T2DM test data association system based on adaptive multivariate correction model

Abstract

The invention relates to a T2DM test data association system based on an adaptive multivariate correction model. The T2DM test data association system comprises a model construction module, a patient sample anchoring module, an animal sample marking module, an evolution presumption module and a model correction module, based on a relatively stable experimental result of an animal sample as a deduction basis, the difference between animal test data and patient test data in test data is deduced, and then evolution is performed through an evolution model to obtain a corresponding prediction model, so that an abnormal condition can be judged or auxiliary diagnosis analysis can be performed according to the patient test data; the reliability of the data is improved; and the abnormality of the test data caused by uncontrollable patient behaviors can be found in time.

Description

Technical Field

[0001] The present invention relates to the technical field of diabetes data analysis, and more specifically, to a T2DM test data association system based on an adaptive multivariate correction model. Background Art

[0002] With the increasing incidence of diabetes, diabetic nephropathy (DN) has become the main cause of end-stage renal disease. It is currently believed that the occurrence of DN is the result of the long-term combined effects of genetic and environmental factors. Like other tissues and organs, the kidneys will be in a functional self-compensation stage for a long time before obvious structural changes occur under the influence of pathogenic factors such as hyperglycemia, that is, the high filtration rate stage described by Mogensen staging. If the increase of urinary microalbuminuria (UMA) is used as a sign of clinical DN, then the above-mentioned high filtration rate stage, that is, the functional compensation stage when UMA has not yet increased, can be regarded as "subclinical" DN. There are many ways to test and study diabetes, such as the more common ones such as urine, serum, and feces. There are also many schemes for diabetes analysis. For example, the publication number CN115029431B discloses a type 2 diabetes gene detection kit and a type 2 diabetes genetic risk assessment system. By obtaining the gene sequence, the genetic risk of diabetes is determined. For example, the publication number CN109171694B discloses a method and system for diabetes disease assessment based on pulse signals. By obtaining the pulse signal and the characteristic index of the corresponding pulse signal, a model function of the corresponding relationship between the characteristic index of the pulse signal and the diabetes disease is obtained; by obtaining the pulse signal, calculating and according to the characteristic index of the pulse signal, the diabetes disease assessment result is obtained through the model function. The system can also obtain multiple pulse signals, obtain multiple diabetes disease assessment results, record and analyze these results, and obtain the diabetes disease development trend assessment results for daily disease monitoring and control of diabetic patients. For example, the publication number CN117893836B discloses a method for predicting diabetic nephropathy based on fundus vascular geometry parameters, the method comprising the following steps: obtaining a data set, the data set comprising clinical data and retinal vascular imaging data; screening modeling indicators from the clinical data and fundus vascular geometry parameters, and establishing a training set; training the training set based on a logistic regression method to obtain a prediction model; analyzing the data to be tested by the prediction model to predict diabetic nephropathy or non-diabetic nephropathy. Extracting fundus vascular geometry parameters from retinal vascular imaging data, and using the data set to train the prediction model, analyzing the data to be tested, predicting whether the patient has diabetic nephropathy or non-diabetic nephropathy; achieving non-invasive and rapid prediction of diabetic nephropathy; and strongly proving the association between fundus vascular characteristics and diabetic nephropathy.The above data are collectively referred to as test data. Currently, there are certain limitations on the use of test data to predict diabetes. This is because the prediction of diabetes based on a single test data is not a highly correlated causal relationship. It is very important to check other conditions of the patient. For example, the patient has high requirements for eating and resting. Therefore, the test data of clinical patients cannot be directly used as the only basis for judgment. If the patient does not perform the corresponding actions as required, the test data will be affected by other conditions, thereby affecting the analysis results. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a T2DM test data association system based on an adaptive multivariate correction model.

