Whole-course health monitoring method and device for hemodialysis patient and storage medium

Abstract

The invention relates to the technical field of health monitoring, and discloses a whole-course health monitoring method and device for a hemodialysis patient and a storage medium. The method comprises the following steps: acquiring historical dialysis data, basic information and initial physiological data of a patient, and inputting a first preset model to predict dialysis parameters; in the dialysis process, human body dynamic parameters, physiological state data and hemodialysis data are collected according to a preset period; performing statistical analysis on the collected data, judging whether dialysis parameters need to be adjusted or not, and if yes, generating new dialysis parameters and continuing to execute the dialysis process; after dialysis is completed, based on historical dialysis data and dialysis monitoring data, a staged purification evaluation value is generated, and reminding information is generated in combination with historical behavior habit data; and sending the evaluation value and the reminding information to the patient terminal. According to the technical scheme, precise monitoring and personalized management of the whole dialysis process are achieved, and the intelligent level of health monitoring is improved.

Description

Technical Field

[0001] This application relates to the technical field of health monitoring, and particularly to a method, device, and storage medium for the whole-process health monitoring of hemodialysis patients. Background Art

[0002] With the aging of the population and the increase in chronic diseases, the number of hemodialysis patients is also increasing year by year. Hemodialysis is one of the main treatment methods for patients with end-stage renal disease (ESRD), aiming to replace the filtration function of the kidneys and remove metabolic wastes and excess water from the body. During the dialysis treatment process, the physiological state of patients will change dynamically. Real-time monitoring of these changes is of great significance for timely detecting potential problems and optimizing treatment plans. At the same time, hemodialysis patients need to receive long-term treatment, and there are problems such as poor treatment compliance and low self-management level among patients. Information management and patient education during the treatment process are crucial. With the development of artificial intelligence, big data, and Internet of Things technologies, the medical field has begun to explore the application of these technologies in the health management of hemodialysis patients.

[0003] A similar prior art is the Chinese patent application with the publication number CN118749932A, which discloses a method and system for monitoring patients' health data during hemodialysis. It obtains blood pressure data at the first monitoring time point, and issues an alarm and executes blood pressure abnormality handling measures when the blood pressure is abnormal; obtains heart rate data at the current second monitoring time point, and issues an alarm and executes heart rate abnormality handling measures when it is determined that the heart rate is abnormal within the current analysis time period at the preset analysis time point; for each analysis time point, calculates the short-term heart rate fluctuation degree value and the long-term heart rate fluctuation degree value respectively, and sets the first adjustment threshold and the second adjustment threshold based on the long-term heart rate fluctuation degree value; if the short-term heart rate fluctuation degree value is greater than the first adjustment threshold and less than the second adjustment threshold, executes the frequency adjustment measure; if the short-term heart rate fluctuation degree value is greater than the second adjustment threshold, on the basis of executing the frequency adjustment measure, obtains the heart rate data prediction set, inputs the heart rate data prediction set into the heart rate prediction model to obtain the predicted heart rate data set, and issues an alarm when there is an abnormality in the predicted heart rate data set, and executes the heart rate abnormality handling measures. This method only monitors blood pressure and heart rate, does not cover comprehensive physiological data, and lacks dynamic adjustment of dialysis parameters and overall health management. There is also the Chinese patent application with the publication number CN118737473A, which discloses a method and system for monitoring patients' health based on hemodialysis data analysis. It collects patients' body data and hemodialysis data, analyzes the correlation values between the data to construct a correlation data set; performs clustering calculations through a clustering algorithm according to the patients' correlation data set, and analyzes the patient characteristic set data of different clusters; based on the patient characteristic set data of different clusters, analyzes the patients' body data and calculates the improvement value, predicts the patients' health score, analyzes the improvement value to judge the patients' body conditions, and adjusts the improvement suggestions according to the patients' body conditions; performs data encryption transmission based on different clusters of patients, generates a report and sends the report. This method does not perform precise analysis on nutritional intake and lacks personalized guidance for patients.

[0004] Therefore, it is an urgent problem to provide a method, device and storage medium for the whole-process health monitoring of hemodialysis patients to improve the comprehensiveness, precision and personalization of health monitoring. Summary of the Invention

[0005] This application provides a method, device and storage medium for the whole-process health monitoring of hemodialysis patients. By real-time monitoring and dynamically adjusting dialysis parameters, combined with patients' behavior habit data, it realizes precise monitoring and personalized management throughout the dialysis process, improves the comprehensiveness and personalization of health monitoring while improving the dialysis precision, realizes intelligent and whole-process management, and enhances the treatment experience.

[0006] In the first aspect, this application provides a method for the whole-process health monitoring of hemodialysis patients, and the method includes:

[0007] Step 1: Obtain the patient's historical dialysis data, basic information, and initial physiological data. Input the historical dialysis data, basic information, and initial physiological data into the first preset model to predict the patient's dialysis parameters and obtain the dialysis parameters.

[0008] Step 2: Execute the dialysis process based on the dialysis parameters. Collect the human dynamic parameters, physiological state data, and hemodialysis data during the dialysis process at preset intervals.

[0009] Step 3: Conduct statistical analysis on the hemodialysis data at each collection time point within the first preset time to determine whether the dialysis parameters need to be adjusted. If so, generate new dialysis parameters and return to Step 2. If not, proceed to Step 4.

[0010] Step 4: Determine whether the collection volume of the physiological state data in the current monitoring stage reaches the first preset value. If not, return to Step 2. If so, conduct statistical analysis on the physiological state data and hemodialysis data to determine whether the dialysis parameters need to be adjusted. If so, generate new dialysis parameters and return to Step 2, and at the same time start the next monitoring stage. If not, return to Step 2.

[0011] Step 5: After dialysis is completed, obtain the dialysis monitoring data for the current treatment course. Generate a phased purification evaluation value based on the historical dialysis data and dialysis monitoring data, and obtain the historical behavior habit data within the second preset time. Generate a reminder message based on the initial physiological data and historical behavior habit data. The dialysis monitoring data includes basic information, initial physiological data, physiological state data, and hemodialysis data.

[0012] Step 6: Send the phased purification evaluation value and the reminder message to the patient terminal.

[0013] Combined with the first aspect, in the first implementation manner of the first aspect of the present application, the physiological state data includes the first type of state data that can be directly obtained and the second type of state data that is indirectly obtained. The obtaining steps of the second type of state data include:

[0014] For any second type of state data, extract the first type of state data related to any second type of state data and define it as the reference state data. Preprocess the reference state data, input the preprocessing result into the corresponding second preset model, and obtain the initial value of any second type of state data. Subsequently, determine whether the human dynamic parameter is greater than the preset threshold. If so, extract the historical state data within the third preset time, and use the average value of the historical state data as the value of any second type of state data. If not, use the initial value as the value of any second type of state data.

