Intelligent monitoring method, system and device for automated peritoneal dialysis

Abstract

The present invention relates to the field of peritoneal dialysis monitoring technology, and specifically to an intelligent monitoring method, system and device for automated peritoneal dialysis. The method obtains various physical parameters of each kidney patient's examination and various dialysis data of each automated peritoneal dialysis; determines similar patients of each kidney patient based on the differences in physical parameter changes between each kidney patient and each other kidney patient; obtains the special degree of each kidney patient of the current automated peritoneal dialysis based on the differences in various dialysis data between each kidney patient of the current automated peritoneal dialysis and each of its similar patients each time they undergo automated peritoneal dialysis, and then determines the current treatment plan for each kidney patient. The present invention determines similar patients for each kidney patient, and then accurately and efficiently determines the current treatment plan for each kidney patient based on similar patients, thereby ensuring that kidney patients receive timely treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of peritoneal dialysis monitoring, and in particular to an intelligent monitoring method, system and device for automated peritoneal dialysis. Background Art

[0002] Peritoneal dialysis is an important way for kidney patients to maintain their lives. Peritoneal dialysis is less harmful to the body of kidney patients. At the same time, kidney patients can perform automated peritoneal dialysis at home through an automated peritoneal dialysis machine, which provides great convenience for kidney patients. Among them, the doctor first sets the various indicators of the automated peritoneal dialysis machine in advance according to the physical parameters of the kidney patient, and then the kidney patient can perform automated peritoneal dialysis at home through the automated peritoneal dialysis machine set in advance by the doctor. The terminal of the automated peritoneal dialysis machine is connected to the hospital system, which is convenient for doctors to monitor the peritoneal dialysis of kidney patients in real time, which is conducive to doctors paying attention to the physical condition of kidney patients in real time.

[0003] In the existing method, doctors directly analyze various dialysis data of each renal patient each time they undergo automated peritoneal dialysis, determine the physical condition of each renal patient in real time, and make corresponding treatment plans for each renal patient. However, in actual situations, each doctor may face multiple renal patients at the same time. If the doctor analyzes various dialysis data of each renal patient's automated peritoneal dialysis separately, it will take a lot of time, which is not conducive to timely making reasonable treatment plans for each renal patient, and may delay the reasonable treatment time of renal patients. Summary of the invention

[0004] In order to solve the technical problem that it takes a lot of time for doctors to analyze various dialysis data of each kidney patient's automated peritoneal dialysis separately, which is not conducive to timely making reasonable treatment plans for each kidney patient, the purpose of the present invention is to provide an intelligent monitoring method, system and device for automated peritoneal dialysis. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides an intelligent monitoring method for automated peritoneal dialysis, the method comprising the following steps:

[0006] Obtain various physical parameters of each renal patient in the hospital system for each hospital examination and various dialysis data for each automated peritoneal dialysis;

[0007] According to the difference in the changes of each physical parameter between each renal patient and each other renal patient, the similarity degree of each renal patient with each other renal patient is obtained, and then the similar patients of each renal patient are determined;

[0008] Obtaining the special degree of each renal patient currently undergoing automated peritoneal dialysis according to the differences in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient undergoing automated peritoneal dialysis each time;

[0009] The current management of each renal patient is determined by the degree of specificity.

[0010] Furthermore, the method for obtaining the similarity degree is:

[0011] According to the difference in the change of each physical parameter of each renal patient with each other physical parameter, the consistency degree of each physical parameter of each renal patient with each other physical parameter is obtained;

[0012] Obtain similar weights for each physical parameter based on the fluctuations in how well each physical parameter agrees with each other physical parameter across all renal patients;

[0013] According to the difference of each physical parameter between each renal patient and each other renal patient and the similarity weight of each physical parameter, the similarity degree of each renal patient with each other renal patient is obtained.

[0014] Furthermore, the method for obtaining the similarity weight is:

[0015] According to the fluctuation of the consistency degree between each physical parameter and each other physical parameter in all kidney patients, the overall consistency degree of each physical parameter with each other physical parameter is obtained;

[0016] The result of accumulating and normalizing the overall consistency value of each body parameter with each other body parameter is taken as the similarity weight of each body parameter.

