A quantitative assessment system for the progression of obstructive renal fibrosis based on data analysis

Abstract

The present invention relates to the technical field of data processing, and in particular to a quantitative evaluation system for the progression of obstructive renal fibrosis based on data analysis, including a data acquisition module for collecting characteristic data of obstructive renal fibrosis; a data processing module for preprocessing the characteristic data of obstructive renal fibrosis to output standard obstructive renal fibrosis data; an obstructive renal fibrosis analysis module for determining an evaluation value of obstructive renal fibrosis according to the standard obstructive renal fibrosis data; and a control module for adjusting the number of data polling directions when it is determined that the stability of the evaluation does not meet the requirements according to the variance of the response delay of the obstructive renal fibrosis evaluation model, or for determining the accuracy of data acquisition by combining the variance of the response delay and the linear fitting degree between the abnormal data volume of data acquisition and the error volume of the evaluation model. The present invention realizes the improvement of the comprehensiveness and accuracy of the quantitative evaluation method.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a quantitative evaluation system for the progression of obstructive renal fibrosis based on data analysis. Background Art

[0002] Obstructive renal fibrosis is a common urinary system disease, and its disease progression may cause serious damage to kidney function. Traditional evaluation methods for obstructive renal fibrosis often rely on doctors' subjective judgments and limited detection indicators, such as the degree of pelvicalyceal separation in ultrasound examinations and the renal parenchymal thickness in CT examinations. These methods have certain limitations in terms of accuracy and comprehensiveness. With the continuous development of medical technology, various advanced detection devices and technologies have been applied, generating a large amount of data related to obstructive renal fibrosis, including imaging data, biochemical index data, patient clinical symptom and sign data, etc. However, these data are usually scattered and isolated, making it difficult to be effectively integrated and analyzed. The main purpose of this study is to evaluate the degree and progression of obstructive renal fibrosis through urine obstructive renal fibrosis biomarker data and hydronephrosis imaging data, so as to more accurately, comprehensively and individually complete the evaluation of obstructive renal fibrosis.

[0003] Chinese Patent Publication No.: CN113069146A discloses an evaluation method for unilateral hydronephrosis in children. Two-dimensional ultrasound is used to measure and calculate the volumes of the two kidneys of the child in detail. According to K = VL / VR, the K value is calculated. By comparing the calculated K value with the K value threshold obtained from previous studies, the changes in the patient's condition can be intuitively reflected. The work carried out by us previously shows that this index can not only be used for the evaluation of the recovery of hydronephrosis and the surgical effect after UPJO surgery, but also can be applied to the prognosis judgment before congenital hydronephrosis surgery, providing clinicians with an assessment of the changes in the condition. It can be seen that the evaluation method for unilateral hydronephrosis in children has the problem of insufficient evaluation stability. Summary of the Invention

[0004] Therefore, the present invention provides a quantitative evaluation system for the progression of obstructive renal fibrosis based on data analysis to overcome the problem of insufficient quality stability in the evaluation of the progression of obstructive renal fibrosis in the prior art.

[0005] To achieve the above object, the present invention provides a quantitative evaluation system for the progression of obstructive renal fibrosis based on data analysis, including:

[0006] A data acquisition module for collecting data on the characteristics of obstructive renal fibrosis, which includes urine obstructive renal fibrosis biomarker data and hydronephrosis imaging data;

[0007] A data processing module, which is connected to the data acquisition module and is used to preprocess the obstructive renal fibrosis characteristic data to output standard obstructive renal fibrosis data, including a comparison and correction unit for performing comparative analysis on the obstructive renal fibrosis characteristic data to output corrected obstructive renal fibrosis characteristic data;

[0008] An obstructive renal fibrosis analysis module, which is connected to the data processing module and is used to determine the obstructive renal fibrosis evaluation value according to the standard obstructive renal fibrosis data, including a mapping establishment unit for generating a number of mappings by training the standard obstructive renal fibrosis data and a model generation unit connected to the mapping establishment unit for generating an obstructive renal fibrosis evaluation model according to the number of mappings;

[0009] A control module, which is respectively connected to the data acquisition module, the data processing module and the obstructive renal fibrosis analysis module, and is used to adjust the number of data polling directions when it is determined that the stability of the evaluation does not meet the requirements according to the variance of the response delay of the obstructive renal fibrosis evaluation model, or to judge the accuracy of data acquisition by combining the variance of the response delay and the linear fitting degree of the abnormal data volume of the acquired data and the model error volume, and determine the number of adjusted organ interaction parameter mappings according to the determination result of the accuracy, or adjust the number of comparison types according to the average difference volume of the acquired data.

