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WO2015079079A1 - Méthode de modélisation du taux de glycémie au moyen d'une programmation génétique - Google Patents

Méthode de modélisation du taux de glycémie au moyen d'une programmation génétique Download PDF

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Publication number
WO2015079079A1
WO2015079079A1 PCT/ES2014/000190 ES2014000190W WO2015079079A1 WO 2015079079 A1 WO2015079079 A1 WO 2015079079A1 ES 2014000190 W ES2014000190 W ES 2014000190W WO 2015079079 A1 WO2015079079 A1 WO 2015079079A1
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Prior art keywords
insulin
glucose
solutions
time
error
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PCT/ES2014/000190
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English (en)
Spanish (es)
Inventor
José Ignacio HIDALGO PÉREZ
Antonio Oscar GARNICA ALCAZAR
Juan LANCHARES DÁVILA
Jose Luis RISCO MARTÍN
Jose Manuel COLMENAR VERDUGO
Alfredo CUESTA INFANTE
Estjer MÁQUEDA VILLAIZÁN
Marta BOTELLA SERRANO
José Antonio RUBIO GARCÍA
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Universidad Complutense De Madrid
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Publication of WO2015079079A1 publication Critical patent/WO2015079079A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00—Subject matter not provided for in other main groups of this subclass
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures

Definitions

  • the present invention proposes to apply genetic programming to find a personalized model that describes and predicts a patient's glucose levels.
  • the present invention describes a method that, from the historical data of a patient consisting of previous values of glucose, carbohydrates taken and insulin injected, obtains an expression that can be used to predict glucose values in the near future. .
  • the field of application of the present invention is the estimation of a patient's glucose from the measured data. Due to the nature of the invention, the glucose estimation of the present invention allows devices that operate according to the method of the present invention to predict a patient's glucose levels. Among all patients, the method of the present invention is especially indicated for subjects with Diabetes Mellitus.
  • Diabetes Mellitus is a disease caused by a defect in the secretion or action of insulin, which is essential for the control of blood glucose levels. In both cases the result is that the cells do not assimilate sugar and as a result, there is an increase in blood glucose levels or hyperglycemia.
  • ADA American Diabetes Association
  • Type 1 Diabetes Cells do not produce insulin due to an autoimmune process. This currently makes it necessary for the person to inject exogenous insulin, either by point injections or using an insulin pump.
  • Type 2 Diabetes T2DM: It is the result of insulin resistance, where cells fail to use insulin properly. Sometimes it is combined with an absence, either partial or total insulin.
  • Gestational Diabetes Appears during pregnancy in one in ten pregnant women. Pregnancy is a change in metabolism, since the fetus uses the mother's energy to obtain food, oxygen and other resources. This causes a decrease in the secretion of insulin by the mother.
  • Evolutionary techniques such as genetic programming - (PG) - have certain characteristics that make them especially useful for addressing problems of optimization and complex modeling. In the first place they are "simple" conceptually speaking and so is their application. However, they have a well-defined and widely studied theoretical basis. Genetic programming has demonstrated its applicability to a multitude of real problems and is intrinsically parallel to work with a set of solutions. Moreover, evolutionary algorithms have great potential to incorporate knowledge about the domain in which they work and to incorporate other search mechanisms that are not necessarily evolutionary.
  • GE evolutionary grammar is an evolutionary computing technique established in 1998 by Conor Ryan's group at the University of Limerick (Ireland). Genetic programming tries to find executable programs or functions that respond to reference data. The main advantage is that GE evolutionary grammar applies genetic operators to a complete string which simplifies the application of search in different programming languages. It also has no memory problems, unlike basic genetic programming, where representation in Tree can lead to the known problem of bloating (an excessive growth of computer data structures in memory).
  • the present invention proposes a new technique that involves obtaining the particularized patient model using "GP" genetic programming.
  • the "GP” genetic programming eliminates the barriers related to the construction of the model, such as linearity or the limitation of the input parameters.
  • the method of the present invention takes the historical data of a patient consisting of previous values of glucose carbohydrates taken and insulin injected and, from them, obtains an expression that can be used to predict glucose values in the near future.
  • a first aspect of the invention is a method for modeling the level of blood glucose that allows to predict the glucose of an individual.
  • the method comprises the following steps: i) obtain from an individual data that comprise, for a time k, at least: o GL glucose levels;
  • GL (k + 1) f (GL, CH, IS, IL).
  • Step ii) additionally comprises carrying out the following sub-steps:
  • a) generate the set of solutions with random N-solutions where each solution is formed by a string of characters (chromosome);
  • c) calculate the error Ek that entails: calculate ek as the difference between the data obtained from the patient and the N-expressions GL k ; and, to apply a function of f ⁇ tness to each one of the errors previously calculated ei ⁇ ; such that an associated error Ek is obtained for each GL k expression;
  • N-1 solutions resulting from: taking the N-solutions and separating the solution with the lowest error Ek from the N-solutions; face the N-1 solutions taken two by two, selecting the solution with the lowest error E;
  • the crossover probability algorithm comprises:
  • the mutation probability algorithm comprises:
  • the predefined stop condition is at least one of the following conditions:
  • the fitness function is one of the following functions (see table 2):
  • the predicted glucose prediction was obtained from four input data taken from the patient. However, the number of input data can be variable up to a total of twenty-five input variables. Thus, the starting solution GL (k + 1) would be extended until it had an expression like the following:
  • GL (k + 1) f (P, E, k, SI, IG, PG, CI, M, IC, IP, DI, OC, TI, FV, VI, PC, TE, FA, G (-), C (-), IS (-),
  • IL (-), F (-), E (-), Z) where the operator ⁇ represents any current or previous instant in time and where / is a function that is calculated using a BNF grammar comprising the following form:
  • ⁇ P> :: "the individual's weight in kg.”
  • ⁇ SI> :: "glycemic units that lower 1 unit of insulin in mg / dl";
  • ⁇ IG> :: "glycemic intake index” (that of glucose is 1, and may be higher or lower;)
  • FSI insulin sensitivity or mg / dl that lowers blood glucose per unit of insulin
  • IG is the glycemic index
  • PG protein / fat units: amount of food provided by lOOKcal in the form of fat and / or proteins, is measured in units;
  • IC Circulating insulin, insulin units
  • IP interval between the beginning of the infusion of prandial insulin and the beginning of the intake, in 15-minute intervals
  • DI duration of the last intake in 15 minute intervals
  • OC order in which food is consumed; (natural number corresponding to a row in a table of possible ordinations)
  • FV absorption variability factor, value between 0 and 1;
  • VI insulin variability, value between 0 and 1;
  • PC food preparation (fried, roasted, condiments %), value between 0 and 1;