[0017] Furthermore, the method for obtaining the similarity degree is:

[0018] For any renal patient, a matching pair of each physical parameter between the renal patient and every other renal patient is obtained by using the DTW algorithm;

[0019] Correcting the difference between two body parameters in each matching pair of each body parameter according to the similarity weight of each body parameter to obtain a corrected difference of each matching pair;

[0020] According to the corrected difference of each matching pair between the kidney patient and each other kidney patient, the similarity between the kidney patient and each other kidney patient is obtained; wherein the corrected difference is negatively correlated with the similarity.

[0021] Furthermore, the method for obtaining similar patients of each kidney patient is:

[0022] For any kidney patient, the similarity values ​​between the kidney patient and each other kidney patient are arranged from large to small to obtain a similarity value sequence of the kidney patient;

[0023] Other kidney patients corresponding to the first preset number of similarity values ​​in the similarity value sequence are all regarded as similar patients of the kidney patient.

[0024] Furthermore, the method for obtaining the special degree is:

[0025] For any renal patient currently undergoing automated peritoneal dialysis, the differences between the current various dialysis data of the renal patient and the various dialysis data of each automated peritoneal dialysis of each similar patient are obtained and accumulated, as the difference between the current automated peritoneal dialysis of the renal patient and each automated peritoneal dialysis of each similar patient;

[0026] The result of normalizing the minimum difference degree is taken as the special degree of the kidney patient.

[0027] Further, the method for determining the current treatment plan for each kidney patient according to the degree of specialness is:

[0028] When the degree of specificity is within the range of the degree of specificity, determining a current treatment plan for the corresponding kidney patient with reference to historical treatment plans for similar patients of the corresponding kidney patient;

[0029] When the special degree is not within the special degree range, the corresponding kidney patient goes to the hospital for testing to determine the current treatment plan for the corresponding kidney patient.

[0030] Furthermore, the renal patients in the hospital system include historical renal patients and current renal patients; wherein, the historical renal patients are renal patients who no longer undergo automated peritoneal dialysis, and the current renal patients are renal patients who undergo automated peritoneal dialysis.

[0031] In a second aspect, another embodiment of the present invention provides an intelligent monitoring system for automated peritoneal dialysis, the system comprising: a data acquisition module, a similar patient acquisition module, a special degree acquisition module and a data processing module.

[0032] A data acquisition module, used for acquiring various physical parameters of each kidney patient in the hospital system each time he is examined in the hospital and various dialysis data of each automated peritoneal dialysis;

[0033] A similar patient acquisition module is used to acquire the similarity between each renal patient and each other renal patient according to the difference in the change of each physical parameter between each renal patient and each other renal patient, and then determine the similar patients of each renal patient;

[0034] A special degree acquisition module, used for acquiring the special degree of each renal patient currently undergoing automated peritoneal dialysis according to the differences in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient each time undergoing automated peritoneal dialysis;

[0035] A data processing module is used to determine the current treatment plan for each kidney patient according to the degree of specialization.

[0036] In a third aspect, another embodiment of the present invention provides an intelligent monitoring device for automated peritoneal dialysis, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0037] The present invention has the following beneficial effects:

[0038] According to the difference in changes in each physical parameter between each kidney patient and each other kidney patient, the similarity between each kidney patient and each other kidney patient is obtained, accurately reflecting the similarity between the physical conditions of any two kidney patients, and then accurately determining the similar patients of each kidney patient, so as to facilitate the subsequent accurate and efficient analysis of the current treatment plan for each kidney patient currently undergoing automated peritoneal dialysis, and timely and reasonable treatment of kidney patients; and then according to the differences in various dialysis data between each kidney patient currently undergoing automated peritoneal dialysis and each similar patient each time undergoing automated peritoneal dialysis, the special degree of each kidney patient currently undergoing automated peritoneal dialysis is obtained, accurately reflecting the situation in which the dialysis data of each kidney patient currently undergoing automated peritoneal dialysis appears in the historical dialysis data of its similar patients. Therefore, accurately and efficiently determining the current treatment plan for each kidney patient based on the special degree is conducive to timely and reasonable treatment of kidney patients, saving a lot of manpower and material resources for analyzing treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic flow chart of an intelligent monitoring method for automated peritoneal dialysis provided by one embodiment of the present invention;

[0041] Figure 2 A flow chart of a method for obtaining a similarity level provided by an embodiment of the present invention;

[0042] Figure 3 A flow chart of a method for obtaining similarity weights provided by an embodiment of the present invention;

[0043] Figure 4 A structural diagram of an intelligent monitoring system for automated peritoneal dialysis provided by one embodiment of the present invention;

[0044] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent monitoring method, system and device for automated peritoneal dialysis proposed by the present invention, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The specific scheme of the intelligent monitoring method, system and device for automated peritoneal dialysis provided by the present invention is described in detail below with reference to the accompanying drawings.

