CN112133398B

CN112133398B - Blood glucose management and control method, system and storage medium

Info

Publication number: CN112133398B
Application number: CN202011042192.4A
Authority: CN
Inventors: 张发宝; 李欣梅
Original assignee: Shanghai Medsci Medical Technology Co ltd
Current assignee: Shanghai Medsci Medical Technology Co ltd
Priority date: 2020-09-28
Filing date: 2020-09-28
Publication date: 2021-09-24
Anticipated expiration: 2040-09-28
Also published as: CN112133398A

Abstract

The invention provides a blood sugar management and control method, a blood sugar management and control system, wearable equipment and a storage medium, wherein the method comprises the following steps: determining the current management and control link grade of the diabetic at the current moment according to the basic parameters of the diabetic at the current moment; calculating the association probability value of the control link grade and the blood sugar test value through a Bayesian algorithm; and adjusting and updating the level of the management and control link according to the association probability value so that the blood sugar test value obtained by retesting after adjustment is close to the corresponding normal blood sugar range value. The invention automatically updates the regulation scheme comprising the blood sugar regulation and control link grade, so that the diabetic can regulate and control the blood sugar according to the regulation scheme, thereby being beneficial to improving the self-management compliance of the diabetic and improving or improving the physical quality of the diabetic.

Description

Blood glucose management and control method, system and storage medium

Technical Field

The invention relates to the technical field of medical data mining, in particular to a blood glucose control method, a blood glucose control system and a storage medium.

Background

Diabetes is a metabolic disease characterized by hyperglycemia, a chronic lifelong disease. The chronic disease management refers to the medical behaviors and processes of regular detection, continuous monitoring, evaluation and comprehensive intervention management of chronic non-infectious diseases and risk factors thereof, and mainly comprises the early screening of chronic diseases, the prediction, early warning and comprehensive intervention of chronic disease risks, the comprehensive management of patients suffering from chronic diabetes, the evaluation of chronic disease management effects and the like. In fact, chronic disease management is the management of patients with chronic diabetes and high risk groups, and comprises the management and intervention of reasonable diet, behavior habits, health psychology and other aspects; the correct management concept, knowledge, skill and the like of the chronic diseases are publicized, and the comprehensive prevention and treatment work of the chronic diseases can be well done.

Diabetes not only needs standardized treatment, but also more importantly, management of chronic diseases, so that patients have compliance, and better prevention and control effects can be achieved by matching with ways such as diet and exercise.

Traditional chronic disease management methods are too mechanized or programmed to meet the individual needs of patients. The personalized demand depending on the traditional chronic disease management is too high in cost and difficult to implement. Meanwhile, the personalized blood sugar management and control scheme is mostly based on the experience of doctors and rehabilitation doctors, and does not have automatic measures.

Disclosure of Invention

The invention aims to provide a blood sugar control method, a blood sugar control system, wearable equipment and a storage medium, which are applied to an internet medical management system, can automatically update an adjustment scheme comprising a blood sugar control link grade based on continuous monitoring and effects of blood sugar values (fasting blood sugar value and postprandial blood sugar value) of a diabetic patient, are favorable for improving self-management compliance of the diabetic patient and improve or improve physical quality of the diabetic patient.

The technical scheme provided by the invention is as follows:

the invention provides a blood sugar management and control method, which comprises the following steps:

determining the current management and control link grade of the diabetic at the current moment according to the basic parameters of the diabetic at the current moment;

calculating the association probability value of the control link grade and the blood sugar test value through a Bayesian algorithm;

and adjusting and updating the level of the management and control link according to the association probability value so that the blood sugar test value obtained by retesting after adjustment is close to the corresponding normal blood sugar range value.

Further, determining the current management and control link level of the diabetic patient at the current moment according to the basic parameters of the diabetic patient at the current moment includes:

obtaining a normal blood sugar range value;

acquiring basic parameters of a diabetic patient at the current moment; the basic parameters comprise name, age, sex, height, weight, medication information, blood sugar test value and physiological parameters; the glycemic test value comprises a fasting blood glucose value and a postprandial blood glucose value;

grading the diabetes patient in a management and control link according to a preset rule and the basic parameters;

the control link comprises a medicine control link, a diet control link and a sport control link, and each control link comprises at least two levels.

Further, the calculating the associated probability value of the management and control link level and the blood glucose test value by the bayesian algorithm includes:

according to the blood sugar test value of the diabetic patient at the current moment and the current management link grade, calculating in the following Bayesian algorithm formula to obtain a first probability value;

according to the normal blood sugar range value of the diabetic patient at the current moment and the current management link grade, calculating in the following Bayesian algorithm formula to obtain a second probability value;

calculating the difference value of the first probability value and the second probability value to obtain a corresponding association probability value;

wherein, P (Gi | E) is a first probability value, P (Gi | F) is a second probability value, P (E | Gi) is an occurrence probability that a blood glucose test value is E under a condition that a current management and control link level is determined, P (F | Gi) is an occurrence probability that a normal blood glucose range value is F under a condition that a current management and control link level is determined, P (Gi) is an occurrence probability of a current management and control link level, E is a blood glucose test value, F is a normal blood glucose range value, P (gj) is an occurrence probability of other management and control link levels, j > i, and i and j are positive integers.