  • FTSI transient insulin sensitivity factor
  • G (-) is the history of blood glucose (or part of it);
  • C (-) is the carbohydrate history
  • IS (-) is short-acting insulin
  • IL (-) is long-acting insulin
  • F (-) is physical exercise
  • ⁇ ( ⁇ ) is the stress level.
  • mapping function indicated above, it includes the following expression:
  • Choice ⁇ is the selected choice for non-terminal i
  • CIV is the codon we are decoding
  • MOD is the module function
  • (# of choices ⁇ ) is the number of possible options for the rule in terminal i.
  • the present invention comprises a computer program for the execution of a method for modeling blood glucose level glucose prediction according to any one of the embodiments described above or in the section "description of a or various embodiments of the invention. "
  • the present invention comprises a storage medium containing a computer program for the execution of a method for modeling blood glucose level glucose prediction according to any one of the embodiments described above or in the section "description of one or more embodiments of the invention".
  • the last aspect of the present invention is comprised of a computer system in which the computer program described above is loaded.
  • Figure 1. Represents a flow chart to calculate the glucose prediction model.
  • Figure 2. Represents a flow chart to calculate the glucose prediction model in a web implementation.
  • Figure 6. It is a third example of BNF grammar.
  • Figure 7. It is a fourth example of BNF grammar. DESCRIPTION OF ONE OR SEVERAL FORMS OF REALIZATION OF THE
  • Figure 1 shows a flow chart of the method according to the present invention.
  • a collection of data 1 is carried out during a certain period of time k, for example seven days.
  • the data collected are at least GL glucose levels, CH intake levels and insulin levels with rapid effect IS and slow effect -IL.
  • an evolutionary algorithm 3 is applied, basically consisting of customizing a grammar in BNF 4 format by applying a mapping function 5.
  • a new model characterized by the function 6 characterized by the function 6:
  • G ⁇ L ⁇ k + 1) f (Gl, CH, IS, IL)
  • GL (k + 1) f (P, E, k, SIIG, PG, CIM, IC, IP, DI OC, TIFV, VI, PC, TE, FA, G (-), C (-), IS ( -),
  • FSI insulin sensitivity or mg / dl that lowers blood glucose per unit of insulin
  • IG is the glycemic index u
  • PG protein / fat units: amount of food provided by the OOKcal in the form of fat and / or protein, is measured in units;
  • IC Circulating insulin, insulin units
  • IP interval between the beginning of the infusion of prandial insulin and the beginning of the intake, in 15-minute intervals
  • DI duration of the last intake in 15 minute intervals
  • FV absorption variability factor, value between 0 and 1;
  • VI insulin variability, value between 0 and 1;
  • PC food preparation (fried, roasted, condiments %), value between 0 and 1;
  • FTSI transient insulin sensitivity factor
  • G (-) is the history of blood glucose (or part of it);
  • C (-) is the carbohydrate history
  • IS (-) is short-acting insulin
  • IL (-) is long-acting insulin
  • F (-) is physical exercise
  • ⁇ ( ⁇ ) is the stress level
  • Figure 2 shows a flow chart where the method of the present invention is implemented within a computer system comprising a database 7 and a web application 8, through which, a patient 9 can enter 11 into the database 7 data corresponding to glucose levels measured by a continuous glucose meter 10.
  • the computing system additionally comprises computing means 15 such as memories, microprocessors, input / output units.
  • the computational means 15 are in charge of storing the grammars and processing the evolutionary algorithm to obtain the mathematical model or expression for GL (k + l) 6 with which to obtain the glucose prediction 13.
  • the computational models 15 can be extended with computational means optional 15 'that would serve to predict glucose 14 and indicate insulin boluses in a future time (for example 2 hours) combine the GL function (k + l) with the "instantaneous" glucose data, expected intake, physical exercise and insulin regulated 12.
  • computational means optional 15 'that would serve to predict glucose 14 and indicate insulin boluses in a future time combine the GL function (k + l) with the "instantaneous" glucose data, expected intake, physical exercise and insulin regulated 12.
  • the objective of the present invention is to find an expression that models the glucose level of a diabetic patient. This expression must be obtained, at least, from previous glucose, carbohydrate and insulin data stored in a system or database. Therefore, we are facing a problem similar to the problem of symbolic regression. Symbolic regression tries to obtain mathematical expressions that reproduce a discrete set of data.
  • Genetic Programming has proven effective in a high number of symbolic regression problems, although it has some limitations, which often come from the mode of representation such as "bloating". Another point to consider is that in Genetic Programming (PG), evolution occurs in the phenotype of the individual and not in their representation (genotype). During recent years, variants of genetic programming, evolutionary grammars have appeared to propose different approaches to evolution. Evolutionary Grammar (GE) allows the generation of computer programs in arbitrary language. This is achieved by using grammars to specify the rules for obtaining programs Specifically, the present invention uses grammars expressed in the form of BNF (Backus Naur form).
  • BNF Backus Naur form
  • the Evolutionary Grammar GE works (evolves) with a genetic code that determines the production process of the solution.
  • the code translation process is determined by the Grammar represented as BNF.
  • BNF is a technical notation for expressing context-free grammars.
  • a representation in BNF can be any specification of a complete language or a subset of a problem-oriented language.
  • a BNF specification is a set of derivation rules, expressed in the form:
  • the rules are composed of terminal and non-terminal sequences.
  • the symbols that appear on the left are non-terminals, while the terminals never appear on the left side.
  • ⁇ symbol> is non-terminal and, although it is not a complete BNF specification, it can also be affirmed for the present invention that ⁇ expression> will be a non-terminal since they always appear between the pair or . Therefore, in this case, the non-terminal ⁇ symbol> will be replaced by an expression.
