Kidney protection method and system for precise insulin administration

Abstract

The invention discloses a kidney protection method and system for accurate insulin administration, and relates to the technical field of medical control methods, and the method comprises the steps: obtaining body position data and urination data of a target patient, and carrying out the time alignment; performing time compensation on urination intermittence according to the body position data to obtain urination data without body position influence; comparing the compensated urination data with preset standard urination rhythm data, and judging whether the urination rhythm of the patient is abnormal or not; if so, calculating an insulin clearing compensation coefficient; respectively adjusting the insulin infusion rate, the injection time interval or the injection dosage according to different threshold intervals of the coefficient; and finally, generating and sending an insulin administration instruction. The pharmaceutical composition has the beneficial effects that the insulin accumulation risk can be reduced, the hypoglycemia occurrence probability is reduced, meanwhile, the renal function is protected, kidney injury caused by drugs is avoided, stable blood glucose control and kidney protection are better considered, and the pharmaceutical composition is particularly suitable for personalized treatment requirements of elderly diabetics.

Description

Technical Field

[0001] The present invention relates to the technical field of medical control methods, and in particular to a kidney protection method and system for precise insulin administration. Background Art

[0002] Precision insulin delivery technology is an important development direction in the field of diabetes treatment. It aims to achieve effective blood sugar control while reducing drug side effects by personalizing insulin dosage and timing. In recent years, with the advancement of medical monitoring technology and data processing algorithms, insulin delivery plans have gradually developed in a dynamic and intelligent direction.

[0003] Existing technologies mostly adjust insulin dosage through continuous blood glucose monitoring and renal function indicators such as glomerular filtration rate to achieve dynamic control of blood glucose and kidney protection. These methods can effectively reduce the risk of hypoglycemia in most diabetic patients, and can appropriately adjust insulin dosage according to the patient's renal function status, with a certain degree of individual adaptability. However, for elderly diabetic patients, due to the deterioration of urination function, they often have abnormal conditions such as irregular urination time or significantly prolonged urination intervals. This abnormality can cause fluctuations in the kidney's ability to process insulin, making the clearance rate of insulin in the body unstable. Existing technologies fail to adjust insulin dosage regimens based on this characteristic of elderly diabetic patients, which can easily cause insulin accumulation or deficiency in the patient's body, increasing the risk of hypoglycemia and kidney damage in patients.

[0004] Therefore, a kidney protection method with precise insulin delivery is proposed. Summary of the Invention

[0005] In view of the above-mentioned state of the art, the present application is proposed. The embodiments of the present application provide a kidney protection method and system for precise insulin delivery, which can improve the personalization and accuracy of insulin delivery regimens for elderly diabetic patients and reduce the risk of hypoglycemia and kidney damage.

[0006] According to one aspect of the present application, a kidney protection method and system for precise insulin administration are provided, including: obtaining first body position data representing changes in the body shape of a target patient and first urination data representing the urination interval period of the target patient; time-aligning the first urination data with the first body position data; performing time compensation for the urination interval corresponding to the first urination data according to the first body position data to obtain second urination data that excludes the influence of body position; comparing the second urination data with preset standard urination rhythm data to determine whether the urination rhythm of the target patient is abnormal, and if so, calculating the insulin clearance of the target patient according to the second urination data. a clearance compensation coefficient; determining the insulin clearance compensation coefficient: if it is within a first threshold range, adjusting the insulin infusion rate of the target patient according to the clearance compensation coefficient; if it is within a second threshold range, adjusting the insulin injection time interval of the target patient according to the clearance compensation coefficient; if it is within a third threshold range, adjusting the insulin injection dose of the target patient according to the clearance compensation coefficient; wherein the first threshold range, the second threshold range and the third threshold range have no intersection and are increasing; generating an insulin administration instruction for the target patient according to the determination result of the insulin clearance compensation coefficient; and sending the insulin administration instruction to the instruction execution end.