Further, the adjusting and updating the management and control link level according to the association probability value includes:

if the association probability value of the current management and control link is lower than a first preset threshold value or higher than a second preset threshold value, adjusting and updating the level of the management and control link; the first preset threshold is smaller than a second preset threshold;

informing the diabetic to execute blood sugar management and control according to the updated management and control link level; the glycemic control includes drug management, dietary management, and exercise management.

Further, the method also comprises the following steps:

and if the blood sugar test value continuously exceeds the normal blood sugar range value after the blood sugar control is executed according to the updated control link grade, sending out warning information and sending out help seeking information to a preset emergency contact person.

The invention also provides a blood sugar management and control system, comprising:

the processing module is used for determining the current management and control link grade of the diabetic at the current moment according to the basic parameters of the diabetic at the current moment;

the calculation module is used for calculating the association probability value of the management and control link grade and the blood sugar test value through a Bayesian algorithm;

and the control module is used for adjusting and updating the grade of the management and control link according to the association probability value so that the blood sugar test value obtained by retesting after adjustment is close to the corresponding normal blood sugar range value.

Further, the processing module comprises:

the first acquisition unit is used for acquiring a normal blood sugar range value;

the second acquisition unit is used for acquiring basic parameters of the diabetic at the current moment; the basic parameters comprise name, age, sex, height, weight, medication information, blood sugar test value and physiological parameters; the glycemic test value comprises a fasting blood glucose value and a postprandial blood glucose value;

the processing unit is used for carrying out grade division on a management and control link on the diabetic according to a preset rule and the basic parameters;

Further, the calculation module comprises:

the first calculation unit is used for calculating a first probability value through the following Bayesian algorithm formula according to the blood sugar test value of the diabetic patient at the current moment and the current management link grade;

the second calculation unit is used for calculating a second probability value through the following Bayesian algorithm formula according to the normal blood glucose range value of the diabetic patient at the current moment and the current management link grade;

a difference value calculating unit, configured to perform difference value calculation on the first probability value and the second probability value to obtain a corresponding associated probability value;

Further, the control module includes:

the adjusting unit is used for adjusting and updating the grade of the management and control link if the association probability value of the current management and control link is lower than a first preset threshold value or higher than a second preset threshold value; the first preset threshold is smaller than a second preset threshold;

the notification unit is used for notifying the diabetic to execute blood sugar management and control according to the updated management and control link grade; the glycemic control includes drug management, dietary management, and exercise management.

Further, the control module includes:

and the warning unit is used for sending warning information and sending distress information to a preset emergency contact if the blood sugar test value continuously exceeds the normal blood sugar range value after the blood sugar management and control is executed according to the updated management and control link level.

The invention also provides a wearable device, which comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor is used for executing the computer program stored in the memory to realize the operation executed by the blood glucose management and control method.

The invention also provides a storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to realize the operation executed by the blood glucose management and control method.

By the blood sugar control method, the blood sugar control system, the wearable device and the storage medium, compliance of a diabetic in a management link in a chronic disease management process can be analyzed through continuous monitoring of blood sugar values (fasting blood sugar values and postprandial blood sugar values) of the diabetic, and an adjustment scheme including the grade of the blood sugar control link is automatically updated based on a Bayesian algorithm, so that the diabetic can perform blood sugar control according to the adjustment scheme, self-management compliance of the diabetic is improved, and physical quality of the diabetic is improved.

Drawings

The above features, technical features, advantages and implementations of a method, system, wearable device and storage medium for blood glucose management are further described in the following detailed description of preferred embodiments in conjunction with the accompanying drawings.

FIG. 1 is a flow chart of one embodiment of a method of glycemic management of the present invention;

FIG. 2 is a flow chart of another embodiment of a method of glycemic management of the present invention;

FIG. 3 is a flow chart of another embodiment of a method of glycemic management of the present invention;

FIG. 4 is a schematic structural diagram of an embodiment of a blood glucose management system of the present invention;

fig. 5 is a schematic structural diagram of an embodiment of a wearable device of the present invention.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

In addition, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.

In accordance with an embodiment of the present invention, there is provided an embodiment of a method for glycemic management, it should be noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.

Diabetes mellitus is a common disease in the world and is a group of clinical syndromes caused by the interaction of genetic and environmental factors. A series of metabolic disorders such as sugar, protein, fat, water and electrolytes are caused by absolute or relative insufficiency of insulin secretion and reduced sensitivity of target tissue cells to insulin. Hyperglycemia is taken as a main marker in clinic, and a plurality of systemic damages can be caused by long-term illness. Acute metabolic disturbance of Luketosis acidosis can occur in severe cases.