[0010] Further, the obstructive renal fibrosis analysis module further includes a model update unit connected to the model generation unit for updating the obstructive renal fibrosis model.

[0011] Further, the control module is respectively connected to the obstructive renal fibrosis analysis module and the data acquisition module, and is used to obtain the response delay of the obstructive renal fibrosis evaluation model after inputting several batches of data to calculate the variance of the response delay, determine that the stability of the evaluation does not meet the requirements when the variance of the response delay is greater than a preset first variance, and increase the number of data polling directions when the variance of the response delay is greater than a preset second variance.

[0012] Further, the increased number of data polling directions is determined by the difference between the variance of the response delay and the preset second variance.

[0013] Further, the control module is respectively connected to the data acquisition module and the obstructive renal fibrosis analysis module, and is also used to preliminarily determine that the accuracy of data acquisition does not meet the requirements when the variance of the response delay is greater than or equal to the preset first variance and less than or equal to the preset second variance, collect the obstructive renal fibrosis characteristic data, and calculate the error volume of the evaluation model according to the evaluation model, so as to calculate the linear fitting degree of the abnormal data volume of the data acquisition and the error volume of the evaluation model, where

[0014] If the linear fitting degree between the abnormal data volume of the data acquisition and the error volume of the evaluation model is greater than a preset second fitting degree, the control module re-determines that the accuracy of the data acquisition does not meet the requirements, and increases the number of mappings between the non-renal organ operation parameters and the obstructive renal fibrosis characteristic data.

[0015] Furthermore, the increase amplitude of the number of groups of the organ interaction parameter mappings is determined by the difference between the linear fitting degree between the abnormal data volume of the data acquisition and the error volume of the evaluation model and the preset second fitting degree.

[0016] Furthermore, the control module is respectively connected to the data acquisition module and the obstructive renal fibrosis analysis module, and is also used to collect the obstructive renal fibrosis characteristic data to calculate the average value of the difference volume of the collected data. When the average value of the difference volume of the collected data is greater than the preset average value of the first data difference volume, it is determined that the accuracy of the evaluated data acquisition does not meet the requirements, and when the average value of the difference volume of the collected data is greater than the preset average value of the second data difference volume, the number of comparison types of the obstructive renal fibrosis characteristic data is increased.

[0017] Furthermore, the increase amplitude of the number of comparison types of the collected data is determined by the average value of the difference volume of the data acquisition and the preset average value of the second data difference volume.

[0018] Furthermore, the linear fitting degree between the abnormal data volume of the data acquisition and the error volume of the evaluation model is the average value of the sum of the straight-line distances between several coordinate points in the coordinate system and the straight-line image of the linear regression function generated by linear regression calculation with the parameter represented by the abscissa and the parameter represented by the ordinate in this coordinate system;

[0019] Wherein, the coordinate system takes the abnormal data volume of the obstructive renal fibrosis characteristic data acquisition as the abscissa and the difference volume of the evaluation model as the ordinate.

[0020] Furthermore, the average value of the difference volume of the collected data is the ratio of the sum of the difference volumes between several batches of collected data and the standard collected data to the number of batches, wherein the data volume of the collected data in each batch is the same.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows. By setting up a data acquisition module, a data processing module, an obstructive renal fibrosis analysis module, and a control module, and using the obstructive renal fibrosis evaluation model generated by the model generation unit in the set obstructive renal fibrosis analysis module to evaluate the degree of obstructive renal fibrosis, the accuracy of the evaluation is improved. By adjusting the number of directions of data polling, the impact of the overall system operation lag that may be caused by the simultaneous operation of a large amount of data required when the model is updated and generated, resulting in insufficient evaluation stability, is reduced. By adjusting the number of mappings between the operating parameters of non-renal organs and the characteristic data of obstructive renal fibrosis according to the linear fitting degree between the abnormal data volume of data acquisition and the error volume of the evaluation model, the interference degree caused by the changes of non-renal organs to ureteral data or fibrosis data is reduced. By adjusting the number of comparison types according to the average value of the difference amount of the collected data, the impact of the signal interference of external devices on the data acquisition device, resulting in a decrease in data acquisition accuracy, is reduced, achieving an improvement in the stability of the evaluation of the progression of obstructive renal fibrosis.