[0007] According to another aspect of the present application, a kidney protection method and system for precise insulin administration are provided, including: a data acquisition module for acquiring first body posture data representing changes in the target patient's body shape and first urination data representing the target patient's urination interval period; a time alignment module for time-aligning the first urination data with the first body posture data; a time compensation module for time-compensating the corresponding urination interval in the first urination data according to the first body posture data to obtain second urination data that excludes the influence of body posture; a rhythm analysis module for comparing the second urination data with preset standard urination rhythm data to determine whether the target patient has an abnormal urination rhythm, and if so, calculating the target patient's urination rhythm according to the second urination data. an insulin clearance compensation coefficient for the target patient; an adjustment decision module, configured to determine the insulin clearance compensation coefficient: if it is within a first threshold range, adjusting the insulin infusion rate of the target patient according to the clearance compensation coefficient; if it is within a second threshold range, adjusting the insulin injection time interval of the target patient according to the clearance compensation coefficient; and if it is within a third threshold range, adjusting the insulin injection dose of the target patient according to the clearance compensation coefficient; wherein the first threshold range, the second threshold range, and the third threshold range have no intersection and are increasing; an instruction generation module, configured to generate an insulin administration instruction for the target patient according to the determination result of the insulin clearance compensation coefficient; and an instruction sending module, configured to send the insulin administration instruction to an instruction execution terminal.

[0008] According to another aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which implement the steps of the above-described method when executed by the processor.

[0009] According to another aspect of the present application, a computer storage medium is provided, on which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the steps of the above method are implemented.

[0010] Compared with the prior art, the kidney protection method and system using precise insulin administration according to the embodiments of the present application can: collect the first body posture data and the first urination data and perform time alignment, and eliminate body posture interference in combination with the body posture compensation model, so as to accurately identify the real urination rhythm abnormality and improve the accuracy of abnormality judgment; dynamically estimate the insulin clearance compensation coefficient, and comprehensively evaluate the insulin clearance ability based on multiple factors such as urination rhythm deviation, estimated glomerular filtration rate and body mass index; adjust the dosing regimen according to the abnormality intensity, and adopt corresponding compensation strategies for different degrees of urination abnormalities to achieve precise drug administration; adaptively update the threshold interval to improve the system sensitivity and reduce false triggering, so that the system can adapt to changes in patient status; compared with the prior art, the present invention can reduce the risk of insulin accumulation and the probability of hypoglycemia, while protecting renal function and avoiding drug-induced kidney damage, better balancing blood sugar stability control and kidney protection, and is particularly suitable for the personalized treatment needs of elderly diabetic patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0012] Figure 1 This is a flow chart of the kidney protection method for precise insulin administration of the present invention.

[0013] Figure 2 This is a flow chart of adjusting the first threshold interval, the second threshold interval, and the third threshold interval of the kidney protection method for precise insulin delivery of the present invention.

[0014] Figure 3 This is a block diagram of the kidney protection system for precise insulin delivery of the present invention.

[0015] Figure 4 The present invention is a block diagram of an electronic device. DETAILED DESCRIPTION

[0016] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0017] Application Overview

[0018] In the existing technology, the field of diabetes treatment achieves blood sugar control and kidney protection by adjusting insulin dosage through continuous blood sugar monitoring and renal function indicators. Although these methods can reduce the risk of hypoglycemia and adapt to individual differences in renal function, they do not consider the impact of abnormal urination rhythm in elderly patients on the rate of insulin clearance. Elderly patients often accumulate or lack insulin in the body due to prolonged or irregular urination intervals. The existing technology lacks a dynamic adjustment mechanism for such abnormalities, which can easily lead to the risk of hypoglycemia and kidney damage.

