Data processing device for diabetic peripheral neuropathy

Abstract

The invention provides a data processing device for diabetic peripheral neuropathy, which is characterized in that basic information and current clinical symptom data of a user are acquired, and the clinical symptom data comprise image data, user symptom description and detection data; when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy, determining whether a symptom identical to the clinical symptom data exists in a preset historical time period of the user or not; if yes, processing based on the current clinical symptom data of the user and the clinical symptom data of the same symptom last time, and determining the change of the oxidative stress level; and if the change of the oxidative stress level is determined to be in a descending trend, determining that the processing scheme of the user last time is effective. The change of the oxidative stress level is determined according to the current clinical symptom data of the user and the previous clinical symptom data, so that the adjustment processing scheme is determined by adjusting the oxidative stress level.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a data processing device for diabetic peripheral neuropathy. Background Art

[0002] Diabetic peripheral neuropathy (DPN) is the most common type of diabetic neuropathy and one of the most common chronic complications of diabetes. Specifically, it refers to the symptoms related to peripheral nerve dysfunction in diabetic patients excluding other reasons, with clinical manifestations of symmetrical pain and paresthesia, and lower limb symptoms being more common than upper limb symptoms, thus affecting the quality of life.

[0003] Currently, the images of the nervous system are often screened by manual determination to determine the degree of diabetic peripheral neuropathy by determining moderate to severe peripheral neuropathy affecting large nerve fibers, and then a treatment plan is given based on the doctor's experience; due to the lack of doctors with very rich experience, the optimal treatment plan cannot be given. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a data processing device for diabetic peripheral neuropathy to solve the problem of not being able to give the optimal treatment plan in the prior art.

[0005] To achieve the above object, the embodiment of the present invention provides the following technical solutions:

[0006] A first aspect of the embodiment of the present invention shows a data processing method for diabetic peripheral neuropathy, and the method includes:

[0007] Obtain the basic information of the user and the current clinical symptom data, where the clinical symptom data includes image data, user symptom description, and detection data;

[0008] When it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy, determine whether there are the same symptoms as the clinical symptom data within the preset historical time period of the user;

[0009] If so, process based on the current clinical symptom data of the user and the clinical symptom data of the same symptoms last time to determine the change in the oxidative stress level;

[0010] If it is determined that the change in the oxidative stress level is a downward trend, determine that the treatment plan of the user last time is effective.

[0011] Optionally, it further includes:

[0012] When it is determined that the change in the oxidative stress level is not a downward trend, the current clinical symptom data of the user, the previous clinical symptom data, and the previous treatment plan of the user are input into a pre-constructed treatment model to output and display the current treatment plan of the user; wherein, the treatment model is pre-trained based on the clinical symptom data and corresponding treatment plans of different simulation experiment objects at different times.

[0013] Optionally, determining that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy includes:

[0014] Input the image data, user symptom description, and detection data into a preset recognition model, and the preset recognition model is trained based on the image data, user symptom description, and detection data of different historical users.

[0015] The preset recognition model processes the image data, user symptom description, and detection data and outputs a recognition result.

[0016] If the recognition result is that the user has diabetic peripheral neuropathy, it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy.

[0017] Optionally, determining that the user has the same symptoms as the clinical symptom data within a preset historical time period includes:

[0018] Traverse the database based on the user's basic information to check if there is historical clinical symptom data within the preset historical time period.

[0019] If there is, obtain the historical clinical symptom data of the user within the preset historical time period.

[0020] Based on the symptom similarity between each historical clinical symptom data and the clinical symptom data, determine whether the user has the same symptoms as the clinical symptom data within the preset historical time period.

[0021] If there is, perform the step of processing the current clinical symptom data of the user and the previous clinical symptom data to determine the change in the oxidative stress level.

[0022] Optionally, processing the current clinical symptom data of the user and the previous clinical symptom data to determine the change in the oxidative stress level includes:

[0023] Process the current clinical symptom data of the user to determine a first parameter value.

[0024] Process the previous clinical symptom data of the same symptoms to determine a second parameter value.

[0025] Determine the corresponding change in the level of oxidative stress based on the first parameter value and the second parameter value.

[0026] Optionally, it further includes:

[0027] If it is determined that there are no symptoms in the user's preset historical time period that are the same as the clinical symptom data, process based on the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user, and determine the current treatment plan for the user.

