A monitoring method, system and device for patients with endocrine and metabolic diseases

Abstract

The present invention relates to the field of medical health data processing technology, and specifically to a monitoring method, system, and device for patients with endocrine metabolic diseases, comprising: obtaining the patient's blood sugar data and other dimensional physiological data as well as a blood sugar standard data set and standard physiological data; screening all moments to obtain blood sugar peak moments based on the difference between the blood sugar data distribution at each moment and the adjacent moments within a time neighborhood; obtaining the initial abnormality level at the current moment based on the blood sugar data distribution at each blood sugar peak moment and the rising and falling trends of the blood sugar data over time, combined with the blood sugar change rate; adjusting the initial abnormality level based on the number of blood sugar peak moments, combined with the patient's physiological data in other dimensions and standard physiological data, obtaining a comprehensive abnormality level at the current moment, and determining the patient's monitoring results based on the comprehensive abnormality level. The present invention can obtain more accurate data abnormality monitoring results.

Description

Technical Field

[0001] The present invention relates to the technical field of medical health data processing, and in particular 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 sugar levels has become a key tool for assessing patient health and controlling the disease. Currently, continuous glucose monitoring (CGM) systems are widely used in the daily management of diabetic patients, providing minute-by-minute and even second-by-second blood sugar concentration data, helping patients and doctors track their condition in real time.

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

[0004] To address the technical problem that existing methods have difficulty distinguishing between normal blood sugar fluctuations caused by eating and abnormal blood sugar changes caused by complications, resulting in low monitoring accuracy, the present invention aims to provide a monitoring method, system, and device for patients with endocrine and metabolic diseases. The technical solutions adopted are as follows:

[0005] In a first aspect, the present invention provides a method for monitoring patients with endocrine and metabolic diseases, comprising:

[0006] Obtain the patient's blood sugar data and other physiological data at each moment after the meal and before the current moment, as well as the patient's blood sugar standard data set and standard physiological data in other dimensions under normal conditions;

[0007] Based on 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;

[0008] 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 over time, combined with the blood sugar change rate in the blood sugar standard data set, the initial abnormality level at the current moment is obtained;

[0009] According to the number of blood sugar peak moments, the initial abnormality level is adjusted in combination with the patient's physiological data of other dimensions and standard physiological data to obtain the comprehensive abnormality level at the current moment, and the patient's monitoring results are determined based on the comprehensive abnormality level.

[0010] Preferably, the step of screening all moments to obtain the blood glucose peak moment based on the difference between the blood glucose data distribution at each moment and adjacent moments within a time neighborhood specifically includes:

[0011] The blood glucose change degree at each moment is obtained based on the difference between the blood glucose data of each two adjacent moments within the preset time window at each moment; the moment when the blood glucose change degree is less than or equal to the preset change threshold is taken as the blood glucose peak moment.

[0012] Preferably, obtaining the blood glucose variation degree at each moment according to the difference between the blood glucose data at each two adjacent moments within the preset time window at each moment specifically includes:

[0013] 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 within the time window of each moment is used as the blood glucose characteristic value of the moment corresponding to the time window;

[0014] 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 each 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 used as the blood glucose change degree at the target moment.

[0015] Preferably, the blood glucose standard data set of the patient in a normal state includes the blood glucose rising rate in the blood glucose rising stage, the blood glucose falling rate in the blood glucose falling stage and the blood glucose peak data of the patient in a normal state.

[0016] Preferably, the initial abnormality degree at the current moment is obtained based on the distribution of blood glucose data at each blood glucose peak moment and the rising and falling trends of blood glucose data after eating over time, combined with the blood glucose change rate in the blood glucose standard data set, specifically including:

[0017] The time periods are divided according to the distribution of blood sugar peak moments to obtain blood sugar rising time period, blood sugar peak time period and blood sugar recovery time period;

[0018] 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 time of eating 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 during the blood sugar rising stage of the patient under normal conditions is normalized to obtain a first initial abnormality degree of the blood sugar rising time period;

[0019] The ratio of the difference between the blood glucose data at the first moment and the last moment within the blood glucose recovery period to the length of the period is used as a blood glucose decrease trend factor, and the difference between the blood glucose decrease trend factor and the blood glucose decrease rate during the blood glucose decrease phase under the patient's normal state is normalized to obtain a second initial abnormality degree during the blood glucose recovery period;

[0020] The initial abnormality level at the current moment includes the first initial abnormality level and the second initial abnormality level.