[0015] In combination with the first aspect, in the second implementation manner of the first aspect of the present application, the hemodialysis data includes the volume of body fluid flowing into the dialysis device within a preset period, and step 3 includes:

[0016] Calculate the difference between the volume of body fluid at the current time point and the volume of body fluid at the first time point, which is defined as the flow difference, calculate the ratio of the flow difference to the preset period, and define it as the first ratio. Subsequently, calculate the difference between the first ratio at the current time point and the first ratio at the first time point, and define it as the first change amount. When the first change amount exceeds the preset range, adjust the dialysis parameters, where the first time point is the previous collection time point of the current time point.

[0017] In combination with the first aspect, in the third implementation manner of the first aspect of the present application, step 4 includes:

[0018] Calculate the difference between the volume of body fluid at any time point and the second time point within the current monitoring stage, which is defined as the flow difference, where the second time point is the previous collection time point of any time point;

[0019] Extract any physiological state data, calculate the difference between the state values of any physiological state data at any time point and the second time point within the current monitoring stage, and define it as the state difference;

[0020] Extract all the flow differences and all the state differences within the current monitoring stage, combine the flow difference and the state difference at the same collection time point together to form a data point in the state-flow space, and calculate the geometric center of all the data points in the state-flow space;

[0021] Based on the similarity between the data points, divide all the data points into multiple arrays, define the data points in the array as array data points, extract any array, calculate the average position of all the array data points in any array, and calculate the first relative distance from any array data point in any array to the average position, and define the maximum value of the first relative distance as the first distance;

[0022] After traversing all the arrays, calculate the second relative distance between each average position and the geometric center point respectively. Subsequently, determine whether all the first distances are less than or equal to the second preset value and all the second relative distances are less than or equal to the third preset value. If not, it is determined that the dialysis parameters need to be adjusted. If so, it is determined that the dialysis parameters do not need to be adjusted.

[0023] In combination with the first aspect, in the fourth implementation manner of the first aspect of the present application, the hemodialysis data includes the volume of body fluid flowing into the dialysis device within a preset period, and the historical dialysis data and the dialysis monitoring data include the first solute concentration and the second solute concentration of a preset solute before and after dialysis treatment, the single operation time of the dialysis device, and the dialysis treatment time. The steps for step 5 to generate the stage purification evaluation value include:

[0024] Extract the historical dialysis data within the fourth preset time, which is defined as the first dialysis data, and define the dialysis treatment process corresponding to the dialysis monitoring data and the first dialysis data as the reference treatment process;

[0025] Extract any reference treatment process, calculate the total body fluid volume during the entire treatment process, define the ratio of the total body fluid volume to the single operation time as the second ratio, perform a first predetermined algorithm on the first solute concentration, the second solute concentration, and the second ratio to generate a renal function index value, and then generate a feature vector of any reference treatment process. The feature vector includes the renal function index value, the single operation time, the dialysis treatment time, and basic information;

[0026] Construct a third preset model for describing the absorption, distribution, metabolism, and excretion processes of solutes in the body, input all feature vectors into the third preset model, obtain the maximum solute concentration value, and perform a second predetermined algorithm on the maximum solute concentration value to generate a phased purification evaluation value.

[0027] Combined with the first aspect, in the fifth implementation manner of the first aspect of the present application, the basic information includes body weight. Performing a second predetermined algorithm on the maximum solute concentration value to generate a phased purification evaluation value includes:

[0028] Calculate the interval time between any two adjacent reference treatment processes based on the dialysis treatment time, and then calculate the first standard deviation, the second standard deviation, and the third standard deviation of the single operation time, the interval time, and the body weight in the reference treatment process respectively;

[0029] Perform a weighted average on the first standard deviation, the second standard deviation, and the third standard deviation to obtain a comprehensive fluctuation evaluation value;

[0030] Construct a negative correlation function, input the comprehensive fluctuation evaluation value into the negative correlation function to obtain a compensation parameter, and adjust the evaluation value obtained by performing the second predetermined algorithm based on the compensation parameter to generate a phased purification evaluation value.

[0031] Combined with the first aspect, in the sixth implementation manner of the first aspect of the present application, generating a reminder message based on the initial physiological data and the historical behavior habit data includes:

[0032] Extract the component type and total intake amount of each nutrient component ingested by the patient within the second preset time from the historical behavior habit data, compare the total intake amount of each nutrient component with the corresponding recommended total intake amount respectively, identify the nutrient components with insufficient or excessive intake, and define them as components to be analyzed;

[0033] Extract the biochemical index data in the initial physiological data, compare the biochemical index data with the corresponding standard range, calculate the degree of kidney damage based on the comparison result, and then perform a third predetermined algorithm on the degree of kidney damage to generate a body index value;

[0034] Obtain the historical contribution rates of all components to be analyzed, accumulate all the historical contribution rates to obtain an accumulated value, update the historical contribution rate of any component to be analyzed based on the accumulated value, obtain the current contribution rate, and use the product of the current contribution rate and the body index value as the current contribution value of any component to be analyzed;

[0035] Obtain the current contribution value of any nutrient component and all historical contribution values within the fifth preset time, and define the average value of all historical contribution values and the current contribution value as the comprehensive contribution value of any nutrient component;

[0036] Generate a reminder message based on the current contribution value and the contribution index value.

[0037] In a second aspect, the present application provides a device for full-course health monitoring of hemodialysis patients, and the device includes:

[0038] A parameter setting module, configured to obtain the patient's historical dialysis data, basic information, and initial physiological data, input the historical dialysis data, basic information, and initial physiological data into a first preset model to predict the patient's dialysis parameters, and obtain the dialysis parameters;

[0039] A data acquisition module, configured to perform a dialysis process according to the dialysis parameters, and collect human dynamic parameters, physiological state data, and hemodialysis data during the dialysis process at a preset period;

[0040] A first judgment module, configured to perform statistical analysis on the hemodialysis data at each collection time point within the first preset time, judge whether it is necessary to adjust the dialysis parameters, if so, generate new dialysis parameters and return them to the data acquisition module, if not, enter the second judgment module;

[0041] A second judgment module, configured to judge whether the acquisition amount of the physiological state data in the current monitoring stage reaches a first preset value, if not, return to the data acquisition module, if so, perform statistical analysis on the physiological state data and the hemodialysis data, judge whether it is necessary to adjust the dialysis parameters, if so, generate new dialysis parameters and return them to the data acquisition module, and at the same time start the next monitoring stage, if not, return to the data acquisition module;

[0042] A notification generation module, configured to, after the dialysis is completed, obtain the dialysis monitoring data of the current treatment course, generate a stage purification evaluation value based on the historical dialysis data and the dialysis monitoring data, and obtain the historical behavior habit data within the second preset time, and generate a reminder message based on the initial physiological data and the historical behavior habit data, where the dialysis monitoring data includes basic information, initial physiological data, physiological state data, and hemodialysis data;

[0043] A notification sending module, configured to send the stage purification evaluation value and reminder information to the patient terminal.