[0022] Furthermore, by setting up an obstructive renal fibrosis model update unit, when an error occurs in the evaluation of the obstructive renal fibrosis evaluation model, the obstructive renal fibrosis model is updated, overcoming the problem of unstable operation of the obstructive renal fibrosis model, enabling model training to be realized, and improving the accuracy of the obstructive renal fibrosis analysis system.

[0023] Furthermore, by setting up a control module and a data acquisition module to conduct polling after collecting the characteristic data of obstructive renal fibrosis, the problem of response delay caused by too much data accumulation during the data acquisition process of the obstructive renal fibrosis evaluation system is overcome, and the system operation efficiency is improved by increasing the number of directions of the data polling.

[0024] Furthermore, by setting up a control module and an obstructive renal fibrosis analysis module to calculate the variance of the response delay and the linear fitting degree between the abnormal data volume of the collected data and the error volume of the evaluation model, the problem of being unable to control the floating trend of the response delay data and the error volume between the abnormal data volume of the collected data and the evaluation model is overcome, enhancing the accuracy of the data in the system.

[0025] Furthermore, by setting up a data processing module to preprocess the collected data, convert the characteristic data of obstructive renal fibrosis into standard characteristic data of obstructive renal fibrosis, and conduct comparative analysis on the characteristic data of obstructive renal fibrosis, the problem of chaotic collection of the characteristic data of obstructive renal fibrosis is overcome, and the efficiency of the obstructive renal fibrosis analysis system is improved. Description of the Drawings

[0026] Figure 1This is the overall structural block diagram of the obstructive renal fibrosis progression quantitative assessment system based on data analysis according to an embodiment of the present invention;

[0027] Figure 2 This is the structural block diagram of the obstructive renal fibrosis analysis module of the obstructive renal fibrosis progression quantitative assessment system based on data analysis according to an embodiment of the present invention;

[0028] Figure 3 This is the connection structural block diagram of the connection between the obstructive renal fibrosis analysis module and the control module of the obstructive renal fibrosis progression quantitative assessment system based on data analysis according to an embodiment of the present invention;

[0029] Figure 4 This is the structural block diagram of the data processing module of the obstructive renal fibrosis progression quantitative assessment system based on data analysis according to an embodiment of the present invention. Detailed implementation manners

[0030] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0031] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0032] Those skilled in the art can understand that unless specifically stated, the singular forms "a", "an" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in this specification means the presence of features, integers, steps, operations, elements / components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements / components. It should be understood that when we say that a module is "connected" or "coupled" to another module, it can be directly connected or coupled to other modules, or there may also be intermediate units. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling.

[0033] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 as shown, which are respectively the overall structural block diagram, the structural block diagram of the obstructive renal fibrosis analysis module, the connection structural block diagram of the connection between the obstructive renal fibrosis analysis module and the control module, and the structural block diagram of the data processing module of the obstructive renal fibrosis progression quantitative assessment system based on data analysis according to an embodiment of the present invention. A quantitative assessment system for obstructive renal fibrosis progression based on data analysis according to the present invention includes:

[0034] A data acquisition module for collecting characteristic data of obstructive renal fibrosis, where the characteristic data of obstructive renal fibrosis includes urine obstructive renal fibrosis marker data and renal hydronephrosis imaging data;

[0035] A data processing module, connected to the data acquisition module, for preprocessing the characteristic data of obstructive renal fibrosis to output standard obstructive renal fibrosis data, including a comparison and correction unit for performing comparative analysis on the characteristic data of obstructive renal fibrosis to output corrected characteristic data of obstructive renal fibrosis and a preprocessing unit for preprocessing the data transmitted by the data acquisition module;