[0028] Optionally, processing based on the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user to determine the current treatment plan for the user includes:

[0029] Determine other users of the same type as the user based on the user's basic information and the clinical symptom data;

[0030] Input the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user into a pre-constructed processing model, and output the current treatment plan for the user.

[0031] The second aspect of the embodiments of the present invention shows a data processing device for diabetic peripheral neuropathy, and the device includes:

[0032] An acquisition unit, configured to acquire the user's basic information and current clinical symptom data, where the clinical symptom data includes image data, user symptom descriptions, and detection data;

[0033] A determination unit, configured to determine whether there are symptoms in the user's preset historical time period that are the same as the clinical symptom data when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy;

[0034] A processing unit, configured to, if any, process based on the user's current clinical symptom data and the clinical symptom data of the same symptoms last time to determine the change in the level of oxidative stress; if it is determined that the change in the level of oxidative stress is a downward trend, determine that the previous treatment plan for the user is effective.

[0035] Optionally, the processing unit is further configured to:

[0036] When it is determined that the change in the oxidative stress level is not a downward trend, the current clinical symptom data of the user, the previous clinical symptom data, and the previous treatment plan of the user are input into a pre-constructed treatment model, and the current treatment plan of the user is output and displayed; wherein, the treatment model is pre-trained based on the clinical symptom data and corresponding treatment plans of different simulation subjects at different times.

[0037] Optionally, the processing unit is further configured to:

[0038] If it is determined that there are no symptoms in the user's preset historical time period that are the same as the clinical symptom data, based on the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user, a treatment is performed to determine the current treatment plan of the user.

[0039] Based on the data processing method and device for diabetic peripheral neuropathy provided in the above embodiments of the present invention, the method includes: obtaining the basic information of the user and the current clinical symptom data, where the clinical symptom data includes image data, user symptom descriptions, and detection data; when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy, determining whether there are symptoms in the user's preset historical time period that are the same as the clinical symptom data; if so, based on the current clinical symptom data of the user and the clinical symptom data of the same symptoms last time, a process is performed to determine the change in the oxidative stress level; if it is determined that the change in the oxidative stress level is a downward trend, it is determined that the previous treatment plan of the user is effective. In the embodiments of the present invention, the change in the oxidative stress level is determined through the current clinical symptom data of the user and the previous clinical symptom data, and the optimal treatment plan is determined by adjusting the oxidative stress level. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0041] Figure 1 It is a schematic interaction diagram between the server and the user terminal shown in the embodiments of the present invention.

[0042] Figure 2 It is a schematic flow diagram of a data processing method for diabetic peripheral neuropathy shown in the embodiments of the present invention.

[0043] Figure 3Schematic diagram of the architecture of a data processing device for diabetic peripheral neuropathy shown in an embodiment of the present invention. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] It should be noted that the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0046] In this application, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0047] See Figure 1 , which is an interaction schematic diagram of the server and the client shown in an embodiment of the present invention.

[0048] The server 10 is wirelessly connected to the client 20.

[0049] Specifically, the client 20 is a display interface and an input interface. The display interface is used to display the processing solution sent by the server and the result of whether the solution is effective, and the input interface is used to receive the input of the client.

[0050] The server 10 is used to process based on the basic information of the user input by the client 20 and the current clinical symptom data.

[0051] Based on the above-mentioned schematic diagram of the server and the client, the process of specifically implementing the data processing of diabetic peripheral neuropathy is as Figure 2As shown, it is a schematic flowchart of a data processing method for diabetic peripheral neuropathy shown in an embodiment of the present invention, which is applied to a server. The method includes the following steps.

[0052] Step S201: Obtain the user's basic information and current clinical symptom data. The clinical symptom data includes image data, user symptom descriptions, and detection data.

[0053] Optionally, first, medical staff upload the user's, that is, the patient's, basic information and current clinical symptom data through the user terminal. The clinical symptom data includes image data, user symptom descriptions, and detection data. The user terminal receives the corresponding basic information and current clinical symptom data and sends them to the server for the server to obtain.

[0054] It should be noted that the image data includes images around nerve fibers, etc. The user symptom descriptions include data such as the user's name, age, and disease description. The detection data includes blood glucose levels, electromyogram indicators, hemorheology, and serum oxidative stress indicators, etc.