[0048] Embodiment 1:

[0049] The present invention proposes an intelligent monitoring method for automated peritoneal dialysis. Figure 1 , which shows a schematic flow chart of an intelligent monitoring method for automated peritoneal dialysis provided by an embodiment of the present invention, the method comprising the following steps:

[0050] Step S1: Acquire various physical parameters of each kidney patient in the hospital system each time he or she is examined in the hospital and various dialysis data of each automated peritoneal dialysis.

[0051] Specifically, this embodiment takes any hospital as an example for analysis, and the implementer can take one or more hospitals as examples for analysis according to the actual situation, which is not limited here. It should be noted that the hospitals that appear later are all the hospitals selected by this embodiment. The case information of each kidney patient who comes to the hospital for treatment will be recorded in the hospital system. In the hospital system, each doctor can obtain various physical parameters of each kidney patient who comes to the hospital for examination each time, wherein the physical parameters include white blood cell content, urine protein content, urine red blood cell content, blood creatinine content, blood urea nitrogen content, creatinine clearance rate and other various physical parameters. In actual situations, each kidney patient needs to come to the hospital for examination regularly. Therefore, in the hospital system, each kidney patient may correspond to each physical parameter of multiple examinations. In this embodiment, each kidney patient comes to the hospital for examination every month, and the implementer can set the time interval for each kidney patient to come to the hospital regularly according to the actual situation, which is not limited here. It should be noted that the renal patients in the hospital system include historical renal patients and current renal patients, wherein historical renal patients refer to renal patients who no longer come to the hospital for examination and no longer perform automated peritoneal dialysis at home, and current renal patients refer to renal patients who currently come to the hospital for examination regularly and perform automated peritoneal dialysis at home every day. In order to enable each renal patient to accurately perform peritoneal dialysis detection at home through an automated peritoneal dialysis machine, the doctor needs to set a new index of the automated peritoneal dialysis machine for each renal patient according to the various physical parameters of each renal patient who comes to the hospital for examination each time. The renal patient performs automated peritoneal dialysis at home once a day according to the doctor's advice and the set automated peritoneal dialysis machine, and obtains various dialysis data of each renal patient for each automated peritoneal dialysis, wherein the dialysis data packet contains various dialysis data such as dialysis fluid temperature, dialysis fluid flow rate, ultrafiltration volume, dialysis time, dialysis fluid pressure, 24h urine volume of each renal patient, and blood pressure of each renal patient. Among them, the 24-hour urine volume and blood pressure of each kidney patient are manually uploaded by the kidney patient after self-testing, which is conducive to the doctor to more accurately monitor the physical condition of each kidney patient at the terminal. It should be noted that the terminal of the automated peritoneal dialysis machine is connected to the hospital system, and the doctor can observe the peritoneal dialysis situation of each kidney patient at any time and determine the physical condition of each current kidney patient in a timely manner.

[0052] The scenario of this embodiment is: in actual situations, each doctor faces multiple kidney patients at the same time. If the dialysis data of each kidney patient is analyzed, it will affect the doctor's work efficiency. Therefore, this embodiment first obtains similar patients for each kidney patient based on the differences in changes in each physical parameter of different kidney patients in the hospital system, and then determines the current treatment plan for each current kidney patient based on the treatment plans corresponding to the dialysis data of the similar patients, thereby improving the doctor's work efficiency, and at the same time promptly determining the treatment plan for each kidney patient, so as to more accurately detect the condition of each kidney patient.

[0053] Step S2: According to the difference in changes in each physical parameter between each renal patient and each other renal patient, the similarity between each renal patient and each other renal patient is obtained, and then similar patients of each renal patient are determined.