[0044] The third aspect of the present application provides a computer-readable storage medium, in which instructions are stored. When the instructions run on a computer, the computer is caused to execute the above-mentioned method for whole-course health monitoring of hemodialysis patients.

[0045] Compared with the prior art, the beneficial effects of the technical solution of the present application are at least as follows:

[0046] 1. During the dialysis process, a phased dynamic adjustment mechanism including short-term and medium-term is set. The short-term judgment can quickly respond to emergencies and avoid acute adverse reactions of patients during dialysis; the medium-term judgment can ensure the rationality of dialysis parameters during the entire monitoring stage based on more comprehensive data analysis, reduce the risk of chronic complications. Through the dual judgment of short-term and medium-term, the physiological changes of patients during dialysis can be captured more accurately, avoiding inaccurate parameter adjustment caused by uncaught short-term fluctuations or long-term trends, ensuring that the dialysis process better meets the individual needs of patients, and improving the purification effect and overall treatment effect of dialysis.

[0047] 2. Generating a stage purification evaluation value based on historical dialysis data and dialysis monitoring data can comprehensively and objectively reflect the purification effect of patients during dialysis, provide more accurate treatment effect feedback for patients and medical staff, and provide a scientific basis for subsequent treatment and health management.

[0048] 3. By analyzing the historical behavior habit data and initial physiological data of patients, identify bad behavior habits that may lead to health problems and generate reminder information. The reminder information based on the individual behavior of patients can help patients better manage their behavior habits, improve treatment compliance, thereby improving the quality of life and long-term prognosis of patients, preventing potential health risks, and reducing the occurrence of complications. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 It is a schematic diagram of an embodiment of a method for whole-course health monitoring of hemodialysis patients in an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of an embodiment of dialysis parameter adjustment judgment in an embodiment of the present application;

[0052] Figure 3 This is a schematic diagram of an embodiment of the method for generating the phased purification evaluation value in the embodiment of the present application;

[0053] Figure 4 This is a schematic diagram of an embodiment of a whole-course health monitoring device for hemodialysis patients in the embodiment of the present application. Detailed implementation manners

[0054] The embodiment of the present application provides a whole-course health monitoring method, device and storage medium for hemodialysis patients. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above drawings of the present application are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the term "including" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0055] For easy understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 An embodiment of a whole-course health monitoring method for hemodialysis patients in the embodiment of the present application includes:

[0056] Step 1: Obtain the historical dialysis data, basic information and initial physiological data of the patient, input the historical dialysis data, basic information and initial physiological data into the first preset model to predict the dialysis parameters of the patient, and obtain the dialysis parameters.

[0057] Specifically, the historical dialysis data includes the basic information and physiological data before previous dialysis, as well as various indicators and parameters during previous dialysis, including dialysis treatment time, blood flow rate, dialysis fluid flow rate, dialysis duration, dialysis dose and / or dialysis fluid composition, etc.; the basic information includes the patient's age, gender, weight, height and / or medical history, etc.; the initial physiological data includes the total body water volume in the patient, blood electrolyte levels (such as potassium, sodium, calcium, etc.), blood creatinine, urea concentration, blood pressure, heart rate, blood oxygen saturation, electrocardiogram data and / or body temperature, etc.

[0058] The first preset model is constructed based on big data analysis, machine learning algorithms or other advanced prediction technologies. By analyzing various input data and comprehensively considering the interrelationships among various factors, this model can accurately predict the dialysis parameters suitable for patients, being more scientific and precise, better adapting to individual differences, and improving the dialysis effect.

[0059] Step 2: Perform the dialysis process based on the dialysis parameters, and collect the human dynamic parameters, physiological state data, and hemodialysis data during the dialysis process at a preset cycle.

[0060] The preset cycle is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this. Exemplarily, the preset cycle is 5 minutes, and it can also be dynamically adjusted according to the specific situation of the patient. For example, for patients with more severe conditions, a shorter data collection cycle is set.

[0061] Specifically, the human dynamic parameters include the body movement speed and the time when the body movement speed is collected, etc.; the data types of the physiological state data are the same as the above initial state data; the hemodialysis data includes various indicators and parameters during the dialysis process, including the dialysis treatment time, blood flow rate, flow rate of the dialysis fluid, dialysis dose, dialysis fluid composition, and / or the operating state of the dialysis device, etc.

[0062] Step 3: Perform statistical analysis on the hemodialysis data at each collection time point within the first preset time, and determine whether it is necessary to adjust the dialysis parameters. If so, generate new dialysis parameters and return to Step 2. If not, enter Step 4.

[0063] The first preset time is the time within a period of time before and including the current time, which is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0064] Specifically, by performing real-time statistical analysis on the hemodialysis data during the dialysis process, problems that may occur during the dialysis process can be detected in a timely manner, such as excessive removal or insufficient removal of intravascular fluid. Timely adjustment of the dialysis parameters can avoid the occurrence of these problems, improve the purification effect of dialysis, and reduce complications during the dialysis process.

[0065] Step 4: Determine whether the collection amount of the physiological state data in the current monitoring stage reaches the first preset value. If not, return to Step 2. If so, perform statistical analysis on the physiological state data and the hemodialysis data, and determine whether it is necessary to adjust the dialysis parameters. If so, generate new dialysis parameters and return to Step 2, and at the same time start the next monitoring stage. If not, return to Step 2.

[0066] Specifically, the monitoring phase refers to different time periods divided according to time or specific conditions during the dialysis process. For example, the dialysis process can be divided into three stages: the initial stage, the middle stage, and the later stage, or different monitoring stages can be set according to the changes in the patient's physiological state. The first preset value refers to the amount of physiological state data that needs to be collected within the current monitoring stage. It can be fixed (such as collecting any physiological state data up to 100 in each stage), or it can be dynamically adjusted according to the specific situation of the patient. The first preset values of the data collection amounts corresponding to each monitoring stage can be the same or different, and are specifically set according to the experience of those skilled in the art or according to the actual application scenario. The embodiments of the present application do not limit this.

