Hemodialysis data processing method and system

Abstract

The invention relates to the technical field of medical dialysis systems, in particular to a hemodialysis data processing method and system. The hemodialysis data processing method comprises the following steps that S1, a data request is sent; the portable data processing device is connected with the dialysis machine, a data request is sent to a central database, and the central database returns a treatment scheme; s2, setting parameters of a dialysis machine; automatically setting parameters of the dialysis machine based on the treatment scheme and the type of the dialysis machine; s3, calculating the dialysis completion degree; s4, generating a treatment demand; and S5, creating a predetermined treatment scheme. By arranging the portable data processing device, relatively private dialysis state data and process data are independently stored in the portable data processing device, so that the data processing pressure of a dialysis center is relieved, the privacy of treatment data of a patient is protected, and meanwhile, parameter conversion between dialysis machines is completed; parameter setting differences and data recording errors in different dialysis machines are eliminated, and the dialysis quality of patients using different dialysis machines is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of medical dialysis systems, and in particular to a data processing method and system for hemodialysis. Background Art

[0002] Hemodialysis is mainly used to treat chronic renal failure and some acute renal failure. It uses an artificial kidney (called a dialysis machine) to remove excess water and waste from the body and help restore the body's electrolyte and acid-base balance. The basic principle of hemodialysis is to use a semipermeable membrane. Blood flows in through a tube called a dialyzer. There is a semipermeable membrane in the dialyzer. The pore size of the membrane is small enough to block waste products (such as urea, creatinine, etc.) and excess water in the blood, but allows beneficial macromolecules such as proteins to pass through. Subsequently, the purified blood returns to the patient's body.

[0003] At present, the number of hemodialysis patients is increasing and tends to be younger, and the demand for hemodialysis services is also growing. The dialysis treatment cycle will vary depending on the patient's condition and physical condition, but it is often more frequent. Patients with more serious conditions even require daily dialysis, which not only brings pressure to the operation of the dialysis center, but also puts higher demands on the integrated management and prevention of kidney disease.

[0004] Related technologies are disclosed in the prior art:

[0005] A Chinese patent with publication number CN117379622A discloses a mobile-based hemodialysis data collection and processing method and system, which receives a dialysis request from the patient, and obtains the virtual twin model and patient data corresponding to the patient based on the confirmation information of the dialysis request from the physician. The virtual twin model is updated according to the patient data to obtain a customized twin model and sent to the physician. The customized twin model includes a dialysis module and a patient module. Based on the liquid level configuration data of the dynamic monitoring module in the dialysis module on the physician side, the customized twin model is updated to obtain a monitoring twin model. The liquid level configuration data includes new liquid level data and historical liquid level data. The liquid level monitoring strategy is retrieved to monitor the liquid level data of the monitoring twin model, and the obtained monitoring data is sent to the physician.

[0006] A Chinese patent with publication number CN117153336A discloses a hemodialysis monitoring system and method based on a hemodialysis machine, which relates to the technical field of hemodialysis machines. After processing the monitoring data in an operating condition set, an operating condition value of the hemodialysis machine is established; after summarizing the monitoring results, an operating state set of the hemodialysis machine is established, and the data in the operating state set is processed and associated to generate its operating state coefficient; the regression coefficient of the independent variable is obtained from the regression equation and the influence degree is obtained in association, and the state coefficient is predicted using a trained multiple linear regression model; the working state data of the hemodialysis machine is obtained and the state coefficient is generated again; the corresponding abnormal characteristics are obtained, and the corresponding maintenance plan is matched according to the correspondence between the abnormal characteristics and the maintenance plan in a pre-prepared maintenance plan library; when predicting the working state of the hemodialysis machine, the prediction effect is more authentic and referenceable, and it is also more conducive to adjusting and improving the operating state of the hemodialysis machine.

[0007] However, the above prior art still has the following problems:

[0008] (1) With the increase in the number of hemodialysis patients, the amount of data that the dialysis center needs to process is also increasing rapidly. Each dialysis requires a large amount of data processing and analysis of historical data. Insufficient processing capacity will result in low data processing efficiency and even processing errors.