[0054] In order to more accurately and efficiently obtain the current treatment plan for each kidney patient currently undergoing automated peritoneal dialysis, this embodiment needs to obtain the similarity between each kidney patient and each other kidney patient based on the difference in changes in each physical parameter between each kidney patient and each other kidney patient in the hospital system. The higher the similarity, the more similar the physical conditions of the two kidney patients are. Then, similar patients for each kidney patient are determined based on the similarity, that is, similar patients for each kidney patient currently undergoing automated peritoneal dialysis are determined, and then the current treatment plan for each kidney patient is determined based on the treatment plan made by the doctor for the dialysis data of the similar patients of each kidney patient.

[0055] Preferably, in some possible implementation methods of this embodiment, the method for obtaining the similarity degree can be found in Figure 2 , which shows a flow chart of a method for obtaining a similarity degree provided by an embodiment of the present invention, the method comprising:

[0056] Step S201: Obtain the consistency degree between each physical parameter of each renal patient and each other physical parameter according to the difference in change between each physical parameter of each renal patient and each other physical parameter.

[0057] In order to better analyze the physical condition of each kidney patient, each physical parameter of all the examination times of each kidney patient is fitted into a curve, wherein the horizontal coordinate corresponding to the curve is the number of examinations and is arranged in chronological order, and the vertical coordinate corresponding to the curve is the physical parameter, wherein the curve fitting method is a well-known technology and will not be described in detail. For any kidney patient, the smaller the difference between the fitting curve of a certain physical parameter of the kidney patient and the fitting curve of each other physical parameter, the more consistent the change of this physical parameter of the kidney patient and each other physical parameter. In order to more accurately analyze the degree of consistency of changes between different types of physical parameters of each kidney patient, the present embodiment standardizes the fitting curve of each physical parameter of each kidney patient to ensure that the fluctuation range of each fitting curve is within the same range.

[0058] This embodiment obtains the variance of the tangent slope of all body parameters on each fitting curve as the fluctuation degree value of the corresponding fitting curve. The larger the fluctuation degree value, the greater the degree of change of the corresponding type of body parameters of the corresponding kidney patient. Among them, the method for obtaining the tangent slope is a well-known technology and will not be described in detail. For any kidney patient, when the fluctuation degree value of the x-th body parameter fitting curve of the kidney patient is equal to the fluctuation degree value of the y-th body parameter fitting curve, it means that the consistency degree of the x-th body parameter of the kidney patient is higher. In order to more accurately analyze the consistency degree of the x-th body parameter and the y-th body parameter of the kidney patient, this embodiment specifically quantifies the consistency degree as a consistency degree value, wherein the expression of the consistency degree value is: ; In the formula, is the consistency value between the xth physical parameter and the yth physical parameter of the i-th kidney patient; is the fluctuation degree value of the xth body parameter fitting curve of the i-th kidney patient; is the fluctuation degree value of the yth physical parameter fitting curve of the i-th kidney patient; is an absolute value function; exp is an exponential function with a natural constant as the base. In other embodiments, and The difference, get , not limited here The greater the consistency value, the more consistent the changes of the two corresponding body parameters are.

[0059] Step S202: Obtain a similarity weight of each physical parameter according to the fluctuation of the consistency between each physical parameter of all kidney patients and each other physical parameter.

[0060] When the consistency of a certain physical parameter with each other physical parameter in all kidney patients is the same, it means that this physical parameter is more likely to be the main physical parameter among all the physical parameters of kidney patients, and can better show the characteristics of each kidney patient. The difference of this physical parameter between different kidney patients can more accurately show the similarity between different kidney patients. When participating in obtaining the similarity between different kidney patients, the difference of this physical parameter needs to participate more, so the similarity weight of this physical parameter should be larger. Therefore, this embodiment obtains the similarity weight of each physical parameter according to the fluctuation of the consistency of each physical parameter of all kidney patients with each other physical parameter. Among them, the larger the similarity weight, the more the corresponding physical parameter can characterize the characteristics of each kidney patient.

[0061] In some possible implementation methods of this embodiment, the method of obtaining the similarity weight can be found in Figure 3 , which shows a flow chart of a method for obtaining similarity weights provided by an embodiment of the present invention, the method comprising:

[0062] Step S202-1: according to the fluctuation of the consistency degree between each body parameter and each other body parameter of all kidney patients, the overall consistency degree of each body parameter with each other body parameter is obtained.