[0019] In order to solve the above problems, the basic concept of the present application is to identify abnormal urination rhythm in elderly patients and then dynamically adjust the drug administration parameters. In this process, the inventors found that abnormal urination rhythm is associated with changes in body position, and changes in body position may interfere with the measurement accuracy of the urination cycle. Therefore, it is necessary to synchronously collect body position and urination data to identify the true urination rhythm abnormality. So far, although the patient's true urination rhythm abnormality has been obtained, if the degree of abnormality is not divided into intervals, the adjustment strategy may be too single, resulting in mild abnormalities being over-intervened or severe abnormalities being under-adjusted, reducing the treatment accuracy and increasing the risk of hypoglycemia or kidney damage. Therefore, the inventors further proposed an interval-based progressive adjustment strategy based on the true urination rhythm abnormality, and corrected the infusion rate, interval or dose according to the difference in the degree of abnormality to achieve dynamic matching of insulin administration parameters with the patient's renal insulin clearance capacity, thereby effectively controlling blood sugar while protecting kidney function.

[0020] Exemplary Methods

[0021] Figure 1~Figure 2 The diagram shows a kidney protection method for precise insulin administration according to an embodiment of the present application, comprising the following steps:

[0022] In the first step, first body posture data indicating changes in the body shape of the target patient and first urination data indicating a urination intermittent period of the target patient are obtained.

[0023] Among them, the first body position data refers to the real-time body position change information collected by the wearable device, which can be specifically implemented by an accelerometer or a gyroscope to reflect the impact of the patient's activity status on bladder pressure.

[0024] The second step is to time-align the first urination data with the first body posture data.

[0025] Time alignment here refers to synchronously matching urination events with body position changes according to timestamps. This can be achieved through a time series interpolation algorithm to ensure the accuracy of data correlation analysis.

[0026] The third step is to perform time compensation on the urination interval corresponding to the first urination data according to the first body position data, so as to obtain the second urination data without the influence of the body position.

[0027] Among them, time compensation refers to correcting the urination cycle according to changes in body position to eliminate measurement errors. It is specifically implemented as follows: first, the second body position data within the time period corresponding to the urination interval is extracted based on the first body position data; then the second body position data is input into the preset time compensation model to obtain the time compensation value for the urination interval; finally, the time length of the urination interval is compensated according to the time compensation value.

[0028] Among them, the preset time compensation model refers to a mathematical calculation model that establishes the relationship between body position changes and urination intervals. It can be specifically implemented using a machine learning model based on a gradient boosting decision tree. The model obtains the mapping relationship between body position parameters and urination intervals through historical data training.

[0029] Specifically, within the time window corresponding to the urination interval, the posture change data sequence is synchronously extracted to ensure the spatiotemporal consistency of physiological status monitoring, and the posture data is input into a trained machine learning model. The model analyzes the nonlinear relationship between posture parameters and bladder pressure changes, and outputs the urination interval correction corresponding to the current posture state. Based on this correction, the original urination interval time is dynamically compensated. For example, when the patient maintains a supine position, resulting in a decrease in bladder pressure, the model will automatically extend the recorded urination interval time, thereby eliminating the measurement deviation caused by posture factors.

[0030] Through the above technical solution, the present application effectively eliminates the interference of body position changes on the measurement of urination interval time, accurately restores the patient's true urination rhythm characteristics, and solves the problem of urination interval measurement distortion caused by body position differences through a dynamic compensation mechanism, providing a reliable data basis for subsequent accurate judgment of urination rhythm abnormalities.

[0031] The fourth step is to compare the second urination data with the preset standard urination rhythm data to determine whether the target patient has an abnormal urination rhythm. If so, the insulin clearance compensation coefficient of the target patient is calculated based on the second urination data.

[0032] Among them, the insulin clearance compensation coefficient reflects the degree to which the patient's kidney's ability to clear insulin deviates from the standard level. Its purpose is to identify whether the patient's ability to clear insulin is abnormal based on factors such as individual urination rhythm, body position interference, and estimated glomerular filtration rate, thereby providing a quantitative reference for adjusting the dosing strategy. The calculation process is as follows: first, obtain the estimated glomerular filtration rate and body mass index of the target patient; then calculate the average duration and variance of each urination interval cycle in the second urination data in the current time period; next, calculate the relative difference between the average duration and variance and the baseline average duration and baseline variance of the preset standard urination rhythm data to obtain the urination rhythm deviation; finally, input the urination rhythm deviation, glomerular filtration rate and body mass index into the preset mapping function based on multivariate linear regression training to obtain the insulin clearance compensation coefficient.