[0004] In order to solve the above technical problems, the technical solution of the present invention is: A T2DM test data association system based on an adaptive multivariate correction model, characterized by: a model building module, a patient sample anchoring module, an animal sample labeling module, an evolution inference module and a model correction module; The model building module is used to build a multivariate correction model, the multivariate correction model includes a plurality of diagnosis nodes, each diagnosis node corresponds to a diagnosis result item, and an identification line is formed between the diagnosis nodes, and the identification line reflects the association relationship between the diagnosis nodes. The multivariate correction model includes a plurality of expression layers, each expression layer corresponds to a test item setting, and the expression layer is provided with a patient characterization feature corresponding to the diagnosis node; The patient sample anchoring module includes a reliability screening unit, an anchor clustering unit and an anchor matching unit. The reliability screening unit is configured with a reliability evaluation algorithm, which is used to calculate the reliability value of each patient sample and screen the patient samples according to the pre-generated patient reliability threshold. The anchor clustering unit is configured with an anchor clustering condition. When the cluster cluster of the screened patient sample at the corresponding diagnosis node meets the corresponding anchor clustering condition, the corresponding patient sample is anchored to the corresponding diagnosis node. The anchor matching unit is used to generate patient characterization features according to the test data in the patient sample corresponding to the anchored diagnosis node; The animal sample marking module includes a marking clustering unit and a marking matching unit. The marking clustering unit is configured with a marking clustering condition. When the cluster of the animal sample at the corresponding diagnosis node meets the corresponding marking clustering condition, the corresponding animal sample is marked at the corresponding diagnosis node. The marking matching unit is used to generate an animal characterization feature according to the inspection data of the animal sample corresponding to the marked diagnosis node; The evolutionary inference module includes an evolutionary configuration unit, an evolutionary training unit, and an evolutionary inference unit; the evolutionary configuration unit is configured with an evolutionary inference sub-model corresponding to different test items, the evolutionary training unit is used to obtain patient characterization features and animal characterization features belonging to the same diagnostic node to establish an evolutionary sample, and train the corresponding evolutionary inference sub-model according to the evolutionary sample, and the evolutionary inference unit generates corresponding evolutionary characterization features according to the animal characterization features of other diagnostic nodes according to the evolutionary inference sub-model as the patient characterization features of the diagnostic node; The model correction module is configured with a reliable evaluation unit, a vector configuration unit and a distance configuration unit. The reliable evaluation unit is used to generate dynamic reliability parameters of the identification line, the vector configuration unit is used to generate the vector of the identification line, and the distance configuration unit is used to generate the length of the identification line.

[0005] Furthermore: it also includes a result analysis module, which includes a data acquisition unit, a data matching unit, a result positioning unit and a result output unit. The data acquisition unit is used to obtain measured test information, and the measured test information includes test data corresponding to different test items. The data matching unit matches the corresponding test data according to the patient's characterization characteristics. The result positioning unit determines the estimated coordinates of the measured test information in the multivariate correction model according to the matching results of the test data. The result output unit generates an analysis map according to the estimated coordinates and outputs it.

[0006] Further: the result positioning unit determines the closest diagnostic node coordinates as relative sub-coordinates at each expression layer by matching the patient representation data with the test data, and calculates the estimated coordinates corresponding to the measured test information according to the preset coordinate mean formula, , ,in, is the abscissa value of the estimated coordinate, is the ordinate value of the estimated coordinate, For the The horizontal coordinate value of the relative sub-coordinate in the expression layer, For the The ordinate value of the relative sub-coordinate in the expression layer, For the The reliability weight of the test data in the expression layer, For the The reliability weight of the matched diagnosis node in the expression layer, For the The similarity weights of the test data and the corresponding patient representation features in the expression layer, is the total number of expression layers that match the test information and satisfy the constraints , , , , , ,in, is the preset reliable weight parameter, is the preset similarity weight parameter, For the The reliability value of the test data in the expression layer, is the sum of the reliability values ​​of the test data of all expression layers, For the The reliability value of the diagnosis node matched by the expression layer, is the sum of the reliability values ​​of the matched diagnosis nodes in all expression layers, For the The similarity value between the test data and the corresponding patient representation features in the expression layer, It is the sum of the similarity values ​​of all test data and the corresponding patient characterization features.

