Blood potassium data intelligent management method and system for renal dialysis patient

Abstract

The invention provides a blood potassium data intelligent management method and system for renal dialysis patients, and belongs to the technical field of medical management. The method comprises the following steps: acquiring blood potassium data of a renal dialysis patient uploaded by an uploading channel, carrying out preliminary quality analysis, and judging a quality analysis result; when the quality analysis result is judged to be qualified, calling the individual information of the renal dialysis patient from the blood potassium data of the renal dialysis patient; performing statistics on management queue information of the blood potassium data management center, performing processing in combination with individual information of the renal dialysis patients, and confirming a management queue of the renal dialysis patients; and processing according to the management queue of the renal dialysis patients to obtain a sub-management mode under the management queue of the blood potassium data of the renal dialysis patients. According to the method, the blood potassium data of the renal dialysis patient is matched with the proper management queue and the sub-management mode, so that the effect of finely managing the blood potassium data of the renal dialysis patient is achieved.

Description

Technical Field

[0001] The present invention relates to the field of medical management technology, and in particular to a method and system for intelligent management of blood potassium data for renal dialysis patients. Background Art

[0002] The kidneys play a vital role in the balance of electrolytes in the body, especially in the regulation of blood potassium. The kidneys maintain normal blood potassium concentration in the body by filtering, reabsorbing and excreting potassium ions. Blood potassium is one of the important electrolytes in the body. Potassium ions are directly involved in metabolic activities inside and outside cells, including maintaining cell metabolism, regulating body fluid osmotic pressure, maintaining acid-base balance and maintaining cell stress function. For patients with end-stage renal disease and renal dialysis, abnormal blood potassium levels can significantly increase the risk of adverse cardiovascular events, such as arrhythmias, cardiac arrest and cardiovascular events. Therefore, in the process of chronic disease management of dialysis patients, blood potassium indicators need to be closely monitored to ensure that they remain within the normal range. For renal dialysis medical staff, ensuring the balance of blood potassium levels is a very important part of the daily management of renal dialysis patients. They need to adjust the patient's chronic disease management plan according to the patient's specific situation, conduct risk management of blood potassium according to the patient's situation, and prevent the adverse consequences of abnormal blood potassium. For dialysis patients, blood potassium is one of the biochemical indicators that is routinely tested every month and is greatly affected by individual factors. This results in an abnormally large volume of blood potassium data, which is complex to manage, resulting in an increasing demand for blood potassium data management.

[0003] The existing blood potassium data management of renal dialysis patients usually monitors and archives blood potassium data based on preset algorithms and rules. The unified management model may not meet the blood potassium data characteristics of different renal dialysis patients, which not only makes data management difficult, but also affects the access and use of blood potassium data by personnel. Therefore, it is difficult to manage the blood potassium data of renal dialysis patients in a refined manner. Summary of the invention

[0004] The present invention provides a method and system for intelligent management of blood potassium data of renal dialysis patients, which solves the problem that it is difficult to perform personalized management of blood potassium data of renal dialysis patients in the prior art, and realizes effective management of blood potassium data of renal dialysis patients.

[0005] In order to solve the above-mentioned invention object, the technical solution provided by the present invention is as follows:

[0006] A method and system for intelligent management of blood potassium data of a renal dialysis patient, comprising: obtaining blood potassium data of a renal dialysis patient uploaded through an upload channel for preliminary quality analysis, and determining a quality analysis result, wherein the quality analysis result is qualified or unqualified; when the quality analysis result is determined to be qualified, retrieving individual information of the renal dialysis patient from the blood potassium data of the renal dialysis patient; collecting information of each management queue of a blood potassium data management center, and processing the information in combination with the individual information of the renal dialysis patient to confirm a blood potassium data management queue of the renal dialysis patient; and obtaining blood potassium data management constraint parameters of the renal dialysis patient according to the blood potassium data management queue of the renal dialysis patient and processing the data.

[0007] Optionally, the obtaining of the blood potassium data of the renal dialysis patient uploaded through the upload channel for preliminary quality analysis comprises the following specific processes: extracting data upload channel parameters from the upload channel, wherein the data upload channel parameters include data upload delay time, data upload average rate and channel upload data volume peak value; extracting data feature parameters and image feature parameters from the uploaded blood potassium data of the renal dialysis patient, wherein the data feature parameters include the number of field filling missings, data field filling density and cumulative data format error value, and the image feature parameters include image average resolution, image average contrast and image average signal-to-noise ratio; and performing comprehensive processing based on the data upload channel parameters, the data feature parameters and the image feature parameters to obtain the data quality analysis value of the blood potassium data.

[0008] Optionally, the specific method for obtaining the data quality analysis value of the blood potassium data is:

[0009] ;

[0010] In the formula, Represents the data quality analysis value of blood potassium data, Represents the data upload delay time, Represents the preset data upload delay time threshold. Represents the average data upload rate, Represents the preset average data upload rate threshold. Represents the peak value of the channel uploaded data. Represents the preset channel upload data volume threshold. Represents the number of missing fields to be filled. Represents the preset field filling missing number threshold, Represents the data field filling density, Represents the preset data field filling density reference value, Represents the cumulative data format error value, Represents the preset cumulative data format error value threshold, Represents the average image resolution, Represents the preset average image resolution reference value. represents the average contrast of the image, Represents the preset image average contrast reference value, represents the average signal-to-noise ratio of the image, Represents the preset image average signal-to-noise ratio threshold, is a natural constant.

[0011] Optionally, the determination obtains a quality analysis result, and the specific process is: obtaining a preset data quality analysis threshold, comparing the data quality analysis value of the blood potassium data with the data quality analysis threshold, when the data quality analysis value of the blood potassium data is less than the data quality analysis threshold, the quality analysis result of the blood potassium data of the renal dialysis patient is determined to be unqualified; when the data quality analysis value of the blood potassium data is greater than or equal to the data quality analysis threshold, the quality analysis result of the blood potassium data of the renal dialysis patient is determined to be qualified.