[0033] Among them, the estimated glomerular filtration rate refers to an indicator reflecting the kidney's filtration function calculated by serum creatinine value, age, gender and race. It can be specifically implemented by the CKD-EPI formula and is used to evaluate the kidney's ability to metabolize insulin. Body mass index refers to the ratio of weight to height squared, which can be specifically calculated by dividing weight by height squared, and is used to reflect the patient's body characteristics and drug distribution volume. Mean duration and variance refer to the statistics of the length of the urination interval cycle and the degree of fluctuation. Specifically, the sliding window method can be used to calculate the arithmetic mean and standard deviation square of all urination intervals in the current time period, which is used to quantify the stability of the urination rhythm. The urination rhythm deviation refers to the relative difference between the actual urination interval statistic and the standard value. It can be obtained by calculating the weighted sum of the mean duration difference percentage and the variance difference percentage, and is used to dynamically evaluate the impact of abnormal urination function on insulin clearance. The preset mapping function of multivariate linear regression training refers to establishing a mathematical relationship model between urination rhythm parameters, renal function indicators, body shape parameters and insulin clearance rate through historical data. Specifically, the least squares method can be used to fit the regression coefficients of each variable to comprehensively calculate the insulin clearance compensation coefficient based on multiple factors.

[0034] Specifically, the patient's abnormal urination rhythm is manifested as unstable or significantly prolonged urination intervals, which affects the kidney's efficiency in clearing insulin. By obtaining the glomerular filtration rate, the state of renal metabolic function can be accurately assessed, and combined with the body mass index, the difference in the distribution volume of the drug in the body can be corrected. Calculating the average duration and variance of the urination interval can extract urination cycle characteristics from the time series, which can better reflect the severity of the rhythm disorder than simply counting the number of urinations. Calculating the difference between the actual urination data and the standard rhythm data can eliminate the baseline deviation caused by individual physiological differences and obtain a dynamically changing deviation index. Finally, the physiological parameters, morphological parameters and rhythm parameters are fused through a multivariate linear regression model to obtain an accurate insulin clearance compensation coefficient under the combined action of multiple factors.

[0035] More specifically, the calculation formula for the insulin clearance compensation coefficient is: ,in, is the regression coefficient obtained from the training of the multiple linear regression model, The estimated glomerular filtration rate is used to reflect the renal filtration function. is the body mass index, is the deviation from the average duration of urination interval, is the deviation of the urination interval variance.

[0036] Through the above technical solution, the present application can accurately quantify the impact of abnormal urination rhythm on insulin clearance, dynamically adjust the compensation coefficient based on the patient's renal function status and body characteristics, provide an accurate basis for the formulation of personalized dosing plans, effectively avoid insulin accumulation or deficiency caused by urination rhythm disorders, and reduce the risk of hypoglycemia and kidney damage.

[0037] The fifth step is to determine the insulin clearance compensation coefficient: if it is in the first threshold interval, the insulin infusion rate of the target patient is adjusted according to the clearance compensation coefficient; if it is in the second threshold interval, the insulin injection time interval of the target patient is adjusted according to the clearance compensation coefficient; if it is in the third threshold interval, the insulin injection dose of the target patient is adjusted according to the clearance compensation coefficient; wherein, the first threshold interval, the second threshold interval and the third threshold interval have no intersection and are increasing.

[0038] Through the above technical solution, the present application can achieve precise optimization of insulin administration parameters by adopting a differentiated adjustment strategy based on the degree of deviation of the renal clearance ability reflected by the insulin clearance compensation coefficient. When the compensation coefficient is low, only the infusion rate is adjusted to achieve rapid fine-tuning; when it is in the medium range, the injection time interval is adjusted to maintain stable drug efficacy; when the degree of deviation is large, the injection dose is directly corrected to prevent the risk of hypoglycemia caused by drug accumulation. This solution avoids parameter adjustment conflicts by dividing the compensation coefficient into non-overlapping increasing intervals, so that insulin administration is dynamically matched with the patient's current metabolic state, thereby improving the safety and individualization of blood sugar control and reducing the risk of complications caused by fluctuations in renal function.