With the progress of computer technology, the practical problem of applying data mining technology to solve a large amount of measured data has become a development direction of data mining in recent years. Due to the development of database technology, clinical records and experimental data accumulated in a medical database can find and mine valuable information from a large amount of multi-source, multi-type, multi-precision, multi-temporal and multi-dimensional nonlinear data, and make up for the defects and shortcomings of traditional medical data processing, so that the data mining technology in the database is combined with the existing medical data.

The overall process of data mining includes problem definition, data preparation and preprocessing, data mining, and interpretation and evaluation of results. In data mining, it is when given training data that the best hypothesis in the hypothesis space is determined. The best hypothesis is defined as the most likely hypothesis given the data and knowledge about the prior probabilities of different hypotheses in the hypothesis space. Bayesian algorithms provide a straightforward way to calculate this possibility. Specifically, bayesian algorithms provide a method of calculating the probability of a hypothesis based on the prior probability of the hypothesis, the probability of observing different data given the hypothesis, and the observed data itself.

In an embodiment of the present invention, as shown in fig. 1, a method for managing and controlling blood glucose includes:

s100, determining the current management and control link level of the diabetic at the current moment according to the basic parameters of the diabetic at the current moment;

specifically, the management and control link classification of the diabetic patients is performed according to the severe conditions of the diabetic patients, for example, fasting blood glucose is generally in a mild grade of 3.9-8 mmol/l; the fasting blood sugar is in a moderate grade at 9-14 mmol/l; the fasting blood sugar is more than 15mmol/l, and diabetic ketoacidosis may occur.

The management and control links of medicine, diet and exercise of the diabetic can be graded according to the degree of the diabetes, for example, the diabetic is in a mild grade, so the grade of the medicine management and control link can be set as one grade, the grade of the diet management and control link can be set as one grade, and the grade of the exercise management and control link can be set as one grade. The grading of the management and control links of medicine, diet and exercise is a medical grading scheme, and the embodiment of the application is not limited to the grading.

S200, calculating the association probability value of the control link grade and the blood sugar test value through a Bayesian algorithm;

the application of the Bayesian algorithm to the management of the chronic diseases of the diabetic patients is the technical core of the embodiment of the application, and the Bayesian algorithm is used for analyzing a drug control link, a diet control link and a motion control link, respectively adjusting the levels of the control links according to the associated probability values of the blood glucose test values corresponding to the diabetic patients. The bayesian algorithm is the probability of event a being (occurring) under the condition of event B, which is different from the probability of event B under the condition of event a.

The Bayesian algorithm is that the probability of event A under the condition of event B (occurrence) is different from the probability of event B under the condition of event A; however, there is a definite relationship between them, and the bayesian algorithm is the statement of this relationship, and the bayesian algorithm is about the conditional probability and marginal probability of random events a and B:

where P (A | B) is the probability of A occurring in the case of B. In the Bayesian algorithm, each noun has a reduced name:

pr (A) is the prior probability or marginal probability of A. This is called a priori because it does not take into account any B-aspect factors.

Pr (a | B) is the conditional probability of a after B is known to occur, and is also referred to as a posterior probability of a due to the value derived from B.

Pr (B | a) is the conditional probability of B after a is known to occur, and is also referred to as the a posteriori probability of B due to the value derived from a.

Pr (B) is the prior probability or marginal probability of B, also taken as the normalization constant.

By these terms, the bayesian algorithm can also be expressed as:

the a posteriori probability is (likelihood x prior probability)/normalization constant, that is, the a posteriori probability is proportional to the product of the prior probability and the likelihood.

In addition, the ratio Pr (B | a)/Pr (B) is also sometimes referred to as a standard likelihood (standard likelihood), and the bayesian algorithm can be expressed as:

the posterior probability is the standard likelihood a priori probability.

For more than two variables, Bayes' theorem holds. For example:

P(A|B，C)＝P(B|A)*P(A)*P(C|A，B)/(P(B)*P(C|B))。

this equation can be derived from the Bayesian algorithm using two variables several times and the definition of conditional probability.

S300, adjusting and updating the level of the management and control link according to the association probability value so that the blood sugar test value obtained by retesting after adjustment is close to the corresponding normal blood sugar range value.

Specifically, if the fasting blood sugar value U and the postprandial blood sugar value V of the diabetic patient are relatively high in association with the diet control link, that is, the probability values of the fasting blood sugar value U and the postprandial blood sugar value V of the diabetic patient and the diet control link can be calculated through the bayesian algorithm to be relatively high, the fasting blood sugar value U and the postprandial blood sugar value V of the diabetic patient can be reduced by adjusting the grade of the diet control link, and therefore chronic disease management of the diabetic patient is achieved. For example, the medical scheme may be preset in the system, such as adjusting the medication management link to a second level, the diet management link to a second level, and the exercise management link to a second level.