[0036] An obstructive renal fibrosis analysis module, connected to the data processing module, for determining an obstructive renal fibrosis evaluation value based on the standard obstructive renal fibrosis data, including a mapping establishment unit for generating a number of mappings by training the standard obstructive renal fibrosis data and a model generation unit connected to the mapping establishment unit for generating an obstructive renal fibrosis evaluation model based on the number of mappings;

[0037] A control module, respectively connected to the data acquisition module, the data processing module, and the obstructive renal fibrosis analysis module, for adjusting the number of data polling directions when it is determined that the stability of the evaluation does not meet the requirements according to the variance of the response delay of the obstructive renal fibrosis evaluation model, or for determining the accuracy of data acquisition by combining the variance of the response delay and the linear fitting degree of the abnormal amount of collected data and the model error amount, and determining the number of adjusted organ interaction parameter mappings according to the determination result of the accuracy or adjusting the number of comparison types according to the average difference amount of the collected data.

[0038] Specifically, the standard obstructive renal fibrosis data is the normal value range of each characteristic data of obstructive renal fibrosis. For example, the standard value range of the thickness of the renal parenchyma is [1.5 cm, 2.5 cm], and the corrected characteristic data of obstructive renal fibrosis refers to the data retained after comparing and screening the characteristic data collected by the data acquisition module with the standard obstructive renal fibrosis data.

[0039] Specifically, the specific process of the corrected characteristic data of obstructive renal fibrosis is the process of removing abnormal data.

[0040] Specifically, the obstructive renal fibrosis analysis module determines the obstructive renal fibrosis evaluation value according to the standard obstructive renal fibrosis data. When the urine obstructive renal fibrosis marker data is the content of fibronectin in serum and the hydronephrosis imaging data is the thickness of the renal parenchyma, the obstructive renal fibrosis evaluation value is: the weight coefficient of fibronectin content × the fibronectin content + the weight coefficient of the thickness of the renal parenchyma × the thickness of the renal parenchyma. The sum of the weight coefficient of fibronectin content and the weight coefficient of the thickness of the renal parenchyma is equal to 1. Among them, the preferred embodiment of the weight coefficient of fibronectin content is 0.4, and the preferred embodiment of the weight coefficient of the thickness of the renal parenchyma is 0.6.

[0041] Optionally, the value range of the preset standard obstructive renal fibrosis evaluation value can be [72.9, 113.5], the value range of the preset standard fibronectin content can be [180mg / L, 280mg / L], and the value range of the preset standard thickness of the renal parenchyma can be [1.5cm, 2.5cm];

[0042] In practice, for example, if the patient's fibronectin content is 200mg / L and the thickness of the renal parenchyma is 2cm, then the obstructive renal fibrosis evaluation value is 200×0.4 + 2×0.6 = 81.2, and it is determined that the patient's obstructive renal fibrosis evaluation value is within the range of the standard obstructive renal fibrosis evaluation value;

[0043] Another example, if the patient's fibronectin content is 300mg / L and the thickness of the renal parenchyma is 2cm, then the obstructive renal fibrosis evaluation value is 300×0.4 + 2×0.6 = 121.2, and it is determined that the patient's obstructive renal fibrosis evaluation value is not within the range of the standard obstructive renal fibrosis evaluation value.

[0044] In practice, the present invention sets up a data acquisition module, a data processing module, an obstructive renal fibrosis analysis module and a control module. Through the obstructive renal fibrosis evaluation model generated by the model generation unit in the set obstructive renal fibrosis analysis module, the degree of obstructive renal fibrosis is evaluated, improving the accuracy of the evaluation. By adjusting the number of directions of data polling, the influence of the overall system operation lag that may be caused by the large amount of data required for model update and generation running simultaneously, resulting in insufficient evaluation stability, is reduced. By adjusting the number of mappings between non-renal organ operation parameters and obstructive renal fibrosis characteristic data according to the linear fitting degree between the abnormal data volume of data acquisition and the error volume of the evaluation model, the interference degree caused by the changes of non-renal organs to ureteral data or fibrosis data is reduced; by adjusting the number of comparison types according to the average value of the collected data difference amount, the influence of the signal interference of external devices on the data acquisition device, resulting in a decrease in data acquisition accuracy, is reduced, realizing an improvement in the stability of the obstructive renal fibrosis progression evaluation.

