Diabetes data management and control method and system for elderly patients

Abstract

The invention relates to the technical field of medical care informatics, in particular to a diabetes data management and control method and system for elderly patients, and the method comprises the steps: constructing a sample three-dimensional space through the analysis of the blood sugar, blood fat and blood pressure data of a patient before and after a meal within one day, and calculating the illness state deviation degree and the comprehensive illness state deviation degree of the patient. According to the data characteristic value and the comprehensive illness state deviation degree of the patient in the sample space, the illness state stability degree and the monitoring priority of the patient are evaluated, the illness state stability type of the patient is judged according to the priority, and a personalized monitoring scheme is formulated according to the illness state stability type. According to the invention, the problem of insufficient or excessive monitoring possibly caused by different stability of patients is optimized, and the monitoring priority suitable for each patient is determined through data analysis, so that a more targeted and efficient monitoring and management scheme is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of medical care informatics, and in particular to a diabetes data management method and system for elderly patients. Background Art

[0002] Diabetes is one of the common chronic diseases among the elderly, and elderly diabetic patients require long-term and continuous disease management, including blood sugar monitoring, diet regulation, regular check-ups, etc. Therefore, systematic management of diabetes data can help monitor the health status of patients. At the same time, real-time data monitoring can help doctors adjust treatment plans in a timely manner, better control blood sugar levels, and reduce the risk of acute and chronic complications. However, in the management of elderly diabetic patients, traditional monitoring methods have a fixed monitoring frequency and use a single monitoring indicator, which cannot be adjusted according to the individual characteristics of the patient and the existing condition, which may lead to excessive or insufficient monitoring. Summary of the invention

[0003] The present invention provides a diabetes data management method and system for elderly patients to solve the existing problems.

[0004] The diabetes data management method and system for elderly patients of the present invention adopts the following technical solutions:

[0005] An embodiment of the present invention provides a diabetes data management method for elderly patients, the method comprising the following steps:

[0006] Obtain the blood sugar, blood lipid and blood pressure data of each patient at each sampling time in a day, and construct a three-dimensional sample space corresponding to each patient; wherein all sampling times are divided into pre-meal sampling times and post-meal sampling times;

[0007] At each sampling time, the deviation of the condition data of each patient is obtained according to the distance from the data point of each patient in the sample three-dimensional space to the origin and the distance between the data points of different patients in the sample three-dimensional space; at all sampling times, the deviation of the comprehensive condition data of each patient is obtained according to the deviation of the condition data of each patient;

[0008] The distance from the data point of each patient in the sample three-dimensional space to the origin at each sampling moment is recorded as the data characteristic value of each patient at each sampling moment; the stability of the condition of each patient is obtained according to the data characteristic value of each patient at each sampling moment and the deviation of the comprehensive condition data of each patient;

[0009] Obtain the monitoring priority of each patient according to the stability of each patient's condition; determine the stability type of each patient's condition according to the monitoring priority of each patient;

[0010] A monitoring plan is developed based on each patient's type of medical stability.

[0011] Preferably, at each sampling moment, the deviation of the disease condition data of each patient is obtained according to the distance from the data point of each patient in the sample three-dimensional space to the origin and the distance between the data points of different patients in the sample three-dimensional space, and the specific steps include the following:

[0012] At each sampling time, according to the Euclidean distance between the data point corresponding to each patient in the sample three-dimensional space and the coordinate origin, the distance characteristic value of each patient at each sampling time is obtained;

[0013] According to the distance characteristic value of each patient at each sampling moment and the distance between the data points of different patients in the sample three-dimensional space, the deviation of the disease condition data of each patient is obtained.

[0014] Preferably, the step of obtaining the deviation of the disease condition data of each patient according to the distance characteristic value of each patient at each sampling moment and the distance between the data points of different patients in the sample three-dimensional space includes the following specific steps:

[0015]

[0016] Where U i,t is the deviation of the condition data of the i-th patient at the t-th sampling time; L i,t is the Euclidean distance between the data point corresponding to the i-th patient in the sample three-dimensional space and the coordinate origin at the t-th sampling time; min(L t ) is the minimum value of the Euclidean distance between the corresponding data points of all patients in the sample three-dimensional space and the coordinate origin at the tth sampling time; m is the number of patients; L i,j,t is the distance between the coordinates of the data points of the i-th patient and the j-th patient except the i-th patient in the sample three-dimensional space at the t-th sampling time; norm[] is the linear normalization function, is the distance characteristic value of the i-th patient at the t-th sampling time.