[0067] Through stage monitoring, comprehensive analysis of physiological state data and hemodialysis data is carried out within a longer time range to evaluate the overall physiological state of the patient within the current monitoring stage, so as to identify the long-term change trends of the patient's physiological state and dialysis effect, which helps to take preventive measures in advance, avoid the deterioration of problems, ensure the safety and effectiveness of the dialysis process, and provide a more comprehensive assessment of the patient's health status.

[0068] Step 5: After dialysis is completed, obtain the dialysis monitoring data of the current treatment course, generate a phased purification evaluation value based on the historical dialysis data and the dialysis monitoring data, and obtain the historical behavior habit data within the second preset time. Generate a reminder message based on the initial physiological data and the historical behavior habit data, where the dialysis monitoring data includes basic information, initial physiological data, physiological state data, and hemodialysis data.

[0069] Specifically, generating a phased purification evaluation value based on the historical dialysis data and the dialysis monitoring data can comprehensively and objectively reflect the purification effect of the patient during the current dialysis process, and provide more accurate treatment effect feedback for the patient and medical staff. The initial physiological data is the physiological state data before the start of the current dialysis treatment. The behavior habit data includes diet records (such as sodium, potassium, and protein intake), fluid intake, exercise conditions, and / or drug use conditions, etc., which reflect the patient's behavior habits in daily life. Statistical analysis is carried out on the initial physiological data and the historical behavior habit data within the second preset time to identify the impact of the historical behavior data on the initial physiological data, and further extract the bad behavior habits that lead to health problems, so as to provide personalized health management suggestions for the patient.

[0070] The second preset time refers to a specific time period, such as one week or one month before the collection of the initial physiological data, which is set according to the experience of those skilled in the art or according to the actual application scenario. The embodiments of the present application do not limit this.

[0071] The reminder information includes dietary advice (such as reminding the patient to control sodium and potassium intake and increase protein intake, etc.), fluid management (such as reminding the patient to control fluid intake to avoid excessive weight gain), exercise advice, medication reminders, etc.

[0072] After the dialysis monitoring is completed, the dialysis monitoring data is stored in the historical dialysis data.

[0073] Step 6: Send the stage purification evaluation value and the reminder information to the patient terminal.

[0074] Specifically, after dialysis, it takes time to detect some of the data in the dialysis monitoring data. The sending time of the stage purification evaluation value can be later than the sending time of the reminder information. Sending the evaluation value and the reminder information to the patient terminal can enable the patient to timely understand their own health status, thereby improving the patient's participation and compliance, and urging the patient to adjust their lifestyle and behavior habits according to the reminder information to better cooperate with the dialysis treatment.

[0075] In a specific embodiment, the physiological state data includes the first type of state data that can be directly obtained and the second type of state data that is indirectly obtained. The obtaining steps of the second type of state data include:

[0076] For any second type of state data, extract the first type of state data related to any second type of state data and define it as the reference state data. Preprocess the reference state data, input the preprocessing result into the corresponding second preset model to obtain the initial value of any second type of state data. Subsequently, determine whether the human body dynamic parameter is greater than the preset threshold. If so, extract the historical state data within the third preset time, and use the average value of the historical state data as the value of any second type of state data. If not, use the initial value as the value of any second type of state data.

[0077] Specifically, the first type of state data includes electrocardiogram, pulse, respiratory rate, blood glucose, and body temperature, etc. The second type of state data includes blood volume, blood oxygen level, cardiac output, and / or blood pressure, etc. The direct obtaining method of the second type of state data may not be easy or may affect the dialysis process. Calculating the second type of state data based on the first type of state data can improve the accuracy and reliability of the data while facilitating dialysis treatment. Exemplarily, the blood oxygen level can be estimated based on the pulse waveform data, and the blood pressure can be estimated based on the electrocardiogram data and the pulse waveform data.

[0078] Taking blood pressure as an example, based on the electrocardiogram data and the pulse waveform data, calculate the first interval from the start of ventricular contraction (R wave of electrocardiogram ECG) to the arrival of the pulse wave at the peripheral blood vessels (peak of the pulse waveform), and the second interval from the start of ventricular relaxation (T wave of electrocardiogram ECG) to the arrival of the pulse wave at the peripheral blood vessels (trough of the pulse waveform). Subsequently, using the known relationship between the above intervals and blood pressure, calculate the maximum pressure and the minimum pressure respectively through formulas. For example, maximum pressure = f(first interval), minimum pressure = g(second interval), where f and g are known functions, usually related to the elastic parameters of blood vessels.

[0079] Specifically, the above human body dynamic parameter is the change amount of the speed of a body part (such as the arm, leg, head, etc.) within a unit time. Although the patient is mainly in a lying state during dialysis, there will still be limb movements, and the generated human body dynamic parameters may affect the accuracy of physiological state data. If the human body dynamic parameter is greater than the preset threshold, it indicates that the speed of the body changes rapidly within a short period of time, and the body state is unstable. It is necessary to correct the second type of state data to reduce the influence of motion artifacts. If the human body dynamic parameter is less than or equal to the preset threshold, it indicates that the speed of the body changes slowly within a short period of time, and the body state is stable, and there is no need to adjust the second type of state data. By correcting small movements, the accuracy and reliability of physiological state data can be improved.

[0080] Set a sliding window, and smooth the data through the average value of historical state data within the third preset time, which can reduce the influence of instantaneous fluctuations and improve the accuracy of physiological state data. Among them, the third preset time is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this. Exemplarily, when calculating blood pressure data, the third preset time is 15 minutes. If the current time is 10:00, the system will extract the blood pressure data between 9:45 and 10:00.

[0081] Preferably, the second type of state parameter can also be calculated based on the first type of state data and dialysis parameters.

[0082] In a specific embodiment, the hemodialysis data includes the amount of body fluid flowing into the dialysis device within a preset period. The process of performing step 3 may specifically include the following steps:

[0083] Calculate the difference between the amount of body fluid at the current time point and the amount of body fluid at the first time point, which is defined as the flow difference. Calculate the ratio of the flow difference to the preset period, and define it as the first ratio. Subsequently, calculate the difference between the first ratio at the current time point and the first ratio at the first time point, and define it as the first change amount. When the first change amount exceeds the preset range, adjust the dialysis parameters, where the first time point is the previous acquisition time point of the current time point.

[0084] Specifically, during hemodialysis, the dialysis device processes a certain amount of blood and removes metabolic wastes and excess water from it. In this process, the volume of body fluid flowing into the dialysis device (which can also be understood as blood) is an important parameter that reflects the efficiency of dialysis and the clearance of water load. By monitoring the volume of body fluid flowing into the dialysis device within a preset period, the water clearance condition of the patient during dialysis and the operating state of the dialysis device can be evaluated.