[0063] When the consistency of any two physical parameters of all kidney patients is more similar, it means that the degree of change of the corresponding two physical parameters in all kidney patients is more consistent, and the relationship between the corresponding two physical parameters is more stable. Therefore, according to the fluctuation of the consistency of each physical parameter of all kidney patients with each other physical parameter, the overall consistency of each physical parameter with each other physical parameter is obtained. The higher the overall consistency, the more stable the relationship between the two physical parameters. In order to more accurately show the overall consistency between any two physical parameters, this embodiment specifically quantifies the overall consistency to obtain the overall consistency value. The larger the overall consistency value, the more stable the relationship between the two physical parameters. Among them, the expression of the overall consistency value is: ; In the formula, is the overall consistency value between the xth physical parameter and the qth physical parameter; N is the total number of kidney patients in the hospital system; is the consistency value between the xth physical parameter and the qth physical parameter of the nth kidney patient; is the mean of the consistency between the xth body parameter and the qth body parameter; is the absolute value function; is an exponential function with a natural constant as its base.

[0064] Step S202-2: Accumulate and normalize the overall consistency values ​​of each body parameter with each other body parameter to obtain a similarity weight of each body parameter.

[0065] When the overall consistency value of a certain physical parameter is larger than that of each other physical parameter, it means that the relationship between this physical parameter and each other physical parameter is more stable, and this physical parameter can better reflect the characteristics of each kidney patient. Therefore, this physical parameter needs to be considered when analyzing the similarities between different kidney patients. Therefore, the overall consistency value of each physical parameter and each other physical parameter is accumulated and normalized as the similarity weight of each physical parameter. The larger the similarity weight, the more accurately the corresponding type of physical parameter can characterize the characteristics of each kidney patient. Among them, the expression of similarity weight is: ; In the formula, is the similarity weight of the xth body parameter; M is the total number of body parameter types; is the overall consistency value of the xth body parameter and the qth body parameter; norm is the normalization function. It should be noted that normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0066] Step S203: Obtain the similarity between each renal patient and each other renal patient according to the difference of each physical parameter between each renal patient and each other renal patient and the similarity weight of each physical parameter.

[0067] In order to determine similar patients of each kidney patient, this embodiment corrects the difference of each physical parameter between each kidney patient and each other kidney patient through the similarity weight of each physical parameter, accurately obtains the physical parameter difference between each kidney patient and each other kidney patient, and then determines the similarity degree between each kidney patient and each other kidney patient. The higher the similarity degree, the more similar the physical conditions of the two kidney patients are.

[0068] In some possible implementation methods of this embodiment, the method for obtaining the degree of similarity is as follows: in actual situations, different kidney patients in the hospital system go to the hospital for examination at different times. Therefore, for any kidney patient, this embodiment obtains the matching pairs of each physical parameter between the kidney patient and each other kidney patient through the Dynamic Time Warping (DTW) algorithm; wherein, the Dynamic Time Warping (DTW) algorithm is a well-known technology and will not be described in detail. Each matching pair contains two physical parameters of the same type. Then, according to the similarity weight of each physical parameter, the absolute value of the difference between the two physical parameters in each matching pair of each physical parameter is corrected to obtain the corrected difference of each matching pair. wherein, the expression of the corrected difference is: ; In the formula, is the corrected difference of the kth matched pair of the xth physical parameter between the ath kidney patient and the bth kidney patient; is the similarity weight of the xth body parameter; is the first physical parameter in the kth matching pair of the xth physical parameter between the ath kidney patient and the bth kidney patient; is the second physical parameter in the kth matching pair of the xth physical parameter between the ath kidney patient and the bth kidney patient; is an absolute value function. The larger the corrected difference, the more dissimilar the corresponding two physical parameters are, which indirectly indicates that the physical conditions of the corresponding two kidney patients are more dissimilar. Then, based on the corrected difference of each matching pair between the kidney patient and each other kidney patient, the similarity between the kidney patient and each other kidney patient is obtained; wherein the corrected difference and the similarity are negatively correlated. In order to accurately determine the similarity between each kidney patient and each other kidney patient, this embodiment specifically quantifies the similarity to obtain a similarity value. The larger the similarity value, the more similar the physical conditions of the corresponding two kidney patients are. wherein, the expression of the similarity value is: ; In the formula, is the similarity value between the a-th kidney patient and the b-th kidney patient; M is the total number of types of physical parameters; is the total number of matching pairs of the mth physical parameter between the ath kidney patient and the bth kidney patient; is the corrected difference of the vth matched pair of the mth physical parameter between the ath kidney patient and the bth kidney patient; exp is an exponential function with a natural constant as the base. In other embodiments, Get ,in, It is the first preset constant, which is greater than 0. It is set to 1 here to avoid the denominator being 0. The implementer can set it according to the actual situation. The size of is not limited here. The implementer can determine it according to the actual situation. The method for obtaining is not limited here. So far, the similarity value between each kidney patient and each other kidney patient is obtained.

