitoring , assisting and educating people with diabetes ”, icchp 2006 , lncs 4061 , pp . 1342 - 1349 , 2006 . kk . u . s . pat . no . 6 , 602 , 191 b2 , quy , r ., method and apparatus for health and disease management combining patient data monitoring with wireless internet connectivity , aug . 5 , 2003 . ll . international patent application publication no . wo 2008 / 064053 a2 , patel , et al ., systems and methods for diabetes management using consumer electronic devices , may 29 , 2008 ; international patent application serial no . pct / us2007 / 084769 , filed nov . 15 , 2007 . mm . international patent application publication no . wo 2010 / 138817 a1 , ow - wing , k ., glucose monitoring system with wireless communications , dec . 2 , 2010 ; international patent application serial no . wo 2010 / 138817 a1 , filed may 28 , 2010 . nn . international patent application publication no . wo 2004 / 052204 a1 , kim , kwan - ho , blood glucose monitoring system , jun . 24 , 2004 ; international patent application serial no . pct / kr2003 / 000398 , filed feb . 28 , 2003 . practice of an aspect of an embodiment ( or embodiments ) of the invention will be still more fully understood from the following examples , which are presented herein for illustration only and should not be construed as limiting the invention in any way . a processor - based method for providing posterior assessment of the risk of hypoglycemic of a patient , said method comprises : providing an algorithm to compute a statistic , r hypo ( record ), for the risk of hypoglycemia based on the absolute bg levels , bg variability , and insulin delivery that is highly correlated to the posterior ( conditional ) probability of hypoglycemia , p ( e hypo | record ), where e hypo denotes the event of hypoglycemia in the next day and record refers to the subject &# 39 ; s historical bg , insulin delivery , and activities record ; and providing the computed statistic , r hypo ( record ), whereby actionable prior warning of the possibility of hypoglycemia about the patient is so provided to patient or user . the method of example 1 , wherein the absolute bg levels and bg variability may be data derived from a cgm device and the absolute insulin delivery may be data obtained from an insulin pump device . the method of example 1 , wherein the absolute bg levels and bg variability may be data derived from a cgm device and the absolute insulin delivery may be data obtained from a manual insulin injection device . the method of example 1 , wherein the absolute bg levels and bg variability may be data derived from an smbg device and / or the absolute insulin delivery may be data obtained from an insulin pump device . the method of example 1 , wherein the absolute bg levels and bg variability may be data derived from an smbg device and / or the absolute insulin delivery may be data obtained from a manual insulin injection device . a processor - based method for retroactively providing a safe level of insulin for the patient , said method comprises : providing an algorithm to retroactively compute a risk - based insulation attenuation factor to the subject &# 39 ; s record of insulin delivery ; and providing the computed risk - based insulation attenuation factor and applying the risk - based attenuation factor so that any internal threshold is provided to the patient or user for deciding on reduced temporary basal rates before meals and / or following exercise in the future that may be implemented . the method of example 6 , wherein the record of the insulin delivery may be data obtained from an insulin pump device . the method of example 6 , wherein the record of the insulin delivery may be data obtained from a manual insulin injection device . the method of example 6 , wherein the risk - based attenuation factor would be computed as follows : where r ( t , τ ) is a measure of the risk of hypoglycemia between time t and t + τ based on the historical record of bg and insulin data up to time t , based on the bg symmetrization of function and k patient is a patient - specific “ aggressiveness ” factor . a processor - based method for providing a “ net effect ” based patient adoptive model , said method comprises : wherein said dynamic model includes descriptive parameters of an individual physiology of the model patient ; a corresponding inferred history of behavioral “ net effect ” model that explains the glucose variability in the historical record through the dynamic model ; wherein said “ net effect ” model includes a mathematical representation perturbations of the model patient ; and an update of the patient &# 39 ; s physiological parameters based on both ( i ) the ability of the dynamic model to predict future bg based on known inputs and ( ii ) the ability of the model to produce net effect curves that are consistent with the patient &# 39 ; s record of the perturbations ; and providing said update to the patient or user whereby patient or user can use the update for future course of action . the method of example 10 , wherein said descriptive parameters include a representation of the dynamic relationship between oral carbs d ( g / min ), physical activity e ( cal / min ), subcutaneous insulin u ( u / hr ), and the model patient &# 39 ; s metabolic state vector χ whose elements include glucose and insulin concentrations ( mg / dl ) in various compartments of the body and carbohydrate mass ( mg ) in the gut . the method of example 11 , wherein the glucose concentration ( mg / dl ) may be data derived from a cgm device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . the method of example 11 , wherein the glucose concentration ( mg / dl ) may be data derived from a cgm device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from a manual insulin injection device . the method of example 11 , wherein the glucose concentration ( mg / dl ) may be data derived from a smbg device and / or the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . the method of example 