[0039] Specifically, if the insulin clearance compensation coefficient is in the first threshold interval, it means that the kidney's ability to clear insulin is slightly abnormal. At this time, fine-tuning can bring the body's insulin homeostasis back to the target interval; if the insulin clearance compensation coefficient is in the second threshold interval, it means that the kidney's ability to clear insulin is moderately abnormal. At this time, the insulin injection interval needs to be adjusted to maintain steady state; if the insulin clearance compensation coefficient is in the third threshold interval, it means that the kidney's ability to clear insulin is severely abnormal. At this time, the amount of insulin injection needs to be directly reduced, giving priority to ensuring safety and preventing hypoglycemia and nephrotoxicity.

[0040] Among them, adjusting the insulin infusion rate of the target patient includes: first, obtaining the basal insulin infusion rate of the target patient; then, mapping the insulin clearance compensation coefficient to the infusion adjustment coefficient through a preset first mapping function; finally, calculating the adjusted insulin infusion rate of the target patient according to the infusion adjustment coefficient, and the adjusted insulin infusion rate is the product of the infusion adjustment coefficient and the basal infusion rate.

[0041] Among them, adjusting the insulin injection time interval of the target patient includes: first, obtaining the basal insulin injection time interval of the target patient; then, mapping the insulin clearance compensation coefficient into the injection interval correction amount through a preset second mapping function; finally, calculating the adjusted insulin injection time interval of the target patient based on the injection interval correction amount, and the adjusted insulin injection time interval is the sum of the injection interval correction amount and the basal insulin injection time interval.

[0042] Among them, adjusting the insulin injection dose of the target patient includes: first, obtaining the basal insulin injection dose of the target patient; then, mapping the insulin clearance compensation coefficient to a dose adjustment coefficient through a preset third mapping function; finally, calculating the adjusted insulin injection dose of the target patient based on the dose adjustment coefficient, and the adjusted insulin injection dose is the product of the dose adjustment coefficient and the basal insulin injection dose.

[0043] In the above scheme, the basal insulin infusion rate refers to the routine infusion rate when the patient does not have abnormal urination rhythm, which can be determined through historical treatment records or clinical evaluations and used to establish an individualized adjustment benchmark; the basal insulin injection time interval refers to the insulin administration cycle set in the patient's original treatment plan, which can be obtained through medical records or doctor's order information; the basal insulin injection dose refers to the insulin dose benchmark value used in the patient's current clinical plan, which can be obtained through the electronic medical record system or insulin pump historical data as a benchmark reference for dose adjustment; the first mapping function, the second mapping function and the third mapping function are mathematical models that convert compensation coefficients into rate adjustment parameters, which can be implemented using piecewise linear functions or exponential functions, and are used to quantify the correspondence between physiological parameters and treatment parameters.

[0044] Step 6: Generate insulin administration instructions for the target patient based on the determination result of the insulin clearance compensation coefficient.

[0045] Step 7: Send the insulin administration instruction to the instruction execution terminal.

[0046] Through the above technical solution, the present application achieves interval-based progressive drug administration control based on actual urination rhythm abnormalities, so that the insulin infusion rate, injection interval, and dose can be dynamically matched to the patient's current renal clearance capacity, thereby effectively reducing the risk of hypoglycemia and kidney damage while controlling blood sugar. However, if a static fixed threshold is still used when determining abnormalities, compensation may be falsely triggered when encountering short-term noise, and may be delayed in response to continuous trend drift, resulting in excessive or insufficient adjustment, which will weaken the accuracy and safety of the solution. Therefore, based on the above solution, the present application also proposes that before determining the insulin clearance compensation coefficient, the following steps are also included: performing a sliding window linear regression calculation on the second urination data according to a preset window size to obtain the trend slope and volatility corresponding to the second urination data in each sliding window; determining whether the trend slope is consistent with the preset reference trend direction and whether the volatility is greater than a preset volatility threshold; if so, adjusting the first threshold interval, the second threshold interval, and the third threshold interval upward based on the preset amplitude threshold; if not, restoring the first threshold interval, the second threshold interval, and the third threshold interval to their initial preset values.