[0085] The first ratio reflects the rate of movement of extravascular fluid into the blood vessels. By calculating the first change amount, the change in the water clearance rate during dialysis can be evaluated. If the first change amount is greater than the preset range, it indicates that the intravascular fluid has been excessively removed; if the first change amount is less than the preset range, it indicates that the intravascular fluid removal is insufficient. To ensure the stability and effectiveness of the dialysis process, it is necessary to adjust the dialysis parameters to avoid complications such as hypotension and arrhythmia caused by too fast or too slow body fluid clearance speed.

[0086] Exemplarily, the above dialysis parameter is the ultrafiltration rate. If the first change amount is less than the corresponding preset range, the ultrafiltration rate is decreased; if the first change amount is greater than the preset unit, the ultrafiltration rate is increased.

[0087] Preferably, the dialysis fluid flow rate, dialysis fluid pressure, and / or the volume of body fluid, etc. can also be adjusted.

[0088] In a specific embodiment, the process of executing step 4 may specifically include the following steps:

[0089] (1) Calculate the difference in the volume of body fluid between any time point and the second time point within the current monitoring stage, which is defined as the flow difference. Herein, the second time point is the previous acquisition time point of any time point.

[0090] (2) Extract any physiological state data, and calculate the difference in the state values of any physiological state data at any time point and the second time point within the current monitoring stage, which is defined as the state difference.

[0091] (3) Extract all the flow differences and all the state differences within the current monitoring stage, combine the flow difference and the state difference at the same acquisition time point together to form a data point in the state-flow space, and calculate the geometric center of all the data points in the state-flow space.

[0092] (4) Divide all the data points into multiple arrays based on the similarity between the data points, define the data points in the array as array data points, extract any array, calculate the average position of all the array data points in any array, and calculate the first relative distance from any array data point in any array to the average position, and define the maximum value of the first relative distance as the first distance.

[0093] (5) After traversing all the arrays, calculate the second relative distance between each average position and the geometric center point respectively, and then determine whether all the first distances are less than or equal to the second preset value and all the second relative distances are less than or equal to the third preset value. If not, it is determined that the dialysis parameters need to be adjusted; if so, it is determined that the dialysis parameters do not need to be adjusted.

[0094] In an embodiment of the present application, an example of the dialysis parameter adjustment judgment method is as Figure 2 shown.

[0095] Specifically, by calculating the flow difference, evaluate the change in the amount of blood processed by the dialysis device at two time points; by calculating the state difference, evaluate the change in the physiological state of the patient during dialysis; construct a state-flow space, combine the flow difference and all state differences at the same acquisition time point to form a data point in the state-flow space, and integrate multi-dimensional data into a unified framework to achieve comprehensive analysis of multi-dimensional data. Then use clustering algorithms (such as K-means, hierarchical clustering, etc.) to divide all data points into multiple arrays, and perform statistical analysis on the average position and the first distance of each group, as well as the second relative distance between each average position and the geometric center point, to evaluate the distribution and abnormality degree of the data points. If all the first distances are less than or equal to the second preset value and all the second relative distances are less than or equal to the third preset value are satisfied simultaneously, it indicates that the dialysis process is stable and the dialysis parameters do not need to be adjusted; otherwise, the dialysis parameters need to be adjusted. Among them, the second preset value and the third preset value are set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0096] Preferably, the above physiological data is blood pressure. At this time, the state-flow space is the blood pressure-flow space, and the dialysis parameters include the ultrafiltration rate. The adjustment of the dialysis parameters in step 4 is to reduce the ultrafiltration rate. That is to say, when it is predicted that the blood pressure has a tendency to change, whether the blood pressure rises or falls, the system will reduce the ultrafiltration rate. If the blood pressure has a tendency to decrease, reducing the dehydration speed can reduce the drop in intravascular pressure, thus avoiding the occurrence of hypotension; if the blood pressure has a tendency to rise, reducing the dehydration speed can reduce the excessive rise in intravascular pressure, thus avoiding the occurrence of hypertension.

[0097] In a specific embodiment, the hemodialysis data includes the amount of body fluid flowing into the dialysis device within a preset period, and the historical dialysis data and dialysis monitoring data include the first solute concentration and the second solute concentration of a preset solute before and after dialysis treatment, the single operation time of the dialysis device, and the dialysis treatment time. The steps for step 5 to generate the stage purification evaluation value include:

[0098] (1) Extract the historical dialysis data within the fourth preset time, which is defined as the first dialysis data, and define the dialysis treatment process corresponding to the dialysis monitoring data and the first dialysis data as the reference treatment process.

[0099] (2) Extract any reference treatment process, calculate the total body fluid volume during the entire treatment process, define the ratio of the total body fluid volume to the single running time as the second ratio, perform a first predetermined algorithm on the first solute concentration, the second solute concentration, and the second ratio to generate a renal function index value, and then generate a feature vector of any reference treatment process. The feature vector includes the renal function index value, the single running time, the dialysis treatment time, and the basic information.

[0100] (3) Construct a third preset model for describing the absorption, distribution, metabolism, and excretion processes of solutes in the body, input all the feature vectors into the third preset model to obtain the maximum solute concentration value, and perform a second predetermined algorithm on the maximum solute concentration value to generate a phased purification evaluation value.

[0101] In the embodiments of the present application, an embodiment of the method for generating the phased purification evaluation value is as Figure 3 shown.

[0102] The fourth preset time is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0103] Specifically, a solute refers to a substance dissolved in the blood, such as urea, creatinine, β2-microglobulin, etc. These substances are excreted through urine when the kidney function is normal, but in patients with renal failure (end-stage renal disease, ESRD), these solutes cannot be effectively excreted and will accumulate in the body, resulting in the accumulation of metabolic wastes. The role of hemodialysis is to remove these substances. Between two dialysis sessions, solutes will gradually accumulate in the body, resulting in a peak concentration; during dialysis, solutes are removed, but after dialysis stops, solutes will be released from tissues into the blood again, resulting in a brief rebound in concentration.

[0104] The renal function index value measures the ability of the dialyzer to remove a certain substance (such as urea, creatinine, etc.) from the blood per unit time, and its value can reflect the function status of the kidney. Exemplarily, the first predetermined algorithm is: where FIs is the renal function index value, Q is the first ratio, SC1 is the first solute concentration, and SC2 is the first solute concentration. When there are multiple solutes, calculate the single index value of each solute separately, and then add them up to obtain the above-mentioned renal function index value.