11 , wherein the glucose concentration ( mg / dl ) may be data derived from a smbg device and / or the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from a manual insulin injection device . the method of example 11 , wherein relationship of said descriptive parameters can be described as a set of discrete - time nonlinear difference equations : χ ( k + 1 )= f ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) bg model ( k )= g ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) where f and g are nonlinear system equations and θ ( k ) is a vector of parameter values that are characteristic of the patient , such as body weight , volumes of distribution in various compartments , various time constant that describe the rates of absorption and clearance between various compartments , some of which are prone to varying as a function of time k . the method of example 11 , wherein relationship of said of descriptive parameters can be described as a set of continuous - time nonlinear differential equations : { dot over ( χ )}( t )= f ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )) bg model ( t )= g ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )). the method of example 17 , wherein nonlinear representation can be linearized around any desired operating point ( e . g . steady state glucose concentration ) to yield a linear dynamic model : x ( k + 1 )= ax ( k )+ b u u δ ( k )+ b d d ( k )+ b e e ( k ) where x is the vector of metabolic state differentials ( away from the operating point ), u δ ( u / hr ) is the deviation in insulin delivery from the patient &# 39 ; s steady state ( basal ) insulin delivery rate , a , b u , b d , b e are the state space matrices of the linear model , and y ( k ) represents bg deviation away from the desired operating point , and the dependence on θ ( k ) is embedded within the state space matrices a , b u , b d , b e . the method of example 10 , wherein said perturbations include meal profiles , physical activity , and sleep / awake periods . the method of example 10 , wherein said “ net effect ” model provides a “ history ” of virtual system inputs that reconciles the patient &# 39 ; s historical record of bg and historical record of insulin delivery . the method of example 20 , wherein the patient &# 39 ; s historical record of bg concentration , { bg ( k )} kεday , and historical record of insulin delivery , { u ( k )} kεday , the net effect that reconciles the historical information is the vector of virtual carbohydrate inputs { d n . e . ( k )} kεday that minimizes the error function : dist ({ bg ( k )} kεday ,{ bg model ( k ) kεday |( u ( k )} kεday ,{ d n . e . ( k )} kεday ), where dist measures the distance between two vectors of bg concentration ( in this case actual bg versus model - predicted bg ) given the fixed record of insulin delivery { u ( k )} kεday and the candidate net effect vector { d n . e . ( k )} kεday . the method of example 21 , wherein the resulting optimal net effect vector ( aka . net effect curve ), { d n . e . ( k )} kεday , optimally reconciles the bg and insulin data collected by the patient through a virtual carbohydrate signal , which captures all external influences on the patient as a single external disturbance signal measured in ( mg / min ). when the net effect curve is positive this shall correspond to the patient actually eating , or it may correspond a period of the day in which the patient is experiencing enhanced insulin sensitivity ; and when the net effect curve is negative then this shall correspond to the patient engaging in intense physical activity or exercise . the patients physiological model parameters , { θ ( k )} kεday , includes daily variability due to the patients circadian rhythm ; and the model updater , includes a formula that takes the form having the following : where u is the recursive parameter update function , which could be gradient - based , bg res is a vector of bg model prediction errors ( residuals ) and ne res is a vector of errors between the computed net effect curve and the patient &# 39 ; s record of actual ( verified ) behavioral inputs . the method of example 24 , wherein the dynamic model is adjusted on multiple time scales , whereby parameter updates can be computed daily based on bg residuals : and updates based on net effect mismatch can be computed on a longer time scale , such as every week or month : the method of example 10 , further comprising providing a retroactive assessment of the patient &# 39 ; s optimal rate of insulin delivery , wherein said algorithm : retroactively computes what the patient &# 39 ; s optimal rate of insulin delivery would have been over a predetermined period of historical time given that the disturbances to the system are exactly the historical of net effect curves computed for the patient over that interval of time , wherein for each “ history ” of net effect curves there is a corresponding “ history ” of insulin delivery rates that account for meals , exercise , and corrections for each day in the considered interval of time ; maps between the net effect curve for a given day and the model - based response of an optimal controller , wherein these vectors of optimal responses are collected and analyzed , and presented to the patient or user for a day - by - day review of insulin treatment ; extracts features from the optimal responses that correspond to important but random events by subtracting discrete amounts of insulin associated with meals or accounting for discrete insulin deficits associated with temporary basal rates around exercise , whereby the remaining schedule of insulin delivery corresponds to a representation of the patient &# 39 ; s “ optimal ” basal pattern each day in the historical record ; and identifies consistency in the retroactively computed optimal basal rates , such optimal basal rates in a plurality of duration segments representing the patient &# 39 ; s treatment duration ; and providing to the patient or user the median