Method, system and device for monitoring endocrine metabolic disease patient

Abstract

The invention relates to the technical field of medical health data processing, in particular to a monitoring method, system and device for an endocrine metabolic disease patient, and the method comprises the steps: obtaining blood glucose data and other-dimension physiological data of the patient, a blood glucose standard data set and standard physiological data; screening all moments to obtain a blood sugar peak moment according to the difference between the blood sugar data distribution of each moment and the adjacent moment in the time neighborhood range; according to the blood glucose data distribution condition of each blood glucose peak moment and the rising and falling trend of the blood glucose data along with time change, the initial abnormal degree of the current moment is obtained in combination with the blood glucose change rate; according to the number of the blood sugar peak moments, the initial abnormal degree is adjusted in combination with physiological data of other dimensions of the patient and standard physiological data, the comprehensive abnormal degree of the current moment is obtained, and the monitoring result of the patient is determined based on the comprehensive abnormal degree. According to the invention, a more accurate data exception monitoring result can be obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of medical and health data processing, and particularly relates to a monitoring method, system and device for patients with endocrine and metabolic diseases. Background Art

[0002] Diabetes is a lifelong chronic endocrine and metabolic disease. With the long-term management and treatment of diabetic patients, monitoring blood glucose changes has become a key means to evaluate the health status of patients and control the condition. At present, the Continuous Glucose Monitoring (CGM) system is widely used in the daily management of diabetic patients and can provide blood glucose concentration data every minute or even every second to help patients and doctors track the condition in real time.

[0003] When detecting abnormal blood glucose data in endocrine and metabolic patients, since blood glucose fluctuates to a certain extent after eating, when patients with diabetic complications experience acute hyperglycemia and other complications after eating, existing methods are difficult to distinguish between normal blood glucose fluctuations caused by eating and abnormal blood glucose changes caused by complications, resulting in low accuracy of monitoring results. Summary of the Invention

[0004] In order to solve the technical problem that existing methods are difficult to distinguish between normal blood glucose fluctuations caused by eating and abnormal blood glucose changes caused by complications, resulting in low accuracy of monitoring results, the purpose of the present invention is to provide a monitoring method, system and device for patients with endocrine and metabolic diseases. The specific technical solutions adopted are as follows: In the first aspect, the present invention provides a monitoring method for patients with endocrine and metabolic diseases, including: Obtaining blood glucose data and other-dimensional physiological data at each moment after the patient's eating moment and before the current moment, as well as a set of blood glucose standard data and standard physiological data in other dimensions under the patient's normal state; Screening all moments to obtain blood glucose peak moments according to the difference in the distribution of blood glucose data between each moment and adjacent moments within the time neighborhood range; According to the distribution of blood glucose data at each blood glucose peak moment and the rising and falling trends of blood glucose data over time after the eating moment, combined with the blood glucose change rate in the set of blood glucose standard data, obtaining the initial degree of abnormality at the current moment; Adjusting the initial degree of abnormality according to the number of blood glucose peak moments, combined with the patient's physiological data in other dimensions and standard physiological data to obtain the comprehensive degree of abnormality at the current moment, and determining the monitoring result of the patient based on the comprehensive degree of abnormality.

[0005] Preferably, screening all moments to obtain blood glucose peak moments according to the difference between the blood glucose data distributions at each moment and adjacent moments within the time neighborhood range specifically includes: Obtaining the degree of blood glucose change at each moment according to the difference between the blood glucose data of every two adjacent moments within the preset time window of each moment; taking the moments corresponding to the degree of blood glucose change less than or equal to the preset change threshold as blood glucose peak moments.

[0006] Preferably, obtaining the degree of blood glucose change at each moment according to the difference between the blood glucose data of every two adjacent moments within the preset time window of each moment specifically includes: Taking the preset time length centered on each moment as the time window of each moment, and taking the mean value of the blood glucose data of all moments within the time window of each moment as the blood glucose characteristic value corresponding to the moment of the time window; Denoting any moment as the target moment, within the time window of the target moment, calculating the difference between the blood glucose characteristic values of every two adjacent moments to obtain the data difference coefficient, and taking the normalized value of the mean value of all data difference coefficients within the time window of the target moment as the degree of blood glucose change at the target moment.

[0007] Preferably, the blood glucose standard data set in the normal state of the patient includes the blood glucose rising rate and blood glucose falling rate in the blood glucose rising stage and blood glucose peak data in the normal state of the patient.

[0008] Preferably, obtaining the initial degree of abnormality at the current moment according to the blood glucose data distribution at each blood glucose peak moment and the rising and falling trends of the blood glucose data after the eating moment, in combination with the blood glucose change rate in the blood glucose standard data set specifically includes: Dividing time periods according to the distribution of blood glucose peak moments to obtain a blood glucose rising time period, a blood glucose peak time period, and a blood glucose recovery time period respectively; Taking the ratio of the difference between the blood glucose data of the last moment in the blood glucose rising time period and the eating moment to the time length of the corresponding time period as the blood glucose rising trend factor; normalizing the difference between the blood glucose rising trend factor and the blood glucose rising rate in the blood glucose rising stage in the normal state of the patient to obtain the first initial degree of abnormality in the blood glucose rising time period; Taking the ratio of the difference between the blood glucose data of the first moment and the last moment in the blood glucose recovery time period to the time length of the time period as the blood glucose falling trend factor, and normalizing the difference between the blood glucose falling trend factor and the blood glucose falling rate in the blood glucose falling stage in the normal state of the patient to obtain the second initial degree of abnormality in the blood glucose recovery time period; The initial abnormality degree at the current moment includes the first initial abnormality degree and the second initial abnormality degree.