[0012] Optionally, the confirmation of the blood potassium data management queue of the renal dialysis patient specifically comprises the following process: based on the management queue information of the blood potassium data management center, the management queue information includes the individual sample information of each management queue and the dialysis information parameters of each existing data packet in each management queue; the individual sample information of each management queue includes: age reference value, height reference value and weight reference value; based on the dialysis information parameters of each existing data packet in each management queue, wherein the dialysis information parameters include the cumulative number of dialysis sessions, the dialysis frequency and the initial dialysis interval, the dialysis information parameters of each existing data packet are averaged one by one to obtain the mean of the dialysis information parameters of the existing data packets in each management queue, wherein the mean of the dialysis information parameters includes the mean of the cumulative number of dialysis sessions, the mean of the dialysis frequency and the initial dialysis interval. value and the average of the initial dialysis interval; retrieve the individual information of the renal dialysis patients from the blood potassium data of the renal dialysis patients, specifically including the age, height, weight, cumulative number of dialysis sessions, dialysis frequency and the initial dialysis interval of the renal dialysis patients; based on the individual information of the renal dialysis patients, and synchronously combined with the individual sample information of each management queue and the average of the dialysis information parameters of the existing data packets in each management queue for comprehensive processing, obtain the storage embedding value between the blood potassium data of the renal dialysis patients and each management queue, and obtain the target management queue of the blood potassium data of the renal dialysis patients according to the storage embedding value matching, and use it as the blood potassium data management queue of the renal dialysis patients; the storage embedding value between the blood potassium data of the renal dialysis patients and each management queue is used to characterize the matching degree of the blood potassium data of the renal dialysis patients stored in each management queue.

[0013] Optionally, the target management queue of the blood potassium data of the renal dialysis patients is obtained according to the storage embedding value matching, and the specific process is: obtaining the storage embedding values ​​between the blood potassium data of the renal dialysis patients and each management queue, sorting the storage embedding values ​​between the blood potassium data of the renal dialysis patients and each management queue from large to small, and selecting the management queue corresponding to the maximum storage embedding value as the target management queue of the blood potassium data of the renal dialysis patients.

[0014] Optionally, the processing to obtain the blood potassium data management constraint parameters of the renal dialysis patient is specifically as follows: extracting the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient from the blood potassium data of the renal dialysis patient, and statistically analyzing the characteristic parameters of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient for mean processing to obtain the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patient; based on the information of each management queue of the blood potassium data management center, obtaining the blood potassium characteristic curve set of each existing data packet in the target management queue and extracting the data quality analysis value of each existing data packet, the blood potassium characteristic curve set includes the peak value, valley value, maximum pulse width and change rate of the blood potassium characteristic curve, and calculating the blood potassium of each existing data packet. The characteristic curve set is processed by mean to obtain the blood potassium characteristic curve control set of the data packet in the target management queue; based on the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patient and the blood potassium characteristic curve control set, the data quality analysis value of the blood potassium data and the data quality analysis value of each existing data packet, the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is obtained, and the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is used to characterize the data chimerism degree between the blood potassium data of the renal dialysis patient and the existing data packet, and the blood potassium data management constraint parameter of the renal dialysis patient is determined by the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue.

[0015] Optionally, the first characteristic parameter of the blood potassium concentration change curve includes: a first peak value, a first trough value, a first maximum pulse width and a first change rate of the blood potassium concentration change curve of a renal dialysis patient; the blood potassium characteristic curve set specifically includes an average peak value, an average trough value, an average maximum pulse width and an average change rate of the blood potassium characteristic curve of the data packets in the target management queue.

[0016] Optionally, the blood potassium data management constraint parameter of the renal dialysis patient is obtained by: based on the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue, and obtaining the branch queues corresponding to the preset intervals of each deep aggregation value, the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is mapped with the branch queues corresponding to the preset intervals of each deep aggregation value, and the branch queue corresponding to the interval where the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is located is obtained, and marked as the target sub-queue of the blood potassium data of the renal dialysis patient; the data quality analysis value of the blood potassium data of the renal dialysis patient is counted, and the adaptive inspection cycle corresponding to the preset intervals of each data quality analysis value is matched to obtain the adaptive inspection cycle of the blood potassium data of the renal dialysis patient, which is recorded as the target adaptive inspection cycle of the blood potassium data of the renal dialysis patient; the blood potassium data management queue of the renal dialysis patient, the target sub-queue of the blood potassium data of the renal dialysis patient, and the adaptive inspection cycle of the blood potassium data are collectively marked as the blood potassium data management constraint parameter.

[0017] The embodiment of the present invention provides a system for an intelligent management method of blood potassium data of a renal dialysis patient, comprising: a quality analysis module: used to obtain blood potassium data of a renal dialysis patient uploaded by an upload channel for preliminary quality analysis, and determine to obtain a quality analysis result, wherein the quality analysis result is qualified or unqualified; an information retrieval module: used to retrieve individual information of the renal dialysis patient from the blood potassium data of the renal dialysis patient when the quality analysis result is determined to be qualified; a management queue matching module: used to count the management queue information of each management queue of a blood potassium data management center, and process it in combination with the individual information of the renal dialysis patient to confirm the blood potassium data management queue of the renal dialysis patient; a management constraint parameter acquisition module: used to manage the queue according to the blood potassium data of the renal dialysis patient, and process it to obtain the blood potassium data management constraint parameter of the renal dialysis patient.