level of basal insulin that would have been applied in each segment , wherein the patient or user could use this information to ( i ) decide upon on reduced temporary basal rates before meals and / or following exercise in the future or ( ii ) adjust the patient &# 39 ; s long - term basal rate profile . the method of example 10 , further comprising providing an on - demand adaptive correction of insulin advice model , said method comprises : retrospective detecting for meal and exercise activities ; stochastic modeling to provide a description about the timing and content of meals and exercise ; and providing insulin correction advice to a patient or user that would be in response to a patient and user request . said retrospective detection for meal and exercise activities includes the algorithm for reconciling current history of said patient “ net effect curves ” with the historical record of patient - acknowledged meals and exercise events to produce a validated ( high - confidence ) record of relevant patient behaviors , wherein the reconciling includes identifying discrepancies between ( i ) the net effect curves computed from the available bg and insulin data for the patient and ( ii ) the meal and exercise events that are acknowledged by the patient or user through the systems user interface ; and providing suggestions from said discrepancies , wherein suggestions are communicated to patient or user ; and receiving any responses resultant from user or patient to form the final , validated record of relevant patient activities . said stochastic modeling includes the algorithm for receiving said final , validated record of relevant patient activities and stochastically modeling to represent the timing and content of meals and exercise of the patient &# 39 ; s behavior . said insulin correction includes the algorithm for monitoring the patient &# 39 ; s status and to provide insulin correction advice in the moment the patient or user asks for it , based on ( i ) the stochastic modeling for upcoming behavioral disturbances and ( ii ) the current dynamic model of the patient &# 39 ; s metabolic system that allows for the prediction of the impact of various alternative correction insulin amounts . a system for providing posterior assessment of the risk of hypoglycemic of a patient , said system comprises : a retroactive risk - based safety module having a processor to compute a statistic , r hypo ( record ), for the risk of hypoglycemia based on the absolute bg levels , bg variability , and insulin delivery that is highly correlated to the posterior ( conditional ) probability of hypoglycemia , p ( e hypo | record ), where e hypo denotes the event of hypoglycemia in the next day and record refers to the subject &# 39 ; s historical bg , insulin delivery , and activities record ; and said processor outputs the computed statistic , r hypo ( record ), whereby actionable prior warning of the possibility of hypoglycemia about the patient is so provided to patient or user . the system of example 31 , wherein the absolute bg levels and bg variability may be data derived from a cgm device and the absolute insulin delivery may be data obtained from an insulin pump device . the system of example 31 , wherein the absolute bg levels and bg variability may be data derived from a cgm device and the absolute insulin delivery may be data obtained from a manual insulin injection device . the system of example 31 , wherein the absolute bg levels and bg variability may be data derived from an smbg device and the absolute insulin delivery may be data obtained from an insulin pump device . the system of example 31 , wherein the absolute bg levels and bg variability may be data derived from an smbg device and the absolute insulin delivery may be data obtained from a manual insulin injection device . a cgm device , wherein the absolute bg levels and bg variability may be data derived from said cgm device ; and an insulin pump device , wherein the absolute insulin delivery may be data obtained from said insulin pump device . a cgm device , wherein the absolute bg levels and bg variability may be data derived from said cgm device ; and a manual insulin injection device , wherein the absolute insulin delivery may be data obtained from said manual insulin injection device . an smbg device , wherein the absolute bg levels and bg variability may be data derived from said smbg device ; and / or an insulin pump device , wherein the absolute insulin delivery may be data obtained from said insulin pump device . an smbg device , wherein the absolute bg levels and bg variability may be data derived from said smbg device ; and / or a manual insulin injection device , wherein the absolute insulin delivery may be data obtained from said manual insulin injection device . system for retroactively providing a safe level of insulin for the patient , said system comprises : a retroactive risk - based safety module having a processor to retroactively compute a risk - based insulation attenuation factor to the subject &# 39 ; s record of insulin delivery ; and said processor outputs the computed risk - based insulation attenuation factor and applying the risk - based attenuation factor so that any internal threshold is provided to the patient or user for deciding on reduced temporary basal rates before meals and / or following exercise in the future that may be implemented . the system of example 40 , wherein the insulin delivery may be data obtained from an insulin pump device . the system of example 40 , wherein the insulin delivery may be data obtained from a manual insulin injection device . an insulin pump device , wherein the insulin delivery may be data obtained from said insulin pump device . a manual insulin injection device , wherein the insulin delivery may be data obtained from said manual insulin injection device . the system of example 40 , wherein the risk - based attenuation factor would be computed as follows : where r ( t , τ ) is a measure of