In this embodiment, the bayesian algorithm is applied to the chronic disease management of the diabetic, and it is actually to mine and analyze medical data in the blood sugar management and control scheme of the diabetic so as to find a more appropriate blood sugar management and control scheme, so that the diabetic has more compliance with the management and control link, and a better prevention and control effect can be achieved.

In an embodiment of the present invention, as shown in fig. 2, a method for managing and controlling blood glucose includes:

s110, acquiring a normal blood sugar range value;

specifically, the normal blood glucose range value includes a normal blood glucose range value in a fasting condition, and a normal blood glucose range value in a postprandial condition. The normal blood sugar range value under the fasting condition and the normal blood sugar range value under the postprandial condition are a conventional normal blood sugar range value at the initial time of blood sugar control, namely the blood sugar range value of normal masses in the medical field, and after blood sugar control for a period of time, the normal blood sugar range value is updated, namely the normal blood sugar range value of a diabetic patient is set and updated according to the body condition and the external environment of the diabetic patient, the normal blood sugar range value is updated in real time in the subsequent blood sugar monitoring and management stage, and blood sugar control is performed by taking the updated normal blood sugar range value as the basis.

S120, acquiring basic parameters of the diabetic at the current moment; the basic parameters comprise name, age, sex, height, weight, medication information, blood sugar test value and physiological parameters; the blood glucose test values include fasting blood glucose values and postprandial blood glucose values;

in particular, the physiological parameters include, but are not limited to, heartbeat, blood pressure, blood oxygen saturation.

S130, grading the diabetes mellitus patient in a management and control link according to preset rules and basic parameters;

specifically, the preset rule is a preset registry. The severity of diabetes can be divided by glycemic test values, including fasting glycemic test values and postprandial glycemic test values. For example, in a plurality of diabetic patients who are similar in weight and physiological parameters and have the same medication information in a certain age group (such as the elderly and the middle-aged), the fasting blood glucose test value is set to be 3.9-8mmol/l as a mild grade; the fasting blood glucose test value is in a moderate grade at 9-14 mmol/l; the fasting blood glucose test value is more than 15mmol/l, which is a severe grade, and diabetic ketoacidosis may occur.

For example, in a plurality of diabetic patients who are similar in body weight and physiological parameters and have the same medication information in a certain age group (for example, the elderly, the middle-aged, etc.), the blood glucose test value of two hours after a meal is set to be 6-12mmol/l, which is a mild grade; the blood sugar test value of two hours after meal is in a moderate grade at 13-18 mmol/l; the blood sugar test value of two hours after meal is more than 18mmol/l in a severe grade, and diabetic ketoacidosis may occur.

This allows the degree of disease in a diabetic patient to be divided into three levels: mild, moderate, and severe. The corresponding medicine link, diet link, and exercise link may also be divided according to specific practical situations, for example, the diet management and control link is divided into three levels, the medicine management and control link is divided into four levels, and the exercise management and control link is divided into five levels. Each grade may correspond to a specific blood glucose management and control protocol, and may be specifically set under the guidance of a doctor.

s300, adjusting and updating the level of the management and control link according to the association probability value so that the blood sugar test value obtained by retesting after adjustment is close to the corresponding normal blood sugar range value;

Specifically, the blood sugar test value E of the diabetic patient is tested, the blood sugar test value E comprises a fasting blood sugar value U and a postprandial blood sugar value V, and the blood sugar test value E is a measured value which is continuously monitored. Normal glycemic range values F include a fasting euglycemic range value S and a postprandial euglycemic range value T.

The embodiment can adjust the drug management and control link, the diet management and control link and the exercise management and control link according to the blood sugar test value tested at each time, for example, the fasting blood sugar value U and the postprandial blood sugar value V of the diabetic patient are tested and increased, and then, the grades of the drug management and control link, the diet management and control link and the exercise management and control link are adjusted according to the above example.

In an embodiment of the present invention, as shown in fig. 3, a method for managing and controlling blood glucose includes:

s110, acquiring a normal blood sugar range value;

s210, according to the blood sugar test value of the diabetic patient at the current moment and the current management link grade, calculating in the following Bayesian algorithm formula to obtain a first probability value;

s220, according to the normal blood sugar range value of the diabetic at the current moment and the current management link grade, a second probability value is obtained through calculation in the following Bayesian algorithm formula;

s230, calculating a difference value between the first probability value and the second probability value to obtain a corresponding association probability value;

wherein, P (Gi | E) is a first probability value, P (Gi | F) is a second probability value, P (E | Gi) is an occurrence probability that a blood glucose test value is E under a condition that a current management and control link level is determined, P (F | Gi) is an occurrence probability that a normal blood glucose range value is F under a condition that a current management and control link level is determined, P (Gi) is an occurrence probability of a current management and control link level, E is a blood glucose test value, F is a normal blood glucose range value, P (gj) is an occurrence probability of other management and control link levels, j > i, and i and j are positive integers;

specifically, the core concept of the diet control link of the diabetic is the glycemic index, which is an index easy to understand and control, the concept is that the glycemic index of glucose is set as 100, and then foods with low glycemic indexes are taken when the glycemic index is less than 55, while the glycemic indexes of common foods can be quickly searched through a network, and in the daily diet control link, the aim of controlling blood sugar can be achieved by carrying out hierarchical management on the diet control link.