[0018] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0019] The above scheme accurately allocates the renal dialysis patients to the appropriate management cohort by retrieving the individual information of the renal dialysis patients whose quality analysis results are qualified, and combining the individual information reference value of the management cohort. The target sub-cohort of the renal dialysis patients' blood potassium data is matched according to the deep aggregation value between the renal dialysis patients' blood potassium data and the target management cohort, so that the renal dialysis patients' blood potassium data can obtain the management sub-cohort suitable for their situation, realizing personalized management services and effectively automating the management of blood potassium data.

[0020] By conducting a preliminary quality analysis on the blood potassium data of renal dialysis patients, it is possible to effectively determine whether the uploaded blood potassium data is qualified, avoid management queue allocation errors caused by poor blood potassium data quality, and promptly discover quality problems with blood potassium data. Through multi-level data quality analysis, the accuracy and reliability of blood potassium data of renal dialysis patients can be ensured, which can effectively improve the management quality of blood potassium data.

[0021] By analyzing the curve characteristic parameters of the patient's blood potassium concentration change curve and the blood potassium characteristic curve set of each existing data packet in the target management queue, it is possible to accurately match the target sub-queue that is more similar to the blood potassium data of renal dialysis patients, and to accurately and efficiently manage the blood potassium data of renal dialysis patients.

[0022] The adaptive inspection cycle of the blood potassium data of the renal dialysis patients is obtained by matching the data quality analysis value of the blood potassium data of the renal dialysis patients with the adaptive inspection cycle corresponding to each preset data quality analysis value interval, and the adaptive inspection cycle of the blood potassium data of the renal dialysis patients is accurately determined, so that regular inspection of the management queue of the blood potassium data of the renal dialysis patients is realized, and when the blood potassium data of the renal dialysis patients does not match the current management queue, it can be replaced in time, thereby realizing dynamic management of the blood potassium data of the renal dialysis patients, and being able to better and more efficiently manage the blood potassium data of the renal dialysis patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of a method flow chart involved in an embodiment of the present invention;

[0025] Figure 2 Schematic diagram of system modules involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantitative limitation, but indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0028] It should be noted that the terms "up", "down", "left", "right", "front", "back", etc. used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0029] The present invention aims to solve the problem that it is difficult to carry out refined management of blood potassium data of renal dialysis patients, and provides an intelligent management method and system for blood potassium data of renal dialysis patients, which can effectively manage blood potassium data of renal dialysis patients.

[0030] like Figure 1 As shown, an embodiment of the present invention provides an intelligent management method for blood potassium data of a renal dialysis patient, comprising: obtaining blood potassium data of a renal dialysis patient uploaded through an upload channel for preliminary quality analysis, and determining a quality analysis result, the quality analysis result being qualified or unqualified; when the quality analysis result is determined to be qualified, retrieving individual information of the renal dialysis patient from the blood potassium data of the renal dialysis patient; collecting statistics on each management queue information of a blood potassium data management center, and processing the information in combination with the individual information of the renal dialysis patient to confirm a blood potassium data management queue for the renal dialysis patient; and obtaining blood potassium data management constraint parameters for the renal dialysis patient according to the blood potassium data management queue of the renal dialysis patient and processing the data.

[0031] In this embodiment, in order to ensure the security and privacy protection of the blood potassium data of the renal dialysis patients, the blood potassium data of the renal dialysis patients needs to be encrypted for confidentiality. The blood potassium data of the renal dialysis patients is encrypted using the advanced encryption standard (such as AES-256 bit encryption algorithm) during transmission and storage. It should be noted that the blood potassium data of the renal dialysis patients includes the basic information of the renal dialysis patients (such as age and weight, etc.), the basic dialysis information of the renal dialysis patients, and the blood potassium related information of the renal dialysis patients, as well as the blood potassium concentration data of each renal dialysis of the renal dialysis patients and other related data. The blood potassium data of the renal dialysis patients uploaded by the upload channel may include image data, and the doctor or patient may upload image data related to the renal dialysis patients. The acquired information is integrated to obtain the blood potassium data of the renal dialysis patients, and the data quality analysis is performed on the blood potassium data of the renal dialysis patients. It should be noted that the blood potassium data management center is a place for centralized storage, processing, management and analysis of data. The blood potassium data management center includes multiple management queues, and each management queue includes various branch queues. The management queue is a queue that integrates the basic information of similar patients. It comprehensively manages data packets with similar basic information of a class of patients. The basic information of patients includes individual patient information and dialysis information. The branch queue is a refinement of the management queue. It manages queues with similar characteristic parameters of blood potassium concentration change curves.

[0032] Among them, the blood potassium data of renal dialysis patients uploaded by the upload channel are obtained for preliminary quality analysis. The specific process is as follows: extracting data upload channel parameters from the upload channel, the data upload channel parameters include: data upload delay time, data upload average rate and channel upload data volume peak; extracting data feature parameters and image feature parameters from the uploaded blood potassium data of renal dialysis patients, the data feature parameters include: field filling missing number, data field filling density and cumulative data format error value, the image feature parameters include: image average resolution, image average contrast and image average signal-to-noise ratio; based on the data upload channel parameters, data feature parameters and image feature parameters, comprehensive processing is performed to obtain the data quality analysis value of blood potassium data.

[0033] In this embodiment, the data upload delay time is the time required from the start of data uploading to the completion of uploading, the data upload average rate is the average of the overall rate of data upload, and the channel upload data volume peak refers to the maximum transmission volume of channel upload data. The data upload delay time, the data upload average rate and the channel upload data volume peak are obtained through network performance monitoring software.

[0034] In this embodiment, the data characteristic parameter is a parameter used to express the degree of completeness of the blood potassium data of renal dialysis patients, the number of field filling missing is the number of missing data fields, the data field filling density is the ratio of the filled fields to the total fields, and the cumulative data format error value is the total number of uploaded data that does not meet the predetermined format requirements. The number of field filling missing, the data field filling density and the cumulative data format error value are obtained through data analysis and processing software.