[0069] It is known that the larger the similarity value is, the more similar the physical conditions of the two kidney patients are. Therefore, similar patients of each kidney patient are obtained according to the similarity. Preferably, in some methods that may be implemented in this embodiment, the method for obtaining similar patients is: for any kidney patient, the similarity values ​​between the kidney patient and each other kidney patient are arranged from large to small to obtain a similarity value sequence of the kidney patient; other kidney patients corresponding to the first first preset number of similarity values ​​in the similarity value sequence are all used as similar patients of the kidney patient. In this embodiment, the first preset number is set to 5. The implementer can set the size of the first preset number according to the actual situation, and it is not limited here. At this point, similar patients of each kidney patient are obtained.

[0070] Step S3: Obtain the special degree of each renal patient currently undergoing automated peritoneal dialysis based on the differences in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient each time undergoing automated peritoneal dialysis.

[0071] Specifically, in order to help doctors analyze the treatment plan for each renal patient currently undergoing automated peritoneal dialysis in real time, this embodiment obtains the difference between the current various dialysis data of each renal patient currently undergoing automated peritoneal dialysis and each dialysis data of the same type of each similar patient undergoing automated peritoneal dialysis each time. The greater the difference in dialysis data, the more likely it is that the current dialysis data of the corresponding renal patient may not have appeared in similar patients, which indirectly indicates that the current dialysis data of the corresponding renal patient is more special, and doctors need to focus on analyzing special renal patients. Therefore, this embodiment obtains the degree of specialness of each renal patient currently undergoing automated peritoneal dialysis based on the difference in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient undergoing automated peritoneal dialysis each time.

[0072] Preferably, in some possible methods of this embodiment, the method for obtaining the special degree is: for any renal patient currently undergoing automated peritoneal dialysis, the absolute values ​​of the differences between the current various dialysis data of the renal patient and the various dialysis data of each similar patient undergoing automated peritoneal dialysis are obtained respectively and the results are accumulated as the degree of difference between the current automated peritoneal dialysis of the renal patient and each automated peritoneal dialysis of each similar patient; when the degree of difference is smaller, it means that the current automated peritoneal dialysis of the renal patient is more similar to the results of the corresponding automated peritoneal dialysis of similar patients, and then the current treatment plan of the renal patient can be determined based on the historical treatment plan of the corresponding automated peritoneal dialysis. In order to more accurately analyze the degree of difference between the current automated peritoneal dialysis of each renal patient and each automated peritoneal dialysis of each similar patient, this embodiment specifically quantifies the degree of difference to obtain the degree of difference value. The smaller the degree of difference value, the more consistent the results of the corresponding two automated peritoneal dialysis. Among them, the expression of the degree of difference value is: ; In the formula, is the difference between the current automated peritoneal dialysis of the d-th kidney patient and the g-th automated peritoneal dialysis of the e-th similar patient; H is the total number of types of dialysis data; is the current h-th dialysis data of the d-th kidney patient; The hth dialysis data of the gth automated peritoneal dialysis of the eth similar patient with the dth kidney; is an absolute value function. Then the result of normalizing the minimum difference degree is taken as the special degree of the kidney patient. In order to analyze more clearly, the special degree is specifically quantified to obtain the special degree value, where the larger the characteristic degree value, the less the current treatment plan of the corresponding kidney patient can refer to the historical treatment plan of similar patients. The expression of the characteristic degree value is: ; In the formula, is the characteristic degree value of the dth kidney patient currently undergoing automated peritoneal dialysis; is the minimum difference value corresponding to the dth kidney patient of the current automated peritoneal dialysis; norm is the normalization function. It should be noted that normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0073] Step S4: Determine the current treatment plan for each kidney patient according to the degree of specificity.