[0105] The above-mentioned third preset model is a mathematical model constructed based on the principles of physics and pharmacokinetics to simulate the dynamic changes of solutes in the body, and can accurately predict the concentration changes of solutes. The maximum value of solute concentration reflects the accumulation of solutes in the patient between two dialysis sessions. If the maximum value of solute concentration is too high, it indicates that the dialysis treatment fails to effectively remove solutes in the body, which may lead to uremic symptoms or other complications in the patient.

[0106] Exemplarily, the second preset algorithm is: PE = SC P ×FIs or PE = SC P ×FIs×F D , where PE is the stage purification evaluation value, SC p is the maximum value of solute concentration, and F D is the dialysis frequency. By generating the stage purification evaluation value, the adequacy and treatment effect of dialysis treatment can be quantified, providing more accurate treatment effect feedback for patients and medical staff, and judging whether the patient's dialysis is sufficient.

[0107] The technical solution of the present invention can comprehensively and scientifically evaluate the treatment effect of hemodialysis under the condition that the dialysis conditions (single dialysis duration, dialysis interval time, body weight, etc.) are unstable, providing a basis for personalized treatment and dynamic adjustment, thereby improving the dialysis effect and overall prognosis of patients.

[0108] In a specific embodiment, the basic information includes body weight. Performing the second preset algorithm on the maximum value of solute concentration to generate the stage purification evaluation value includes:

[0109] (1) Calculating the interval time between any two adjacent reference treatment processes based on the dialysis treatment time, and then calculating the first standard deviation, the second standard deviation, and the third standard deviation of the single running time, the interval time, and the body weight in the reference treatment process respectively.

[0110] (2) Performing weighted average on the first standard deviation, the second standard deviation, and the third standard deviation to obtain a comprehensive fluctuation evaluation value.

[0111] (3) Constructing a negative correlation function, inputting the comprehensive fluctuation evaluation value into the negative correlation function to obtain a compensation parameter, and adjusting the evaluation value obtained by performing the second preset algorithm based on the compensation parameter to generate the stage purification evaluation value.

[0112] Specifically, fluctuations in dialysis conditions, such as irregularities in dialysis time, dialysis interval, and dialysis frequency, can lead to uneven clearance of solutes in the body, affecting dialysis adequacy and thus the stage purification assessment value. By calculating the standard deviation or variance of the single-run time, interval time, and body weight, and further calculating the comprehensive fluctuation assessment value, the unevenness of dialysis conditions can be quantified. Based on this, the calculation of the stage purification assessment value can be adjusted to more accurately evaluate the dialysis effect. Among them, the higher the comprehensive fluctuation assessment value, the worse the stability of the dialysis treatment process. Through the compensation parameter, the assessment value should be correspondingly reduced to reflect the true effect of the treatment process.

[0113] Exemplarily, the negative correlation function is f(SE) = 1 - α×SE or f(SE) = β×e -γ×SE , where SE is the comprehensive fluctuation assessment value, and α, β, γ are weight coefficients.

[0114] In a specific embodiment, generating reminder information based on initial physiological data and historical behavior habit data includes:

[0115] (1) Extract the component type and total intake of each nutrient ingested by the patient within the second preset time from the historical behavior habit data, compare the total intake of each nutrient with the corresponding recommended total intake respectively, identify the nutrients with insufficient or excessive intake, and define them as components to be analyzed.

[0116] (2) Extract the biochemical index data in the initial physiological data, compare the biochemical index data with the corresponding standard range, calculate the degree of kidney damage based on the comparison result, and then perform the third predetermined algorithm on the degree of kidney damage to generate a body index value.

[0117] (3) Obtain the historical contribution rates of all components to be analyzed, accumulate all the historical contribution rates to obtain an accumulated value, update the historical contribution rate of any component to be analyzed based on the accumulated value to obtain the current contribution rate, and take the product of the current contribution rate and the body index value as the current contribution value of any component to be analyzed.

[0118] (4) Obtain the current contribution value of any nutrient and all historical contribution values within the fifth preset time, and define the average value of all historical contribution values and the current contribution value as the comprehensive contribution value of any nutrient.

[0119] (5) Generate reminder information based on the current contribution value and the contribution index value.

[0120] Specifically, patients can share data such as diet and exercise with the cloud platform. The system can analyze the above-mentioned diet data to extract the component types and total intake amounts of each nutrient component ingested by the patient. The above-mentioned nutrient components include calcium, sodium, potassium, protein, water, etc. The total intake amount of each nutrient component is compared with the corresponding recommended total intake amount respectively. If the intake amount of a certain nutrient component is lower than the recommended intake amount, then this component is defined as an insufficient intake component. If the intake amount of a certain nutrient component is higher than the recommended intake amount, then this component is defined as an excessive intake component. All insufficient or excessive intake nutrient components are collectively referred to as components to be analyzed, that is, components that have an adverse impact on the purification effect of the stage.

[0121] Biochemical index data includes biochemical indexes reflecting kidney function such as serum creatinine, blood urea nitrogen, blood potassium, blood sodium, etc. Exemplarily, the calculation formula for the degree of kidney damage is: where D B is the degree of kidney damage, AV i is the actual value of the i-th biochemical index. When AV i is lower than the standard range, SV i is the minimum value of the above standard range. When AV i is higher than the standard range, SV i is the maximum value of the above standard range, and ε is the weight coefficient. Exemplarily, the calculation formula for the body index value is T B = a1×a2×D B where TB is the body index value, a1 is the physiological index factor calculated based on the deviation of physiological state data, and a2 is the comprehensive influence factor calculated based on age, gender, underlying diseases, etc. The body index value comprehensively considers various factors such as the patient's kidney function, physiological state, and basic information, and can comprehensively reflect the patient's current overall health status. The patient's body index and nutritional intake will change over time. By regularly calculating the body index value, the change trend of the patient's health status can be monitored.

[0122] The historical contribution rate of any nutrient component reflects the magnitude of its contribution to the degree of kidney damage over a past period. Exemplarily, there are 4 components to be analyzed, namely A1, A2, A3, and A4, and the corresponding historical contribution rates are 20%, 70%, 55%, and 30% respectively. The cumulative value is 175%. The updated current contribution rates of the components A1, A2, A3, and A4 to be analyzed are 31% (20%÷175% + 20%), 100% (the maximum value is 100%, when the calculated contribution rate is greater than 100%, it is set to 100%), 86%, and 47% respectively. Subsequently, based on the current contribution rate and the body index value, the current contribution value of the component to be analyzed is calculated. The greater the current contribution value, the greater the impact of the component to be analyzed on kidney health, and the higher the possibility of being the cause of kidney damage. At different evaluation stages, even if the contribution rates are the same, if the degree of kidney health (body index value) is different, the contribution of the nutrient component to kidney health is also different. For example, when the body index value is relatively high, even if the current contribution rate of a certain nutrient component is relatively low, its current contribution value may be relatively high, and the impact of this nutrient component on health is amplified.