Illustratively, foods are classified according to glycemic index, such as first-grade foods (e.g., foods with high starch content such as potatoes, and foods with high sugar content such as sucrose); second grade foods (high fiber foods with a moderate starch content such as rice); third grade food (such as high protein food such as beef and fish, and high fiber food such as vegetable).

Illustratively, the movements are classified according to a glycemic index, such as third-level movements (e.g., low-motion movements such as lying, sitting, etc.); a second level of motion (e.g., a moderate amount of motion such as walking); first level sports (e.g., jogging, cycling, badminton, etc. high motion).

Illustratively, drugs are classified by glycemic index, such as third class drugs (e.g., rapid acting insulin, short acting insulin); a second grade drug (e.g., intermediate acting insulin); first class drugs (e.g., long acting insulin).

S310, if the association probability value of the current management and control link is lower than a first preset threshold value or higher than a second preset threshold value, adjusting and updating the level of the management and control link; the first preset threshold value is smaller than the second preset threshold value;

s320, informing the diabetic to execute blood sugar management and control according to the updated management and control link grade; blood glucose management and control includes drug management and control, diet management and exercise management and control;

Specifically, if the diabetic detects blood glucose in the fasting state, it is assumed that the blood glucose test value at the current time is the fasting blood glucose value U, and the normal blood glucose range value at the current time is the fasting blood glucose normal range value S. Then, according to the current control link level and the fasting blood glucose value U, a first probability value is obtained through the following Bayesian algorithm formula:

and according to the level of the current control link and the normal range value S of the fasting blood glucose, calculating by the following Bayesian algorithm formula to obtain a second probability value:

wherein, P (Gi | U) is a first probability value in the fasting state, P (Gi | S) is a second probability value in the fasting state, P (U | Gi) is an occurrence probability that a blood glucose test value is a fasting blood glucose value U under the condition that the current management and control link level is determined, P (S | Gi) is an occurrence probability that a normal blood glucose range value is a fasting blood glucose normal range value S under the condition that the current management and control link level is determined, P (Gi) is an occurrence probability of the current management and control link level, U is a fasting blood glucose value, S is a fasting normal blood glucose range value, P (gj) is an occurrence probability of other management and control link levels, and j > i, i and j are positive integers.

Of course, if the blood glucose level of the diabetic patient is measured in a postprandial state, the blood glucose test value at the current time is assumed to be a postprandial blood glucose level V, and the normal blood glucose range value at the current time is assumed to be a postprandial blood glucose normal range value T. Then, according to the current control link grade and the postprandial blood sugar value V, a first probability value is obtained through the following Bayesian algorithm formula:

and according to the current control link grade and the postprandial blood glucose normal range value T, calculating by the following Bayesian algorithm formula to obtain a second probability value:

wherein, P (Gi | V) is a first probability value under the postprandial condition, P (Gi | T) is a second probability value under the postprandial condition, P (V | Gi) is the occurrence probability that the blood glucose test value under the condition that the current control link grade is determined is the postprandial blood glucose value V, P (T | Gi) is the occurrence probability that the normal blood glucose range value under the condition that the current control link grade is determined is the postprandial blood glucose normal range value T, P (Gi) is the occurrence probability of the current control link grade, V is the postprandial blood glucose value, T is the postprandial normal blood glucose range value, P (Gj) is the occurrence probability of other control link grades, j > i, i and j are positive integers.

Whether the blood glucose measurement value at the current moment is a fasting blood glucose value U or a postprandial blood glucose value V, a first probability value and a second probability value of the diabetic under the corresponding situation can be correspondingly calculated, and then the first probability value under the fasting situation and the second probability value under the fasting situation are subjected to difference value calculation to obtain an associated probability value under the fasting situation. Similarly, the first probability value under the postprandial situation and the second probability value under the postprandial situation are subjected to difference value calculation to obtain the associated probability value under the postprandial situation.

And if the association probability value of the current management and control link is calculated to be higher than a preset threshold value, adjusting and updating the level of the management and control link.