  • the rest of the grammar must indicate the different possibilities.
  • a grammar is represented by a 4-tuple ⁇ N, T, P, S ⁇ , where N is the set of non-terminals, T is the set of terminals, P are the production rules for the assignment of elements from N to T , and S is a start symbol that should appear in N. The options within a production rule are separated by the "
  • Figure 3 represents an example of grammar in BNF designed for symbolic regression. The code that represents an expression will consist of elements of a set of T terminals. These have been combined with the grammar rules as explained below.
  • grammars can be adapted to skew the search for the evolutionary process because there is a finite number of production rule options, which limits the search space.
  • mapping process As mentioned above, the present invention applies an evolutionary algorithm to evolve genotypes, which are represented as a chain of integer values. Each genotype maps the start symbol to terminal symbols by reading the codons, which, in this exemplary embodiment, are 8 bits long. The process is similar to the one explained in the previous section but, instead of making random choices, we will make the decisions by reading the genotype. Each codon is therefore an integer value of the genotype, which is processed by the following mapping function:
  • Choice ⁇ is the selected choice for non-terminal i
  • CIV is the codon being decoded
  • MOD is the module function
  • (# of chotees,) is the number of possible options for the rule in the terminal.
  • the mapping function takes the integer value of the chromosome, calculates the module with respect to the number of possibilities of the rule and selects the option according to the result. Since the module function returns values between 0 and (# of choices,) - 1, the first option will correspond to the first value, 0, the second to 1, and so on. Therefore, if a rule has only one possibility, it will always be selected.
  • the mapping process uses the grammar of Figure 3, designed to solve a problem of symbolic regression and glucose model problems, in the absence of particularizing the terminal set, which is described in the next section.
  • An individual is made up of a series of genes (integer values). Each gene can take a value between 0 and 255 since the codons are 8 bits. Assume for example the following individual of 7 genes:
  • This value selects the first option, the non-terminal X.
  • This value selects the third option, the non-terminal ⁇ var>.
  • a model to be complete such a model, for glucose levels, should be based on observable factors as well as other hidden and intrinsic characteristics of the patient's organism.
  • the observable factors are those data that the patient has collected manually or an automatic device, while the unobservable must be inferred.
  • the present invention proposes a model that considers all these factors, applying Evolutionary Grammar (GE) to infer an expression that characterizes the behavior of glucose in diabetic patients. 3.1. Available data and general glucose model.
  • GE Evolutionary Grammar
  • the level of glucose at any given time depends on several factors, some of them intrinsic to the functioning of the organism. In the case of a diabetes patient, the most relevant are the level of glucose that was up to the last measure, the ingested carbohydrates and the injected insulin. These factors are included in the data sets of our patients. Importantly, this data is easy to collect for a real patient.
  • the glucose value is measured with blood analyzers, carbohydrates are measured in units ingested from daily meals, and the amount of insulin as well as the type, are data that the patient obviously knows.
  • Table 1 Example of data taken for a patient "XI" for the values GL, CH, IS IL.
  • the proposed model provides us with the estimated glucose values, represented by GL.
  • the estimated glucose is obtained using the previous estimated values, and the values of carbohydrates and insulin at that instant, formally:
  • GL (+ 1) f (GL; CH; IS; IL); l ⁇ k ⁇ N (ec. l) where GL (k + 1) is the estimated future value for glucose, an instant after the current one, and the rest of the variables have the same interpretation as the one described above.
  • GE Evolutionary Grammar
  • Gl (k + 1) f g i (Gl (km)) + fc (CH (km)) - f in (IS (km); IL (km)); 0 ⁇ m ⁇ k; (gr.l)
  • the concrete form of f g i, f cn and end is obtained by GE with Grammar 1, shown in Figure 4.
  • the three terms ⁇ exprgluc>, ⁇ exprch> and ⁇ exprins> correspond to f g i, fch and end, respectively, and are the expressions that can use prefixes (operands) such as those of rule IX, variables for each of the terms or combinations of them through rule VIII.
  • Gl (k + 1) f g i (Sl (km)) + fc h (CH (km)) - end (IS (km); IL (km)); 0 ⁇ m ⁇ 1; (gr.2)
  • Figure 5 shows the grammar used. You simply have to limit the indexes in rules III, V and VII to 00 and 01.
  • the connective operations are left free, extending them to the basic arithmetic operations.
  • the general expression of the model is (gr.3), where f is the function that connects the three subexpressions for glucose, carbohydrates and insulin.
  • Gl (k + 1) f (f g i (Gl (k - m)); f ch (CH (k - m)); figna(IS (k - m); IL (k - m))) ; 0 ⁇ m ⁇ k; (gr.3)
  • Grammar 3 is responsible for defining this model, which presents a slight modification in rule I of grammar 1. It is about changing the fixed operations + and - for the non-terminal ⁇ op> already The following is defined in rule VIII.
  • Figure 6 shows grammar 3.
  • the general model is similar to grammar 2 but adds freedom to function / which relates the subexpressions of glucose, carbohydrates and insulin.
  • GL (k + 1) f (f g i (GL (k - m)); f ch (CH (k - m)); f in (IS (k - m); IL (k. M))) ; 0 ⁇ m ⁇ 1; (gr. 4)
  • grammar 4 is similar to grammar 2 but giving freedom to the operations in rule I.
  • the grammar is shown in Figure 7.
  • the mission of the "fitness" functions (also known as fitness) is to guide the evolution towards a good solution.
  • “fitness” the first time series of the complete glucose, GL, is obtained from the phenotype generated by the genotype of the individual and the grammar used. In this step the estimated glucose in k is fed back to estimate the glucose in the following k.
  • ek ⁇ GL (k) - GL (k) ⁇ , l ⁇ k ⁇ N
  • the fitness functions contained in Table 2 are: Fi (least squares), F 2 (medium error), F 3 (error maximum), F 4 (mean square error) and F 5 (mean absolute deviation).