[0074] Specifically, this embodiment obtains the characteristic degree value of each renal patient currently undergoing automated peritoneal dialysis, and determines the current treatment plan for each renal patient. In this embodiment of the present invention, the special degree value threshold is set to 0.3, and the implementer can set the size of the special degree value threshold according to the actual situation, which is not limited here. When the special degree value is less than the special degree value threshold, the current treatment plan for the corresponding renal patient can be determined by referring to the historical treatment plan for the corresponding automated peritoneal dialysis of the corresponding similar patient; when the special degree value is greater than or equal to the special degree value threshold, the corresponding renal patient needs to go to the hospital for offline testing, and the doctor determines the current treatment plan for the corresponding renal patient based on more accurate and comprehensive test data, ensures the rationality of the current treatment plan for each renal patient, and promptly makes corresponding treatment for the condition of each renal patient currently undergoing automated peritoneal dialysis. Among them, the treatment plan includes other treatment plans such as renal patients continuing to undergo dialysis at home, going to the hospital for offline monitoring, and direct hospitalization.

[0075] In summary, this embodiment obtains various physical parameters of each examination of a kidney patient and various dialysis data of each automated peritoneal dialysis; determines similar patients of each kidney patient according to the difference in physical parameter changes between each kidney patient and each other kidney patient; obtains the special degree of each kidney patient of the current automated peritoneal dialysis according to the difference in various dialysis data between each kidney patient of the current automated peritoneal dialysis and each similar patient of the current automated peritoneal dialysis, and then determines the current treatment plan of each kidney patient. The present invention determines similar patients of each kidney patient, and then accurately and efficiently determines the current treatment plan of each kidney patient based on similar patients, thereby ensuring that kidney patients receive timely treatment.

[0076] Embodiment 2:

[0077] The present invention also proposes an intelligent monitoring system for automated peritoneal dialysis, see Figure 4 , which shows a structural diagram of an intelligent monitoring system for automated peritoneal dialysis provided by an embodiment of the present invention. The system includes: a data acquisition module 10, a similar patient acquisition module 20, a special degree acquisition module 30 and a data processing module 40.

[0078] The data acquisition module 10 is used to acquire various physical parameters of each kidney patient in the hospital system each time he or she is examined in the hospital and various dialysis data of each automated peritoneal dialysis.

[0079] The similar patient acquisition module 20 is used to acquire the similarity between each renal patient and each other renal patient according to the difference in changes of each physical parameter between each renal patient and each other renal patient, and further determine the similar patients of each renal patient.

[0080] The special degree acquisition module 30 is used to acquire the special degree of each renal patient currently undergoing automated peritoneal dialysis based on the differences in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient each time undergoing automated peritoneal dialysis.

[0081] The data processing module 40 is used to determine the current treatment plan for each kidney patient according to the degree of specialization.

[0082] It should be noted that: the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the intelligent monitoring system for automated peritoneal dialysis and the intelligent monitoring method for automated peritoneal dialysis provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0083] Embodiment 3:

[0084] The present invention also proposes an intelligent monitoring device for automated peritoneal dialysis, the device comprising a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute an intelligent monitoring method for automated peritoneal dialysis provided in an embodiment of the present application. The device can be specifically a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute an intelligent monitoring method for automated peritoneal dialysis provided in the above embodiment.

[0085] In addition, the present application embodiment also protects a computer device, see Figure 5 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein when the processor 402 executes the computer program 403, the computer device can execute any one of the intelligent monitoring methods for automated peritoneal dialysis introduced above.

[0086] Embodiment 4:

[0087] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement an intelligent monitoring method for automated peritoneal dialysis provided in the above embodiment.

[0088] Embodiment 5:

[0089] This embodiment further provides a computer program product. When the computer program product is run on a computer, the computer is caused to execute the above-mentioned related steps to implement an intelligent monitoring method for automated peritoneal dialysis provided in the above embodiment.