[0055] Step S202: Determine whether the current clinical symptom data indicates that the user has diabetic peripheral neuropathy. If so, execute step S203. If not, show the result of no diabetic peripheral neuropathy through the user terminal.

[0056] The specific process of implementing step S202 includes:

[0057] S11: Input the image data, user symptom descriptions, and detection data into a preset recognition model. The preset recognition model is trained based on the image data, user symptom descriptions, and detection data of different historical users.

[0058] Specifically, the process of training a preset recognition model based on the image data, user symptom descriptions, and detection data of different historical users includes:

[0059] The server collects in real-time the image data, user symptom descriptions, and detection data of different historical users with diabetic peripheral neuropathy, as well as the image data, user symptom descriptions, and detection data of diabetic users without diabetic peripheral neuropathy, and uses them as a sample set; divides the sample set into a training set and a test set according to a preset ratio; during the generation process, first uses the obtained training set to train an initial model; then, uses the test set to identify the initial model to obtain an identification result; if the identification result is inconsistent with the symptoms indicated by the sample set, continue to train the initial model based on the training set until the identification result is consistent with the symptoms indicated by the sample set. Otherwise, take the trained initial model as the preset recognition model.

[0060] It should be noted that the recognition result includes that the user has diabetic peripheral neuropathy, or the user does not have diabetic peripheral neuropathy.

[0061] The initial model is constructed based on existing neural network algorithms or algorithms such as deep learning.

[0062] The preset ratio is also pre-constructed and can generally be set to 9:1, with 9 parts for the training set and 1 part for the test set.

[0063] S12: The preset recognition model processes the image data, user symptom description, and detection data, and outputs a recognition result.

[0064] In the process of specifically implementing step S12, the preset recognition model obtained in the above step S11 is used to process the current clinical symptom data input by the user end to obtain a recognition result.

[0065] S13: Determine whether the recognition result is that the user has diabetic peripheral neuropathy. If so, execute S14; otherwise, show the result that the user does not have diabetic peripheral neuropathy through the user end.

[0066] S14: Determine that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy.

[0067] Step S203: Determine whether there are symptoms in the user's preset historical time period that are the same as the clinical symptom data. If so, execute step S204; if not, execute step S208.

[0068] In the process of specifically implementing step S203, the following steps are included:

[0069] S21: Based on the user's basic information, traverse whether there is historical clinical symptom data in the database within the preset historical time period. If so, execute step S22; if not, directly execute step S207.

[0070] It should be noted that the database pre-stores the historical clinical symptom data, user's basic information, and medical treatment time, etc. of all current users in a certain hospital.

[0071] The user's basic information may include information such as the user's ID, age, gender, blood type, and phone number.

[0072] Specifically, traverse each historical clinical symptom data in the data according to the user's basic information to determine whether there is data with the same basic information as the current user. If so, execute step S22; if not, directly execute step S208.

[0073] S22: Obtain the historical clinical symptom data of the user within the preset historical time period.

[0074] It should be noted that the historical clinical symptom data can be one-time or multiple data.

[0075] S23: Determine whether there are symptoms identical to the clinical symptom data within the preset historical time period of the user based on the symptom similarity between each historical clinical symptom data and the clinical symptom data. If so, execute step S204; otherwise, execute step S208.

[0076] In the process of specifically implementing step S23, calculate the symptom similarity between each historical clinical symptom data and the clinical symptom data respectively, and determine whether the symptom similarity is greater than the preset similarity. If it is greater, it is determined that there are symptoms identical to the clinical symptom data within the preset historical time period of the user, and step S204 is executed; otherwise, it means that there are no symptoms identical to the clinical symptom data within the preset historical time period of the user, and step S208 is executed.

[0077] Among them, calculating the symptom similarity between each historical clinical symptom data and the clinical symptom data can be calculated through a model or directly calculated.

[0078] Step S204: Based on the current clinical symptom data of the user and the previous clinical symptom data, perform processing to determine the change in the oxidative stress level.

[0079] It should be noted that in the process of specifically implementing step S204, the following steps are included:

[0080] Step S31: Based on the current clinical symptom data of the user, perform processing to determine the first parameter value.