[0045] Specifically, the obstructive renal fibrosis analysis module further includes a model update unit connected to the model generation unit for updating the obstructive renal fibrosis model.

[0046] Specifically, the update process of the obstructive renal fibrosis evaluation model is as follows: when there is an error in the evaluation of the obstructive renal fibrosis evaluation model, the input data of the obstructive renal fibrosis model this time is locked according to the error, and the input data is marked, and the marked input data is put into the training set for training to update the obstructive renal fibrosis model.

[0047] Specifically, the control module is respectively connected to the obstructive renal fibrosis analysis module and the data acquisition module, and is used to obtain the response delay of the obstructive renal fibrosis evaluation model after inputting several batches of data to calculate the variance of the response delay. When the variance of the response delay is greater than a preset first variance, it is determined that the stability of the evaluation does not meet the requirements, and when the variance of the response delay is greater than a preset second variance, the number of directions of data polling is increased;

[0048] Specifically, the meaning of the variance of the response delay is the variance of the response delay duration generated by the obstructive renal fibrosis evaluation model after inputting the obstructive renal fibrosis feature data of the same data volume several times. The calculation method of the variance of the response delay is a conventional technical means well-known to those skilled in the art. Therefore, the calculation process of the variance of the response delay will not be described in detail here.

[0049] Specifically, the response delay duration is the difference between the duration from when the obstructive renal fibrosis evaluation model inputs the obstructive renal fibrosis feature data of the same data volume several times to when the obstructive renal fibrosis evaluation model processes the above data and outputs the obstructive renal fibrosis evaluation value and the standard time.

[0050] Specifically, the standard time is related to the data volume input by the obstructive renal fibrosis evaluation model each time. Generally speaking, those skilled in the art can understand that the more data volume input by the obstructive renal fibrosis evaluation model each time, the more data the obstructive renal fibrosis evaluation model needs to process. Therefore, generally speaking, the longer the time required for the obstructive renal fibrosis to output the obstructive renal fibrosis evaluation value.

[0051] In implementation, the characteristic data of obstructive renal fibrosis include the content of fibronectin in serum, the thickness of renal parenchyma, the width of pelvicaliectasis, the content of monocyte chemoattractant protein-1 in urine, and the content of ADAMTS18 in urine; the direction of data polling can start from the content of fibronectin, followed by the thickness of renal parenchyma, the width of pelvicaliectasis, the content of monocyte chemoattractant protein-1 in urine, and end with the content of ADAMTS18 in urine; the direction of data polling can also start from the content of ADAMTS18 in urine, followed by the content of monocyte chemoattractant protein-1 in urine, the width of pelvicaliectasis, the thickness of renal parenchyma, and end with the content of fibronectin in serum; the characteristic data of obstructive renal fibrosis can also start polling from the content of fibronectin in serum and the content of ADAMTS18 in urine respectively. The polling order in one direction is the content of fibronectin in serum and the thickness of renal parenchyma, and the polling order in the other direction is the content of ADAMTS18 in urine and the content of monocyte chemoattractant protein-1 in urine, and finally end with the width of pelvicaliectasis.

[0052] Specifically, the ADAMTS18 is a disintegrin-like and metalloproteinase with thrombospondin type 1 motifs 18.

[0053] Specifically, the increased number of directions of the data polling is determined by the difference between the variance of the response delay and the preset second variance.

[0054] Optionally, the value range of the preset first variance can be [1.5s, 2s], and the value range of the preset second variance can be [2s, 3s].

[0055] Preferably, the preferred embodiment of the preset first variance is 1.5s, and the preferred embodiment of the preset second variance is 2.5s.