[0009] (2) As the number of hemodialysis patients increases, the hospital's dialysis equipment will gradually become saturated and unable to fully meet the needs of patients. The same patient may use different dialysis machines, and the data from different dialysis machines will differ, thus affecting the patient's dialysis quality. Summary of the invention

[0010] To achieve the purpose of the present invention, the present invention is implemented by the following technical solution: a hemodialysis data processing method, comprising the following steps:

[0011] S1. Sending a data request: Connecting the portable data processing device to the dialysis machine, sending a data request to the central database, and the central database returns a treatment plan;

[0012] S2. Setting the dialysis machine parameters: Automatically setting the dialysis machine parameters based on the treatment plan and the dialysis machine model;

[0013] S3, calculating the dialysis completion degree; recording the dialysis status data and toxin clearance rate through a portable data processing device, extracting the status allowable interval and target clearance rate in the treatment plan to calculate the dialysis completion degree;

[0014] S4, generating treatment requirements; analyzing historical dialysis process data through a portable data processing device, generating treatment requirements, and sending them to a central database;

[0015] S5. Create a predetermined treatment plan; based on treatment needs and physical indicator data, create a predetermined treatment plan through a clustering algorithm.

[0016] Furthermore, the data request includes a unique identity identification code and physical indicator data; the specific steps of S1 include:

[0017] S11, sending a unique identity identification code to a central database, which accepts the physical index data after successful verification;

[0018] S21, adjusting the predetermined treatment plan based on the physical indicator data, generating a treatment plan and evaluating it;

[0019] S31. If the evaluation is qualified, the treatment plan will be returned; if not, the treatment plan will be optimized.

[0020] Furthermore, the specific steps of S2 include:

[0021] S21, identifying the dialysis machine model through a portable data processing device, and obtaining a parameter mapping template;

[0022] S22, extracting the preset dialysis machine model and parameters from the treatment plan, and completing parameter conversion based on the parameter mapping template;

[0023] S23. Input the converted parameters to complete the dialysis machine parameter setting.

[0024] Furthermore, the parameter mapping template includes parameter conversion relationships of the same catalog between dialysis machines of different models and parameter replacement schemes of different catalogs.

[0025] Furthermore, the calculation formula of the S3 dialysis completion degree is as follows:

[0026]

[0027] Among them, D is the dialysis completion degree, n is the number of toxin indicators, x i is the toxin i clearance rate, is the target clearance rate of toxin i, m is the number of state indicators, z j is the deduction value of state j.

[0028] Furthermore, the calculation formula of the deduction value of state j is as follows:

[0029]

[0030] Among them, y j is the real-time value of state j, is the permissible interval of state j, w j is the deduction weight of state j, max(Δy j) is the maximum difference between state j and the permissible threshold.

[0031] Furthermore, the specific step S4 includes:

[0032] S41, recording historical dialysis process data and patient scores through a portable data processing device, where the patient scores include process scores and effect scores;

[0033] S42, screening the highest historical scores and analyzing the historical dialysis process data;

[0034] S43. Extract demand characteristics through historical dialysis process data and generate treatment needs.

[0035] Furthermore, the specific step S5 includes:

[0036] S51, dividing the physical indicators in the central database into different clusters, and selecting the cluster with the highest similarity to the physical indicators of the current patient as a specific cluster;

[0037] S52, dividing the treatment needs in the specific cluster into different clusters, and selecting the cluster with the highest similarity to the current patient's treatment needs to extract treatment plan features;

[0038] S53. Create a predetermined treatment plan based on the treatment plan characteristics.

[0039] The present invention also includes a hemodialysis data processing system, comprising:

[0040] Portable data processing device, used to achieve parameter conversion between different dialysis machines, record and store dialysis status data and complete data analysis;

[0041] A central database for storing treatment plans for the entire dialysis center, creating a predetermined treatment plan and adjusting the predetermined treatment plan based on physical index data, and generating a treatment plan;

[0042] Dialysis machine, used to perform hemodialysis.

[0043] Furthermore, the portable data processing device includes: a dialysis machine interface, a wireless transmission unit, a data analysis unit, a database, and a display screen.