[0009] Preferably, dividing time periods according to the distribution of blood glucose peak times respectively obtains a blood glucose rising time period, a blood glucose peak time period, and a blood glucose recovery time period, which specifically includes: When the number of time intervals between two adjacent blood glucose peak times is less than the preset number of times, the time period between the two adjacent blood glucose peak times is used as a suspected peak time period; The suspected peak time period where the second blood glucose peak time is located is used as the blood glucose peak time period; the time period from after the eating time to before the blood glucose peak time period is used as the blood glucose rising time period, and the time period from after the blood glucose peak time period to before the current moment is used as the blood glucose recovery time period.

[0010] Preferably, adjusting the initial abnormality degree according to the number of blood glucose peak times, in combination with the physiological data of other dimensions of the patient and the standard physiological data, to obtain the comprehensive abnormality degree at the current moment, which specifically includes: Taking the sum of the first initial abnormality degree and the second initial abnormality degree as the first abnormality coefficient; taking the difference between the maximum value of the blood glucose data at all blood glucose peak times and the blood glucose peak data as the second abnormality coefficient; When the total number of blood glucose peak times is less than or equal to the preset number threshold, calculating the difference between the number threshold and the total number of blood glucose peak times, and taking the sum of the product of this difference and the second abnormality coefficient and the second abnormality coefficient as the blood glucose abnormality degree at the current moment; When the total number of blood glucose peak times is greater than the preset number threshold, taking the time length between the first blood glucose peak time and the last blood glucose peak time and the sum of the first abnormality coefficient and the second abnormality coefficient as the blood glucose abnormality degree at the current moment; Adjusting the blood glucose abnormality degree according to the difference between the physiological data of each other dimension of the patient at the current moment and the standard physiological data of the same dimension in the normal state to obtain the comprehensive abnormality degree at the current moment.

[0011] Preferably, adjusting the blood glucose abnormality degree according to the difference between the physiological data of each other dimension of the patient at the current moment and the standard physiological data of the same dimension in the normal state to obtain the comprehensive abnormality degree at the current moment, which specifically includes: Calculating the sum of the differences between the physiological data of each dimension at the current moment and the standard physiological data of the corresponding same dimension to obtain the other dimension abnormality factor; performing normalization processing on the product of the blood glucose abnormality degree at the current moment and the other dimension abnormality factor to obtain the comprehensive abnormality degree at the current moment.

[0012] In a second aspect, the present invention provides a monitoring system for patients with endocrine and metabolic diseases, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the computer program is executed by the processor, it implements the steps of a monitoring method for patients with endocrine and metabolic diseases.

[0013] In a third aspect, the present invention provides a monitoring device for patients with endocrine and metabolic diseases, which is used to implement the steps of a monitoring method for patients with endocrine and metabolic diseases. The monitoring device for patients with endocrine and metabolic diseases specifically includes: A data acquisition module, configured to obtain blood glucose data and other-dimensional physiological data at each moment after the patient's eating moment and before the current moment, as well as a set of blood glucose standard data and standard physiological data in other dimensions under the patient's normal state; A moment screening module, configured to screen all moments to obtain blood glucose peak moments according to the difference in the distribution of blood glucose data between each moment and adjacent moments within a time neighborhood range; An abnormal analysis module, configured to obtain the initial abnormal degree at the current moment according to the distribution of blood glucose data at each blood glucose peak moment and the rising and falling trends of blood glucose data over time after the eating moment, in combination with the blood glucose change rate in the set of blood glucose standard data; An abnormal monitoring module, configured to adjust the initial abnormal degree according to the number of blood glucose peak moments, in combination with the patient's physiological data and standard physiological data in other dimensions, to obtain the comprehensive abnormal degree at the current moment, and determine the monitoring result of the patient based on the comprehensive abnormal degree.

[0014] The embodiments of the present invention have at least the following beneficial effects: The present invention first collects blood glucose data and physiological data in other dimensions, and also collects standard data in each dimension under the patient's normal state, providing a data basis for analyzing the patient's blood glucose abnormality according to the data change difference between the actual monitoring process and the historical normal process. Then, considering that the patient may have diabetes complications resulting in multiple blood glucose peak points, the possible peak moments are screened according to the difference in the data distribution of blood glucose data at each moment within the surrounding local time range. Further, considering that diabetes complications occur in different blood glucose change stages resulting in different blood glucose change trends, the trend of blood glucose data over time is analyzed, and in combination with the change rate of standard data, the blood glucose abnormality at the current moment can be initially quantified. Finally, considering the case where complications result in multiple blood glucose peak moments, in combination with the abnormal degree of physiological information in other dimensions, the preliminary abnormal analysis result is further adjusted, and a more accurate abnormal monitoring result can be obtained, providing more reliable health monitoring support for the patient. Description of the Drawings

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0016] Figure 1 It is a step flowchart of a monitoring method for patients with endocrine and metabolic diseases provided by the present invention; Figure 2 It is the first schematic diagram of the data change trend when complications occur after a patient eats provided by the present invention; Figure 3 It is the second schematic diagram of the data change trend when complications occur after a patient eats provided by the present invention; Figure 4 It is the third schematic diagram of the data change trend when complications occur after a patient eats provided by the present invention; Figure 5 It is the fourth schematic diagram of the data change trend when complications occur after a patient eats provided by the present invention; Figure 6 It is a schematic structural diagram of a monitoring device for patients with endocrine and metabolic diseases provided by the present invention. Detailed implementation manners

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a monitoring method, system, and device for patients with endocrine and metabolic diseases proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solutions of a monitoring method, system, and device for patients with endocrine and metabolic diseases provided by the present invention in combination with the accompanying drawings.