[0017] Preferably, the step of obtaining the comprehensive condition data deviation of each patient according to the condition data deviation of each patient at all sampling moments includes the following specific steps:

[0018]

[0019] In the formula, S i is the deviation of the comprehensive disease data of the i-th patient, U' i,k is the deviation of the condition data of the i-th patient at the k-th post-meal sampling time, is the deviation of the disease data of the i-th patient at the k-th pre-meal sampling time; q is the number of pre-meal sampling times; exp[] is an exponential function with a natural constant as the base.

[0020] Preferably, the step of obtaining the stability of the condition of each patient according to the data characteristic value of each patient at each sampling moment and the deviation of the comprehensive condition data of each patient comprises the following specific steps:

[0021] Obtain the comprehensive data characteristic value of each patient on that day according to the data characteristic value of each patient;

[0022] The stability of each patient's condition is obtained based on the comprehensive data characteristic value of each patient on that day and the deviation of each patient's comprehensive condition data.

[0023] Preferably, the step of obtaining the stability of the condition of each patient according to the comprehensive data characteristic value of each patient on that day and the deviation of the comprehensive condition data of each patient includes the following specific steps:

[0024]

[0025] Where, T i is the stability of the condition of the i-th patient; q is the number of sampling moments before meals, and is also the number of sampling moments after meals; is the data characteristic value of the i-th patient at the k-th pre-meal sampling time, is the data characteristic value of the i-th patient at the k+1-th pre-meal sampling time; E' i,k is the data characteristic value of the i-th patient at the k-th post-meal sampling time; E' i,k+1 The data feature value of the i-th patient at the k+1-th post-meal sampling time; is the average value of the data characteristic values ​​of the i-th patient at all sampling times; is the maximum value of the data characteristic value of all patients at all sampling times; S i is the deviation of the comprehensive disease data of the ith patient; min(S) is the minimum value of the deviation of the comprehensive disease data of all patients; | | is the absolute value; sigmoid[] is the normalization function; It is the comprehensive data feature value of each patient on that day.

[0026] Preferably, the step of obtaining the monitoring priority of each patient according to the stability of the condition of each patient includes the following specific steps:

[0027] P i =1-T i

[0028] Where P i is the monitoring priority of the ith patient; Ti is the stability of the condition of the ith patient.

[0029] Preferably, the step of determining the stable condition type of each patient according to the monitoring priority of each patient comprises the following specific steps:

[0030] When the monitoring priority of the patient is less than or equal to the first preset threshold, the patient is judged to have a highly stable condition;

[0031] When the monitoring priority of the patient is greater than the first preset threshold and less than or equal to the second preset threshold, the patient is judged to have a relatively stable condition;

[0032] When the monitoring priority of the patient is greater than the second preset threshold, the patient is determined to have an acute change in condition.

[0033] Preferably, the monitoring plan is formulated according to the stable condition type of each patient, and the specific steps include the following:

[0034] The preset first monitoring plan is used for patients with highly stable conditions; the preset second monitoring plan is used for patients with relatively stable conditions; and the preset third monitoring plan is used for patients with acute changes in conditions.

[0035] The present invention also proposes a diabetes data management system for elderly patients, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned diabetes data management method for elderly patients.

[0036] The beneficial effect of the technical solution of the present invention is that the present invention comprehensively analyzes the relevant data indicators of patients before and after the three meals, accurately evaluates the degree of impact and the level of change, and thus determines the stability of the disease. Based on this evaluation, the monitoring priority can be accurately determined and the corresponding monitoring plan can be formulated. This method optimizes the problem of insufficient or excessive monitoring that may occur due to different patient stability. Through data analysis, the monitoring priority that best suits each patient can be better determined, thereby providing a more targeted and efficient monitoring and management plan, further optimizing the treatment effect, and improving the utilization of monitoring resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 The present invention is a flowchart of the steps of a diabetes data management method for elderly patients. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a diabetes data management method and system for elderly patients proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The following is a detailed description of a method and system for managing and controlling diabetes data for elderly patients provided by the present invention in conjunction with the accompanying drawings.