[0044] The beneficial effects of the present invention are:

[0045] 1. By setting up a portable data processing device, the relatively private dialysis status data and process data are stored separately in the portable data processing device, which relieves the data processing pressure of the dialysis center and protects the privacy of the patient's treatment data;

[0046] 2. Use portable data processing devices to identify the dialysis machine model, complete parameter conversion between dialysis machines, eliminate parameter setting differences and data recording errors in different dialysis machines, and ensure the dialysis quality of patients when using different dialysis machines;

[0047] 3. The portable data processing device is plug-and-play and can be taken away by the patient after use. It is convenient and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the method flow of the present invention; DETAILED DESCRIPTION

[0049] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0050] Embodiment 1

[0051] according to Figure 1 As shown, this embodiment provides a hemodialysis data processing method, comprising the following steps:

[0052] S1. Sending a data request: Connecting the portable data processing device to the dialysis machine, sending a data request to the central database, and the central database returns a treatment plan;

[0053] S2. Setting the dialysis machine parameters: Automatically setting the dialysis machine parameters based on the treatment plan and the dialysis machine model;

[0054] S3, calculating the dialysis completion degree; recording the dialysis status data and toxin clearance rate through a portable data processing device, extracting the status allowable interval and target clearance rate in the treatment plan to calculate the dialysis completion degree;

[0055] S4, generating treatment requirements; analyzing historical dialysis process data through a portable data processing device, generating treatment requirements, and sending them to a central database;

[0056] S5. Create a predetermined treatment plan; based on treatment needs and physical indicator data, create a predetermined treatment plan through a clustering algorithm.

[0057] More specifically, the data request includes a unique identification code and physical indicator data; the specific steps of S1 include:

[0058] S11, sending a unique identity identification code to a central database, which accepts the physical index data after successful verification;

[0059] S21, adjusting the predetermined treatment plan based on the physical indicator data, generating a treatment plan and evaluating it;

[0060] S31. If the evaluation is qualified, the treatment plan will be returned; if not, the treatment plan will be optimized.

[0061] More specifically, the S2 specific steps include:

[0062] S21, identifying the dialysis machine model through a portable data processing device, and obtaining a parameter mapping template;

[0063] S22, extracting the preset dialysis machine model and parameters from the treatment plan, and completing parameter conversion based on the parameter mapping template;

[0064] S23. Input the converted parameters to complete the dialysis machine parameter setting.

[0065] More specifically, the parameter mapping template includes parameter conversion relationships of the same catalog between dialysis machines of different models and parameter replacement schemes of different catalogs.

[0066] More specifically, the calculation formula of the S3 dialysis completion is as follows:

[0067]

[0068] Among them, D is the dialysis completion degree, n is the number of toxin indicators, x i is the toxin i clearance rate, is the target clearance rate of toxin i, m is the number of state indicators, z j is the deduction value of state j.

[0069] More specifically, the calculation formula for the deduction value of state j is as follows:

[0070]

[0071] Among them, y j is the real-time value of state j, is the permissible interval of state j, w j is the deduction weight of state j, max(Δy j ) is the maximum difference between state j and the permissible threshold.

[0072] More specifically, the S4 specific steps include:

[0073] S41, recording historical dialysis process data and patient scores through a portable data processing device, where the patient scores include process scores and effect scores;

[0074] S42, screening the highest historical scores and analyzing the historical dialysis process data;

[0075] S43. Extract demand characteristics through historical dialysis process data and generate treatment needs.

[0076] More specifically, the S5 specific steps include:

[0077] S51, dividing the physical indicators in the central database into different clusters, and selecting the cluster with the highest similarity to the physical indicators of the current patient as a specific cluster;

[0078] S52, dividing the treatment needs in the specific cluster into different clusters, and selecting the cluster with the highest similarity to the current patient's treatment needs to extract treatment plan features;

[0079] S53. Create a predetermined treatment plan based on the treatment plan characteristics.

[0080] Embodiment 2

[0081] A hemodialysis data processing system, comprising:

[0082] Portable data processing device, used to achieve parameter conversion between different dialysis machines, record and store dialysis status data and complete data analysis;

[0083] A central database for storing treatment plans for the entire dialysis center, creating a predetermined treatment plan and adjusting the predetermined treatment plan based on physical index data, and generating a treatment plan;

[0084] Dialysis machine, used to perform hemodialysis.