[0047] Specifically, by introducing a sliding window linear regression mechanism, local trend analysis is performed on the second urination data over time, enabling real-time capture of continuous deviations or periodic fluctuations in a patient's urination rhythm. For example, if a patient's recent urination interval has been increasing, indicating a gradual decline in renal clearance, maintaining the initial fixed threshold intervals may delay adjustment due to the compensation coefficient failing to cross the threshold, leading to the risk of insulin accumulation. Conversely, if the data exhibits short-term strong fluctuations but lacks a clear trend, premature adjustment may lead to dose perturbations. Therefore, a dual determination mechanism based on trend slope and volatility is used to determine whether there is a directional rhythmic abnormality. Based on this, the boundaries of the three threshold intervals are dynamically adjusted upwards, simultaneously raising the thresholds for triggering compensation coefficient adjustments, thus avoiding the problem of triggering adjustments even with slight deviations. When the trend or fluctuation subsides, the initial threshold is automatically restored, ensuring that the adjustment mechanism maintains a balance between sensitivity and stability. This approach enhances the algorithm's adaptability to long-term trends in an individual's urination rhythm, further improving the dynamic robustness and physiological fit of the dosing strategy.

[0048] Exemplary Systems

[0049] Figure 3The diagram shows a kidney protection system for precise insulin delivery according to an embodiment of the present application, comprising: a data acquisition module for acquiring first body posture data representing changes in the target patient's body shape and first urination data representing the target patient's urination interval period; a time alignment module for time-aligning the first urination data with the first body posture data; a time compensation module for time-compensating the corresponding urination interval in the first urination data according to the first body posture data to obtain second urination data that excludes the influence of body posture; a rhythm analysis module for comparing the second urination data with preset standard urination rhythm data to determine whether the target patient has an abnormal urination rhythm. If so, the target patient's urination rhythm is calculated based on the second urination data. an insulin clearance compensation coefficient; an adjustment decision module, configured to determine the insulin clearance compensation coefficient: if the insulin clearance compensation coefficient is within a first threshold range, adjusting the insulin infusion rate of the target patient according to the clearance compensation coefficient; if the insulin clearance compensation coefficient is within a second threshold range, adjusting the insulin injection time interval of the target patient according to the clearance compensation coefficient; and if the insulin clearance compensation coefficient is within a third threshold range, adjusting the insulin injection dose of the target patient according to the clearance compensation coefficient; wherein the first threshold range, the second threshold range, and the third threshold range have no intersection and are increasing; an instruction generation module, configured to generate an insulin administration instruction for the target patient according to the determination result of the insulin clearance compensation coefficient; and an instruction sending module, configured to send the insulin administration instruction to the instruction execution terminal.

[0050] In one example, the time compensation module performs time compensation for the corresponding urination interval in the first urination data based on the first body position data, including: extracting second body position data within the time period corresponding to the urination interval based on the first body position data; inputting the second body position data into a preset time compensation model to obtain a time compensation value for the urination interval; and performing time compensation for the time length of the urination interval based on the time compensation value.

[0051] In one example, the rhythm analysis module calculates the insulin clearance compensation coefficient of the target patient based on the second urination data, including: obtaining the estimated glomerular filtration rate and body mass index of the target patient; calculating the average duration and variance of each urination interval cycle in the second urination data in the current time period; calculating the relative difference between the average duration and variance and the baseline average duration and baseline variance of the preset standard urination rhythm data to obtain the urination rhythm deviation; inputting the urination rhythm deviation, glomerular filtration rate and body mass index into a preset mapping function based on multivariate linear regression training to obtain the insulin clearance compensation coefficient.