[0123] The fifth preset time is a historical time within a period from the current time point. Exemplarily, there are a total of 5 nutrient components A1, A2, A3, A4, and A5, and a total of 3 contribution value calculations are performed. The contribution values of each nutrient component in each calculation are A1(0.9, 0, 2), A2(0.8, 4.1, 6.4), A3(2.9, 1, 5.5), A4(0, 2.5, 3), and A5(3.2, 1.6, 0) respectively. The comprehensive contribution values of A1, A2, A3, A4, and A5 are 1, 3.8, 3.1, 1.8, and 1.6 respectively.

[0124] Based on the current contribution value and the comprehensive contribution value, the long-term and short-term impacts of a certain nutrient component on kidney health can be comprehensively evaluated. Preferably, when generating a reminder message, the current contribution value and the comprehensive contribution value are sorted respectively based on the numerical size, and the top N1 components to be analyzed are extracted as the first reminder components (nutrient components with greater short-term impact), and the top N2 nutrient components are extracted as the second reminder components (nutrient components with greater long-term impact). Exemplarily, the reminder message is "Your sodium intake has been insufficient recently. It is recommended to appropriately increase salt intake", "Your potassium intake has been too high recently. Please reduce the intake of high-potassium foods", or "Your kidney function has declined. It is recommended to control protein intake and increase water intake", etc.

[0125] According to the technical solution of the present invention, factors that have a greater impact on the kidney health of a patient can be identified based on the patient's daily behavior habits, enabling the patient to realize the causal relationship between daily behavior habits and kidney health, thereby improving bad behavior habits, enhancing treatment compliance, and preventing potential health risks.

[0126] The above describes a method for full-course health monitoring of hemodialysis patients in an embodiment of the present application. Next, a device for full-course health monitoring of hemodialysis patients in an embodiment of the present application will be described. Please refer to Figure 4 An embodiment of a device for full-course health monitoring of hemodialysis patients in an embodiment of the present application includes:

[0127] A parameter setting module 10, configured to obtain the patient's historical dialysis data, basic information, and initial physiological data, input the historical dialysis data, basic information, and initial physiological data into a first preset model to predict the patient's dialysis parameters, and obtain the dialysis parameters.

[0128] A data acquisition module 20, configured to perform a dialysis process according to the dialysis parameters, and collect human dynamic parameters, physiological state data, and hemodialysis data during the dialysis process at a preset period.

[0129] A first judgment module 30, configured to perform statistical analysis on the hemodialysis data at each acquisition time point within a first preset time, judge whether it is necessary to adjust the dialysis parameters. If so, generate new dialysis parameters and return them to the data acquisition module 20. If not, enter the second judgment module 40.

[0130] A second judgment module 40, configured to judge whether the acquisition amount of the physiological state data in the current monitoring stage reaches a first preset value. If not, return to the data acquisition module 20. If so, perform statistical analysis on the physiological state data and the hemodialysis data, judge whether it is necessary to adjust the dialysis parameters. If so, generate new dialysis parameters and return them to the data acquisition module 20, and at the same time start the next monitoring stage. If not, return to the data acquisition module 20.

[0131] A notification generation module 50, configured to, after dialysis is completed, obtain the dialysis monitoring data of the current treatment course, generate a phased purification evaluation value based on the historical dialysis data and the dialysis monitoring data, and obtain the historical behavior habit data within a second preset time, and generate a reminder message based on the initial physiological data and the historical behavior habit data, where the dialysis monitoring data includes basic information, initial physiological data, physiological state data, and hemodialysis data.

[0132] A notification sending module 60, configured to send the phased purification evaluation value and the reminder message to the patient terminal.

[0133] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is made to execute the steps of the method for full-course health monitoring of hemodialysis patients.

[0134] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described devices, equipment, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0136] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for the whole-process health monitoring of hemodialysis patients, characterized in that, The method includes: Step 1: Obtain the patient's historical dialysis data, basic information, and initial physiological data, and input the historical dialysis data, the basic information, and the initial physiological data into a first preset model to predict the patient's dialysis parameters and obtain the dialysis parameters; Step 2: Execute the dialysis process based on the dialysis parameters, and collect the human dynamic parameters, physiological state data, and hemodialysis data during the dialysis process at a preset cycle; Step 3: Perform statistical analysis on the hemodialysis data at each collection time point within a first preset time to determine whether the dialysis parameters need to be adjusted. If so, generate new dialysis parameters and return to Step 2. If not, proceed to Step 4; Step 4: Determine whether the collection amount of the physiological state data in the current monitoring stage reaches a first preset value. If not, return to Step 2. If so, perform statistical analysis on the physiological state data and the hemodialysis data to determine whether the dialysis parameters need to be adjusted. If so, generate new dialysis parameters and return to Step 2, and at the same time start the next monitoring stage. If not, return to Step 2; Step 5: After dialysis is completed, obtain the dialysis monitoring data for the current treatment course, generate a stage purification evaluation value based on the historical dialysis data and the dialysis monitoring data, and obtain the historical behavior habit data within a second preset time, and generate a reminder message based on the initial physiological data and the historical behavior habit data, where the dialysis monitoring data includes the basic information, the initial physiological data, the physiological state data, and the hemodialysis data; Step 6: Send the stage purification evaluation value and the reminder message to the patient terminal.

2. The whole-course health monitoring method for hemodialysis patients according to claim 1, characterized in that, The physiological state data includes the first type of state data that can be directly obtained and the second type of state data that is indirectly obtained. The obtaining steps of the second type of state data include: For any second type of state data, extract the first type of state data related to any second type of state data and define it as the reference state data. Preprocess the reference state data, input the preprocessing result into the corresponding second preset model, obtain the initial value of any second type of state data, and then determine whether the human dynamic parameter is greater than a preset threshold. If so, extract the historical state data within a third preset time, and use the average value of the historical state data as the value of any second type of state data. If not, use the initial value as the value of any second type of state data.

3. The whole-course health monitoring method for hemodialysis patients according to claim 1, wherein, The hemodialysis data includes the amount of body fluid flowing into the dialysis device within the preset cycle, and Step 3 includes: Calculate the difference between the amount of body fluid at the current time point and the amount of body fluid at the first time point, and define it as the flow difference. Calculate the ratio of the flow difference to the preset cycle, and define it as the first ratio. Then calculate the difference between the first ratio at the current time point and the first ratio at the first time point, and define it as the first change amount. When the first change amount exceeds the preset range, adjust the dialysis parameters, where the first time point is the previous collection time point of the current time point.