the risk of hypoglycemia between time t and t + τ based on the historical record of bg and insulin data up to time t , based on the bg symmetrization of function and k patient is a patient - specific “ aggressiveness ” factor . a system for providing a “ net effect ” based patient adoptive model , said system comprises : wherein said dynamic model includes descriptive parameters of an individual physiology of the model patient ; and a corresponding inferred history of behavioral “ net effect ” model that explains the glucose variability in the historical record through the dynamic model ; wherein said “ net effect ” model includes a mathematical representation perturbations of the model patient ; and a model updater module having a processor to compute : an update of the patient &# 39 ; s physiological parameters based on both ( i ) the ability of the dynamic model to predict future bg based on known inputs and ( ii ) the ability of the model to produce net effect curves that are consistent with the patient &# 39 ; s record of the perturbations ; and said system outputs said update to the patient or user whereby patient or user can use the update for future course of action . the system of example 46 , wherein said descriptive parameters include a representation of the dynamic relationship between oral carbs d ( g / min ), physical activity e ( cal / min ), subcutaneous insulin u ( u / hr ), and the model patient &# 39 ; s metabolic state vector x whose elements include glucose and insulin concentrations ( mg / dl ) in various compartments of the body and carbohydrate mass ( mg ) in the gut . the system of example 47 , wherein the glucose concentration ( mg / dl ) may be data derived from a cgm device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . the system of example 47 , wherein the glucose concentration ( mg / dl ) may be data derived from a cgm device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from a manual insulin injection device . the system of example 47 , wherein the glucose concentration ( mg / dl ) may be data derived from a smbg device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . the system of example 47 , wherein the glucose concentration ( mg / dl ) may be data derived from a smbg device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from a manual insulin injection . an cgm device , wherein the glucose concentration ( mg / dl ) may be data derived from said cgm device ; and an insulin pump , wherein the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . an smbg device , wherein the glucose concentration ( mg / dl ) may be data derived from said smbg device ; and an insulin pump device or an insulin injection device , wherein the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from said insulin pump device or said insulin injection device . the system of example 47 , wherein relationship said descriptive parameters can be described as a set of discrete - time nonlinear difference equations : χ ( k + 1 )= f ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) bg model ( k )= g ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) where f and g are nonlinear system equations and θ ( k ) is a vector of parameter values that are characteristic of the patient , such as body weight , volumes of distribution in various compartments , various time constant that describe the rates of absorption and clearance between various compartments , some of which are prone to varying as a function of time k . the system of example 47 , wherein relationship of said of descriptive parameters can be described as a set of continuous - time nonlinear differential equations : { dot over ( χ )}( t )= f ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )) bg model ( t )= g ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )). the system of example 55 , wherein nonlinear representation can be linearized around any desired operating point ( e . g . steady state glucose concentration ) to yield a linear dynamic model : x ( k + 1 )= ax ( k )+ b u u δ ( k )+ b d d ( k )+ b e e ( k ) where x is the vector of metabolic state differentials ( away from the operating point ), u δ ( u / hr ) is the deviation in insulin delivery from the patient &# 39 ; s steady state ( basal ) insulin delivery rate , a , b u , b d , b e are the state space matrices of the linear model , and y ( k ) represents bg deviation away from the desired operating point , and the dependence on θ ( k ) is embedded within the state space matrices a , b u , b d , b e . the system of example 46 , wherein said perturbations include meal profiles , physical activity , and sleep / awake periods . the system of example 46 , wherein said “ net effect ” model provides a “ history ” of virtual system inputs that reconciles the patient &# 39 ; s historical record of bg and historical record of insulin delivery . the system of example 58 , wherein the patient &# 39 ; s historical record of bg concentration , { bg ( k )} kεday , and historical record of insulin delivery , { u ( k )} kεday , the net effect that reconciles the historical information is the vector of virtual carbohydrate inputs { d n . e . ( k )} kεday that minimizes the error function : dist ({ bg ( k )} kεday ,{ bg model ( k )} kεday |{ u ( k )} kεday ,{ d n . e . ( k )} kεday ), where dist measures the distance between two vectors of bg concentration ( in this case actual bg versus model - predicted bg ) given the fixed record of insulin delivery { u ( k )} kεday and the candidate net effect vector { d n . e . ( k )} kεday . the system of example 59 , wherein the resulting optimal net effect vector ( aka . net effect curve ), { d n . e . ( k )} kεday , optimally reconciles the bg and insulin data collected by the patient through a virtual carbohydrate signal , which captures all external influences on the patient as a single external disturbance signal measured in ( mg / min ). when the net effect curve is positive this shall correspond to the patient actually eating , or