[0007] Further: it also includes a data processing module, the data processing module includes a quantitative feature processing unit, an image feature processing unit and a sequence feature processing unit, the expression layer includes a serum test expression layer, a stool test expression layer, a urine test expression layer, an eye pattern recognition expression layer, a pulse test expression layer and a gene test expression layer; The quantitative feature processing unit is used to process the test data whose data type is a numerical value. The quantitative feature processing unit is pre-configured with a test mapping function corresponding to different test sub-items, and re-assigns the test data according to the test mapping function. When the cluster to which it belongs meets the preset quantitative feature extraction condition, the patient characterization feature or animal characterization feature is generated according to the mapping rule between the test data after the different test sub-items are re-assigned; The image feature processing unit is used to process the inspection data whose data type is an image. After graying the image, the image processing unit generates a binarization threshold according to the grayscale mean value corresponding to the image to binarize the image. When the cluster to which it belongs meets the preset image feature extraction condition, the element shape graphic in the inspection sub-item is extracted as the patient characterization feature or the animal characterization feature; The sequence feature processing unit pre-stores a number of different gene identification fragments. The sequence feature processing unit identifies and marks the gene identification fragments in the test data. When the cluster meets the preset sequence feature extraction conditions, the patient characterization feature or animal characterization feature is generated according to the set of gene identification fragments.

[0008] Further: the reliability assessment algorithm includes: ,in, To verify the reliability of the data, is the macro reliability corresponding to the test data, and the macro reliability is negatively correlated with the discrete degree corresponding to the test sub-item. For the a reliable weight corresponding to an influencing factor item related to the test data, wherein the reliable weight reflects the stability of the influencing factor item itself, For the a controllable selection value corresponding to an influencing factor item related to the inspection data, wherein the controllable selection value reflects the controllability of the selection content corresponding to the influencing factor item, is the total number of influencing factors related to the test data, For the a difference value corresponding to a micro-difference item related to the test data, wherein the difference value reflects the dispersion degree of the category corresponding to the micro-difference item, is the total number of micro-difference items related to the test data, is the preset macro reliability weight, is the preset influencing factor weight, is the preset micro-difference weight, .

[0009] Further: the anchor clustering condition includes a similarity sub-condition, a reliability sub-condition and a quantity sub-condition, and the tag clustering condition includes a similarity sub-condition, a reliability sub-condition and a quantity sub-condition. When the similarity sub-condition, the reliability sub-condition and the quantity sub-condition are all satisfied, it is deemed that the corresponding anchor clustering condition or tag clustering condition is satisfied. The similarity sub-condition is that the similarity mean between the test data of the clusters is greater than a preset similarity benchmark; The reliability sub-condition is that the mean of the reliability values ​​between the test data of the clusters is greater than a preset reliability value benchmark; The quantity sub-condition is that the quantity of the inspection data of the cluster is greater than a preset quantity benchmark.

[0010] Further: the vector configuration unit includes obtaining the disease course migration value and the test migration value between two diagnosis nodes, and the vector expression of the identification connection line is ,in, Indicates the number The diagnostic nodes and numbers are A vector of identification lines between diagnostic nodes, For the number The diagnostic nodes and numbers are The disease course migration value between the diagnosis nodes, For the number The diagnostic nodes and numbers are The test migration value between the diagnostic nodes, the disease course migration value reflects the disease course relationship between the two diagnostic nodes, the test migration value reflects the characterization feature difference between the two diagnostic nodes, each characterization feature is pre-associated with a characterization feature value, and the characterization feature difference is the difference between the characterization feature values ​​between the diagnostic nodes.