[0021] Preferably, the division of time periods according to the distribution of blood glucose peak moments to obtain blood glucose rising time period, blood glucose peak time period and blood glucose recovery time period specifically includes:

[0022] When the number of time intervals between two adjacent blood glucose peak moments is less than the preset number of time intervals, the time period between the two adjacent blood glucose peak moments is regarded as a suspected peak time period;

[0023] 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, and the time period from the blood glucose peak time period to the current moment is taken as the blood glucose recovery time period.

[0024] Preferably, the initial abnormality degree is adjusted according to the number of blood glucose peak moments in combination with physiological data of other dimensions of the patient and standard physiological data to obtain the comprehensive abnormality degree at the current moment, specifically including:

[0025] 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;

[0026] When the total number of blood sugar peak moments is less than or equal to a preset number threshold, calculating the difference between the number threshold and the total number of blood sugar peak moments, and using the product of the difference and the second abnormality coefficient plus the cumulative sum of the second abnormality coefficient as the blood sugar abnormality level at the current moment;

[0027] When the total number of blood glucose peak moments is greater than a preset number threshold, the sum of the time length between the first blood glucose peak moment and the last blood glucose peak moment, the first abnormality coefficient, and the second abnormality coefficient is used as the blood glucose abnormality level at the current moment;

[0028] 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.

[0029] Preferably, the abnormal blood sugar level is adjusted based on 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:

[0030] 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.

[0031] In a second aspect, the present invention provides 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, wherein when the computer program is executed by the processor, the steps of a monitoring method for patients with endocrine and metabolic diseases are implemented.

[0032] 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:

[0033] The data acquisition module is used to obtain the patient's blood sugar data and other physiological data at each moment after the meal and before the current moment, as well as the patient's blood sugar standard data set and standard physiological data in other dimensions under normal conditions;

[0034] A moment screening module is used to screen all moments to obtain the blood glucose peak moment based on the difference between the blood glucose data distribution at each moment and the adjacent moments within the time neighborhood;

[0035] The abnormality analysis module is used to obtain the initial abnormality level 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 over time, combined with the blood sugar change rate in the blood sugar standard data set;

[0036] 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.

[0037] The embodiments of the present invention have at least the following beneficial effects:

[0038] The present invention first collects blood sugar data and physiological data of other dimensions, and collects standard data of each dimension under the normal state of the patient, providing a data basis for the subsequent analysis of the patient's abnormal blood sugar situation based on the data change differences between the actual monitoring process and the historical normal process. Then, taking into account the phenomenon that the patient may have diabetic complications leading to multiple blood sugar peak points, the possible peak moments are screened based on the data distribution differences of the blood sugar data at each moment within the surrounding local time range. Furthermore, taking into account the different blood sugar change trends caused by diabetic complications at different blood sugar change stages, the trend of blood sugar data changing over time is analyzed, and combined with the change rate of standard data, the blood sugar abnormality at the current moment can be preliminarily quantified. Finally, taking into account the situation where complications lead to multiple blood sugar peak moments, combined with the abnormal degree of physiological information in other dimensions, the preliminary abnormal analysis results are further adjusted, so that more accurate abnormal monitoring results can be obtained, providing patients with more reliable health monitoring support. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 any creative work.

[0040] Figure 1 This is a flowchart of the steps of a method for monitoring patients with endocrine and metabolic diseases provided by the present invention;

[0041] Figure 2 This is a first schematic diagram of the data change trend when a patient develops complications after eating, provided by the present invention;

[0042] Figure 3 This is a second schematic diagram of the data change trend when complications occur after a patient eats, provided by the present invention;

[0043] Figure 4 This is a third schematic diagram of data change trends when complications occur after a patient eats, provided by the present invention;

[0044] Figure 5 This is a fourth schematic diagram of data change trends when complications occur after a patient eats, provided by the present invention;

[0045] Figure 6 It is a structural schematic diagram of a monitoring device for patients with endocrine and metabolic diseases provided by the present invention. DETAILED DESCRIPTION

[0046] To further illustrate the technical means and efficacy employed by the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method, system, and device for monitoring patients with endocrine and metabolic diseases according to the present invention, including its specific implementation, structure, features, and efficacy. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0047] 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.