[0052] In one example, the adjustment decision module adjusts the insulin infusion rate of the target patient according to the clearance compensation coefficient, including: obtaining the basal insulin infusion rate of the target patient; mapping the insulin clearance compensation coefficient to an infusion adjustment coefficient through a preset first mapping function; and calculating the adjusted insulin infusion rate of the target patient according to the infusion adjustment coefficient, wherein the adjusted insulin infusion rate is the product of the infusion adjustment coefficient and the basal infusion rate.

[0053] In one example, the adjustment decision module adjusts the insulin injection time interval of the target patient according to the clearance compensation coefficient, including: obtaining the basal insulin injection time interval of the target patient; mapping the insulin clearance compensation coefficient to an injection interval correction amount through a preset second mapping function; and calculating the adjusted insulin injection time interval of the target patient according to the injection interval correction amount, the adjusted insulin injection time interval being the sum of the injection interval correction amount and the basal insulin injection time interval.

[0054] In one example, the adjustment decision module adjusts the target patient's insulin injection dose according to the clearance compensation coefficient, including: obtaining the target patient's basal insulin injection dose; mapping the insulin clearance compensation coefficient to a dose adjustment coefficient through a preset third mapping function; and calculating the target patient's adjusted insulin injection dose according to the dose adjustment coefficient, the adjusted insulin injection dose being the product of the dose adjustment coefficient and the basal insulin injection dose.

[0055] Exemplary electronic devices

[0056] Figure 4 The figure shows an electronic device according to an embodiment of the present application. The electronic device can be the mobile device itself, or a stand-alone device independent of the mobile device, which can communicate with the mobile device to receive collected input signals from the mobile device and send the selected target driving behavior to the mobile device.

[0057] Figure 4 The figure shows a block diagram of an electronic device according to an embodiment of the present application.

[0058] like Figure 4 As shown, the electronic device includes one or more processors and memory.

[0059] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0060] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the driving behavior decision-making method of each embodiment of the present application described above and / or other desired functions.

[0061] In one example, the electronic device may further include an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0062] Of course, to simplify, Figure 4 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0063] Exemplary computer-readable media

[0064] An embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor executes the steps of the driving behavior decision-making method according to various embodiments of the present application described in the above “Exemplary Method” section of this specification.

[0065] Computer-readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or components, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0066] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0067] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0068] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0069] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0070] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for protecting kidneys by precise insulin administration, characterized in that: include: Acquiring first body posture data representing a change in the target patient's body shape and first urination data representing a urination interval period of the target patient; Time-aligning the first urination data with the first body position data; Performing time compensation on the urination interval corresponding to the first urination data according to the first body posture data to obtain second urination data excluding the influence of body posture; comparing the second urination data with preset standard urination rhythm data to determine whether the target patient has an abnormal urination rhythm, and if so, calculating the insulin clearance compensation coefficient of the target patient based on the second urination data; Determine the insulin clearance compensation coefficient: If it is within the first threshold range, adjusting the insulin infusion rate of the target patient according to the clearance compensation coefficient; If it is within the second threshold range, adjusting the insulin injection time interval of the target patient according to the clearance compensation coefficient; If it is within the third threshold range, adjusting the insulin injection dose of the target patient according to the clearance compensation coefficient; Wherein, the first threshold interval, the second threshold interval and the third threshold interval have no intersection and are increasing; generating an insulin administration instruction for the target patient according to a determination result of the insulin clearance compensation coefficient; and The insulin administration instruction is sent to the instruction execution end.

2. The method for kidney protection by precise insulin delivery according to claim 1, characterized in that: Before determining the insulin clearance compensation coefficient, the method further includes: performing a sliding window linear regression calculation on the second urination data according to a preset window size to obtain a trend slope and a volatility corresponding to the second urination data in each sliding window; Determining whether the trend slope is consistent with a preset reference trend direction and whether the fluctuation is greater than a preset fluctuation threshold; If so, the first threshold interval, the second threshold interval, and the third threshold interval are adjusted upward based on the preset amplitude threshold; If not, the first threshold interval, the second threshold interval and the third threshold interval are restored to initial preset values.