it may correspond a period of the day in which the patient is experiencing enhanced insulin sensitivity ; and when the net effect curve is negative then this shall correspond to the patient engaging in intense physical activity or exercise . the patients physiological model parameters , { θ ( k )} kεday , includes daily variability due to the patients circadian rhythm ; and the processor of the model updater module is configured to compute the following : where u is the recursive parameter update function , which could be gradient - based , bg res is a vector of bg model prediction errors ( residuals ) and ne res is a vector of errors between the computed net effect curve and the patient &# 39 ; s record of actual ( verified ) behavioral inputs . the system of example 62 , wherein the dynamic model is adjusted on multiple time scales , whereby parameter updates can be computed daily based on bg residuals : and updates based on net effect mismatch can be computed on a longer time scale , such as every week or month : the system of example 46 , further configured to provide a retroactive assessment of the patient &# 39 ; s optimal rate of insulin delivery , wherein said system comprises : retroactively compute what the patient &# 39 ; s optimal rate of insulin delivery would have been over a predetermined period of historical time given that the disturbances to the system are exactly the historical of net effect curves computed for the patient over that interval of time , wherein for each “ history ” of net effect curves there is a corresponding “ history ” of insulin delivery rates that account for meals , exercise , and corrections for each day in the considered interval of time ; and map between the net effect curve for a given day and the model - based response of an optimal controller , wherein these vectors of optimal responses are collected and analyzed , and presented to the patient or user for a day - by - day review of insulin treatment ; extract features from the optimal responses that correspond to important but random events by subtracting discrete amounts of insulin associated with meals or accounting for discrete insulin deficits associated with temporary basal rates around exercise , whereby the remaining schedule of insulin delivery corresponds to a representation of the patient &# 39 ; s “ optimal ” basal pattern each day in the historical record ; and identify consistency in the retroactively computed optimal basal rates , such optimal basal rates in a plurality of duration segments representing the patient &# 39 ; s treatment duration ; and provide an output to the patient or user the median level of basal insulin that would have been applied in each segment , wherein the patient or user could use this information to ( i ) decide upon on reduced temporary basal rates before meals and / or following exercise in the future or ( ii ) adjust the patient &# 39 ; s long - term basal rate profile . the system of example 46 , further configured to provide an on - demand adaptive correction of insulin advice model , said system comprises : a retrospective meal and exercise detector module having a processor to provide retrospective detecting for meal and exercise activities ; a meal and exercise stochastic modeler module having a processor to provide stochastic modeling to provide a description about the timing and content of meals and exercise ; and a correction bolus advisor module having a processor to provide and output insulin correction advice to a patient or user that would be in response to a patient and user request . said retrospective detection for meal and exercise activities includes the algorithm for reconciling current history of said patient “ net effect curves ” with the historical record of patient - acknowledged meals and exercise events to produce a validated ( high - confidence ) record of relevant patient behaviors , wherein the reconciling includes identifying discrepancies between ( i ) the net effect curves computed from the available bg and insulin data for the patient and ( ii ) the meal and exercise events that are acknowledged by the patient or user through the systems user interface ; and an output module to provide suggestions from said discrepancies , wherein suggestions are communicated to patient or user ; and an input module to receive any responses resultant from user or patient to form the final , validated record of relevant patient activities . said processor of said stochastic modeling module being configured for receiving said final , validated record of relevant patient activities and stochastically modeling to represent the timing and content of meals and exercise of the patient &# 39 ; s behavior . said processor of said correction bolus advisor module being configured for monitoring the patient &# 39 ; s status and to provide insulin correction advice output in the moment the patient or user asks for it , based on ( i ) the stochastic modeling for upcoming behavioral disturbances and ( ii ) the current dynamic model of the patient &# 39 ; s metabolic system that allows for the prediction of the impact of various alternative correction insulin amounts . a non - transitory computer readable medium containing program instructions for providing posterior assessment of the risk of hypoglycemic of a patient , wherein execution of the program instructions by one or more processors of a computer system causes the processor to carry out the following steps of : providing an algorithm to compute a statistic , r hypo ( record ), for the risk of hypoglycemia based on the absolute bg levels , bg variability , and insulin delivery that is highly correlated to the posterior ( conditional ) probability of hypoglycemia , p ( e hypo | record ), where e hypo denotes the event of hypoglycemia in the next day and record refers to the subject &# 39 ; s historical bg , insulin delivery , and activities record ; and providing the computed statistic , r hypo ( record ), whereby actionable