[0090] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0091] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent monitoring method for automated peritoneal dialysis, characterized in that: The method comprises the following steps: Obtain various physical parameters of each renal patient in the hospital system for each hospital examination and various dialysis data for each automated peritoneal dialysis; Among them, physical parameters include white blood cell count, urine protein content, urine red blood cell content, blood creatinine content, blood urea nitrogen content, and creatinine clearance rate; Among them, the dialysis data package includes dialysate temperature, dialysate flow rate, ultrafiltration volume, dialysis time, dialysate pressure, 24h urine volume of each kidney patient, and blood pressure of each kidney patient; According to the difference in the changes of each physical parameter between each renal patient and each other renal patient, the similarity degree of each renal patient with each other renal patient is obtained, and then the similar patients of each renal patient are determined; Obtaining the special degree of each renal patient currently undergoing automated peritoneal dialysis according to the differences in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient each time undergoing automated peritoneal dialysis; Determine the current management options for each kidney patient based on the degree of specificity; The method for obtaining the similarity degree is: According to the difference in the change of each physical parameter of each renal patient with each other physical parameter, the consistency degree of each physical parameter of each renal patient with each other physical parameter is obtained; Obtain similar weights for each physical parameter based on the fluctuations in how well each physical parameter agrees with each other physical parameter across all renal patients; According to the difference of each physical parameter between each renal patient and each other renal patient and the similarity weight of each physical parameter, obtain the similarity degree between each renal patient and each other renal patient; The method for obtaining the similarity weight is: According to the fluctuation of the consistency degree between each physical parameter and each other physical parameter in all kidney patients, the overall consistency degree of each physical parameter with each other physical parameter is obtained; The result of accumulating and normalizing the overall consistency value of each body parameter with each other body parameter is taken as the similarity weight of each body parameter; The method for obtaining the similarity between each renal patient and each other renal patient according to the difference of each physical parameter between each renal patient and each other renal patient and the similarity weight of each physical parameter is as follows: For any renal patient, a matching pair of each physical parameter between the renal patient and every other renal patient is obtained by using the DTW algorithm; Correcting the difference between two body parameters in each matching pair of each body parameter according to the similarity weight of each body parameter to obtain a corrected difference of each matching pair; According to the corrected difference of each matching pair between the kidney patient and each other kidney patient, the similarity between the kidney patient and each other kidney patient is obtained; wherein the corrected difference is negatively correlated with the similarity; The method for obtaining similar patients for each kidney patient is: For any kidney patient, the similarity values ​​between the kidney patient and each other kidney patient are arranged from large to small to obtain a similarity value sequence of the kidney patient; Other kidney patients corresponding to the first preset number of similarity values ​​in the similarity value sequence are all regarded as similar patients of the kidney patient; The method for obtaining the special degree is: For any renal patient currently undergoing automated peritoneal dialysis, the differences between the current various dialysis data of the renal patient and the various dialysis data of each automated peritoneal dialysis of each similar patient are obtained and accumulated, as the difference between the current automated peritoneal dialysis of the renal patient and each automated peritoneal dialysis of each similar patient; The result of normalizing the minimum difference degree is taken as the special degree of the kidney patient; The method for determining the current treatment plan for each kidney patient according to the degree of specificity is: When the degree of specificity is within the range of the degree of specificity, determining a current treatment plan for the corresponding kidney patient with reference to historical treatment plans for similar patients of the corresponding kidney patient; When the special degree is not within the special degree range, the corresponding kidney patient goes to the hospital for testing to determine the current treatment plan for the corresponding kidney patient.

2. The intelligent monitoring method for automated peritoneal dialysis according to claim 1, characterized in that: The kidney patients in the hospital system include historical kidney patients and current kidney patients; wherein, the historical kidney patients are kidney patients who no longer undergo automated peritoneal dialysis, and the current kidney patients are kidney patients who undergo automated peritoneal dialysis.

3. An intelligent monitoring system for automated peritoneal dialysis, characterized in that: The method is implemented by using the intelligent monitoring method for automated peritoneal dialysis according to claim 1 or 2, wherein the system comprises: A data acquisition module, used for acquiring various physical parameters of each kidney patient in the hospital system each time he is examined in the hospital and various dialysis data of each automated peritoneal dialysis; A similar patient acquisition module is used to acquire the similarity between each renal patient and each other renal patient according to the difference in the change of each physical parameter between each renal patient and each other renal patient, and then determine the similar patients of each renal patient; A special degree acquisition module, used for acquiring the special degree of each renal patient currently undergoing automated peritoneal dialysis according to the differences in various dialysis data between each renal patient currently undergoing automated peritoneal dialysis and each similar patient each time undergoing automated peritoneal dialysis; A data processing module is used to determine the current treatment plan for each kidney patient according to the degree of specialization.

4. An intelligent monitoring device for automated peritoneal dialysis, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When executing the computer program, the processor implements the steps of the intelligent monitoring method for automated peritoneal dialysis as described in claim 1 or 2.

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