[0042] See also Figure 1 , which shows a flowchart of a method for managing diabetes data for elderly patients provided by an embodiment of the present invention, the method comprising the following steps:

[0043] Step S001: Obtain blood sugar, blood lipid and blood pressure data of each patient at each sampling time in a day, and construct a three-dimensional sample space corresponding to each patient; wherein all sampling times are divided into pre-meal sampling times and post-meal sampling times.

[0044] It should be noted that: in this embodiment, 10 patients are selected, and the sampling time is 30 minutes before and 1 hour after the three meals in a day. This is used as an example for description. The blood sugar, blood lipid and blood pressure data of each patient at each sampling time in a day are used as a data point to construct a sample three-dimensional space corresponding to each patient. The horizontal axis, vertical axis and vertical axis of the sample three-dimensional space are blood sugar, blood lipid and blood pressure data respectively. Among them, blood sugar data is obtained by a portable blood glucose meter or a continuous blood glucose monitoring system (CGMS), blood pressure is obtained by an automated sphygmomanometer, and blood lipids are obtained by a blood analyzer. This is used as an example for description, and this embodiment is not limited. And blood sugar, blood lipid and blood pressure data are all processed by Z-score standardization, which is a known technology.

[0045] Step S002: At each sampling moment, the deviation of each patient's condition data is obtained based on the distance from each patient's data point in the sample three-dimensional space to the origin and the distance between the data points of different patients in the sample three-dimensional space; at all sampling moments, the deviation of each patient's comprehensive condition data is obtained based on the deviation of each patient's condition data.

[0046] Each data point in the sample three-dimensional space corresponds to the condition data of each patient at the same sampling time. If one or more of the patient's blood sugar, blood lipids, and blood pressure data are high, the greater the deviation of the corresponding data point in the sample three-dimensional space, the more serious the patient's condition is at this moment. On the contrary, the smaller the deviation of the patient's data, the milder the condition.

[0047] This embodiment calculates the corresponding disease data deviation by analyzing the distribution of the data points corresponding to each patient in the sample three-dimensional space. The calculation method is as follows:

[0048]

[0049] Where U i,t is the deviation of the condition data of the i-th patient at the t-th sampling time; L i,t is the Euclidean distance between the data point corresponding to the i-th patient in the sample three-dimensional space and the coordinate origin at the t-th sampling time; min(L t ) is the minimum value of the Euclidean distance between the corresponding data points of all patients in the sample three-dimensional space and the coordinate origin at the tth sampling time; m is the number of patients; L i,j,t is the distance between the coordinates of the data points of the i-th patient and the j-th patient except the i-th patient in the sample three-dimensional space at the t-th sampling time; norm[] is the linear normalization function; is the distance characteristic value of the i-th patient at the t-th sampling time.

[0050] It represents the relative distance between the mapping value corresponding to the i-th patient and the coordinate origin, and represents the severity of the disease. The larger the value, the higher the overall blood sugar, blood lipid, and blood pressure values ​​of the patient. The more serious the disease, the greater the deviation. is the relative size of the condition data of the ith patient relative to other patients. If the value is larger, it means that the data of the ith patient is larger relative to other patients, and the more serious the condition is, the greater the corresponding data deviation is.

[0051] Step S003: The distance from the data point of each patient in the sample three-dimensional space at each sampling moment to the origin is recorded as the data characteristic value of each patient at each sampling moment; the stability of the condition of each patient is obtained according to the data characteristic value of each patient at each sampling moment and the deviation of the comprehensive condition data of each patient.

[0052] The deviation of the condition data represents the severity of each patient's condition at a specific sampling time. After the patient eats, the deviation of the data may increase. Therefore, this embodiment obtains the stability of the patient's condition by comparing the difference in the deviation of the patient's data before and after each meal. If the deviation of the patient's data after a meal is greater than that before a meal, it means that the diet has a significant impact on the patient's condition, and at the same time indicates that the stability of the patient's condition is relatively low; otherwise, the stability is relatively high.