[0085] More specifically, the portable data processing device includes: a dialysis machine interface, a wireless transmission unit, a data analysis unit, a database, and a display screen.

[0086] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for processing hemodialysis data, characterized in that: The following steps are involved: S1. Sending a data request: Connecting the portable data processing device to the dialysis machine, sending a data request to the central database, and the central database returns a treatment plan; S2. Setting the dialysis machine parameters: Automatically setting the dialysis machine parameters based on the treatment plan and the dialysis machine model; S3, calculating the dialysis completion degree; recording the dialysis status data and toxin clearance rate through a portable data processing device, extracting the status allowable interval and target clearance rate in the treatment plan to calculate the dialysis completion degree; S4, generating treatment requirements; analyzing historical dialysis process data through a portable data processing device, generating treatment requirements, and sending them to a central database; S5. Create a predetermined treatment plan; based on treatment needs and physical indicator data, create a predetermined treatment plan through a clustering algorithm.

2. A hemodialysis data processing method according to claim 1, characterized in that: The data request includes a unique identification code and physical indicator data; the specific steps of S1 include: S11, sending a unique identity identification code to a central database, which accepts the physical index data after successful verification; S21, adjusting the predetermined treatment plan based on the physical indicator data, generating a treatment plan and evaluating it; S31. If the evaluation is qualified, the treatment plan will be returned; if not, the treatment plan will be optimized.

3. The method for processing hemodialysis data according to claim 1, characterized in that: The specific steps of S2 include: S21, identifying the dialysis machine model through a portable data processing device, and obtaining a parameter mapping template; S22, extracting the preset dialysis machine model and parameters from the treatment plan, and completing parameter conversion based on the parameter mapping template; S23. Input the converted parameters to complete the dialysis machine parameter setting.

4. The method for processing hemodialysis data according to claim 1, characterized in that: The parameter mapping template includes parameter conversion relationships of the same catalog between dialysis machines of different models and parameter replacement schemes of different catalogs.

5. The method for processing hemodialysis data according to claim 1, characterized in that: The calculation formula of S3 dialysis completion is as follows: Among them, D is the dialysis completion degree, n is the number of toxin indicators, x i is the toxin i clearance rate, is the target clearance rate of toxin i, m is the number of state indicators, z j is the deduction value of state j.

6. The method for processing hemodialysis data according to claim 5, characterized in that: The calculation formula of the deduction value of state j is as follows: Among them, y j is the real-time value of state j, is the permissible interval of state j, w j is the deduction weight of state j, max(Δy j ) is the maximum difference between state j and the permissible threshold.

7. The method for processing hemodialysis data according to claim 1, characterized in that: The specific steps of S4 include: S41, recording historical dialysis process data and patient scores through a portable data processing device, where the patient scores include process scores and effect scores; S42, screening the highest historical scores and analyzing the historical dialysis process data; S43. Extract demand characteristics through historical dialysis process data and generate treatment needs.

8. The method for processing hemodialysis data according to claim 1, characterized in that: The specific steps of S5 include: S51, dividing the physical indicators in the central database into different clusters, and selecting the cluster with the highest similarity to the physical indicators of the current patient as a specific cluster; S52, dividing the treatment needs in the specific cluster into different clusters, and selecting the cluster with the highest similarity to the current patient's treatment needs to extract treatment plan features; S53. Create a predetermined treatment plan based on the treatment plan characteristics.

9. A hemodialysis data processing system, used for implementing a hemodialysis data processing method according to any one of claims 1 to 8, characterized in that: include: Portable data processing device, used to achieve parameter conversion between different dialysis machines, record and store dialysis status data and complete data analysis; A central database for storing treatment plans for the entire dialysis center, creating a predetermined treatment plan and adjusting the predetermined treatment plan based on physical index data, and generating a treatment plan; Dialysis machine, used to perform hemodialysis.

10. A hemodialysis data processing system according to claim 9, characterized in that: The portable data processing device comprises: a dialysis machine interface, a wireless transmission unit, a data analysis unit, a database, and a display screen.

Images