[0056] In implementation, when the value that the variance of the response delay exceeds the preset second variance reaches 4s, the number of directions of the data polling is increased to 3 times the current number of directions of the data polling; when the value that the variance of the response delay exceeds the preset second variance exceeds 0.5s, the number of directions of the data polling is increased by 1. For example, in a possible embodiment, when the variance of the response delay is 3.5s and the original number of directions of the data polling is 1, the number of directions of the data polling becomes: 1 + [(3.5 - 2.5) / 0.5]×1 = 3, that is, when the variance of the response delay is 3.5s, the number of directions of the data polling becomes 3.

[0057] For another example, in a possible embodiment, when the variance of the response delay is 6.5 s, the number of directions of the original data polling is 1, and the number of directions of the data polling becomes: 1 + [(6.5 - 2.5) / 4] × 3 = 4, that is, when the variance of the response delay is 6.5 s, the number of directions of the data polling becomes 4.

[0058] Specifically, the control module is respectively connected to the data acquisition module and the obstructive renal fibrosis analysis module, and is further configured to preliminarily determine that the accuracy of data acquisition does not meet the requirements when the variance of the response delay is greater than or equal to a preset first variance and less than or equal to the preset second variance, collect obstructive renal fibrosis characteristic data, and calculate the error amount of the evaluation model according to the evaluation model, so as to calculate the linear fitting degree between the abnormal data amount of data acquisition and the error amount of the evaluation model, where

[0059] If the linear fitting degree between the abnormal data amount of data acquisition and the error amount of the evaluation model is greater than a preset second fitting degree, the control module re-determines that the accuracy of data acquisition does not meet the requirements, and increases the number of mappings between non-renal organ operation parameters and obstructive renal fibrosis characteristic data.

[0060] Optionally, the value range of the preset second fitting degree can be [1, 2].

[0061] Preferably, a preferred embodiment of the preset second fitting degree is 1.5.

[0062] In implementation, when the value by which the linear fitting degree exceeds the preset second fitting degree exceeds 0.5, the number of mappings between non-renal organ operation parameters and obstructive renal fibrosis characteristic data increases by 1. For example, in a possible embodiment, when the linear fitting degree is 3, the number of mappings between the original non-renal organ operation parameters and obstructive renal fibrosis characteristic data is 1, and the number of mappings between non-renal organ operation parameters and obstructive renal fibrosis characteristic data becomes: 1 + [(3 - 1.5) / 0.5] × 1 = 4, that is, when the linear fitting degree is 3, the number of mappings between the original non-renal organ operation parameters and obstructive renal fibrosis characteristic data becomes 4.

[0063] For another example, when the linear fitting degree is 2.5, the number of mappings between the original non-renal organ operation parameters and obstructive renal fibrosis characteristic data is 1, and the number of mappings between non-renal organ operation parameters and obstructive renal fibrosis characteristic data becomes: 1 + [(2.5 - 2) / 0.5] × 1 = 2, that is, when the linear fitting degree is 2.5, the number of mappings between the original non-renal organ operation parameters and obstructive renal fibrosis characteristic data becomes 2.

[0064] Specifically, the number of groups of organ interaction parameter mappings is the number of one-to-one corresponding relationships between the data in the two sets of non-renal organ operation parameters and obstructive renal fibrosis characteristic data;

[0065] Specifically, when the obstruction causes a stress response in the body, the blood pressure value in the operating parameters of non-renal organs and the content of aspartate aminotransferase in the liver in the characteristic data of obstructive renal fibrosis form a set of mappings. When other characteristic data remain unchanged, the blood pressure value is directly proportional to the content of aspartate aminotransferase, and the blood pressure value increases as the content of aspartate aminotransferase increases.

[0066] Specifically, the increase amplitude of the number of groups of the organ interaction parameter mappings is determined by the difference between the linear fitting degree of the abnormal data volume of the data collection and the error volume of the evaluation model and a preset second fitting degree.

[0067] Specifically, the control module is respectively connected to the data collection module and the obstructive renal fibrosis analysis module, and is also used to collect the characteristic data of obstructive renal fibrosis to calculate the average value of the difference amount of the collected data. When the average value of the difference amount of the collected data is greater than the preset average value of the first data difference amount, it is determined that the accuracy of the data collection of the evaluation does not meet the requirements, and when the average value of the difference amount of the collected data is greater than the preset average value of the second data difference amount, the number of comparison types of the characteristic data of obstructive renal fibrosis is increased.