[0020] The specific scenario targeted by the present invention is as follows: In the daily management of diabetic patients, during the process of collecting blood glucose data through a continuous glucose monitoring system (CGM) and monitoring abnormal conditions of the blood glucose data, due to various factors such as eating, exercise, and mood fluctuations, it is difficult to distinguish normal fluctuations from abnormal changes. Therefore, a solution combining the fusion analysis of blood glucose dynamics and physiological parameters is proposed. Specific application scenarios include: dynamic monitoring of blood glucose fluctuations after a diabetic patient eats, early warning of sudden acute hyperglycemia or complications (such as diabetic ketoacidosis, severe hyperglycemia), and real-time assessment of the health status under multi-factor interference.

[0021] Please refer to Figure 1 , which shows a step flowchart of a monitoring method for patients with endocrine and metabolic diseases provided by an embodiment of the present invention. The method includes the following steps: Step S100, obtain the blood glucose data and other-dimensional physiological data at each moment after the patient's eating moment and before the current moment, as well as the set of blood glucose standard data and the standard physiological data in other dimensions under the patient's normal state.

[0022] Since the blood glucose concentration in the body of diabetic patients is not stable, it is necessary to monitor the blood glucose concentration in their bodies to prevent the recurrence of diabetes or complications. Under normal circumstances, according to the change of the diabetes concentration, it can be detected whether the patient has recurrence or complications. However, after a patient eats, due to various reasons, it may be difficult to control the blood glucose concentration in their body, and acute hyperglycemia and other complications may occur after eating. It is difficult to distinguish whether it is a normal blood glucose change caused by eating or a blood glucose change caused by complications on this basis. Therefore, it is necessary to judge whether there is an abnormality at the current moment of the patient through the changes in the patient's blood glucose and physiological data. To provide a data basis for the subsequent abnormal analysis process, this embodiment first collects the patient's blood glucose information and various types of physiological information.

[0023] Specifically, continuously collect the blood glucose data at each moment after the patient eats to monitor the change of the patient's blood glucose information in real time. More specifically, after the patient's eating moment and until before the current moment, collect the physiological data of the patient at each moment and the physiological data of each dimension. In this embodiment, the time interval between two adjacent moments is 1 minute. Among them, the physiological data in other dimensions includes the patient's body temperature, heart rate, blood pressure, etc. Each type of data corresponds to the physiological data of one dimension. It can be understood that in order to avoid the influence of the dimension problem on the subsequent data analysis results, in this embodiment, the collected blood glucose data and physiological data are both data after standardization processing. The method for standardizing the data is a well-known technology and will not be introduced in detail here.

[0024] Further, data information on the blood glucose change in the normal state after a patient eats is obtained through the historical monitoring process of the same patient, which is used to compare the abnormal conditions of blood glucose change in the current blood glucose monitoring process. Specifically, during the historical monitoring process, the blood glucose peak data in the normal state, the blood glucose rising rate during the blood glucose rising stage, and the blood glucose falling rate during the blood glucose falling stage are obtained after the patient eats.

[0025] It can be understood that the cycle of blood glucose change in the normal state after a person eats is generally that the blood glucose shows a continuous rising trend until it reaches a peak state, and then continues to decline and tend to be stable. That is, before the peak state is the stage of continuous blood glucose rising, and after the peak state is the stage of continuous blood glucose falling. Furthermore, the specific methods for obtaining the blood glucose peak data, blood glucose rising rate, and blood glucose falling rate in the normal state are well-known technologies and will not be introduced in detail here.

[0026] More specifically, for physiological data in other dimensions, it is also necessary to obtain the standard physiological data of the patient in the normal state. There is a standard physiological data corresponding to each type of corresponding dimension, which is used to represent the normal range of the patient's physiological information under the corresponding dimension of the type. It can be understood that the normal value of physiological information under each type of corresponding dimension is usually a data range. For example, in the dimension corresponding to blood pressure, the blood pressure is maintained within a certain blood pressure range in the normal state, and in the dimension corresponding to heart rate, it is also maintained within a certain heart rate range in the normal state. Based on this, in this embodiment, the standard physiological data under each dimension is a range data.

[0027] By collecting the data in the normal state during the historical monitoring process of the patient, a standard health model of the patient can be effectively constructed, which also reflects the performance of the patient's physiological information of each type in the healthy state.

[0028] Step S200: According to the difference between the blood glucose data distributions at each moment and the adjacent moments within the time neighborhood range, all moments are screened to obtain the blood glucose peak moment.

[0029] During the patient's eating cycle, under normal circumstances, it includes the blood glucose rising stage, peak point, blood glucose recovery stage, and the blood glucose stable stage after reduction after eating. When the patient develops diabetic complications at different stages after eating, it will disrupt the regular trend of the above blood glucose change, and thus different peak points may appear at different stages, manifested as the blood glucose peak state at multiple moments in the blood glucose data. Based on this, by analyzing the difference in blood glucose data distribution between each moment and the adjacent moment, the moment when the blood glucose peak state appears is screened and obtained.