[0048] The following describes in detail a method, system and device for monitoring patients with endocrine and metabolic diseases provided by the present invention with reference to the accompanying drawings.

[0049] This invention addresses the following specific scenarios: In the daily management of diabetic patients, blood sugar data is collected through a continuous glucose monitoring (CGM) system. Monitoring abnormal blood sugar data is difficult due to interference from multiple factors such as eating, exercise, and mood swings, making it difficult to distinguish between normal fluctuations and abnormal changes. This invention proposes a solution that combines blood sugar dynamics with physiological parameter fusion analysis. Specific application scenarios include: dynamic monitoring of blood sugar fluctuations after eating in diabetic patients, early warning of sudden acute hyperglycemia or complications (such as ketoacidosis and severe hyperglycemia), and real-time health status assessment under multi-factor interference.

[0050] See also Figure 1 , which shows a flowchart of a method for monitoring patients with endocrine and metabolic diseases provided by one embodiment of the present invention, the method comprising the following steps:

[0051] Step S100, obtaining the blood sugar data and other dimensional physiological data of the patient at each moment after the patient eats and before the current moment, as well as the blood sugar standard data set and standard physiological data in other dimensions under the patient's normal state.

[0052] Since the blood sugar concentration in diabetic patients is not stable, it is necessary to monitor the blood sugar concentration in their bodies to prevent the recurrence of diabetes or complications. Under normal circumstances, the change in the concentration of diabetes can be used to detect whether the patient has recurrence or complications. However, after a meal, the patient's blood sugar concentration may be difficult to control due to various reasons, and complications such as acute hyperglycemia may occur after a meal. It is difficult to distinguish whether it is a normal blood sugar change caused by a meal or a blood sugar change caused by complications on this basis. Therefore, it is necessary to judge whether the patient has an abnormality at the current moment based on the changes in the patient's blood sugar and physiological data. To provide a data basis for the subsequent abnormality analysis process, this implementation first collects the patient's blood sugar information and various types of physiological information.

[0053] Specifically, blood glucose data is continuously collected at each moment after the patient eats, so as to monitor the changes in the patient's blood glucose information in real time. More specifically, the patient's physiological data at each moment and physiological data of each dimension are collected from the moment the patient eats until the current moment. In this embodiment, the time interval between two adjacent moments is 1 minute, and the physiological data of other dimensions include the patient's body temperature, heart rate, blood pressure, etc. Each type of data corresponds to physiological data of one dimension. It can be understood that in order to avoid the influence of dimensionality problems on the subsequent data analysis results, in this embodiment, the collected blood glucose data and physiological data are all standardized data. The method of standardizing the data is a well-known technology and will not be introduced in detail here.

[0054] Furthermore, data on the patient's normal blood sugar changes after eating is obtained from historical monitoring of the same patient, which is then used to compare abnormal blood sugar changes during the current blood sugar monitoring process. Specifically, during the historical monitoring process, the patient's normal blood sugar peak data after eating, the blood sugar rise rate during the blood sugar rise phase, and the blood sugar fall rate during the blood sugar fall phase are obtained.

[0055] It is understandable that the cycle of blood sugar changes in a person under normal conditions after eating is generally a continuous upward trend in blood sugar until it reaches a peak state, and then continues to decline and stabilizes. That is, before the peak state is a stage of continuous blood sugar rise, and after the peak state is a stage of continuous blood sugar decline. The specific method of obtaining blood sugar peak data, blood sugar rise rate, and blood sugar decline rate under normal conditions is a well-known technology and will not be introduced in detail here.

[0056] More specifically, for physiological data in other dimensions, it is also necessary to obtain standard physiological data of the patient under normal conditions. Each dimension corresponding to each category corresponds to a standard physiological data set, which is used to represent the normal range of the patient's physiological information under the corresponding dimension of that category. It is understandable that the normal value of physiological information under each dimension corresponding to a category is generally a data range. For example, under normal conditions, blood pressure is maintained within a certain blood pressure range, and under normal conditions, heart rate is also maintained within a certain heart rate range. Based on this, in this embodiment, the standard physiological data under each dimension is a range of data.