[0068] Specifically, the comparison types of the characteristic data of obstructive renal fibrosis include comparing with the characteristic data of obstructive renal fibrosis in the previous month and the month before last that are consistent with the collection time point of the characteristic data of obstructive renal fibrosis, and comparing with the characteristic data of obstructive renal fibrosis with the same air temperature as the collection environment of the current characteristic data of obstructive renal fibrosis in the previous collection cycle.

[0069] Optionally, taking the fibronectin content data as an example, the value range of the preset average value of the first data difference amount can be [1 mg / L, 1.5 mg / L], and the preset average value of the second data difference amount can be [2 mg / L, 3 mg / L];

[0070] Preferably, the preferred embodiment of the average value of the preset average value of the first data difference amount is 1.5 mg / L, and the preferred embodiment of the average value of the second data difference amount is 2.5 mg / L;

[0071] In one or more embodiments, every time the average value of several difference amounts of the collected data exceeds the preset average value of the second data difference amount by 3 mg / L, the number of comparison types of the collected data increases by 3;

[0072] For example, if the average value of several difference amounts of the collected data is 5.5 mg / L and the current number of comparison types of the collected data is 5, then the number of comparison types of the collected data increases to: 3 + [(5.5 - 2.5) / 3]×3 = 6.

[0073] Specifically, the increase amplitude of the quantity of the collected data comparison types is determined by the average value of the data collection difference quantity and the average value of a preset second data difference quantity.

[0074] Specifically, the linear fitting degree between the abnormal data quantity collected for the obstructive renal fibrosis characteristic data and the error quantity of the evaluation model is the average value of the sum of the linear distances between several coordinate points in a coordinate system and the straight line image of a linear regression function generated through linear regression calculation with the parameter represented by the abscissa and the parameter represented by the ordinate in this coordinate system;

[0075] Wherein, in this coordinate system, the abscissa is the abnormal data quantity collected for the obstructive renal fibrosis characteristic data, and the ordinate is the difference quantity of the evaluation model.

[0076] Optionally, the preset standard linear fitting degree is [0.9, 1.1];

[0077] Preferably, a preferred embodiment of the preset standard linear fitting degree is 1;

[0078] In an embodiment, if there are n points for the abnormal data quantity (x) collected in data collection and the error quantity (y) of the evaluation model in the coordinate system, the linear regression function can be obtained:

[0079] y = a + bx + c;

[0080] a is the intercept, b is the slope, and c is the error term;

[0081] In the coordinate system, for a point P(x, y), the steps to determine the fitting straight line Ax + By + C = 0 by the linear regression function are as follows: The intercept a represents the value of y when x = 0, and the slope b represents the average change amount of y when x increases by one unit.

[0082] In the coordinate system, the calculation formula for the distance d from the point P(x, y) to the straight line Ax + By + C = 0 is:

[0083]

[0084] Then, the linear fitting degree S between the abnormal data quantity collected for the obstructive renal fibrosis characteristic data and the error quantity of the evaluation model is:

[0085]

[0086] Wherein, S is the linear fitting degree between the abnormal data quantity collected for the obstructive renal fibrosis characteristic data and the error quantity of the evaluation model, and d n is the straight line distance from the nth coordinate point to the straight line Ax + By + C = 0, n is the number of coordinate points, and n is a natural number greater than or equal to 1.

[0087] Specifically, the average value of the collected data difference amounts is the ratio of the sum of the collected data difference amounts for several times to the total number of collections.