[0053] This embodiment provides a calculation method for obtaining the stability of the patient's condition based on the deviation of the condition data:

[0054]

[0055] In the formula, S i is the deviation of the comprehensive disease data of the i-th patient, U' i,k is the deviation of the condition data of the i-th patient at the k-th post-meal sampling time, is the deviation of the disease data of the i-th patient at the k-th pre-meal sampling time; q is the number of pre-meal sampling times, and is also the number of post-meal sampling times; exp[] is an exponential function with a natural constant as the base.

[0056] This shows that the more obvious the trend of increasing data corresponding to each indicator of this patient is after a meal, the greater the impact of the meal on the patient's condition, and the lower the corresponding condition stability.

[0057] Furthermore, if a patient's data before and after a meal are not significantly affected, but the data of various test indicators are at a relatively high level, and the monitoring indicators before or after each meal fluctuate greatly, this may indicate that the patient's condition is in a dangerous state.

[0058] This embodiment evaluates the stability of the patient's condition by considering the deviation of the comprehensive condition data and the overall fluctuation level of the data. If the deviation of the patient's comprehensive condition data is smaller and the overall fluctuation level is smaller, it is considered that the patient's condition is more stable.

[0059] The Euclidean distance between the data point corresponding to the i-th patient in the sample three-dimensional space and the coordinate origin at the t-th sampling time is recorded as the data feature value of the i-th patient at the t-th sampling time.

[0060] This embodiment provides a calculation method for obtaining the stability of the patient's condition as follows:

[0061]

[0062] Where, T i is the stability of the condition of the i-th patient; q is the number of sampling moments before meals, and is also the number of sampling moments after meals; is the data characteristic value of the i-th patient at the k-th pre-meal sampling time, is the data characteristic value of the i-th patient at the k+1-th pre-meal sampling time; E' i,k is the data characteristic value of the i-th patient at the k-th post-meal sampling time; E' i,k+1 The data feature value of the i-th patient at the k+1-th post-meal sampling time; is the average value of the data characteristic values ​​of the i-th patient at all sampling times; is the maximum value of the data characteristic value of all patients at all sampling times; S i is the deviation of the comprehensive disease data of the ith patient; min(S) is the minimum value of the deviation of the comprehensive disease data of all patients; | | is the absolute value; sigmoid[] is the normalization function; It is the comprehensive data feature value of each patient on that day.

[0063] It should be noted that when When the denominator is 0, the denominator is set to 1 to prevent the denominator from being 0. This is used as an example for description. Other values ​​can be set in other implementation modes, and this embodiment is not limited.

[0064] In the above formula, The difference in characteristic values ​​corresponding to the monitoring indicators of the i-th patient before and after each meal reflects the horizontal fluctuation degree of each monitoring indicator of the patient. If the difference value is larger, it means that the natural fluctuation degree of each monitoring indicator of the i-th patient is greater, and it is considered that the patient's condition is more unstable.

[0065] Represents the relative level of the indicators of the i-th patient before and after the three meals, through the average characteristic value of the i-th patient at all sampling times With the maximum eigenvalue If the difference is expressed as The larger the value, the The smaller it is, the lower the indexes of the i-th patient are, and the higher the corresponding disease stability is. i -min(S) represents the relative degree of impact of the ith patient, S i The smaller the value of -min(S), the higher the S iThe smaller it is, the less the indicators of the i-th patient are affected by meals, and the more stable the condition is. The larger the value of , the more stable the disease of the i-th patient.

[0066] Step S004: Obtain the monitoring priority of each patient according to the degree of stability of the condition of each patient; determine the type of stability of the condition of each patient according to the monitoring priority of each patient.

[0067] When determining the indicator monitoring priority of different patients, first adjust it according to the stability of their condition. For patients with a higher degree of condition stability, it is necessary to moderately lower the monitoring priority of each indicator; while for patients with a lower degree of condition stability, it is necessary to moderately increase the monitoring priority of the indicator. Since the degree of condition stability is in the range of [0,1], this embodiment provides a method for obtaining the monitoring priority of a patient as follows:

[0068] P i =1-T i

[0069] Where P i is the monitoring priority of the ith patient; T i is the stability of the condition of the ith patient.