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Abstract

L'invention concerne une méthode qui, en appliquant des algorithmes évolutifs à des solutions aléatoires et à des données prélevées chez un patient souffrant de glycémie, permet d'établir une modélisation du taux de glycémie afin d'obtenir une modélisation du taux de glucose à des instants dans le futur par rapport aux moments d'obtention des données du patient. Les données du patient comprennent au moins les taux de glucose, d'ingesta et d'insuline rapide et lente pour un intervalle de temps. L'algorithme évolutif consiste essentiellement à appliquer une programmation génétique à sa variante de grammaires évolutives ou à son évolution grammaticale. C'est-à-dire, il consiste à appliquer des grammaires personnalisées sous format BNF, des processus de mappage personnalisés et des évaluations d'erreur concrètes aux solutions aléatoires ou aux modèles générés antérieurement pour obtenir une expression qui décrit et prédit les taux de glucose chez un patient. Parmi l'ensemble des patient, la méthode selon la présente invention est particulièrement indiquée aux patients souffrant de diabète sucré.
PCT/ES2014/000190 2013-11-27 2014-11-05 Méthode de modélisation du taux de glycémie au moyen d'une programmation génétique WO2015079079A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023135346A1 (fr) 2022-01-12 2023-07-20 Universidad Complutense De Madrid Procédé et système pour la prédiction de valeurs de glucose et la génération d'alertes d'hypoglycémie et d'hyperglycémie
US12205686B2 (en) * 2020-02-27 2025-01-21 The Cleveland Clinic Foundation Identifying patients for intensive hyperglycemia management

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005113036A1 (fr) * 2004-05-13 2005-12-01 The Regents Of The University Of California Procede et dispositif de regulation du glucose et de dosage de l'insuline pour des sujets diabetiques
US20080154513A1 (en) * 2006-12-21 2008-06-26 University Of Virginia Patent Foundation Systems, Methods and Computer Program Codes for Recognition of Patterns of Hyperglycemia and Hypoglycemia, Increased Glucose Variability, and Ineffective Self-Monitoring in Diabetes
WO2010019919A1 (fr) * 2008-08-14 2010-02-18 University Of Toledo Système de réseau neuronal multifonctionnel, et ses utilisations pour des prévisions glycémiques
US20110077930A1 (en) * 2008-02-12 2011-03-31 Alferness Clifton A Computer-implemented method for providing a personalized tool for estimating 1,5-anhydroglucitol

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005113036A1 (fr) * 2004-05-13 2005-12-01 The Regents Of The University Of California Procede et dispositif de regulation du glucose et de dosage de l'insuline pour des sujets diabetiques
US20080154513A1 (en) * 2006-12-21 2008-06-26 University Of Virginia Patent Foundation Systems, Methods and Computer Program Codes for Recognition of Patterns of Hyperglycemia and Hypoglycemia, Increased Glucose Variability, and Ineffective Self-Monitoring in Diabetes
US20110077930A1 (en) * 2008-02-12 2011-03-31 Alferness Clifton A Computer-implemented method for providing a personalized tool for estimating 1,5-anhydroglucitol
WO2010019919A1 (fr) * 2008-08-14 2010-02-18 University Of Toledo Système de réseau neuronal multifonctionnel, et ses utilisations pour des prévisions glycémiques

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12205686B2 (en) * 2020-02-27 2025-01-21 The Cleveland Clinic Foundation Identifying patients for intensive hyperglycemia management
WO2023135346A1 (fr) 2022-01-12 2023-07-20 Universidad Complutense De Madrid Procédé et système pour la prédiction de valeurs de glucose et la génération d'alertes d'hypoglycémie et d'hyperglycémie

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