[0035] In this embodiment, the average image resolution refers to the clarity of details in the image, which refers to the average number of pixels per unit area in the image. The average image contrast is the average value of the brightness difference between the brightest and darkest areas in the image. The average image signal-to-noise ratio is the average value of the ratio of the useful signal to the background noise in the image. The average image resolution, the average image contrast and the average image signal-to-noise ratio are obtained through image processing software.

[0036] Among them, the specific method for obtaining the data quality analysis value of blood potassium data is:

[0037] ;

[0038] In the formula, Represents the data quality analysis value of blood potassium data, Represents the data upload delay time, Represents the preset data upload delay time threshold. Represents the average data upload rate, Represents the preset average data upload rate threshold. Represents the peak value of the channel uploaded data. Represents the preset channel upload data volume threshold. Represents the number of missing fields to be filled. Represents the preset field filling missing number threshold, Represents the data field filling density, Represents the preset data field filling density reference value, Represents the cumulative data format error value, Represents the preset cumulative data format error value threshold, Represents the average image resolution, Represents the preset average image resolution reference value. represents the average contrast of the image, Represents the preset image average contrast reference value, represents the average signal-to-noise ratio of the image, Represents the preset image average signal-to-noise ratio threshold, is a natural constant.

[0039] In this embodiment, the data upload delay time threshold, the data upload average rate threshold, the channel upload data volume threshold, the field filling missing number threshold, the data field filling density reference value, the cumulative data format error value threshold, the image average resolution reference value, the image average contrast reference value and the image average signal-to-noise ratio threshold are obtained from the database.

[0040] In this embodiment, the data upload channel parameters, data feature parameters and image feature parameters are interrelated and affect each other, and also jointly reflect the quality of blood potassium data of renal dialysis patients. The data upload delay time is related to the average data upload rate and the peak value of the channel upload data volume. When the delay is high, the data transmission efficiency is low, and the average data upload rate may become low. When the data upload delay time is too high, the upload may fail, resulting in an increase in the peak value of the channel upload data volume. When the average data upload rate is high, the corresponding data upload delay time will be low. At the same time, the data transmission process is fast, and the peak value of the channel upload data volume may also be low. A large peak value of the channel upload data volume will increase the data upload delay and may cause data packet loss or transmission errors, affecting the overall quality of blood potassium data. More field filling missing numbers usually mean poor data integrity, low field filling density usually reflects incomplete data, more missing fields, affecting data quality, more field filling missing numbers, and data field filling density is usually low. Long data delay time or low average data upload rate may cause delays in the field filling process, which may increase the number of field filling missing numbers. When there is a format error in data upload, some fields may not be correctly identified and filled, thereby increasing the number of missing fills, that is, a large cumulative data format error value may cause an increase in the number of missing field fills. As a result, the data field filling density becomes lower. The number of missing field fills is closely related to the cumulative data format error value. A large number of missing field fills will lead to upload failure, thereby increasing the peak value of the channel upload data volume. More field types may increase the probability of format errors. Images with higher image average resolution usually require longer upload time because the data volume is large and may cause higher delays. Higher image average contrast usually means that the image signal is stronger and the noise is relatively small, so the image average signal-to-noise ratio is higher. Images with high image average resolution can better present the image average contrast difference. Images with higher image average resolution and higher image average contrast usually have higher signal-to-noise ratio. Images with low signal-to-noise ratio may cause errors during upload due to quality problems, resulting in repeated uploads, thereby increasing the peak value of the channel upload data volume. In short, the data upload channel parameters, data feature parameters, and image feature parameters all affect the quality of blood potassium data. This embodiment combines the above factors to effectively improve the quality of blood potassium data.

[0041] In this embodiment, the quality of blood potassium data is closely related to the stability and reliability of data transmission. If the average data upload rate is too low, it may cause packet loss, transmission errors or delays during data transmission, thereby affecting the quality of blood potassium data. If the average data upload rate is unstable or insufficient, some blood potassium data may fail to be uploaded successfully, or the uploaded data may be incomplete, thereby reducing the accuracy and integrity of the data. The long data upload delay time may be related to problems such as unstable network connection and complex data processing. These factors will affect the quality of blood potassium data. If the peak value of the channel upload data volume is too high, it may cause network congestion or bandwidth overload, resulting in loss or delay of data transmission, thereby affecting the quality of blood potassium data.

[0042] Among them, the quality analysis result is determined, and the specific process is: obtaining a preset data quality analysis threshold, comparing the data quality analysis value of the blood potassium data with the data quality analysis threshold, when the data quality analysis value of the blood potassium data is less than the data quality analysis threshold, the quality analysis result of the blood potassium data of the renal dialysis patient is determined to be unqualified, and when the data quality analysis value of the blood potassium data is greater than or equal to the data quality analysis threshold, the quality analysis result of the blood potassium data of the renal dialysis patient is determined to be qualified.

[0043] In this embodiment, when the quality analysis result is determined to be qualified, the individual information of the renal dialysis patient is retrieved from the blood potassium data of the renal dialysis patient, and the allocation of the management queue is performed based on the individual information of the renal dialysis patient.