4. A method for the whole-course health monitoring of hemodialysis patients according to claim 1, characterized in that, Step 4 includes: Calculating the difference between the body fluid volume at any time point within the current monitoring phase and the second time point, which is defined as the flow difference. Herein, the second time point is the previous collection time point of any of the time points; Extracting any physiological state data, and calculating the difference between the state values of any of the physiological state data at any time point and the second time point within the current monitoring phase, which is defined as the state difference; Extracting all the flow differences and all the state differences within the current monitoring phase, combining the flow difference and the state difference at the same collection time point together to form a data point in the state-flow space, and calculating the geometric center of all the data points in the state-flow space; Dividing all the data points into multiple arrays based on the similarity between the data points, defining the data points in the array as array data points, extracting any array, calculating the average position of all the array data points in any of the arrays, and calculating the first relative distance from any of the array data points in any of the arrays to the average position, and defining the maximum value of the first relative distance as the first distance; After traversing all the arrays, calculating the second relative distance between each average position and the geometric center point respectively, and then determining whether all the first distances are less than or equal to a second preset value and all the second relative distances are less than or equal to a third preset value. If not, it is determined that the dialysis parameters need to be adjusted. If so, it is determined that the dialysis parameters do not need to be adjusted.

5. A method for the whole-process health monitoring of hemodialysis patients according to claim 1, characterized in that, The hemodialysis data includes the body fluid volume flowing into the dialysis device within the preset period, and the historical dialysis data and the dialysis monitoring data include the first solute concentration and the second solute concentration of the preset solute before and after dialysis treatment, the single operation time of the dialysis device, and the dialysis treatment time. The steps of step 5 for generating the stage purification evaluation value include: Extracting the historical dialysis data within the fourth preset time, which is defined as the first dialysis data, and defining the dialysis treatment process corresponding to the dialysis monitoring data and the first dialysis data as the reference treatment process; Extracting any reference treatment process, calculating the total body fluid volume during the entire treatment process, defining the ratio of the total body fluid volume to the single operation time as the second ratio, performing a first predetermined algorithm on the first solute concentration, the second solute concentration, and the second ratio to generate a renal function index value, and then generating a feature vector of any of the reference treatment processes, where the feature vector includes the renal function index value, the single operation time, the dialysis treatment time, and the basic information; Constructing a third preset model for describing the absorption, distribution, metabolism, and excretion processes of solutes in the body, inputting all the feature vectors into the third preset model to obtain the maximum solute concentration, and performing a second predetermined algorithm on the maximum solute concentration to generate the stage purification evaluation value.

6. The whole-process health monitoring method for hemodialysis patients according to claim 5, characterized in that The basic information includes the body weight, and performing the second predetermined algorithm on the maximum solute concentration to generate the stage purification evaluation value includes: Calculate the interval time between any two adjacent reference treatment processes based on the dialysis treatment time, and then calculate the first standard deviation, the second standard deviation, and the third standard deviation of the single-run time, the interval time, and the body weight during the reference treatment process, respectively; Perform a weighted average on the first standard deviation, the second standard deviation, and the third standard deviation to obtain a comprehensive fluctuation evaluation value; Construct a negative correlation function, input the comprehensive fluctuation evaluation value into the negative correlation function to obtain a compensation parameter, and adjust the evaluation value obtained by executing the second predetermined algorithm based on the compensation parameter to generate the stage purification evaluation value.

7. A method for the whole-process health monitoring of hemodialysis patients according to claim 1, characterized in that, The generating the reminder information based on the initial physiological data and the historical behavior habit data includes: Extract the component type and total intake of each nutrient ingested by the patient within the second preset time from the historical behavior habit data, compare the total intake of each nutrient with the corresponding recommended total intake respectively, identify the nutrients with insufficient or excessive intake, and define them as components to be analyzed; Extract the biochemical index data in the initial physiological data, compare the biochemical index data with the corresponding standard range, calculate the degree of kidney damage based on the comparison result, and then execute a third predetermined algorithm on the degree of kidney damage to generate a body index value; Obtain the historical contribution rates of all components to be analyzed, accumulate all the historical contribution rates to obtain an accumulated value, update the historical contribution rate of any component to be analyzed based on the accumulated value to obtain the current contribution rate, and use the product of the current contribution rate and the body index value as the current contribution value of any component to be analyzed; Obtain the current contribution value of any nutrient and all historical contribution values within the fifth preset time, and define the average value of all the historical contribution values and the current contribution value as the comprehensive contribution value of any nutrient; Generate the reminder information based on the current contribution value and the contribution index value.

8. A whole-course health monitoring device for hemodialysis patients, characterized in that, The device includes: A parameter setting module, configured to obtain the patient's historical dialysis data, basic information, and initial physiological data, input the historical dialysis data, the basic information, and the initial physiological data into a first preset model to predict the patient's dialysis parameters, and obtain the dialysis parameters; A data acquisition module, configured to perform a dialysis process according to the dialysis parameters, and collect human dynamic parameters, physiological state data, and hemodialysis data during the dialysis process at a preset period; A first judgment module, configured to perform statistical analysis on the hemodialysis data at each acquisition time point within a first preset time, judge whether it is necessary to adjust the dialysis parameters, if so, generate new dialysis parameters and return them to the data acquisition module, if not, enter the second judgment module; A second judgment module, configured to judge whether the acquisition amount of the physiological state data in the current monitoring stage reaches a first preset value. If not, it returns to the data acquisition module. If so, it statistically analyzes the physiological state data and the hemodialysis data to judge whether it is necessary to adjust the dialysis parameters. If so, it generates new dialysis parameters, returns to the data acquisition module, and simultaneously starts the next monitoring stage. If not, it returns to the data acquisition module; A notification generation module, configured to, after dialysis is completed, obtain the dialysis monitoring data of the current treatment course, generate a phased purification evaluation value based on the historical dialysis data and the dialysis monitoring data, and obtain the historical behavior habit data within a second preset time, and generate a reminder message based on the initial physiological data and the historical behavior habit data, wherein the dialysis monitoring data includes the basic information, the initial physiological data, the physiological state data, and the hemodialysis data; A notification sending module, configured to send the phased purification evaluation value and the reminder message to the patient terminal.

9. A computer-readable storage medium having instructions stored thereon, characterized in that, When the instruction is executed by a processor, it implements a method for full-course health monitoring of hemodialysis patients as described in any one of claims 1-7.

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