[0081] The first parameter value includes the values of malondialdehyde (MDA), superoxide dismutase (SOD), and total antioxidant capacity (T-AOC) detected by enzyme-linked immunosorbent assay.

[0082] Among them, MDA is an index reflecting the severity of lipid peroxidation in the body, SOD can reflect the antioxidant level of the body, and T-AOC can reflect both the functional state of the body's antioxidant system and the antioxidant enzyme activity, indirectly reflecting the degree of lipid peroxidation damage. That is to say, the oxidative stress level of the user is determined by the values of MDA, SOD, and T-AOC.

[0083] In one embodiment, the current clinical symptom data is input into a serum analyzer to detect the values of MDA, SOD, and T-AOC in the current clinical symptom data by enzyme-linked immunosorbent assay.

[0084] In another embodiment, the current clinical symptom data is input into a detection model so that the detection model processes based on the current clinical symptom data to obtain the values of MDA, SOD, and T-AOC.

[0085] It should be noted that the detection model is trained based on historical clinical symptom data and the corresponding values of MDA, SOD, and T-AOC.

[0086] S32: Process the clinical symptom data of the previous same symptom to determine the second parameter value.

[0087] It should be noted that the process of specifically implementing step S32 is the same as that of specifically implementing step S31 and can be referred to each other.

[0088] The second parameter value includes the values of MDA, SOD, and T-AOC.

[0089] S33: Determine the change in the corresponding oxidative stress level based on the first parameter value and the second parameter value.

[0090] In the process of specifically implementing step S33, calculate the first difference between MDA in the first parameter value and MDA in the second parameter value; calculate the second difference between SOD in the first parameter value and SOD in the second parameter value; calculate the third difference between T-AOC in the first parameter value and T-AOC in the second parameter value; and use the first difference, second difference, and third difference as the change in the oxidative stress level.

[0091] Step S205: Determine whether the change in the oxidative stress level is a downward trend. If it is determined that the change in the oxidative stress level is a downward trend, execute step S206; otherwise, execute step S207.

[0092] In the process of specifically implementing step S205, first, determine whether the first difference, second difference, and third difference are all negative. If at least two of them are negative, the change in the oxidative stress level is a downward trend, and execute step S206; otherwise, it is not a downward trend, and execute step S207.

[0093] Step S206: Determine that the previous treatment plan of the user is effective.

[0094] It should be noted that the treatment plan is a diagnosis and treatment plan, generally including acupuncture at acupoints such as Hegu, Sanyinjiao, Xuehai, Fenglong, Quchi, Zusanli, Taixi, and Yongquan, using an acupuncture treatment plan.

[0095] In the process of specifically implementing step S206, after determining that the previous user's treatment plan is effective, the previous user's treatment plan is used as the current treatment plan to continue treating the user using the previous user's treatment plan again.

[0096] Step S207: Based on the current clinical symptom data of the user, the previous clinical symptom data, and the previous user's treatment plan, input them into a pre-constructed treatment model, output the current user's treatment plan, and display it.

[0097] Among them, the treatment model is pre-trained based on the clinical symptom data and corresponding treatment plans of different simulation experiment objects at different times.

[0098] Specifically, the server collects in real time the clinical symptom data and corresponding treatment plans of different simulation experiment objects at different times and uses them as a sample set; uses the sample set to train a preset algorithm to construct an initial model. Then, using a certain piece of the clinical symptom data in the sample set, its corresponding previous clinical symptom data, and the previous user's treatment plan, input them into a pre-constructed treatment model, and output the current user's treatment plan; if this treatment plan is consistent with the current treatment plan in the sample set, it is determined that the training is completed at this time, and the trained initial model is used as the treatment model; if this treatment plan is inconsistent with the current treatment plan in the sample set, continue to train the initial model based on the sample set until the training is completed.

[0099] It should be noted that the simulation experiment objects are experimental groups of users with diabetic peripheral neuropathy.

[0100] In the process of specifically implementing step S207, use the above-trained treatment model to process the previous clinical symptom data and the previous user's treatment plan to obtain the current user's treatment plan, and display it to the user through the user terminal.

[0101] Step S208: Process the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user to determine the current user's treatment plan.