[0070] So far, the monitoring priority of each patient is obtained. In this embodiment, the stable condition type of each patient is determined according to the monitoring priority of each patient.

[0071] Specifically, when the patient's monitoring priority is less than or equal to a first preset threshold, the patient corresponding to the monitoring priority is judged as having a highly stable condition; when the patient's monitoring priority is greater than the first preset threshold and less than or equal to a second preset threshold, the patient corresponding to the monitoring priority is judged as having a relatively stable condition; when the patient's monitoring priority is greater than the second preset threshold, the patient corresponding to the monitoring priority is judged as having an acute change condition.

[0072] In this embodiment, the first preset threshold is 0.35, and the second preset threshold is 0.75. This is used as an example for description. Other implementations may set the threshold to other values, which are not limited in this embodiment.

[0073] Step S005: Develop a monitoring plan based on the stable condition type of each patient.

[0074] Different indicator monitoring frequencies are adopted for patients with different priorities. The higher the priority and the more serious the stable condition type, the more attention will be paid to the patient and the higher the indicator monitoring frequency will be.

[0075] In this embodiment, a preset first monitoring plan is adopted for patients with a highly stable condition; a preset second monitoring plan is adopted for patients with a relatively relatively stable condition; and a preset third monitoring plan is adopted for patients with an acute change condition.

[0076] Specifically, in this embodiment, the first monitoring scheme is preset as HbA1c test once every 3-6 months, and blood glucose monitoring 1-2 times a day; the second monitoring scheme is preset as HbA1c test once every 1-3 months, and blood glucose monitoring 3-4 times a day; the third monitoring scheme is preset as HbA1c test once a month, and blood glucose monitoring 4-7 times a day. This is described as an example.

[0077] It should be noted that the HbA1c test and blood glucose monitoring times in this embodiment are preset values, which are described as an example. Other values ​​may be set in other implementations, and this embodiment does not limit them.

[0078] The present invention also provides a diabetes data management system for elderly patients, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned diabetes data management method for elderly patients.

[0079] So far, the present invention is completed.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A diabetes data management method for elderly patients, characterized in that: The method comprises the following steps: Obtain the blood sugar, blood lipid and blood pressure data of each patient at each sampling time in a day, and construct a three-dimensional sample space corresponding to each patient; wherein all sampling times are divided into pre-meal sampling times and post-meal sampling times; At each sampling time, the deviation of the condition data of each patient is obtained according to the distance from the data point of each patient in the sample three-dimensional space to the origin and the distance between the data points of different patients in the sample three-dimensional space; at all sampling times, the deviation of the comprehensive condition data of each patient is obtained according to the deviation of the condition data of each patient; The distance from the data point of each patient in the sample three-dimensional space to the origin at each sampling moment is recorded as the data characteristic value of each patient at each sampling moment; the stability of the condition of each patient is obtained according to the data characteristic value of each patient at each sampling moment and the deviation of the comprehensive condition data of each patient; Obtain the monitoring priority of each patient according to the stability of each patient's condition; determine the stability type of each patient's condition according to the monitoring priority of each patient; A monitoring plan is developed based on each patient's type of medical stability.

2. A diabetes data management method for elderly patients according to claim 1, characterized in that: At each sampling moment, the deviation of the disease condition data of each patient is obtained according to the distance from the data point of each patient in the sample three-dimensional space to the origin and the distance between the data points of different patients in the sample three-dimensional space, and the specific steps include the following: At each sampling time, according to the Euclidean distance between the data point corresponding to each patient in the sample three-dimensional space and the coordinate origin, the distance characteristic value of each patient at each sampling time is obtained; According to the distance characteristic value of each patient at each sampling moment and the distance between the data points of different patients in the sample three-dimensional space, the deviation of the disease condition data of each patient is obtained.