[0057] By collecting data on the patient's normal state during historical monitoring, a standard health model of the patient can be effectively constructed, which reflects the performance of various types of physiological information of the patient in a healthy state.

[0058] Step S200 , based on 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 moments.

[0059] A patient's eating cycle normally includes a blood sugar rise phase after eating, a peak point, a blood sugar recovery phase, and a blood sugar stabilization phase after a meal. However, when a patient develops diabetic complications at different stages after eating, this regular trend of blood sugar changes may be disrupted, resulting in different peak points at different stages, which is reflected in the blood sugar data at multiple moments. Based on this, by analyzing the differences in blood sugar data distribution between each moment and adjacent moments, the moments when blood sugar peaks occur are screened.

[0060] In this embodiment, the differences in adjacent blood glucose data within the time range surrounding each moment are analyzed to quantify the degree of blood glucose variation at each moment. Considering that the degree of variation in blood glucose data is minimal when the blood glucose data reaches its peak, while the degree of variation at each moment during the rising and falling phases of the blood glucose data is greater, all moments can be filtered based on the degree of blood glucose variation at each moment to determine the peak blood glucose moment. Peak blood glucose moments may occur when a patient develops diabetic complications.

[0061] Specifically, the blood glucose variation at each moment is determined based on the difference between the blood glucose data at each adjacent moment within a preset time window. The moment at which the blood glucose variation is less than or equal to a preset variation threshold is considered the blood glucose peak moment. In this embodiment, the variation threshold is set to 0.1; in other embodiments, the implementer may set this threshold based on the specific implementation scenario.

[0062] The smaller the blood sugar variation at each moment, the smaller the change in blood sugar data at that moment, and the more likely that the corresponding moment is a peak point, that is, a blood sugar peak state. The larger the blood sugar variation at each moment, the larger the change in blood sugar data at that moment, and the less likely that the corresponding moment is a peak point.

[0063] In one specific embodiment, a preset time length is obtained with each moment as the center as the time window for each moment, and the mean of the blood glucose data at all moments within each time window is used as the blood glucose characteristic value at the corresponding moment in the time window. Any moment is marked as a target moment, and 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 a data difference coefficient. The normalized value of the mean of the data difference coefficients within the time window of the target moment is used as the blood glucose change degree at the target moment.

[0064] To prevent local fluctuations in the patient's blood glucose data within a local time range from interfering with subsequent analysis results, characteristic data within each local time range, i.e., the blood glucose characteristic value, is obtained by averaging. In this embodiment, the preset time length is 11 minutes, meaning that blood glucose data is obtained for five time periods on both sides of each time point to define the time window for each time point. It is understood that if a sufficient time period cannot be obtained before each time point, no analysis will be performed.

[0065] As a specific example, taking any moment as an example, the i-th moment is taken as the target moment, and the calculation formula for the blood sugar change degree at the i-th moment, that is, the blood sugar change degree at the target moment, can be expressed as: ;in, Indicates the degree of change in blood sugar at the i-th moment, i.e., the target moment, i indicates the i-th moment, Represents the total number of moments contained in the time window of the i-th moment, represents the blood glucose characteristic value at the nth moment in the time window of the i-th moment, represents the blood glucose characteristic value at the n-1th moment in the time window of the i-th moment, is the normalization function.

[0066] It reflects the changes in blood glucose concentration at adjacent moments within the local time range of a detection moment. The smaller the average change in blood glucose concentration, the more stable the blood glucose change at the detection moment, and the more likely the detection moment is the peak point, that is, the corresponding blood glucose change value is larger.

[0067] Step S300, based on the blood sugar data distribution at each blood sugar peak moment and the rising and falling trends of the blood sugar data after eating with time, combined with the blood sugar change rate in the blood sugar standard data set, the initial abnormality level at the current moment is obtained.

[0068] Under normal circumstances, the blood sugar fluctuation cycle of patients after each meal generally includes the blood sugar rising stage, blood sugar peak and blood sugar falling stage, and the stable stage after blood sugar recovery. When patients develop diabetic complications at different stages after eating, the patient's blood sugar changes are different. Diabetic complications can cause the patient's blood sugar to rise, resulting in an additive effect.