[0030] In this embodiment, the differences in adjacent blood glucose data within the surrounding time range for each moment are analyzed respectively, and the degree of blood glucose change at each moment can be quantified. Considering that when the blood glucose data reaches the peak point, the degree of change in the blood glucose data is small, while the degree of change at each moment is large during the rising and falling stages of the blood glucose data. Therefore, all moments can be screened based on the degree of blood glucose change at each moment to obtain the blood glucose peak moment. When a patient has diabetic complications, there may be multiple blood glucose peak moments.

[0031] Specifically, the degree of blood glucose change at each moment is obtained according to the differences between the blood glucose data of every two adjacent moments within the preset time window for each moment; the moments corresponding to the degree of blood glucose change less than or equal to the preset change threshold are used as the blood glucose peak moments. In this embodiment, the value of the change threshold is 0.1, and in other embodiments, the implementer can set it according to the specific implementation scenario.

[0032] The smaller the degree of blood glucose change at each moment, the smaller the change in the blood glucose data corresponding to that moment, and the more likely the corresponding moment belongs to the peak point, that is, the blood glucose peak state. The larger the degree of blood glucose change at each moment, the greater the change in the blood glucose data corresponding to that moment, and the less likely the moment belongs to the peak point.

[0033] In a specific embodiment, a preset time length is obtained centered on each moment as the time window for each moment, and the mean value of the blood glucose data of all moments within the time window of each moment is used as the blood glucose characteristic value corresponding to the moment of the time window. Denote any moment as the target moment. Within the time window of the target moment, calculate the differences between the blood glucose characteristic values of every two adjacent moments to obtain the data difference coefficient, and use the normalized value of the mean of all data difference coefficients within the time window of the target moment as the degree of blood glucose change at the target moment.

[0034] Among them, in order to avoid the interference of local fluctuations in the patient's blood glucose data within the local time range on the subsequent analysis results, the characteristic data within the local time range for each moment is obtained by taking the mean value, that is, the blood glucose characteristic value. In this embodiment, the preset time length is 11 minutes, that is, the blood glucose data of five moments on both the left and right sides of each moment are obtained to determine the time window for each moment. It can be understood that if there is not enough time length before each moment, no analysis will be performed.

[0035] As a specific example, taking any moment as an example for illustration, if the i-th moment is taken as the target moment, the calculation formula for the degree of blood glucose change at the i-th moment, that is, the degree of blood glucose change at the target moment, can be expressed as: ; where Indicates the degree of blood glucose change at the i-th moment, i.e., the target moment. Here, i represents the i-th moment. Indicates the total number of moments included within the time window of the i-th moment. Indicates the blood glucose characteristic value at the n-th moment within the time window of the i-th moment. Indicates the blood glucose characteristic value at the (n - 1)-th moment within the time window of the i-th moment. Is a normalization function.

[0036] It reflects the change in blood glucose concentration between adjacent moments within the local time range of a detection moment. When the average change in blood glucose concentration is smaller, it indicates that the blood glucose change at this detection moment is smoother, and this detection moment is more likely to be the peak point moment, that is, the corresponding value of the degree of blood glucose change is larger.

[0037] Step S300: According to the distribution of blood glucose data at each blood glucose peak moment and the rising and falling trends of blood glucose data over time after the eating moment, combined with the blood glucose change rate in the blood glucose standard data set, obtain the initial degree of abnormality at the current moment.

[0038] Under normal circumstances, the blood glucose fluctuation cycle after each meal of a patient generally includes a blood glucose rising stage, a blood glucose peak, a blood glucose falling stage, and a stable stage after blood glucose recovery. When the patient has diabetes complications at different stages after eating, the blood glucose change situation of the patient is different, and the diabetes complications will cause a superimposed effect on the blood glucose rise of the patient.

[0039] Such as Figure 2 Is the first blood glucose change trend corresponding to the occurrence of complications after the patient eats under normal circumstances. Figure 2 It is that the patient has complications during the blood glucose rising stage after eating, resulting in a superimposed effect on the speed of blood glucose rising, so it may cause abnormal phenomena in the patient's blood glucose data during the rising stage.

[0040] Such as Figure 3 Is the second blood glucose change trend corresponding to the occurrence of complications after the patient eats under normal circumstances. The patient has diabetes complications during the blood glucose falling stage after eating. Due to the interference of the complications, a secondary peak phenomenon appears in the blood glucose data. Such as Figure 3 As shown, the blood glucose data starts to decline at the peak point. After a period of time, the occurrence of complications will cause the blood glucose to rise again and a secondary peak point appears.

[0041] Based on this, considering the multi-peak phenomenon of blood glucose data caused by diabetes complications, by analyzing the distribution of blood glucose peak times, the blood glucose change cycle of the patient is divided into three stages, specifically including the blood glucose rising stage before reaching the peak, the peak duration stage with the multi-peak phenomenon, and the blood glucose decreasing and recovering stage after reaching the peak.

[0042] Specifically, time periods are divided according to the distribution of blood glucose peak times to obtain the blood glucose rising time period, the blood glucose peak time period, and the blood glucose recovering time period. More specifically, when the number of time intervals between two adjacent blood glucose peak times is less than the preset number of times, the time period between the two adjacent blood glucose peak times is used as the suspected peak time period; the suspected peak time period where the second blood glucose peak time is located is used as the blood glucose peak time period; the time period from after the eating time to before the blood glucose peak time period is used as the blood glucose rising time period, and the time period from after the blood glucose peak time period to before the current time is used as the blood glucose recovering time period.