[0044] Among them, the blood potassium data management queue of renal dialysis patients is confirmed, and the specific process is: based on the management queue information of the blood potassium data management center, the information of each management queue includes the individual sample information of each management queue and the dialysis information parameters of each existing data packet in each management queue; the individual sample information of each management queue includes: age reference value, height reference value and weight reference value; based on the dialysis information parameters of each existing data packet in each management queue, wherein the dialysis information parameters include the cumulative number of dialysis sessions, the dialysis frequency and the initial dialysis interval, the dialysis information parameters of each existing data packet are averaged one by one to obtain the mean of the dialysis information parameters of the existing data packets in each management queue, and the mean of the dialysis information parameters includes the mean of the cumulative number of dialysis sessions, the mean of the dialysis frequency and the initial dialysis interval of the existing data packets in each management queue. The mean duration of the dialysis interval; the individual information of the renal dialysis patients is retrieved from the blood potassium data of the renal dialysis patients, specifically including the age, height, weight, cumulative number of dialysis sessions, dialysis frequency and duration of the first dialysis interval of the renal dialysis patients; based on the individual information of the renal dialysis patients, the individual sample information of each management cohort and the mean value of the dialysis information parameters of the existing data packets in each management cohort are synchronously processed to obtain the storage embedding value between the blood potassium data of the renal dialysis patients and each management cohort, and the target management cohort of the blood potassium data of the renal dialysis patients is obtained according to the matching of the storage embedding value, and is used as the management cohort of the blood potassium data of the renal dialysis patients; the storage embedding value between the blood potassium data of the renal dialysis patients and each management cohort is used to characterize the matching degree of the blood potassium data of the renal dialysis patients stored in each management cohort.

[0045] In this embodiment, when matching the blood potassium data management queue of renal dialysis patients, the hyperkalemia risk screening scale score of renal dialysis patients can be added. Each management queue is provided with a standard hyperkalemia risk screening scale score interval. For example, the standard hyperkalemia risk screening scale score interval of renal dialysis patients in a certain management queue is 4-6 points. The above is just a simple example. The standard hyperkalemia risk screening scale score interval of each management queue is determined according to the actual situation. The middle value of the standard hyperkalemia risk screening scale score interval of each management queue is taken as the standard hyperkalemia risk screening scale score of each management queue. Before the renal dialysis patients are assigned to the management queue, the hyperkalemia risk screening scale score of the renal dialysis patients can be obtained. It can be obtained by filling out the hyperkalemia risk screening scale for maintenance hemodialysis patients and calculating it, or by calculating it according to the information provided by the renal dialysis patients. The hyperkalemia risk screening scale for maintenance hemodialysis patients provided in this embodiment is shown in Table 1:

[0046] Table 1 Hyperkalemia risk screening scale for maintenance hemodialysis patients

[0047]

[0048]

[0049] In this embodiment, the specific method for obtaining the stored embedded values ​​between the blood potassium data of the renal dialysis patient and each management queue is as follows:

[0050] ;

[0051] In the formula, Representative potassium data for renal dialysis patients manages the storage embedding values ​​between queues, , To manage the total number of queues, represents the age of the renal dialysis patient, Representative A reference value for the age of existing packets in the management queue, represents the height of a renal dialysis patient, Representative The height reference value of the existing data packets in the management queue, represents the body weight of a renal dialysis patient, Representative The weight reference value of the existing data packets in the management queue, Represents the cumulative number of dialysis sessions for renal dialysis patients, Representative The average number of dialysis times of existing data packets in the management queue, represents the dialysis frequency of renal dialysis patients, Representative The mean dialysis frequency of existing data packets in the management queue, Represents the duration of the initial dialysis interval for renal dialysis patients, Representative The average duration of the first dialysis interval of the existing data packets in the management queue, Represents the score of the Hyperkalemia Risk Screening Scale for Renal Dialysis Patients, Representative Standard hyperkalemia risk screening scale scores in the management cohort, is a natural constant.

[0052] In this embodiment, the individual sample information of each management queue is the sample reference value set by the existing data packet in the comprehensive management queue of each management queue. The setting of the individual sample information of each management queue is used to determine the degree of matching between the individual information of renal dialysis patients and the individual sample information of the management queue.

[0053] In this embodiment, the cumulative number of dialysis sessions is the cumulative number of times a renal dialysis patient undergoes renal dialysis, the dialysis frequency is the frequency of renal dialysis of the renal dialysis patient, the initial dialysis interval is the time between the initial dialysis of the renal dialysis patient and the time when the blood potassium data of the renal dialysis patient is obtained, and the age, height, weight, cumulative number of dialysis sessions, dialysis frequency, and initial dialysis interval are extracted and obtained from the blood potassium data of the renal dialysis patient.

[0054] In the present embodiment, there is a certain correlation between the individual information related parameters, the individual sample information reference value and the dialysis information parameter reference value, and there is an inseparable relationship. The closer the age, height, weight, cumulative dialysis times, dialysis frequency and initial dialysis interval duration of the renal dialysis patient are to the age reference value, height reference value, weight reference value, cumulative dialysis times mean, dialysis frequency mean and initial dialysis interval duration mean, the higher the matching degree of the renal dialysis patient and the management queue is. The age, height and weight of the renal dialysis patient have a certain correlation with the cumulative dialysis times, dialysis frequency and initial dialysis interval duration, and the age, height and weight of the renal dialysis patient affect the frequency and dialysis effect of dialysis. The increase of dialysis frequency is usually accompanied by the increase of the cumulative dialysis times, and the cumulative dialysis times are higher, and the initial dialysis interval duration is shorter, which means that the dialysis frequency is higher, and the larger the dialysis frequency and initial dialysis interval duration are, the smaller the cumulative dialysis times are. The age, height, and weight of kidney dialysis patients reflect the physiological characteristics of the human body. Elderly patients usually suffer from chronic kidney disease for a longer period of time and often need longer dialysis treatment. Therefore, as age increases, the cumulative number of dialysis treatments of patients tends to be greater. Taller people may need more dialysis to maintain electrolyte balance and eliminate waste in the body. Heavier patients have more fluid and metabolic waste in their bodies, so their dialysis needs are usually greater and may need to increase the frequency of dialysis.