[0069] like Figure 2 This is the first blood sugar change trend that occurs when a patient develops complications after eating under normal circumstances. Figure 2 The patient develops complications during the stage of rising blood sugar after eating, which leads to a superimposed effect on the speed of blood sugar rise, and therefore may cause abnormal phenomena in the patient's blood sugar data during the rising stage.

[0070] like Figure 3 The second blood sugar change trend is when the patient has complications after eating under normal circumstances. The patient has diabetic complications during the blood sugar drop phase after eating. Due to the interference of complications, the blood sugar data has a secondary peak phenomenon, such as Figure 3 As shown, the blood sugar data begins to drop at the peak point, and after a period of time, complications may occur, causing the blood sugar to rise again and form a second peak point.

[0071] Based on this, taking into account the multi-peak phenomenon in blood glucose data caused by diabetic complications, the patient's blood glucose change cycle is divided into three stages by analyzing the distribution of blood glucose peak moments, including the blood glucose rising stage before blood glucose reaches the peak, the peak continuation stage where the multi-peak phenomenon occurs, and the blood glucose decline recovery stage after reaching the peak.

[0072] Specifically, the time periods are divided according to the distribution of blood glucose peak moments into a blood glucose rise period, a blood glucose peak period, and a blood glucose recovery period. More specifically, when the number of time intervals between two adjacent blood glucose peak moments is less than a preset number of time intervals, the time period between the two adjacent blood glucose peak moments is considered a suspected peak period; the suspected peak period of the second blood glucose peak moment is considered a blood glucose peak period; the time period from the time of eating to the time before the blood glucose peak period is considered a blood glucose rise period, and the time period from the blood glucose peak period to the current time is considered a blood glucose recovery period.

[0073] In this embodiment, the number of preset moments is set to 5. In other embodiments, the implementer may set the number based on the specific implementation scenario. When the time interval between two adjacent blood glucose peak moments is short, it indicates that multiple peaks may occur within a short time range, which is more likely to be caused by complications.

[0074] Furthermore, during the actual monitoring process of the patient, during the blood sugar rising stage and the blood sugar recovery stage, the blood sugar changes in the actual monitoring process are compared with the blood sugar changes in the corresponding stages in the patient's standard health model, and a preliminary analysis can be made as to whether there are any data abnormalities in the patient's blood sugar rising stage and the blood sugar recovery stage during the actual monitoring process.

[0075] Specifically, for a patient's blood sugar rising period, the ratio of the difference between the blood sugar data at the last moment of the period and the moment of eating to the length of the corresponding 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 during the patient's normal blood sugar rising phase is normalized to obtain a first initial abnormality level for the blood sugar rising period. This first initial abnormality level reflects the abnormality of the data during the patient's actual blood sugar rising phase during monitoring.

[0076] For a patient's blood sugar recovery period, the ratio of the difference between the blood sugar data at the first and last moments within the blood sugar recovery period to the length of the period is used as a blood sugar decline trend factor. The difference between the blood sugar decline trend factor and the blood sugar decline rate during the patient's normal blood sugar decline phase is normalized to obtain a second initial abnormality level for the blood sugar recovery period. The initial abnormality level at the current moment includes the first initial abnormality level and the second initial abnormality level. The second initial abnormality level reflects the data abnormality during the patient's actual blood sugar recovery phase during monitoring.

[0077] As a specific strength, the calculation formula of the first initial abnormality degree and the second initial abnormality degree can be expressed as:

[0078]

[0079]

[0080] in, Indicates the first initial abnormality level during the period of blood sugar rise. Indicates the second initial abnormal process of blood sugar recovery period, Indicates the absolute value of the difference between the last moment of the blood sugar rising period and the blood sugar data at the time of eating. Indicates the length of time during which blood sugar levels rise. It represents the blood sugar rising rate during the normal blood sugar rising phase of the patient in the standard health model; Indicates the absolute value of the difference between the blood sugar data at the first moment and the last moment during the blood sugar recovery period. Indicates the length of the blood sugar recovery period, Indicates the blood sugar drop rate during the normal blood sugar drop phase of the patient in the standard health model. is the normalization function.