[0043] In this embodiment, the value of the preset number of times is 5. In other embodiments, the implementer can set it according to the specific implementation scenario. When the time interval between two adjacent blood glucose peak times is short, it indicates that the peak phenomenon may occur multiple times within a short time range, and it is more likely that the blood glucose change is caused by complications.

[0044] Furthermore, during the blood glucose rising stage and the blood glucose recovering stage in the actual monitoring of the patient, the blood glucose change situation in the actual monitoring process is respectively compared with the blood glucose change situation in the corresponding stages of the patient's standard healthy model, and it can be preliminarily analyzed whether there are data abnormalities in the patient during the blood glucose rising stage and the blood glucose recovering stage in the actual monitoring process.

[0045] Specifically, for the blood glucose rising time period of the patient, the ratio of the difference between the blood glucose data at the last time in the blood glucose rising time period and the blood glucose data at the eating time to the time length of the corresponding time period is used as the blood glucose rising trend factor; the difference between the blood glucose rising trend factor and the blood glucose rising rate in the blood glucose rising stage under the normal state of the patient is normalized to obtain the first initial abnormal degree of the blood glucose rising time period. The first initial abnormal degree reflects the data abnormality situation in the blood glucose rising stage during the actual monitoring of the patient.

[0046] For the blood glucose recovery time period of the patient, the ratio of the difference between the blood glucose data at the first moment and the last moment within the blood glucose recovery time period to the time length of this time period is used as the blood glucose decline trend factor, and the difference between the blood glucose decline trend factor and the blood glucose decline rate during the blood glucose decline stage in the normal state of the patient is normalized to obtain the second initial abnormality degree of the blood glucose recovery time period. The initial abnormality degree at the current moment includes the first initial abnormality degree and the second initial abnormality degree. The second initial abnormality degree reflects the data abnormality situation during the blood glucose recovery stage in the actual monitoring of the patient.

[0047] As a specific example, the calculation formulas for the first initial abnormality degree and the second initial abnormality degree can be expressed as: Among them, represents the first initial abnormality degree of the blood glucose rising time period, represents the second initial abnormality degree of the blood glucose recovery time period, represents the absolute value of the difference between the blood glucose data at the last moment and the eating moment within the blood glucose rising time period, represents the time length of the blood glucose rising time period, represents the blood glucose rising rate during the blood glucose rising stage in the normal state of the patient in the standard healthy model; represents the absolute value of the difference between the blood glucose data at the first moment and the last moment within the blood glucose recovery time period, represents the time length of the blood glucose recovery time period, represents the blood glucose decline rate during the blood glucose decline stage in the normal state of the patient in the standard healthy model, is the normalization function.

[0048] is the blood glucose rising trend factor, which reflects the blood glucose rising change speed during the actual monitoring process, is the blood glucose decline trend factor, which reflects the blood glucose decline change speed during the actual monitoring process. By analyzing the difference between the actual speed of blood glucose change and the normal speed of the standard healthy model in the historical record, the data change abnormality degree of blood glucose rising and blood glucose declining of the patient during the actual monitoring process is quantified.

[0049] The larger the value of the first initial difference degree, the greater the data abnormality degree of the patient during the blood glucose rising stage. The larger the value of the second initial difference degree, the greater the data abnormality degree of the patient during the blood glucose recovery stage. That is, the initial abnormality degree at the current moment reflects the size degree of data abnormality respectively from the data change trends of the blood glucose rising and blood glucose recovery stages.

[0050] Step S400: Adjust the initial degree of abnormality based on the number of blood glucose peak moments, in combination with the patient's physiological data in other dimensions and the standard physiological data, to obtain the comprehensive degree of abnormality at the current moment, and determine the patient's monitoring result based on the comprehensive degree of abnormality.

[0051] When diabetic complications occur during the blood glucose rising stage of a patient, it will cause a relatively high peak value of the patient's blood glucose concentration, and the patient will remain at the peak state for a relatively long time, resulting in the data change trend not entering the blood glucose recovery stage, or the speed of blood glucose data change after the recovery stage is slower than normal, as Figure 4 shown. When diabetic complications occur during the blood glucose recovery stage of a patient, it will cause the blood glucose data of the patient to have a peak point again during the recovery stage, and the peak value remains unchanged for a period of time, or there is a recovery situation but the recovery rate is slow, as Figure 5 shown.

[0052] When there is only one blood glucose peak moment, it indicates that there is only one peak point in the blood glucose change cycle after the current patient eats. Therefore, it is necessary to focus on whether there is an abnormal change in the blood glucose rate caused by complications during the blood glucose rising stage of the patient. When there are two blood glucose peak moments, it indicates that there are two peak points in the blood glucose change cycle after the current patient eats, indicating that there is a phenomenon of rising and then falling during the actual monitoring of the patient. Its overall trend is relatively similar to the normal change trend, but the peak height and the speed of data decline may be abnormal, reflected as a large fluctuation range of the data. The data change stage before the second blood glucose peak moment can be approximately regarded as the blood glucose rising stage, that is, it indicates that diabetic complications have occurred during the blood glucose rising stage of the patient. When the number of blood glucose peak moments is greater than 2, it indicates that the blood glucose data of the patient shows an initial rise followed by a short recovery stage, but new peak points appear during the recovery process, manifested as abnormal fluctuations in the blood glucose data. Therefore, it indicates that there is one or more peak phenomena caused by complications during the blood glucose recovery stage of the patient at this time.