[0055] Among them, the target management queue of the blood potassium data of the renal dialysis patients is obtained according to the storage embedding value matching. The specific process is: obtaining the storage embedding values ​​between the blood potassium data of the renal dialysis patients and each management queue, sorting the storage embedding values ​​between the blood potassium data of the renal dialysis patients and each management queue from large to small, and selecting the management queue corresponding to the maximum storage embedding value as the target management queue of the blood potassium data of the renal dialysis patients.

[0056] In this embodiment, the storage embedding value between the blood potassium data of the renal dialysis patient and each management queue represents the matching degree between the individual information of the renal dialysis patient and the individual sample information of each management queue and the dialysis information parameters of the existing data packets in each management queue. The larger the storage embedding value, the closer the parameters related to the individual information of the renal dialysis patient and the individual sample information of the management queue and the dialysis information parameters of the existing data packets in the management queue, that is, the higher the matching degree with the management queue. Therefore, the management queue corresponding to the largest storage embedding value is selected as the target management queue for the blood potassium data of the renal dialysis patient.

[0057] Among them, the blood potassium data management constraint parameters of the renal dialysis patients are obtained by processing, and the specific process is: extracting the blood potassium concentration change curve of each renal dialysis of the renal dialysis patients from the blood potassium data of the renal dialysis patients, and statistically analyzing the characteristic parameters of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patients for mean processing to obtain the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patients; based on the information of each management queue of the blood potassium data management center, obtaining the blood potassium characteristic curve set of each existing data packet in the target management queue and extracting the data quality analysis value of each existing data packet, the blood potassium characteristic curve set includes the peak value, valley value, maximum pulse width and change rate of the blood potassium characteristic curve, and the blood potassium characteristic curve set of each existing data packet is averaged. The characteristic curve set is processed by mean to obtain the blood potassium characteristic curve control set of the data packet in the target management queue; based on the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patient and the blood potassium characteristic curve control set, the data quality analysis value of the blood potassium data and the data quality analysis value of each existing data packet, the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is obtained, and the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is used to characterize the data chimerism degree between the blood potassium data of the renal dialysis patient and the existing data packet, and the blood potassium data management constraint parameter of the renal dialysis patient is determined by the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue.

[0058] In this embodiment, the specific method for obtaining the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is:

[0059] ;

[0060] In the formula, Represents the deep aggregation value between the serum potassium data of renal dialysis patients and the target management cohort, Represents the data quality analysis value of blood potassium data, Represents the target management queue Data quality analysis value of each data packet, , is the total number of packets, Represents the first peak of the blood potassium concentration curve of renal dialysis patients. Represents the average peak value of the potassium profile of the data packets in the target management cohort. Represents the first valley value of the blood potassium concentration curve of renal dialysis patients. Represents the average trough value of the blood potassium characteristic curve of the data packets in the target management queue, Represents the first maximum pulse width of the blood potassium concentration change curve of renal dialysis patients, Represents the average maximum pulse width of the blood potassium characteristic curve of the data packets in the target management queue, Represents the first rate of change of the blood potassium concentration curve of renal dialysis patients, Represents the average rate of change of the blood potassium characteristic curve of the data packets in the target management queue, is a natural constant, the first The data quality analysis value of each data packet is obtained from the database.

[0061] In this embodiment, the blood potassium concentration change curve of each renal dialysis of a renal dialysis patient reflects the change of blood potassium concentration during the dialysis process. For many renal dialysis patients, the blood potassium concentration before dialysis is usually high. When dialysis starts, the excess potassium in the patient's body will be removed from the blood, and the blood potassium concentration will usually drop rapidly. At the end of dialysis, the patient's blood potassium concentration will usually drop significantly. It should be noted that each existing data packet is a blood potassium data packet of each historical doctor and patient in the target management queue of blood potassium data of renal dialysis patients. The characteristic parameters of the blood potassium concentration change curve are the peak value, valley value, maximum pulse width and change rate of the blood potassium concentration change curve. The characteristic parameters of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient are averaged to obtain the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patient.

[0062] Among them, the first characteristic parameter of the blood potassium concentration change curve includes: the first peak value, the first valley value, the first maximum pulse width and the first change rate of the blood potassium concentration change curve of the renal dialysis patient; the blood potassium characteristic curve set specifically includes the average peak value, the average valley value, the average maximum pulse width and the average change rate of the blood potassium characteristic curve of the data packets in the target management queue.

[0063] In this embodiment, the first peak value is the mean of the peak values ​​of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient, the peak value is the maximum value of the blood potassium concentration change curve, the first valley value is the mean of the valley values ​​of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient, the valley value is the minimum value of the blood potassium concentration change curve, the first maximum pulse width is the mean of the maximum pulse width of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient, the maximum pulse width is the maximum duration in the blood potassium concentration curve, the first change rate is the mean of the change rate of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient, and the change rate is the rate at which the blood potassium concentration changes the most. The peak value, valley value, change amplitude and change rate can be obtained through Python software.

[0064] Among them, the blood potassium data management constraint parameters of the renal dialysis patients are obtained, and the specific process is: based on the deep aggregation value between the blood potassium data of the renal dialysis patients and the target management queue, and obtaining the branch queues corresponding to the preset deep aggregation value intervals, the deep aggregation value between the blood potassium data of the renal dialysis patients and the target management queue is mapped with the branch queues corresponding to the preset deep aggregation value intervals, and the branch queue corresponding to the interval where the deep aggregation value between the blood potassium data of the renal dialysis patients and the target management queue is located is obtained, and it is marked as the target sub-queue of the blood potassium data of the renal dialysis patients; the data quality analysis value of the blood potassium data of the renal dialysis patients is counted, and the adaptive inspection cycle corresponding to the preset data quality analysis value interval is matched to obtain the adaptive inspection cycle of the blood potassium data of the renal dialysis patients, which is recorded as the target adaptive inspection cycle of the blood potassium data of the renal dialysis patients; the blood potassium data management queue of the renal dialysis patients, the target sub-queue of the blood potassium data of the renal dialysis patients, and the blood potassium data adaptive inspection cycle are collectively marked as the blood potassium data management constraint parameters.