3. The method for kidney protection by precise insulin administration according to claim 1, characterized in that: The compensating the urination interval corresponding to the first urination data according to the first body position data includes: extracting second body position data within a time period corresponding to the urination interval according to the first body position data; Inputting the second body position data into a preset time compensation model to obtain a time compensation value for the urination interval; and The time length of the urination pause is time compensated according to the time compensation value.

4. The method for kidney protection by precise insulin delivery according to claim 1, characterized in that: Calculating the insulin clearance compensation coefficient of the target patient according to the second urination data includes: obtaining the estimated glomerular filtration rate and body mass index of the target patient; Calculating the average duration and variance of each urination interval period in the second urination data within the current time period; Calculating the relative differences between the average duration and variance and the baseline average duration and baseline variance of the preset standard urination rhythm data to obtain a urination rhythm deviation; The urination rhythm deviation, glomerular filtration rate and body mass index are input into a preset mapping function based on multiple linear regression training to obtain the insulin clearance compensation coefficient.

5. The kidney protection method for precise insulin administration according to claim 1 or 4, characterized in that: The adjusting the insulin infusion rate of the target patient according to the clearance compensation coefficient comprises: Obtaining the basal insulin infusion rate of the target patient; Mapping the insulin clearance compensation coefficient to an infusion adjustment coefficient by a preset first mapping function; The adjusted insulin infusion rate of the target patient is calculated according to the infusion adjustment coefficient, and the adjusted insulin infusion rate is the product of the infusion adjustment coefficient and the basal infusion rate.

6. The method for kidney protection by precise insulin delivery according to claim 5, characterized in that: The adjusting the insulin injection time interval of the target patient according to the clearance compensation coefficient includes: Obtaining the basal insulin injection time interval of the target patient; Mapping the insulin clearance compensation coefficient to an injection interval correction value by a preset second mapping function; The adjusted insulin injection time interval of the target patient is calculated according to the injection interval correction amount, and the adjusted insulin injection time interval is the sum of the injection interval correction amount and the basal insulin injection time interval.

7. The method for kidney protection by precise insulin delivery according to claim 6, characterized in that: The step of adjusting the insulin injection dose of the target patient according to the clearance compensation coefficient comprises: Obtaining the basal insulin injection dose of the target patient; Mapping the insulin clearance compensation coefficient to a dosage adjustment coefficient by a preset third mapping function; The adjusted insulin injection dose of the target patient is calculated according to the dose adjustment coefficient, and the adjusted insulin injection dose is the product of the dose adjustment coefficient and the basal insulin injection dose.

8. A kidney protection system for precise insulin delivery, characterized by: include: a data acquisition module, configured to acquire first body posture data representing a change in the target patient's body shape and first urination data representing a urination interval period of the target patient; a time alignment module, configured to perform time alignment on the first urination data and the first body position data; a time compensation module, configured to perform time compensation on the urination interval corresponding to the first urination data according to the first body position data, to obtain second urination data excluding the influence of body position; a rhythm analysis module, configured to compare the second urination data with preset standard urination rhythm data to determine whether the target patient has an abnormal urination rhythm, and if so, calculate an insulin clearance compensation coefficient for the target patient based on the second urination data; The adjustment decision module is used to determine the insulin clearance compensation coefficient: If it is within the first threshold range, adjusting the insulin infusion rate of the target patient according to the clearance compensation coefficient; If it is within the second threshold range, adjusting the insulin injection time interval of the target patient according to the clearance compensation coefficient; If it is within the third threshold range, adjusting the insulin injection dose of the target patient according to the clearance compensation coefficient; Wherein, the first threshold interval, the second threshold interval and the third threshold interval have no intersection and are increasing; an instruction generating module, configured to generate an insulin administration instruction for the target patient according to a determination result of the insulin clearance compensation coefficient; The instruction sending module is used to send the insulin administration instruction to the instruction execution end.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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