[0053] Based on this, through the distribution of the number of blood glucose peak moments, and respectively for the situations of different stages when complications occur, combining the analysis results of the initial degree of abnormality, the final abnormal situation of the blood glucose at the current moment is quantified. Specifically, the sum of the first initial degree of abnormality and the second initial degree of abnormality is used as the first abnormal coefficient; the difference between the maximum value of the blood glucose data at all blood glucose peak moments and the blood glucose peak data is used as the second abnormal coefficient.

[0054] When the total number of blood glucose peak moments is less than or equal to the preset number threshold, calculate the difference between the number threshold and the total number of blood glucose peak moments, and take the sum of the product of this difference and the second abnormal coefficient and the second abnormal coefficient as the degree of blood glucose abnormality at the current moment.

[0055] When the total number of blood glucose peak moments is greater than a preset number threshold, the time length between the first blood glucose peak moment and the last blood glucose peak moment, along with the cumulative sum of the first abnormal coefficient and the second abnormal coefficient, is taken as the degree of blood glucose abnormality at the current moment.

[0056] It can be understood that, through the above analysis, in this embodiment, the value of the number threshold is 2. As a specific example, the degree of blood glucose abnormality at the current moment can be expressed as: Where, represents the degree of blood glucose abnormality at the current moment, represents the first initial abnormal degree during the blood glucose rising period, represents the second initial abnormal degree during the blood glucose recovery period, represents the total number of blood glucose peak moments, represents the difference between the maximum value of the blood glucose data at all blood glucose peak moments and the blood glucose peak data, represents the time length between the first blood glucose peak moment and the last blood glucose peak moment.

[0057] is the first abnormal coefficient, which reflects the degree of difference in the data change rate between the actual monitoring process and the historical normal process after preliminary comparison, is the second abnormal coefficient, which reflects the degree of difference in the peak points between the actual monitoring process and the historical normal process.

[0058] When the number of blood glucose peak points is less than or equal to 2, it indicates that there are complications in the blood glucose rising stage of the patient during the current actual monitoring process. Therefore, the focus is on whether there is an abnormal degree in the blood glucose change rate during the rising stage. When the number of blood glucose peak points is greater than 2, it indicates that there are complications in the blood glucose recovery stage of the patient during the current actual monitoring process. Complications may lead to a phenomenon where the peak persists at a relatively high level. The longer the duration of the blood glucose data peak and the higher the peak, the greater the corresponding abnormal degree, that is, the larger the value of the blood glucose abnormality degree. The degree of blood glucose abnormality at the current moment characterizes the magnitude and possibility of blood glucose data abnormalities during the real-time monitoring process of the patient.

[0059] Furthermore, considering that when a patient has diabetes complications, significant changes may also occur in physiological information such as blood pressure, heart rate, and body temperature. For example, when a patient has diabetes complications such as diabetic ketoacidosis or severe hyperglycemia, it can lead to overactivation of the sympathetic nervous system and a sharp increase in heart rate. Additionally, diabetic ketoacidosis can cause fluid loss and electrolyte disorders, potentially resulting in a rapid increase in blood pressure. To obtain a more accurate abnormal quantification result, the current degree of blood glucose abnormality is further adjusted by combining the deviation degrees of physiological data in other dimensions.

[0060] Based on this, according to the difference between the physiological data of each other dimension of the patient at the current moment and the standard physiological data of the same dimension in the normal state, the degree of blood glucose abnormality is adjusted to obtain the comprehensive abnormal degree at the current moment.

[0061] Specifically, calculate the cumulative sum of the differences between the physiological data of each dimension at the current moment and the standard physiological data of the corresponding same dimension to obtain the abnormal factor of other dimensions; perform normalization on the product of the degree of blood glucose abnormality at the current moment and the abnormal factor of other dimensions to obtain the comprehensive abnormal degree at the current moment. Among them, the normalization method is a well-known technology and will not be introduced in detail here.

[0062] It can be understood that when the standard physiological data is a data range, the method for calculating the difference between the physiological data and the standard data of the corresponding same dimension can be specifically as follows: Taking the body temperature data as an example, if the body temperature data at the current moment is greater than the upper limit value of the standard body temperature data range, calculate the absolute value of the difference between the body temperature data and the upper limit value of the standard body temperature data range, that is, the greater the deviation degree of the body temperature data from the upper limit value of the standard body temperature data range, the greater the degree of deviation of the body temperature data from the normal value at the current moment, and thus the greater the abnormal degree of the physiological data of the corresponding dimension. If the body temperature data at the current moment is less than the lower limit value of the standard body temperature data range, calculate the absolute value of the difference between the body temperature data and the lower limit value of the standard body temperature data range, that is, the greater the deviation degree of the body temperature data from the lower limit value of the standard body temperature data range, the greater the degree of deviation of the body temperature data from the normal value at the current moment, and thus the greater the abnormal degree of the physiological data of the corresponding dimension. If the body temperature data at the current moment is greater than or equal to the lower limit value of the standard body temperature data range and less than or equal to the upper limit value of the standard body temperature data range, it means that the body temperature data at the current moment is within the normal range, and at this time, the abnormal degree of the physiological data of the corresponding dimension is 0, that is, the value of the difference between the physiological data and the standard data of the corresponding same dimension is 0.