Continuing the content of the grade division, exemplarily, if the association probability value of the grade corresponding to the current drug control link and the blood glucose test value is lower than a first preset threshold value, which indicates that the current drug control link has little effect of reducing the blood glucose value, the grade of the drug control link is subjected to grade increase adjustment. For example, the drug management and control link is at the second level, and if the calculated association probability value is lower than the first preset threshold value, the drug management link is adjusted from the second level to the third level, so that the blood glucose test value can be more effectively reduced to the normal blood glucose range value. Of course, if the association probability value of the level corresponding to the current drug management and control link and the blood glucose test value is higher than the second preset threshold, which indicates that the current drug management and control link has an obvious effect of reducing the blood glucose value, the level of the drug management and control link is adjusted in a level-reducing manner. For example, the drug management and control link is at the second level, and if the calculated association probability value is higher than the second threshold, the link of drug management is adjusted from the second level to the first level, so that the blood sugar test value can be effectively reduced to the normal range value of blood sugar, and the appropriate drug dosage is ensured, so that the drug is suitable for the body of the diabetic patient.

And if the association probability value of the level corresponding to the current diet control link and the blood sugar test value is lower than a first preset threshold value, indicating that the effect of the current diet control link on reducing the blood sugar value is not large, and performing grade-increasing adjustment on the level of the diet control link. For example, the diet management and control link is at the second level, and if the calculated association probability value is lower than the first preset threshold value, the diet management link is adjusted from the second level to the third level, so that the blood sugar test value can be more effectively reduced to the normal blood sugar range value. Of course, if the association probability value of the level corresponding to the current diet control link and the blood sugar test value is higher than the second preset threshold, which indicates that the current diet control link has an obvious effect on reducing the blood sugar value, the level of the diet control link is subjected to level reduction adjustment. For example, the diet management and control link is at a third level, and if the calculated association probability value is higher than a second threshold, the link of diet management is adjusted from the third level to the second level, so that the blood glucose test value can be more effectively reduced to the normal blood glucose range value.

And if the association probability value of the level corresponding to the current motion control link and the blood sugar test value is lower than a first preset threshold value, which indicates that the current motion control link has little effect of reducing the blood sugar value, the level of the motion control link is subjected to level reduction adjustment. For example, the exercise management and control link is of the second level, and if the calculated association probability value is lower than the first preset threshold value, the exercise management link is adjusted from the second level to the first level, so that the blood sugar test value can be effectively reduced to the normal blood sugar range value. Of course, if the association probability value of the level corresponding to the current exercise control link and the blood glucose test value is higher than the second preset threshold, which indicates that the current exercise control link has an obvious effect of reducing the blood glucose value, the level of the exercise control link is subjected to level-up adjustment. For example, the exercise management and control link is of a first level, and if the calculated association probability value is higher than a second threshold, the exercise management link is adjusted and upgraded from the first level to the second level, so that the blood sugar test value can be effectively reduced to the normal blood sugar range value, and meanwhile, the exercise amount is ensured to be appropriate, and the exercise management and control link is suitable for the body of the diabetic patient.

Of course, the above is only one embodiment of the present application, and the present application may also adjust the drug management and control link, the diet management and control link, and the exercise management and control link simultaneously, or adjust one or two of them, specifically, the probability value of the relevance calculated by the bayesian algorithm and the result of the ranking by the predetermined rule specified by the system are related, so the embodiment of the present application is not limited thereto.

Preferably, a diet log needs to be established for the diabetic, the diabetic is required to fill in the name and the meal amount of each meal for eating in detail every day, the blood sugar value of the diabetic is measured automatically all day long, the dietary fiber is the wall layer of plant cells which cannot be decomposed by digestive enzymes of a human body, and the dietary fiber can be combined with water in intestinal tracts to form a micelle, so that the diabetic has strong satiety, the food intake is reduced, and the purpose of controlling the blood sugar is achieved. The dietary fiber can be combined with carbohydrate in human intestinal tract to reduce enzymolysis speed of starch and secretion of glucagon, so that postprandial blood sugar can be reduced by eating vegetables rich in dietary fiber first. Protein food can be digested and absorbed by human body, and the physical properties of protein and dietary fiber can adsorb water to form micelle, thereby increasing satiety; the protein can also slow down the speed of sugar released by the saccharides and stimulate the pancreas to secrete digestive enzymes of the protein, thereby promoting the human body to secrete insulin to reduce the blood sugar. The dietary fiber food or protein-containing food can reduce fasting blood sugar and postprandial blood sugar of diabetic patients, and has positive effect in improving diabetes. Of course, an exercise log and a medication log can be established for the diabetic, and blood glucose influencing factors (diet, exercise and medication) are monitored through the established logs, so that the blood glucose management and control of the diabetic can be monitored in real time.

Preferably, if the blood glucose test value continuously exceeds the normal blood glucose range value after the blood glucose management and control is executed according to the updated management and control link level, a warning message is sent out, and a distress message is sent out to the preset emergency contact person.