[0081] It is the blood sugar rising trend factor, which reflects the rate of change of blood sugar rising during the actual monitoring process. This is the blood sugar decline trend factor, reflecting the rate of change in blood sugar decline during actual monitoring. By comparing the actual rate of blood sugar change with the normal rate of a standard health model in historical records, the degree of abnormal changes in blood sugar rise and fall during actual monitoring is quantified.

[0082] A larger value for the first initial difference indicates a greater degree of data anomaly during the blood sugar rise phase, while a larger value for the second initial difference indicates a greater degree of data anomaly during the blood sugar recovery phase. In other words, the current initial anomaly level reflects the magnitude of the data anomaly, based on the data change trends during both the blood sugar rise and recovery phases.

[0083] Step S400, according to the number of blood glucose 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.

[0084] When diabetic complications occur during the rising blood sugar stage, the patient's blood sugar concentration peak will be higher and remain at the peak for a longer period of time, causing the data change trend to not enter the blood sugar recovery stage, or the blood sugar data change rate after the recovery stage will be slower than normal. Figure 4 When diabetic complications occur during the patient's blood sugar recovery phase, the patient's blood sugar data will peak again during the recovery phase, and the peak value will remain unchanged for a period of time, or recovery may occur but the recovery rate is slow, as shown in the following example. Figure 5 shown.

[0085] When there is only one blood sugar peak moment, it means that there is only one peak point in the blood sugar change cycle after the current patient eats, and it is necessary to focus on whether there is an abnormal change in blood sugar rate caused by complications during the patient's blood sugar rising stage. When there are two blood sugar peak moments, it means that there are two peak points in the blood sugar change cycle after the current patient eats, which means that the patient's actual monitoring process has experienced a phenomenon of rising and then falling. The overall trend is similar to the normal change trend, but the peak height and data decline rate may be abnormal, which is reflected in the large data fluctuation amplitude. The data change stage before the second blood sugar peak moment can be approximately regarded as the blood sugar rising stage, which means that diabetic complications have occurred in the patient's blood sugar rising stage. When the number of blood sugar peak moments is greater than 2, it means that the patient's blood sugar data change has an initial rise and then a short recovery stage, but a new peak point appears during the recovery process, which is manifested as abnormal blood sugar data fluctuations, which means that one or more peak phenomena caused by complications have occurred in the patient's blood sugar recovery stage.

[0086] Based on this, the final abnormality of blood sugar at the current moment is quantified by analyzing the distribution of the number of blood sugar peaks and the initial abnormality levels at different stages of the complication. Specifically, the sum of the first and second initial abnormality levels is used as the first abnormality coefficient; the difference between the maximum value of the blood sugar data at all blood sugar peak moments and the peak blood sugar data is used as the second abnormality coefficient.

[0087] When the total number of blood sugar peak moments is less than or equal to a preset number threshold, the difference between the number threshold and the total number of blood sugar peak moments is calculated, and the product of the difference and the second abnormality coefficient and the cumulative sum of the second abnormality coefficient are used as the blood sugar abnormality level at the current moment.

[0088] When the total number of blood sugar peak moments is greater than a preset threshold, the cumulative sum of the time length between the first blood sugar peak moment and the last blood sugar peak moment and the first abnormality coefficient and the second abnormality coefficient is used as the blood sugar abnormality degree at the current moment.

[0089] It is understandable that, after the above analysis, in this embodiment, the value of the quantity threshold is 2. As a specific example, the abnormal blood sugar level at the current moment can be expressed as:

[0090]

[0091] in, Indicates the current level of blood sugar abnormality. Indicates the first initial abnormality level during the period of blood sugar rise. Indicates the second initial abnormal process of blood sugar recovery period, Indicates the total number of blood sugar 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, Indicates the time length between the first blood glucose peak and the last blood glucose peak.

[0092] is the first abnormal coefficient, which reflects the 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 difference between the peak points of the actual monitoring process and the historical normal process.

[0093] When the number of blood sugar peak points is less than or equal to 2, it indicates that complications have occurred in the patient's blood sugar rising stage during the current actual monitoring process, and the focus is on whether there is an abnormality in the rate of change of blood sugar in the rising stage. When the number of blood sugar peak points is greater than 2, it indicates that complications have occurred in the patient's blood sugar recovery stage during the current actual monitoring process. Complications may cause the peak to appear continuously high. The longer the peak of the blood sugar data lasts and the higher the peak, the greater the corresponding abnormality, that is, the greater the value of the blood sugar abnormality. The current blood sugar abnormality level represents the magnitude and possibility of abnormalities in the blood sugar data during the patient's real-time monitoring process.