[0088] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A quantitative assessment system for the progression of obstructive renal fibrosis based on data analysis, characterized in that: include: A data acquisition module is used to collect characteristic data of obstructive renal fibrosis, wherein the characteristic data of obstructive renal fibrosis include urine obstructive renal fibrosis marker data and hydronephrosis imaging data; A data processing module connected to the data acquisition module, used for preprocessing the obstructive renal fibrosis characteristic data to output standard obstructive renal fibrosis data, including a comparison and correction unit used for performing comparative analysis on the obstructive renal fibrosis characteristic data to output corrected obstructive renal fibrosis characteristic data; an obstructive renal fibrosis analysis module, connected to the data processing module, for determining an obstructive renal fibrosis evaluation value according to the standard obstructive renal fibrosis data, comprising a mapping establishment unit for generating a plurality of mappings by training the standard obstructive renal fibrosis data and a model generation unit connected to the mapping establishment unit for generating an obstructive renal fibrosis evaluation model according to the plurality of mappings; A control module, which is respectively connected to the data acquisition module, the data processing module and the obstructive renal fibrosis analysis module, and is used to adjust the number of data polling directions when the stability of the evaluation determined by the variance of the response delay of the obstructive renal fibrosis evaluation model does not meet the requirements, or to determine the accuracy of data acquisition by combining the variance of the response delay and the linear fit between the abnormal amount of the acquired data and the model error amount, and to determine the number of organ interaction parameter mapping groups to be adjusted according to the accuracy determination result or to adjust the number of comparison types according to the average difference amount of the acquired data.

2. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 1, characterized in that: The obstructive renal fibrosis analysis module further includes a model updating unit connected to the model generating unit for updating the obstructive renal fibrosis model.

3. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 2, characterized in that: The control module is connected to the obstructive renal fibrosis analysis module and the data acquisition module, respectively, and is used to obtain the response delay of the obstructive renal fibrosis assessment model after inputting several batches of data to calculate the variance of the response delay, and when the variance of the response delay is greater than a preset first variance, it is determined that the stability of the assessment does not meet the requirements, and when the variance of the response delay is greater than a preset second variance, the number of directions of the data polling is increased.

4. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 3, characterized in that: The increased number of directions of the data polling is determined by the difference between the variance of the response delay and the preset second variance.

5. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 4, characterized in that: The control module is connected to the data acquisition module and the obstructive renal fibrosis analysis module respectively, and is also used to preliminarily determine that the accuracy of data acquisition does not meet the requirements when the variance of the response delay is greater than or equal to the preset first variance and less than or equal to the preset second variance, collect obstructive renal fibrosis characteristic data, and calculate the error amount of the evaluation model according to the evaluation model, thereby calculating the linear fit between the abnormal data amount of data acquisition and the error amount of the evaluation model, wherein, If the linear fit between the amount of abnormal data collected and the error amount of the evaluation model is greater than a preset second fit, the control module determines for the second time that the accuracy of the data collection does not meet the requirements, and increases the number of mappings between non-renal organ operating parameters and obstructive renal fibrosis characteristic data.

6. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 5, characterized in that: The increase in the number of mappings of the non-renal organ operating parameters and the obstructive renal fibrosis characteristic data is determined by the difference between the linear fit between the amount of abnormal data collected and the error amount of the evaluation model and a preset second fit.

7. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 6, characterized in that: The control module is connected to the data acquisition module and the obstructive renal fibrosis analysis module respectively, and is also used to collect obstructive renal fibrosis characteristic data to calculate the average value of the difference amount of the collected data, and when the average value of the difference amount of the collected data is greater than the average value of the preset first data difference amount, it is determined that the evaluated data acquisition accuracy does not meet the requirements, and when the average value of the difference amount of the collected data is greater than the average value of the preset second data difference amount, the number of comparison types of the obstructive renal fibrosis characteristic data is increased.

8. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 7, characterized in that: The increase range of the number of the collected data comparison types is determined by the average value of the data collection difference amount and the average value of the preset second data difference amount.

9. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 8, characterized in that: The linear fit between the abnormal data amount of the data collection and the error amount of the evaluation model is the average value of the sum of the straight-line distances between a number of coordinate points in the coordinate system and a straight-line image of a linear regression function in the coordinate system generated by linear regression calculation using the horizontal coordinate characterization parameters and the vertical coordinate characterization parameters; The coordinate system uses the amount of abnormal data collected from the characteristic data of obstructive renal fibrosis as the horizontal coordinate and the amount of difference in the evaluation model as the vertical coordinate.

10. The data analysis-based quantitative assessment system for obstructive renal fibrosis progression according to claim 9, characterized in that: The average value of the difference in collected data is the ratio of the sum of the differences between several batches of collected data and standard collected data to the number of batches, wherein the data amount of each batch of collected data is the same.

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