[0011] Further: the reliability evaluation unit is configured with a transfer function table, the transfer function table stores a number of reliability adjustment parameters, each reliability adjustment parameter is indexed by an evolution element, when the evolution inference module generates an evolution characterization feature, the evolution element is generated according to the original fit and target fit of the evolution characterization feature, the original fit reflects the fit between the generated evolution characterization feature and the animal characterization feature, the target fit reflects the fit between the evolution characterization feature and the patient sample corresponding to the diagnostic node, the dynamic reliability parameter is generated according to the reliability adjustment parameter, and the reliability value of the patient characterization feature corresponding to the diagnostic node pointed to by the identification line is calculated according to the dynamic reliability parameter.

[0012] Furthermore: the model correction module also includes a similarity learning unit, which is used to obtain new patient samples, match corresponding diagnostic nodes according to the diagnostic results of the patient samples, and extract test data in the patient samples, and compare the corresponding patient characterization features and test data to generate deviation correction information, the deviation correction information includes an association correction index, a characterization deviation index and a characterization reliability index, and the deviation correction information is substituted into the evolutionary inference module to correct the evolutionary inference sub-model.

[0013] Furthermore: the distance configuration unit performs a correlation analysis on all patient samples corresponding to two diagnosis nodes between the identification lines of each expression layer, and is configured with a correlation mapping function, through which the length of the corresponding identification line is calculated.

[0014] The technical effects of the present invention are mainly reflected in the following aspects: through such a setting, the experimental results based on relatively stable animal samples are used as the basis for deduction, the differences between animal test data and patient test data in the test data are deduced, and then the corresponding prediction model is evolved through the evolution model. In this way, abnormal situations can be judged according to the patient test data or auxiliary diagnosis and analysis can be carried out, thereby improving the reliability of the data and being able to promptly discover abnormalities in the test data caused by uncontrollable patient behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 : The system architecture principle diagram of the T2DM test data association system based on the adaptive multivariate correction model of the present invention; Figure 2: Schematic diagram of the patient sample anchoring module of the T2DM test data association system based on the adaptive multivariate correction model of the present invention; Figure 3 : Schematic diagram of the animal sample labeling module of the T2DM test data association system based on the adaptive multivariate correction model of the present invention; Figure 4 : Schematic diagram of the evolutionary inference module of the T2DM test data association system based on the adaptive multivariate correction model of the present invention; Figure 5 : Schematic diagram of the model correction module of the T2DM test data association system based on the adaptive multivariate correction model of the present invention; Figure 6 : Schematic diagram of result analysis of T2DM test data association system based on adaptive multivariate correction model of the present invention; Figure 7 : Schematic diagram of the data processing module of the T2DM test data association system based on the adaptive multivariate correction model of the present invention.

[0016] Figure numerals: 100, model building module; 200, patient sample anchoring module; 210, reliability screening unit; 220, anchor clustering unit; 230, anchor matching unit; 300, animal sample marking module; 310, marker clustering unit; 320, marker matching unit; 400, evolutionary inference module; 410, evolutionary configuration unit; 420, evolutionary training unit; 430, evolutionary inference unit; 500, model correction module; 510, reliability evaluation unit; 520, vector configuration unit; 530, distance configuration unit; 540, similarity learning unit; 600, result analysis module; 610, data acquisition unit; 620, data matching unit; 630, result positioning unit; 640, result output unit; 700, data processing module; 710, quantitative feature processing unit; 720, image feature processing unit; 730, sequence feature processing unit. DETAILED DESCRIPTION

[0017] The specific implementation modes of the present invention are further described below in conjunction with the accompanying drawings to make the technical solutions of the present invention easier to understand and grasp.

[0018] A T2DM test data association system based on an adaptive multivariate correction model includes a model building module 100, a patient sample anchoring module 200, an animal sample marking module 300, an evolution inference module 400, and a model correction module 500; the purpose of the present invention is that if the test data is taken as an example of a patient, such as drinking water, eating, exercising, or living conditions, it is difficult to control. If the test data is biased due to these correlation variables, and it is difficult for the tester to find it, it will lead to the identification of all test data as the same, which will further lead to deviations in the test results. Affecting the reliability of the test data.