[0094] Furthermore, considering that patients may experience significant changes in physiological information such as blood pressure, heart rate, and body temperature when they develop diabetic complications, such as diabetic ketoacidosis and severe hyperglycemia, the sympathetic nervous system may be overactivated and the heart rate may rise sharply. Furthermore, diabetic ketoacidosis can also lead to fluid loss and electrolyte imbalance, which may cause a rapid increase in blood pressure. In order to obtain more accurate abnormal quantification results, the current degree of blood sugar abnormality is adjusted in combination with the degree of deviation of physiological data in other dimensions.

[0095] Based on this, 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.

[0096] Specifically, the difference 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 abnormality factor of the other dimension. The product of the current blood glucose abnormality level and the abnormality factor of the other dimension is normalized to obtain the comprehensive abnormality level at the current moment. The normalization method is well known in the art and will not be further described here.

[0097] 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 corresponding to the same dimension can be specifically as follows: Taking 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, the absolute value of the difference between the body temperature data and the upper limit value of the standard body temperature data range is calculated, that is, the greater the degree of deviation between the body temperature data and the upper limit value of the standard body temperature data range, the greater the degree of deviation of the body temperature data at the current moment from the normal value, and thus the greater the degree of abnormality 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, the absolute value of the difference between the body temperature data and the lower limit value of the standard body temperature data range is calculated, that is, the greater the degree of deviation between the body temperature data and the lower limit value of the standard body temperature data range, the greater the degree of deviation of the body temperature data at the current moment from the normal value, and thus the greater the degree of abnormality 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 of the standard body temperature data range, and less than or equal to the upper limit of the standard body temperature data range, it means that the body temperature data at the current moment is within the normal range. At this time, the abnormality degree of the physiological data of the corresponding dimension is 0, that is, the difference between the physiological data and the standard data of the same dimension is 0.

[0098] The same method can be used to obtain the corresponding abnormality degree of physiological data in other dimensions, and then the abnormalities of physiological data in all dimensions can be accumulated to obtain the abnormal changes in other types of physiological data of the patient in addition to blood sugar information.

[0099] Ultimately, the comprehensive abnormality level reflects the patient's blood sugar abnormality from multiple aspects. The larger the value of the comprehensive abnormality level, the greater the possibility that the patient's data information at the current moment is abnormal, and thus the greater the possibility that the patient will develop complications.

[0100] Furthermore, the patient's monitoring results can be determined based on the comprehensive abnormality level. Specifically, when the current comprehensive abnormality level is greater than or equal to a preset abnormality threshold, it indicates that the patient is more likely to develop complications at that moment, and it is necessary to notify the doctor and the patient's emergency contact, so that the doctor can make further judgments based on the actual situation. In this embodiment, the abnormality threshold is set to 0.8. In other embodiments, the implementer can set it according to the specific implementation scenario.

[0101] In summary, this embodiment, by integrating blood glucose data with other physiological parameters and leveraging key characteristics of blood glucose fluctuations (such as fluctuation amplitude, rate of rise, and duration of peak), can effectively distinguish normal fluctuations caused by eating from abnormal fluctuations due to diabetic complications, reducing the risk of misjudgment and missed diagnosis, and providing more reliable health monitoring support for patients. This enhances the comprehensiveness and robustness of anomaly detection and improves the monitoring method's adaptability to complex situations.

[0102] An embodiment of the present invention also provides 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. When the computer program is executed by the processor, the steps of a monitoring method for patients with endocrine and metabolic diseases are implemented.

[0103] like Figure 6 As shown, an embodiment of the present invention further provides a monitoring device for patients with endocrine metabolic diseases, which is used to implement the steps of a monitoring method for patients with endocrine metabolic diseases. The monitoring device for patients with endocrine metabolic diseases specifically includes:

[0104] The data acquisition module is used to obtain the patient's blood sugar data and other physiological data at each moment after the meal and before the current moment, as well as the patient's blood sugar standard data set and standard physiological data in other dimensions under normal conditions;

[0105] A moment screening module is used to screen all moments to obtain the blood glucose peak moment based on the difference between the blood glucose data distribution at each moment and the adjacent moments within the time neighborhood;

[0106] The abnormality analysis module is used to obtain the initial abnormality level 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 over time, combined with the blood sugar change rate in the blood sugar standard data set;

[0107] 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.