In particular, since events that affect changes in blood glucose include, but are not limited to, meals, medication, exercise, similar activity events may include many more, such as watching a stimulating movie, singing a song, deep sleep, and so forth. And if the association probability value in the current management and control link is higher than a second preset threshold value, and after the blood sugar management and control is executed according to the updated management and control link level, the blood sugar test value still exceeds the normal blood sugar range value, warning information is sent out so as to remind a diabetic patient, and in addition, help seeking information is sent out to preset emergency contacts (doctors, family members and sick and friends). Of course, the above situation may also occur when a hardware device fails or a software goes down. No matter what reason the association probability value under the current management and control link exceeds the corresponding preset threshold (for example, is higher than a second preset threshold or is lower than a first preset threshold), and after the blood sugar management and control is executed according to the updated management and control link level, the blood sugar test value exceeds the normal blood sugar range value, corresponding information is respectively sent to the diabetic and the preset emergency contact person for notification and reminding, and the scene that the sudden blood sugar test value of the diabetic exceeds the normal blood sugar range value is monitored in real time in the whole process in case of prevention.

Certainly, it can be judged that the associated probability value under the current management and control link exceeds a corresponding preset threshold (for example, is higher than a second preset threshold or is lower than a first preset threshold) within a preset time, and after the blood glucose management and control is executed according to the updated management and control link level, when the blood glucose test value at each moment within the preset time exceeds a normal blood glucose range value, the alarm information is sent out again and the distress information is sent out to the preset emergency contact person, so that the situation that a song is sung because of watching a stimulating movie can be avoided, the situations of false alarm and misinformation are reduced or avoided, the power consumption is reduced, and the workload of the preset emergency contact person is reduced.

In one embodiment of the present invention, as shown in fig. 4, a blood glucose management system includes:

the processing module 10 is configured to determine a current management and control link level of the diabetic at the current time according to the basic parameters of the diabetic at the current time;

the calculating module 20 is used for calculating the association probability value of the management and control link grade and the blood sugar test value through a Bayesian algorithm;

and the control module 30 is used for adjusting and updating the level of the management and control link according to the association probability value so that the blood sugar test value obtained by retesting after adjustment is close to the corresponding normal blood sugar range value.

Specifically, this embodiment is a device embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which is not described in detail herein.

Based on the foregoing embodiment, the processing module 10 includes:

the second acquisition unit is used for acquiring basic parameters of the diabetic at the current moment; the basic parameters comprise name, age, sex, height, weight, medication information, blood sugar test value and physiological parameters; the blood glucose test values include fasting blood glucose values and postprandial blood glucose values;

the processing unit is used for carrying out grade division on the management and control link of the diabetic according to preset rules and basic parameters;

Based on the foregoing embodiment, the calculation module 20 includes:

the first calculating unit is used for establishing a first feature corpus according to the acquired first sample data and calculating the first feature corpus according to a Bayesian algorithm to obtain a corresponding association probability value; the first sample data comprises a blood sugar test value and a normal blood sugar range value of the diabetic at the current moment, and the current management link grade;

the second calculation unit is used for calculating a third probability value and a fourth probability value respectively through a Bayesian algorithm according to a postprandial blood glucose value, a postprandial normal blood glucose range value and a current management link grade which are currently measured by the diabetic patient, and calculating a difference value of the third probability value and the fourth probability value to obtain a corresponding associated probability value;

wherein, P (Gi | E) is a corresponding associated probability value, P (Gi | F) is a corresponding associated probability value, P (E | Gi) is a probability of the event E under the condition that the current management and control link level is determined, P (F | Gi) is a probability of the event F under the condition that the current management and control link level is determined, Gi is the current management and control link level under the same management and control type, E is an fasting blood glucose value, F is a postprandial blood glucose value, Gj is other management and control link levels under the same management and control type, j > i, and i and j are positive integers.

Based on the foregoing embodiment, the control module 30 includes:

the adjusting unit is used for adjusting and updating the grade of the management and control link if the association probability value of the current management and control link is lower than a first preset threshold value or higher than a second preset threshold value; the first preset threshold value is smaller than the second preset threshold value;

the notification unit is used for notifying the diabetic to execute blood sugar management and control according to the updated management and control link grade; glycemic control includes drug management, dietary management, and exercise management.

Based on the foregoing embodiment, the control module 30 includes:

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of program modules is illustrated, and in practical applications, the above-described distribution of functions may be performed by different program modules, that is, the internal structure of the apparatus may be divided into different program units or modules to perform all or part of the above-described functions. Each program module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one processing unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software program unit. In addition, the specific names of the program modules are only used for distinguishing the program modules from one another, and are not used for limiting the protection scope of the application.

One embodiment of the present invention, as shown in fig. 5, a wearable device 100, comprises a processor 110, a memory 120, wherein the memory 120 is used for storing a computer program 121; the processor 110 is configured to execute the computer program 121 stored in the memory 120 to implement the blood glucose management method in the above corresponding method embodiment.