prior warning of the possibility of hypoglycemia about the patient is so provided to patient or user . the non - transitory computer readable medium of example 69 , wherein the absolute bg levels and bg variability may be data derived from a cgm device and the absolute insulin delivery may be data obtained from an insulin pump device . the non - transitory computer readable medium of example 69 , wherein the absolute bg levels and bg variability may be data derived from a cgm device and the absolute insulin delivery may be data obtained from a manual insulin injection device . the non - transitory computer readable medium of example 69 , wherein the absolute bg levels and bg variability may be data derived from an smbg device and / or the absolute insulin delivery may be data obtained from an insulin pump device . the non - transitory computer readable medium of example 69 , wherein the absolute bg levels and bg variability may be data derived from an smbg device and / or the absolute insulin delivery may be data obtained from a manual insulin injection device . a non - transitory computer readable medium containing program instructions for retroactively providing a safe level of insulin for the patient , wherein execution of the program instructions by one or more processors of a computer system causes the processor to carry out the following steps of : providing an algorithm to retroactively compute a risk - based insulation attenuation factor to the subject &# 39 ; s record of insulin delivery ; and providing the computed risk - based insulation attenuation factor and applying the risk - based attenuation factor so that any internal threshold is provided to the patient or user for deciding on reduced temporary basal rates before meals and / or following exercise in the future that may be implemented . the non - transitory computer readable medium of example 74 , wherein the record of the insulin delivery may be data obtained from an insulin pump device . the non - transitory computer readable medium of example 74 , wherein the record of the insulin delivery may be data obtained from a manual insulin injection device . the non - transitory computer readable medium of example 202 , wherein the risk - based attenuation factor would be computed as follows : where r ( t , τ ) is a measure of the risk of hypoglycemia between time t and t + τ based on the historical record of bg and insulin data up to time t , based on the bg symmetrization of function and k patient is a patient - specific “ aggressiveness ” factor . a non - transitory computer readable medium containing program instructions for providing a “ net effect ” based patient adoptive model , wherein execution of the program instructions by one or more processors of a computer system causes the processor to carry out the following steps of : wherein said dynamic model includes descriptive parameters of an individual physiology of the model patient ; computing a corresponding inferred history of behavioral “ net effect ” model that explains the glucose variability in the historical record through the dynamic model ; wherein said “ net effect ” model includes a mathematical representation perturbations of the model patient ; computing an update of the patient &# 39 ; s physiological parameters based on both ( i ) the ability of the dynamic model to predict future bg based on known inputs and ( ii ) the ability of the model to produce net effect curves that are consistent with the patient &# 39 ; s record of the perturbations ; and providing said update to the patient or user whereby patient or user can use the update for future course of action . the non - transitory computer readable medium of example 78 , wherein said descriptive parameters include a representation of the dynamic relationship between oral carbs d ( g / min ), physical activity e ( cal / min ), subcutaneous insulin u ( u / hr ), and the model patient &# 39 ; s metabolic state vector x whose elements include glucose and insulin concentrations ( mg / dl ) in various compartments of the body and carbohydrate mass ( mg ) in the gut . the non - transitory computer readable medium of example 79 , wherein the glucose concentration ( mg / dl ) may be data derived from a cgm device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . the non - transitory computer readable medium of example 79 , wherein the glucose concentration ( mg / dl ) may be data derived from a cgm device and the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from a manual insulin injection device . the non - transitory computer readable medium of example 79 , wherein the glucose concentration ( mg / dl ) may be data derived from a smbg device and / or the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from an insulin pump device . the non - transitory computer readable medium of example 79 , wherein the glucose concentration ( mg / dl ) may be data derived from a smbg device and / or the subcutaneous insulin u and the insulin concentration ( mg / dl ) may be data obtained from a manual insulin injection device . the non - transitory computer readable medium of example 79 , wherein relationship said descriptive parameters can be described as a set of discrete - time nonlinear difference equations : χ ( k + 1 )= f ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) bg model ( k )= g ( χ ( k ), u ( k ), d ( k ), e ( k ); θ ( k )) where f and g are nonlinear system equations and θ ( k ) is a vector of parameter values that are characteristic of the patient , such as body weight , volumes of distribution in various compartments , various time constant that describe the rates of absorption and clearance between various compartments , some of which are prone to varying as a function of time k . the non - transitory computer readable medium of example 79 , wherein relationship of said of descriptive parameters can be described as a set of continuous - time nonlinear differential equations : { dot over ( χ )}( t )= f ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )) bg model ( t )= g ( χ ( t ), u ( t ), d ( t ), e ( t ); θ ( t )). the non - transitory computer readable medium of example 185 , wherein nonlinear representation can be linearized around any desired operating point ( e . g . steady state glucose concentration ) to yield a linear dynamic model : x ( k + 1 )= ax ( k )+ b u u δ ( k )+ b d d ( k )+ b e e ( k ) where x is the vector of metabolic state differentials ( away from the operating point ), u δ ( u / hr ) is the deviation in insulin delivery from the patient &# 39 ; s steady state ( basal ) insulin delivery rate , a , b u , b d , b e are the state space matrices of the linear model , and y ( k ) represents bg deviation away from the desired operating point , and the dependence on θ ( k ) is embedded within the state space matrices a , b u , b d , b e . the non - transitory computer readable medium of example 78 , wherein said perturbations include meal profiles , physical activity , and sleep / awake periods . the non - transitory computer readable medium of example 78 , wherein said “ net effect ” model provides a “ history ” of virtual system inputs that reconciles the patient &# 39 ; s historical record of bg and historical record of insulin delivery . the non - transitory computer readable medium of example 88 , wherein the patient &# 39 ; s historical record of bg concentration , { bg ( k )} kεday , and historical record of insulin delivery , { u ( k )} kεday , the net effect that reconciles the historical information is the vector of virtual carbohydrate inputs { d n . e . ( k )} kεday that minimizes the error function : dist ({ bg ( k )} kεday ,{ bg model ( k )} kεday |{ u ( k )} kεday ,{ d n . e . ( k )} kεday ), where dist measures the distance between two vectors of bg concentration ( in this case actual bg versus model - predicted bg ) given the fixed record of insulin delivery { u ( k )} kεday and the candidate net effect vector {( d n . e . ( k )} kεday . the non - transitory computer readable medium of example 89 , wherein the resulting optimal net effect vector ( aka . net effect curve ), { d n . e . ( k )} kεday , optimally reconciles the bg and insulin data collected by the patient through a virtual carbohydrate signal , which captures all external influences on the patient as a single external disturbance signal measured in ( mg / min ). when the net effect curve is positive this shall correspond to the patient actually eating , or it may correspond a period of the day in which the patient is experiencing enhanced insulin sensitivity ; and when the net effect curve is negative then this shall correspond to the patient engaging in intense physical activity or exercise . the patients physiological model parameters , { θ ( k )} kεday , includes daily variability due to the patients circadian rhythm ; and the model updater , includes a formula that takes the form having the following : where u is the recursive parameter update function , which could be gradient - based , bg res is a vector of bg model prediction errors ( residuals ) and ne res is a vector of errors between the computed net effect curve and the patient &# 39 ; s record of actual ( verified ) behavioral inputs . the non - transitory computer readable medium of example 92 , wherein the dynamic model is adjusted on multiple time scales , whereby parameter updates can be computed daily based on bg residuals : and updates based on net effect mismatch can be computed on a longer time scale , such as every week or month : the non - transitory computer readable medium of example 78 , further comprising providing a retroactive assessment of the patient &# 39 ; s optimal rate of insulin delivery , wherein execution of the program instructions by one or more processors of a computer system causes the processor to carry out the following steps of : retroactively computing what the patient &# 39 ; s optimal rate of insulin delivery would have been over a predetermined period of historical time given that the disturbances to the system are exactly the historical of net effect curves computed for the patient over that interval of time , wherein for each “ history ” of net effect curves there is a corresponding “ history ” of insulin delivery rates that account for meals , exercise , and corrections for each day in the considered interval of time ; mapping between the net effect curve for a given day and the model - based response of an optimal controller , wherein these vectors of optimal responses are collected and analyzed , and presented to the patient or user for a day - by - day review of insulin treatment ; extracting features from the optimal responses that correspond to important but random events by subtracting discrete amounts of insulin associated with meals or accounting for discrete insulin deficits associated with temporary basal rates around exercise , whereby the remaining schedule of insulin delivery corresponds to a representation of the patient &# 39 ; s “ optimal ” basal pattern each day in the historical record ; identifying consistency in the retroactively computed optimal basal rates , such optimal basal rates in a plurality of duration segments representing the patient &# 39 ; s treatment duration ; and providing to the patient or user the median level of basal insulin that would have been applied in each segment , wherein the patient or user could use this information to ( i ) decide upon on reduced temporary basal rates before meals and / or following exercise in the future or ( ii ) adjust the patient &# 39 ; s long - term basal rate profile . the non - transitory computer readable medium of example 78 , further comprising providing an on - demand adaptive correction of insulin advice model , wherein execution of the program instructions by one or more processors of a computer system causes the processor to carry out the following steps of : stochastic modeling to provide a description about the timing and content of meals and exercise ; and providing insulin correction advice to a patient or user that would be in response to a patient and user request . said retrospective detection for meal and exercise activities includes the algorithm for reconciling current history of said patient “ net effect curves ” with the historical record of patient - acknowledged meals and exercise events to produce a validated ( high - confidence ) record of relevant patient behaviors , wherein the reconciling includes identifying discrepancies between ( i ) the net effect curves computed from the available bg and insulin data for the patient and ( ii ) the meal and exercise events that are acknowledged by the patient or user through the systems user interface ; and wherein execution of the program instructions by one or more processors of a computer system causes the processor to carry out the following steps of : providing suggestions from said discrepancies , wherein suggestions are communicated to patient or user ; and receiving any responses resultant from user or patient to form the final , validated record of relevant patient activities . said stochastic modeling includes the algorithm for receiving said final , validated record of relevant patient activities and stochastically modeling to represent the timing and content of meals and exercise of the patient &# 39 ; s behavior . said insulin correction includes the algorithm for monitoring the patient &# 39 ; s status and to provide insulin correction advice in the moment the patient or user asks for it , based on ( i ) the stochastic modeling for upcoming behavioral disturbances and ( ii ) the current dynamic model of the patient &# 39 ; s metabolic system that allows for the prediction of the impact of various alternative correction insulin amounts . it should be appreciated that any one or more of the example nos . 1 - 98 may be combined with any one or more of example nos . 1 - 98 as desired or required . it should be appreciated that as discussed herein , a subject or patient 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 . unless clearly specified to the contrary , there is no requirement for any particular described or illustrated activity or element , any particular sequence or such activities , any particular size , speed , material , duration , contour , dimension or frequency , or any particularly interrelationship of such elements . moreover , any activity can be repeated , any activity can be performed by multiple entities , and / or any element can be duplicated . further , any activity or element can be excluded , the sequence of activities can vary , and / or the interrelationship of elements can vary . it should be appreciated that aspects of the present invention may have a variety of sizes , contours , shapes , compositions and materials as desired or required . in summary , while the present invention has been described with respect to specific embodiments , many modifications , variations , alterations , substitutions , and equivalents will be apparent to those skilled in the art . the present invention is not to be limited in scope by the specific embodiment described herein . indeed , various modifications of the present invention , in addition to those described herein , will be apparent to those of skill in the art from the foregoing description and accompanying drawings . accordingly , the invention is to be considered as limited only by the spirit and scope of the following claims , including all modifications and equivalents . still other embodiments will become readily apparent to those skilled in this art from reading the above - recited detailed description and drawings of certain exemplary embodiments . it should be understood that numerous variations , modifications , and additional embodiments are possible , and accordingly , all such variations , modifications , and embodiments are to be regarded as being within the spirit and scope of this application . for example , regardless of the content of any portion ( e . g ., title , field , background , summary , abstract , drawing figure , etc .) of this application , unless clearly specified to the contrary , there is no requirement for the inclusion in any claim herein or of any application claiming priority hereto of any particular described or illustrated activity or element , any particular sequence of such activities , or any particular interrelationship of such elements . moreover , any activity can be repeated , any activity can be performed by multiple entities , and / or any element can be duplicated . further , any activity or element can be excluded , the sequence of activities can vary , and / or the interrelationship of elements can vary . unless clearly specified to the contrary , there is no requirement for any particular described or illustrated activity or element , any particular sequence or such activities , any particular size , speed , material , dimension or frequency , or any particularly interrelationship of such elements . accordingly , the descriptions and drawings are to be regarded as illustrative in nature , and not as restrictive . moreover , when any number or range is described herein , unless clearly stated otherwise , that number or range is approximate . when any range is described herein , unless clearly stated otherwise , that range includes all values therein and all sub ranges therein . any information in any material ( e . g ., a united states / foreign patent , united states / foreign patent application , book , article , etc .) that has been incorporated by reference herein , is only incorporated by reference to the extent that no conflict exists between such information and the other statements and drawings set forth herein . in the event of such conflict , including a conflict that would render invalid any claim herein or seeking priority hereto , then any such conflicting information in such incorporated by reference material is specifically not incorporated by reference herein .