[0065] In this embodiment, each preset depth aggregation value interval corresponds to a different branch queue. It should be noted that the branch queue is a branch queue under the management queue. Each branch queue stores corresponding blood potassium data. The depth aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is matched with each preset depth aggregation value interval, and the branch queue corresponding to the depth aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is found, and this branch queue is defined as a target sub-queue of blood potassium data of the renal dialysis patient, and the blood potassium data of the renal dialysis patient is allocated to the target sub-queue, and the management measures of the target sub-queue are executed.

[0066] In this embodiment, the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is used to characterize the degree of data integration between the blood potassium data of the renal dialysis patient and each existing data packet. By using the deep integration value, the branch queue of the data packet most similar to the blood potassium data of the renal dialysis patient is obtained, and the blood potassium data of the renal dialysis patient can be automatically allocated and managed effectively. In addition, the target adaptive inspection cycle of the blood potassium data of the renal dialysis patient is the adaptive inspection cycle of the target sub-queue of the blood potassium data of the renal dialysis patient. According to the target adaptive inspection cycle of the blood potassium data of the renal dialysis patient, the target management queue and the target sub-queue are regularly re-checked and replaced to realize the dynamic management of the blood potassium data of the renal dialysis patient.

[0067] The embodiment of the present invention provides a system for intelligent management of blood potassium data of renal dialysis patients, including: a quality analysis module: used to obtain the blood potassium data of renal dialysis patients uploaded by the upload channel for preliminary quality analysis, and determine the quality analysis result, which is qualified or unqualified; an information retrieval module: used to retrieve the individual information of renal dialysis patients from the blood potassium data of renal dialysis patients when the quality analysis result is determined to be qualified; a management queue matching module: used to count the management queue information of the blood potassium data management center, and process it in combination with the individual information of the renal dialysis patients to confirm the blood potassium data management queue of the renal dialysis patients; a management constraint parameter acquisition module: used to manage the queue according to the blood potassium data of the renal dialysis patients, and process it to obtain the blood potassium data management constraint parameters of the renal dialysis patients. The following points need to be explained:

[0068] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0069] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0070] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0071] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intelligent management of blood potassium data for renal dialysis patients, characterized in that: include: Obtaining the blood potassium data of the renal dialysis patient uploaded by the upload channel for preliminary quality analysis, and determining the quality analysis result, wherein the quality analysis result is qualified or unqualified; When the quality analysis result is judged as qualified, the individual information of the renal dialysis patient is retrieved from the blood potassium data of the renal dialysis patient; Collect the information of each management cohort of the blood potassium data management center, and process it in combination with the individual information of renal dialysis patients to confirm the blood potassium data management cohort of renal dialysis patients; A queue is managed according to the blood potassium data of renal dialysis patients, and the blood potassium data management constraint parameters of renal dialysis patients are obtained by processing.

2. The intelligent management method for blood potassium data of renal dialysis patients according to claim 1, characterized in that: The blood potassium data of the renal dialysis patients uploaded by the upload channel is obtained and a preliminary quality analysis is performed, and the specific process is as follows: Extracting data upload channel parameters from the upload channel, wherein the data upload channel parameters include: data upload delay time, data upload average rate and channel upload data volume peak value; Extracting data characteristic parameters and image characteristic parameters from the uploaded blood potassium data of renal dialysis patients, wherein the data characteristic parameters include: the number of missing fields filled, the density of data field filling and the accumulated data format error value, and the image characteristic parameters include: the average image resolution, the average image contrast and the average image signal-to-noise ratio; The data quality analysis value of blood potassium data is obtained by comprehensive processing based on data upload channel parameters, data feature parameters and image feature parameters.

3. The intelligent management method for blood potassium data of renal dialysis patients according to claim 2, characterized in that: The specific method for obtaining the data quality analysis value of the blood potassium data is: ； In the formula, Represents the data quality analysis value of blood potassium data, Represents the data upload delay time, Represents the preset data upload delay time threshold. Represents the average data upload rate, Represents the preset average data upload rate threshold. Represents the peak value of the channel uploaded data. Represents the preset channel upload data volume threshold. Represents the number of missing fields to be filled. Represents the preset field filling missing number threshold, Represents the data field filling density, Represents the preset data field filling density reference value, Represents the cumulative data format error value, Represents the preset cumulative data format error value threshold, Represents the average image resolution, Represents the preset average image resolution reference value. represents the average contrast of the image, Represents the preset image average contrast reference value, represents the average signal-to-noise ratio of the image, Represents the preset image average signal-to-noise ratio threshold, is a natural constant.

4. The intelligent management method for blood potassium data of renal dialysis patients according to claim 2, characterized in that: The determination results in quality analysis, and the specific process is as follows: A preset data quality analysis threshold is obtained, and the data quality analysis value of the blood potassium data is compared with the data quality analysis threshold. When the data quality analysis value of the blood potassium data is less than the data quality analysis threshold, the quality analysis result of the blood potassium data of the renal dialysis patient is judged to be unqualified; when the data quality analysis value of the blood potassium data is greater than or equal to the data quality analysis threshold, the quality analysis result of the blood potassium data of the renal dialysis patient is judged to be qualified.