The wearable device 100 may be a desktop computer, a notebook, a palm computer, a tablet computer, a mobile phone, a human-computer interaction screen, or the like. The wearable device 100 may include, but is not limited to, a processor 110, a memory 120. Those skilled in the art will appreciate that fig. 5 is merely an example of wearable device 100, does not constitute a limitation of wearable device 100, and may include more or fewer components than shown, or combine certain components, or different components, such as: wearable device 100 may also include input/output interfaces, display devices, network access devices, communication buses, communication interfaces, and the like. A communication interface and a communication bus, and may further include an input/output interface, wherein the processor 110, the memory 120, the input/output interface and the communication interface complete communication with each other through the communication bus. The memory 120 stores a computer program 121, and the processor 110 is configured to execute the computer program 121 stored in the memory 120, so as to implement the blood glucose management method in the above-described corresponding method embodiment.

The Processor 110 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 120 may be an internal storage unit of the wearable device 100, such as: a hard disk or memory of the wearable device. The memory may also be an external storage device of the wearable device, such as: the wearable device is provided with a plug-in hard disk, an intelligent memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like. Further, the memory 120 may also include both an internal storage unit and an external storage device of the wearable device 100. The memory 120 is used for storing the computer program 121 and other programs and data required by the wearable device 100. The memory may also be used to temporarily store data that has been output or is to be output.

A communication bus is a circuit that connects the described elements and enables transmission between the elements. For example, the processor 110 receives commands from other elements through the communication bus, decrypts the received commands, and performs calculations or data processing according to the decrypted commands. The memory 120 may include program modules such as a kernel (kernel), middleware (middleware), an Application Programming Interface (API), and applications. The program modules may be comprised of software, firmware or hardware, or at least two of the same. The input/output interface forwards commands or data entered by a user via the input/output interface (e.g., sensor, keyboard, touch screen). The communication interface connects the wearable device 100 with other network devices, user devices, networks. For example, the communication interface may be connected to a network by wire or wirelessly to connect to external other network devices or user devices. The wireless communication may include at least one of: wireless fidelity (WiFi), Bluetooth (BT), Near Field Communication (NFC), Global Positioning Satellite (GPS) and cellular communications, among others. The wired communication may include at least one of: universal Serial Bus (USB), high-definition multimedia interface (HDMI), asynchronous transfer standard interface (RS-232), and the like. The network may be a telecommunications network and a communications network. The communication network may be a computer network, the internet of things, a telephone network. The wearable device 100 may connect to the network through a communication interface, and a protocol by which the wearable device 100 communicates with other network devices may be supported by at least one of an application, an Application Programming Interface (API), middleware, a kernel, and a communication interface.

In an embodiment of the present invention, a storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operations performed by the corresponding embodiments of the blood glucose management method. For example, the storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

They may be implemented in program code that is executable by a computing device such that it is executed by the computing device, or separately, or as individual integrated circuit modules, or as a plurality or steps of individual integrated circuit modules. Thus, the present invention is not limited to any specific combination of hardware and software.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/wearable device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/wearable device are merely illustrative, and for example, the division of the modules or units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units may be stored in a storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by sending instructions to relevant hardware by the computer program 121, where the computer program 121 may be stored in a storage medium, and when the computer program 121 is executed by a processor, the steps of the above-described embodiments of the method may be implemented. The computer program 121 may be in a source code form, an object code form, an executable file or some intermediate form, etc. The storage medium may include: any entity or device capable of carrying the computer program 121, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content of the storage medium may be increased or decreased as appropriate according to the requirements of legislation and patent practice in the jurisdiction, for example: in certain jurisdictions, in accordance with legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunications signals.

It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. A blood glucose management and control method is characterized by comprising the following steps:

the step of determining the current management and control link level of the diabetic patient at the current moment according to the basic parameters of the diabetic patient at the current moment comprises the following steps:

obtaining a normal blood sugar range value;

the control link comprises a medicine control link, a diet control link and a sport control link, and each control link comprises at least two levels;

the calculating of the associated probability values of the control link grade and the blood sugar test value through the Bayesian algorithm comprises the following steps:

2. The method of claim 1, wherein the adjusting the updated regulation link level according to the association probability value comprises:

3. The glycemic management method of claim 1, further comprising the steps of:

4. A blood glucose management system, comprising:

the processing module comprises:

the calculation module comprises:

5. The blood glucose management system of claim 4, wherein the control module comprises:

6. The blood glucose management system of claim 4, wherein the control module comprises:

7. A wearable device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor configured to execute the computer program stored in the memory to perform the operations performed by the blood glucose management method of any one of claims 1-3.

8. A storage medium having stored therein at least one instruction that is loaded and executed by a processor to perform operations performed by the method of glycemic management of any of claims 1 to 3.