[0063] The abnormal degrees of physiological data in other dimensions can all obtain the corresponding abnormal degrees according to the same method. Furthermore, by accumulating the abnormal conditions of physiological data in all dimensions, the abnormal changes in other types of physiological data of the patient except for blood glucose information can be obtained.

[0064] Finally, the comprehensive abnormality degree reflects the blood glucose abnormality of the patient from multiple aspects. When the value of the comprehensive abnormality degree is larger, it indicates that the possibility of the patient's data information being abnormal at the current moment is greater, and further indicates that the possibility of the patient developing complications is greater.

[0065] Furthermore, the monitoring result of the patient can be determined based on the comprehensive abnormality degree. Specifically, when the comprehensive abnormality degree at the current moment is greater than or equal to the preset abnormality threshold, it indicates that the possibility of the patient developing complications at the current moment is greater, and it is necessary to notify the doctor and the patient's emergency contact, and the doctor can make a further judgment according to the actual situation. Among them, in this embodiment, the value of the abnormality threshold is 0.8, and in other embodiments, the implementer can set it according to the specific implementation scenario.

[0066] In summary, in this embodiment, by fusing blood glucose data with other physiological parameters and using the key characteristics of blood glucose changes (such as fluctuation amplitude, rising speed, peak maintenance time, etc.), it can effectively distinguish the normal fluctuations caused by eating from the abnormal fluctuations caused by diabetic complications, reduce the risks of misjudgment and missed judgment, and provide more reliable health monitoring support for patients. Enhance the comprehensiveness and robustness of anomaly detection, and improve the adaptability of the monitoring method to complex situations.

[0067] The embodiment of the present invention also provides a monitoring system for patients with endocrine and metabolic diseases, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the computer program is executed by the processor, it implements the steps of a monitoring method for patients with endocrine and metabolic diseases.

[0068] As Figure 6 shown, the embodiment of the present invention also provides a monitoring device for patients with endocrine and metabolic diseases. The device is used to implement the steps of a monitoring method for patients with endocrine and metabolic diseases. The monitoring device for patients with endocrine and metabolic diseases specifically includes: A data acquisition module, configured to obtain the blood glucose data and other-dimensional physiological data at each moment after the patient's eating moment and before the current moment, as well as the blood glucose standard data set and the standard physiological data in other dimensions under the patient's normal state; A moment screening module, configured to screen all moments according to the difference in the blood glucose data distribution between each moment and its adjacent moments within the time neighborhood range to obtain the blood glucose peak moment; An anomaly analysis module, configured to obtain the initial anomaly degree at the current moment according to the blood glucose data distribution at each blood glucose peak moment and the rising and falling trends of the blood glucose data after the eating moment over time, in combination with the blood glucose change rate in the blood glucose standard data set; Anomaly monitoring module, which is used to adjust the initial anomaly degree according to the number of blood glucose peak moments, in combination with the physiological data of other dimensions of the patient and the standard physiological data, to obtain the comprehensive anomaly degree at the current moment, and determine the monitoring result of the patient based on the comprehensive anomaly degree.

[0069] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring patients with endocrine and metabolic diseases, characterized in that: The method comprises the following steps: Obtain the patient's blood sugar data and other dimensional physiological data at each moment after the patient's meal and before the current moment, as well as the patient's blood sugar standard data set under normal conditions and standard physiological data in other dimensions; According to the difference between the blood glucose data distribution at each moment and the adjacent moments within the time neighborhood, all moments are screened to obtain the blood glucose peak moment; According to the distribution of blood sugar data at each blood sugar peak moment and the rising and falling trends of blood sugar data after eating over time, combined with the blood sugar change rate in the blood sugar standard data set, the initial abnormality degree at the current moment is obtained; According to the number of blood sugar peak moments, the initial abnormality degree is adjusted in combination with the patient's physiological data of other dimensions and standard physiological data to obtain the comprehensive abnormality degree at the current moment, and the patient's monitoring result is determined based on the comprehensive abnormality degree.

2. The method for monitoring patients with endocrine and metabolic diseases according to claim 1, characterized in that: The method of screening all moments to obtain the blood glucose peak moment according to the difference between the blood glucose data distribution at each moment and the adjacent moments within the time neighborhood range specifically includes: The blood sugar change degree at each moment is obtained according to the difference between the blood sugar data of each two adjacent moments within the preset time window at each moment; the moment corresponding to the blood sugar change degree being less than or equal to the preset change threshold is taken as the blood sugar peak moment.

3. The method for monitoring patients with endocrine and metabolic diseases according to claim 2, characterized in that: The step of obtaining the blood sugar change degree at each moment according to the difference between the blood sugar data at each two adjacent moments within the preset time window at each moment specifically includes: Taking each moment as the center, a preset time length is obtained as the time window of each moment, and the mean of the blood glucose data of all moments in the time window of each moment is taken as the blood glucose characteristic value of the moment corresponding to the time window; Any moment is recorded as the target moment. Within the time window of the target moment, the difference between the blood glucose characteristic values ​​of every two adjacent moments is calculated to obtain the data difference coefficient. The normalized value of the mean of all data difference coefficients within the time window of the target moment is taken as the blood glucose change degree at the target moment.

4. The method for monitoring patients with endocrine and metabolic diseases according to claim 1, characterized in that: The blood sugar standard data set of the patient in a normal state includes the blood sugar rising rate in the blood sugar rising stage and the blood sugar falling rate in the blood sugar falling stage as well as the blood sugar peak data of the patient in a normal state.