5. The intelligent management method for blood potassium data of renal dialysis patients according to claim 1, characterized in that: The specific process of confirming the blood potassium data management cohort of renal dialysis patients is as follows: Based on the management queue information of the blood potassium data management center, the management queue information includes the individual sample information of each management queue and the dialysis information parameters of each existing data packet in each management queue; The individual sample information of each management cohort includes: age reference value, height reference value and weight reference value; Based on the dialysis information parameters of each existing data packet in each management queue, wherein the dialysis information parameters include the cumulative number of dialysis sessions, the dialysis frequency, and the initial dialysis interval duration, the dialysis information parameters of each existing data packet are averaged one by one to obtain the average of the dialysis information parameters of the existing data packets in each management queue, wherein the average of the dialysis information parameters includes the average of the cumulative number of dialysis sessions, the average of the dialysis frequency, and the initial dialysis interval duration of the existing data packets in each management queue; Retrieving individual information of renal dialysis patients from their blood potassium data, including age, height, weight, cumulative number of dialysis sessions, dialysis frequency, and duration of the first dialysis session; Based on the individual information of renal dialysis patients, the individual sample information of each management queue and the mean value of the dialysis information parameters of the existing data packets in each management queue are synchronously processed to obtain the storage embedding value between the blood potassium data of renal dialysis patients and each management queue, and the target management queue of the blood potassium data of renal dialysis patients is obtained according to the matching of the storage embedding value, and used as the blood potassium data management queue of renal dialysis patients; The storage embedding value between the blood potassium data of the renal dialysis patient and each management cohort is used to characterize the matching degree of the blood potassium data of the renal dialysis patient stored in each management cohort.

6. The intelligent management method for blood potassium data of renal dialysis patients according to claim 5, characterized in that: The target management queue of the blood potassium data of the renal dialysis patients is obtained by matching the stored embedded values, and the specific process is as follows: The storage embedding values ​​between the blood potassium data of renal dialysis patients and each management queue are obtained, the storage embedding values ​​between the blood potassium data of renal dialysis patients and each management queue are sorted from large to small, and the management queue corresponding to the maximum storage embedding value is selected as the target management queue for the blood potassium data of renal dialysis patients.

7. The intelligent management method for blood potassium data of renal dialysis patients according to claim 1, characterized in that: The processing to obtain the blood potassium data management constraint parameters of the renal dialysis patient is specifically performed as follows: Extracting the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient from the blood potassium data of the renal dialysis patient, and statistically analyzing the characteristic parameters of the blood potassium concentration change curve of each renal dialysis of the renal dialysis patient for mean processing to obtain the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patient; Based on the information of each management queue of the blood potassium data management center, the blood potassium characteristic curve set of each existing data packet in the target management queue is obtained and the data quality analysis value of each existing data packet is extracted. The blood potassium characteristic curve set includes the peak value, valley value, maximum pulse width and change rate of the blood potassium characteristic curve. The blood potassium characteristic curve set of each existing data packet is averaged to obtain the blood potassium characteristic curve control set of the data packet in the target management queue; Based on the first characteristic parameter of the blood potassium concentration change curve of the renal dialysis patient and the control set of the blood potassium characteristic curve, the data quality analysis value of the blood potassium data and the data quality analysis value of each existing data packet, the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is processed to obtain the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue. The deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue is used to characterize the degree of data chimerism between the blood potassium data of the renal dialysis patient and the existing data packets, and the blood potassium data management constraint parameters of the renal dialysis patient are determined through the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management queue.

8. The intelligent management method for blood potassium data of renal dialysis patients according to claim 7, characterized in that: The first characteristic parameter of the blood potassium concentration change curve includes: a first peak value, a first valley value, a first maximum pulse width and a first change rate of the blood potassium concentration change curve of the renal dialysis patient; The blood potassium characteristic curve set specifically includes an average peak value, an average valley value, an average maximum pulse width, and an average change rate of the blood potassium characteristic curves of the data packets in the target management queue.

9. The intelligent management method for blood potassium data of renal dialysis patients according to claim 7, characterized in that: The specific process of obtaining the blood potassium data management constraint parameters of the renal dialysis patient is as follows: Based on the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management cohort, the branch cohorts corresponding to the preset intervals of each deep aggregation value are obtained, and the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management cohort is mapped with the branch cohorts corresponding to the preset intervals of each deep aggregation value, so as to obtain the branch cohort corresponding to the interval where the deep aggregation value between the blood potassium data of the renal dialysis patient and the target management cohort is located, and the branch cohort is marked as the target sub-cohort of the blood potassium data of the renal dialysis patient; The data quality analysis values ​​of the blood potassium data of the renal dialysis patients are counted, and matched with the adaptive inspection cycles corresponding to the preset data quality analysis value intervals to obtain the adaptive inspection cycle of the blood potassium data of the renal dialysis patients, which is recorded as the target adaptive inspection cycle of the blood potassium data of the renal dialysis patients; The blood potassium data management queue of renal dialysis patients, the blood potassium data target sub-queue of renal dialysis patients, and the blood potassium data adaptive inspection period are collectively marked as blood potassium data management constraint parameters.

10. A system using the method for intelligent management of blood potassium data of renal dialysis patients as claimed in any one of claims 1 to 9, characterized in that: include: Quality analysis module: used to obtain the blood potassium data of the renal dialysis patients uploaded by the upload channel for preliminary quality analysis, and determine the quality analysis result, which is qualified or unqualified; Information retrieval module: used to retrieve individual information of renal dialysis patients from their blood potassium data when the quality analysis result is determined to be qualified; Management queue matching module: used to collect statistics on the management queue information of the blood potassium data management center, and process it in combination with the individual information of renal dialysis patients to confirm the blood potassium data management queue of renal dialysis patients; Management constraint parameter acquisition module: used to manage the queue according to the blood potassium data of the renal dialysis patients, and process and obtain the blood potassium data management constraint parameters of the renal dialysis patients.

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