[0102] It should be noted that in the process of specifically implementing step S208, the following steps are included:

[0103] S41: Determine other users of the same type as the user based on the user's basic information and the clinical symptom data.

[0104] It should be noted that the user types are pre-divided according to the basic information of the users and the clinical symptom data. For example, users with the same complication in diabetic peripheral neuropathy, similar ages, the same gender and blood type belong to the same type.

[0105] Other users can be simulated users or real users. That is to say, if they are simulated users, the effects of different treatment plans for this type of user can be simulated through simulation experiments, and the treatment method with the highest effect can be recorded.

[0106] In the process of specifically implementing step S41, other users of the same type as the user are searched for in the data.

[0107] S42: Input the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user into a pre-constructed processing model, and output the current treatment plan for the user.

[0108] It should be noted that when the pre-constructed processing model is trained, it can also be trained through the clinical symptom data of users of the same type and the corresponding treatment plans.

[0109] The process of specifically implementing step S42 is the same as the process of the above-mentioned specific implementation step S207, and reference can be made to each other.

[0110] In the embodiment of the present invention, when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy and the user has the same symptoms as the clinical symptom data within the preset historical period, the change in the oxidative stress level is determined through the current clinical symptom data of the user and the previous clinical symptom data, so as to determine the adjusted treatment plan by adjusting the oxidative stress level.

[0111] Optionally, based on a data processing method for diabetic peripheral neuropathy in the embodiment of the present invention, correspondingly, the present invention also shows a data processing device for diabetic peripheral neuropathy, as Figure 3 shown, the device includes:

[0112] An acquisition unit 301, configured to acquire the basic information of the user and the current clinical symptom data, where the clinical symptom data includes image data, user symptom descriptions, and detection data.

[0113] A determination unit 302, configured to determine whether the user has the same symptoms as the clinical symptom data within the preset historical period when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy.

[0114] A processing unit 303, which, if any, processes based on the current clinical symptom data of the user and the clinical symptom data of the same symptom last time to determine the change in the oxidative stress level; if it is determined that the change in the oxidative stress level shows a downward trend, it is determined that the treatment plan for the user last time is effective.

[0115] For the specific principles and execution processes of each unit in the data processing system for diabetic peripheral neuropathy disclosed in the embodiments of the present invention above, they are the same as the corresponding content in the data processing method for diabetic peripheral neuropathy provided in the embodiments of the present invention above. Reference can be made to the corresponding parts in the data processing method for diabetic peripheral neuropathy disclosed in the embodiments of the present invention above, and details will not be elaborated here.

[0116] In the embodiments of the present invention, when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy and the user has the same symptoms as the clinical symptom data within a preset historical period, the change in the oxidative stress level is determined through the current clinical symptom data of the user and the clinical symptom data last time, so as to determine the adjusted treatment plan by adjusting the oxidative stress level.

[0117] Optionally, the processing unit is further configured to:

[0118] If it is determined that the change in the oxidative stress level is not a downward trend, the current clinical symptom data of the user, the clinical symptom data last time, and the treatment plan for the user last time are input into a pre-constructed processing model, and the current treatment plan for the user is output and displayed; wherein, the processing model is pre-trained based on the clinical symptom data and corresponding treatment plans of different simulation experimental objects at different times.

[0119] Optionally, the processing unit is further configured to:

[0120] If it is determined that there are no symptoms the same as the clinical symptom data within the preset historical period of the user, based on the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user, the current treatment plan for the user is determined.

[0121] Among them, determining the current treatment plan for the user based on the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user includes:

[0122] Based on the basic information of the user and the clinical symptom data, other users of the same type as the user are determined.

[0123] Input the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user into a pre-constructed processing model, and output the current treatment plan for the user.

[0124] Optionally, the determination unit for determining that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy is specifically configured to:

[0125] Input the image data, user symptom description, and detection data into a preset recognition model, and the preset recognition model is trained based on the image data, user symptom description, and detection data of different historical users.

[0126] The preset recognition model processes the image data, user symptom description, and detection data, and outputs a recognition result.

[0127] If the recognition result is that the user has diabetic peripheral neuropathy, determine that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy.

[0128] Optionally, the determination unit for determining that the user has the same symptoms as the clinical symptom data within a preset historical time period is specifically configured to:

[0129] Traverse the database based on the user's basic information to check if there is historical clinical symptom data within the preset historical time period.