5. The method for monitoring patients with endocrine and metabolic diseases according to claim 4, characterized in that: The initial abnormality degree at the current moment is obtained based on the distribution of blood sugar data at each blood sugar peak moment and the rising and falling trends of blood sugar data after eating with time, combined with the blood sugar change rate in the blood sugar standard data set, specifically including: According to the distribution of blood sugar peak moments, the time periods are divided into blood sugar rising time period, blood sugar peak time period and blood sugar recovery time period; The ratio of the difference between the blood sugar data at the last moment of the blood sugar rising time period and the blood sugar data at the eating moment to the length of the corresponding time period is used as the blood sugar rising trend factor; the difference between the blood sugar rising trend factor and the blood sugar rising rate in the blood sugar rising stage of the patient under normal conditions is normalized to obtain the first initial abnormality degree of the blood sugar rising time period; The ratio of the difference between the blood sugar data at the first moment and the last moment in the blood sugar recovery time period to the length of the time period is used as the blood sugar decrease trend factor, and the difference between the blood sugar decrease trend factor and the blood sugar decrease rate in the blood sugar decrease stage of the patient under normal conditions is normalized to obtain the second initial abnormality degree of the blood sugar recovery time period; The initial abnormality level at the current moment includes the first initial abnormality level and the second initial abnormality level.

6. The method for monitoring patients with endocrine and metabolic diseases according to claim 5, characterized in that: The division of time periods according to the distribution of blood sugar peak moments into blood sugar rising time periods, blood sugar peak time periods and blood sugar recovery time periods specifically includes: When the number of time intervals between two adjacent blood sugar peak moments is less than the preset number of time intervals, the time period between the two adjacent blood sugar peak moments is regarded as a suspected peak time period; The suspected peak time period of the second blood glucose peak moment is taken as the blood glucose peak time period; the time period from the eating time to the blood glucose peak time period is taken as the blood glucose rising time period; the time period from the blood glucose peak time period to the current moment is taken as the blood glucose recovery time period.

7. The method for monitoring patients with endocrine and metabolic diseases according to claim 6, characterized in that: The initial abnormality degree is adjusted according to the number of blood sugar peak moments in combination with the physiological data of other dimensions of the patient and the standard physiological data to obtain the comprehensive abnormality degree at the current moment, specifically including: The cumulative sum of the first initial abnormality degree and the second initial abnormality degree is used as the first abnormality coefficient; the difference between the maximum value of the blood glucose data at all blood glucose peak moments and the blood glucose peak data is used as the second abnormality coefficient; When the total number of blood sugar peaks is less than or equal to a preset number threshold, the difference between the number threshold and the total number of blood sugar peaks is calculated, and the product of the difference and the second abnormal coefficient and the cumulative sum of the second abnormal coefficient are used as the blood sugar abnormality degree at the current moment; When the total number of blood sugar peak moments is greater than a preset number threshold, the time length between the first blood sugar peak moment and the last blood sugar peak moment and the cumulative sum of the first abnormal coefficient and the second abnormal coefficient are used as the blood sugar abnormality degree at the current moment; According to the difference between the physiological data of each other dimension of the patient at the current moment and the standard physiological data of the same dimension under normal conditions, the abnormal blood sugar level is adjusted to obtain the comprehensive abnormality level at the current moment.

8. The method for monitoring patients with endocrine and metabolic diseases according to claim 7, characterized in that: The abnormal blood sugar level is adjusted according to the difference between the physiological data of each other dimension of the patient at the current moment and the standard physiological data of the same dimension under normal conditions to obtain the comprehensive abnormality level at the current moment, specifically including: The cumulative sum of the differences between the physiological data of each dimension at the current moment and the standard physiological data corresponding to the same dimension is calculated to obtain the abnormal factors of other dimensions; the product of the abnormal blood sugar level at the current moment and the abnormal factors of other dimensions is normalized to obtain the comprehensive abnormality level at the current moment.

9. A monitoring system for patients with endocrine and metabolic diseases, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the computer program is executed by a processor, the steps of a method for monitoring a patient with an endocrine metabolic disease as described in any one of claims 1 to 8 are implemented.

10. A monitoring device for patients with endocrine and metabolic diseases, characterized in that: The device is used to implement the steps of a method for monitoring patients with endocrine and metabolic diseases as described in any one of claims 1 to 8, and the monitoring device for patients with endocrine and metabolic diseases specifically comprises: A data acquisition module is used to obtain the blood sugar data and other physiological data of each moment after the patient's meal time and before the current moment, as well as the blood sugar standard data set and standard physiological data of other dimensions under the patient's normal state; A moment screening module is used to screen all moments to obtain the blood glucose peak moment according to the difference between the blood glucose data distribution of each moment and the adjacent moments within the time neighborhood; The abnormality analysis module is used to obtain the initial abnormality degree at the current moment based on the distribution of blood sugar data at each blood sugar peak moment and the rising and falling trends of blood sugar data after eating time, combined with the blood sugar change rate in the blood sugar standard data set; The abnormality monitoring module is used to adjust the initial abnormality level according to the number of blood sugar peak moments, combined with the patient's physiological data of other dimensions and standard physiological data, to obtain the comprehensive abnormality level at the current moment, and determine the patient's monitoring results based on the comprehensive abnormality level.

Images