3. A diabetes data management method for elderly patients according to claim 2, characterized in that: The specific steps of obtaining the deviation of the disease data of each patient according to the distance characteristic value of each patient at each sampling moment and the distance between the data points of different patients in the sample three-dimensional space are as follows: Where U i,t is the deviation of the condition data of the i-th patient at the t-th sampling time; L i,T is the Euclidean distance between the data point corresponding to the i-th patient in the sample three-dimensional space and the coordinate origin at the t-th sampling time; min(L t ) is the minimum value of the Euclidean distance between the corresponding data points of all patients in the sample three-dimensional space and the coordinate origin at the tth sampling time; m is the number of patients; L i,j,t is the distance between the coordinates of the data points of the i-th patient and the j-th patient except the i-th patient in the sample three-dimensional space at the t-th sampling time; norm[] is the linear normalization function, is the distance characteristic value of the i-th patient at the t-th sampling time.

4. A diabetes data management method for elderly patients according to claim 1, characterized in that: The method of obtaining the comprehensive condition data deviation of each patient according to the condition data deviation of each patient at all sampling moments includes the following specific steps: In the formula, S i is the deviation of the comprehensive disease data of the i-th patient, U' i,k is the deviation of the condition data of the i-th patient at the k-th post-meal sampling time, is the deviation of the disease data of the i-th patient at the k-th pre-meal sampling time; q is the number of pre-meal sampling times; exp[] is an exponential function with a natural constant as the base.

5. The diabetes data management method for elderly patients according to claim 1, characterized in that: The specific steps of obtaining the stability of each patient's condition according to the data characteristic value of each patient at each sampling time and the deviation of each patient's comprehensive condition data are as follows: Obtain the comprehensive data characteristic value of each patient on that day according to the data characteristic value of each patient; The stability of each patient's condition is obtained based on the comprehensive data characteristic value of each patient on that day and the deviation of each patient's comprehensive condition data.

6. A diabetes data management method for elderly patients according to claim 5, characterized in that: The specific steps of obtaining the stability of the condition of each patient according to the comprehensive data characteristic value of each patient on the day and the deviation of the comprehensive condition data of each patient are as follows: Where, T i is the stability of the condition of the i-th patient; q is the number of sampling moments before meals, and is also the number of sampling moments after meals; is the data characteristic value of the i-th patient at the k-th pre-meal sampling time, is the data characteristic value of the i-th patient at the k+1-th pre-meal sampling time; E' i,k is the data characteristic value of the i-th patient at the k-th post-meal sampling time; E' i,k+1 The data feature value of the i-th patient at the k+1-th post-meal sampling time; is the average value of the data characteristic values ​​of the i-th patient at all sampling times; is the maximum value of the data characteristic value of all patients at all sampling times; s i is the deviation of the comprehensive disease data of the ith patient; min(S) is the minimum value of the deviation of the comprehensive disease data of all patients; || is the absolute value; sigmoid[] is the normalization function; It is the comprehensive data feature value of each patient on that day.

7. A diabetes data management method for elderly patients according to claim 1, characterized in that: The specific steps of obtaining the monitoring priority of each patient according to the stability of the condition of each patient are as follows: P i =1-T i Where P i is the monitoring priority of the i-th patient; T i is the stability of the condition of the ith patient.

8. The diabetes data management method for elderly patients according to claim 1, characterized in that: The specific steps of determining the stable condition type of each patient according to the monitoring priority of each patient are as follows: When the monitoring priority of the patient is less than or equal to the first preset threshold, the patient is judged as having a highly stable condition; When the monitoring priority of the patient is greater than the first preset threshold and less than or equal to the second preset threshold, the patient is judged to have a relatively stable condition; When the monitoring priority of the patient is greater than the second preset threshold, the patient is determined to have an acute change in condition.

9. A diabetes data management method for elderly patients according to claim 8, characterized in that: The monitoring plan is formulated according to the stable condition type of each patient, and the specific steps include the following: The preset first monitoring plan is used for patients with highly stable conditions; the preset second monitoring plan is used for patients with relatively stable conditions; and the preset third monitoring plan is used for patients with acute changes in conditions.

10. A diabetes data management system for elderly patients, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a diabetes data management method for elderly patients as described in any one of claims 1 to 9 are implemented.

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