[0130] If there is, obtain the historical clinical symptom data of the user within the preset historical time period.

[0131] Based on the symptom similarity between each historical clinical symptom data and the clinical symptom data, determine whether the user has the same symptoms as the clinical symptom data within the preset historical time period.

[0132] If there is, perform the step of processing based on the current clinical symptom data of the user and the previous clinical symptom data to determine the change in oxidative stress level.

[0133] Optionally, the processing unit for processing based on the current clinical symptom data of the user and the previous clinical symptom data to determine the change in oxidative stress level is specifically configured to:

[0134] Process the current clinical symptom data of the user to determine a first parameter value.

[0135] Process the previous clinical symptom data of the same symptoms to determine a second parameter value.

[0136] Based on the first parameter value and the second parameter value, determine the corresponding change in oxidative stress level.

[0137] An embodiment of the present application further provides a data processing system for diabetic peripheral neuropathy. The system includes a processor and a memory. The memory is used to store program codes and data for data processing of diabetic peripheral neuropathy, and the processor is used to call program instructions in the memory to execute the data processing method for diabetic peripheral neuropathy shown in the above embodiments.

[0138] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing device for diabetic peripheral neuropathy, characterized in that: The device comprises: An acquisition unit, used to acquire basic information of the user and current clinical symptom data, wherein the clinical symptom data includes image data, user symptom description and detection data; A determination unit, configured to determine whether the user has symptoms identical to the clinical symptom data within a preset historical time period when it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy; A processing unit is used to determine the change in the oxidative stress level based on the current clinical symptom data of the user and the clinical symptom data of the same symptoms last time, if any; if it is determined that the change in the oxidative stress level is a downward trend, determine that the treatment plan of the user last time is effective.

2. The device according to claim 1, characterized in that The processing unit is further used for: If it is determined that the change in the oxidative stress level is not a downward trend, the user's current clinical symptom data, the previous clinical symptom data, and the user's previous treatment plan are input into a pre-constructed treatment model, and the user's current treatment plan is output and displayed; wherein the treatment model is pre-trained based on the clinical symptom data of different simulated experimental subjects at different periods and the corresponding treatment plans.

3. The device according to claim 1, characterized in that The processing unit is further used for: If it is determined that there is no symptom identical to the clinical symptom data within the preset historical time period of the user, the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user are processed to determine the current treatment plan for the user; The process of processing the current clinical symptom data and the clinical symptom data corresponding to other users of the same type as the user to determine the current treatment plan for the user includes: Determining other users of the same type as the user based on the basic information of the user and the clinical symptom data; The current clinical symptom data and clinical symptom data corresponding to other users of the same type as the user are input into a pre-built processing model, and the user's current processing plan is output.

4. The device according to claim 1, characterized in that The determination unit for determining that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy is specifically configured to: Inputting the image data, user symptom description and test data into a preset recognition model, wherein the preset recognition model is trained based on the image data, user symptom description and test data of different historical users; The preset recognition model processes the image data, the user's symptom description and the detection data, and outputs a recognition result; If the identification result is that the user has diabetic peripheral neuropathy, it is determined that the current clinical symptom data indicates that the user has diabetic peripheral neuropathy.

5. The device according to claim 1, characterized in that The determining unit for determining whether the same symptom as the clinical symptom data exists within the historical time period preset by the user is specifically configured to: Based on the basic information of the user, it is searched in the database whether there is historical clinical symptom data within a preset historical time period; If it exists, obtain the historical clinical symptom data within the historical time period preset by the user; Determine whether there is a symptom identical to the clinical symptom data in the historical time period preset by the user based on the symptom similarity between each historical clinical symptom data and the clinical symptom data; If so, a step of determining a change in the oxidative stress level is performed based on the current clinical symptom data of the user and the previous clinical symptom data.

6. The device according to claim 1, characterized in that The processing unit for determining the change of the oxidative stress level based on the current clinical symptom data of the user and the last clinical symptom data is specifically used for: Processing based on the current clinical symptom data of the user to determine a first parameter value; Processing based on the clinical symptom data of the same symptom last time to determine a second parameter value; A corresponding change in the oxidative stress level is determined based on the first parameter value and the second parameter value.

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