[0108] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection 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 physiological data at each moment after the meal and before the current moment, as well as the patient's blood sugar standard data set and standard physiological data in other dimensions under normal conditions; Based on 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; 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 over time, combined with the blood sugar change rate in the blood sugar standard data set, the initial abnormality level at the current moment is obtained; According to the number of blood sugar peak moments, the initial abnormality level is adjusted in combination with the patient's physiological data of other dimensions and standard physiological data to obtain a comprehensive abnormality level at the current moment, and the patient's monitoring result is determined based on the comprehensive abnormality level; The blood glucose standard data set under the normal state of the patient includes the blood glucose rising rate in the blood glucose rising stage and the blood glucose falling rate in the blood glucose falling stage as well as the blood glucose peak data; The method for obtaining the initial abnormality degree at the current moment includes: The time periods are divided according to the distribution of blood sugar peak moments to obtain 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 time of eating 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 during the blood sugar rising stage of the patient under normal conditions is normalized to obtain a first initial abnormality degree of the blood sugar rising time period; The ratio of the difference between the blood glucose data at the first moment and the last moment within the blood glucose recovery period to the length of the period is used as a blood glucose decrease trend factor, and the difference between the blood glucose decrease trend factor and the blood glucose decrease rate during the blood glucose decrease phase under the patient's normal state is normalized to obtain a second initial abnormality degree during the blood glucose recovery period; The initial abnormality level at the current moment includes the first initial abnormality level and the second initial abnormality level.

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 based on the difference between the blood glucose data distribution at each moment and the adjacent moments within the time neighborhood specifically includes: The blood glucose change degree at each moment is obtained based on the difference between the blood glucose data of each two adjacent moments within the preset time window at each moment; the moment when the blood glucose change degree is less than or equal to the preset change threshold is taken as the blood glucose 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 variation degree at each moment based on 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 within the time window of each moment is used 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 each 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 used 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 division of time periods according to the distribution of blood glucose peak moments into blood glucose rising time periods, blood glucose peak time periods and blood glucose recovery time periods specifically includes: When the number of time intervals between two adjacent blood glucose peak moments is less than the preset number of time intervals, the time period between the two adjacent blood glucose 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, and the time period from the blood glucose peak time period to the current moment is taken as the blood glucose recovery time period.

5. The method for monitoring patients with endocrine and metabolic diseases according to claim 4, characterized in that: The initial abnormality level is adjusted based on the number of blood glucose peak moments in combination with the patient's physiological data of other dimensions and standard physiological data to obtain the comprehensive abnormality level 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 peak moments is less than or equal to a preset number threshold, calculating the difference between the number threshold and the total number of blood sugar peak moments, and using the product of the difference and the second abnormality coefficient plus the cumulative sum of the second abnormality coefficient as the blood sugar abnormality level at the current moment; When the total number of blood glucose peak moments is greater than a preset number threshold, the sum of the time length between the first blood glucose peak moment and the last blood glucose peak moment, the first abnormality coefficient, and the second abnormality coefficient is used as the blood glucose abnormality level 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.

6. The method for monitoring patients with endocrine and metabolic diseases according to claim 5, characterized in that: The adjustment of the abnormal blood sugar level 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 includes: 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.

7. 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 the method for monitoring patients with endocrine and metabolic diseases as described in any one of claims 1 to 6 are implemented.

8. 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 6, wherein the device for monitoring patients with endocrine and metabolic diseases specifically comprises: The data acquisition module is used to obtain the patient's blood sugar data and other physiological data at each moment after the meal and before the current moment, as well as the patient's blood sugar standard data set and standard physiological data in other dimensions under normal conditions; A moment screening module is used to screen all moments to obtain the blood glucose peak moment based on the difference between the blood glucose data distribution at each moment and the adjacent moments within the time neighborhood; The abnormality analysis module is used to obtain the initial abnormality level 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 over 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.

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