[0019] The model building module 100 is used to build a multivariate correction model, which includes a plurality of diagnostic nodes, each of which corresponds to a diagnostic result item, and an identification line is formed between the diagnostic nodes, and the identification line reflects the association relationship between the diagnostic nodes. The multivariate correction model includes a plurality of expression layers, each of which corresponds to a test item setting, and the expression layer is provided with a patient characterization feature corresponding to the diagnostic node; first of all, this model can be understood as a diagnostic node composed of a constellation diagram and a network of diagnostic nodes associated with the identification line, the diagnostic node reflects the disease condition or the diagnostic result, and all diagnostic results about T2DM are classified and assigned with a label to form a diagnostic node, and then each sample can find the corresponding diagnostic node, and the relative position of the diagnostic node in the expression layer can be changed, for example, the position of the same diagnostic node in the first expression layer and the second expression layer may be different, and each expression layer corresponds to the test item setting, for example, urine test can be configured with different expression layers according to the test item, and serum test and urine test belong to different expression layers, so the corresponding test result corresponds to each diagnostic node The characterization feature is also different. For example, the diagnostic nodes in the test data can be: NC: Normal control group; SDN: Subclinical diabetic nephropathy group; EDN: Early diabetic nephropathy group; which can be used as diagnostic nodes, and the corresponding characterization features can be the features of the test data in various aspects.

[0020] It also includes a data processing module 700, which includes a quantitative feature processing unit 710, an image feature processing unit 720 and a sequence feature processing unit 730. The expression layer includes a serum test expression layer, a stool test expression layer, a urine test expression layer, an eye pattern recognition expression layer, a pulse test expression layer and a gene test expression layer. The purpose of the data processing module 700 is to process the above data in advance.

[0021] The quantitative feature processing unit 710 is used to process the test data whose data type is a numerical value. The quantitative feature processing unit 710 is pre-configured with a test mapping function corresponding to different test sub-items, and the test data is reassigned according to the test mapping function. When the cluster to which it belongs meets the preset quantitative feature extraction conditions, the patient characterization feature or animal characterization feature is generated according to the mapping rule between the test data after the different test sub-items are reassigned; for example, taking urine test as an example: Scr: Serum creatinine; UMA: Urinary microalbuminuria; microglobubin; NAG: N-acetyl D-glucosaminadase; GAL: galactosidase; RBP: Retinol binding protein; UAGT: Urinary angiotensinogen angiotensinogen; The test results of different sub-items are different, and each item has a corresponding range. Therefore, if different situations need to be classified, the range value is used in principle. However, since different detection sub-items exceed different range units, corresponding inspection mapping functions are constructed. In this way, the corresponding numerical deviation can be quantified according to the abnormal situation, and a more accurate response can be made. If the corresponding features are to be extracted, for the digitized features, after mapping, the mapping value range of different items can be used as the characterization feature. At the same time, if the deviation between the actual data and the characterization feature is to be calculated, it can also be determined by the deviation from the center value of the range.

[0022] The image feature processing unit 720 is used to process the inspection data whose data type is an image. After the image processing unit grayscales the image, it generates a binarization threshold value according to the grayscale mean value corresponding to the image to binarize the image. When the cluster to which it belongs meets the preset image feature extraction conditions, the element shape graphics in the inspection sub-item are extracted as the patient characterization feature or animal characterization feature; by simplifying the image, the element shape can be extracted as the recognition basis, and by generating a binarization threshold value through the grayscale mean value, the influence of the environment on the brightness value can be eliminated, so that the recognition result is more accurate and the data volume is smaller. After obtaining the element graphics, a reference feature graphic can be formed as the characterization feature by combining the element graphics. If the similarity between the inspection image and the reference feature graphic exceeds the threshold value, the feature matching is considered successful.

[0023] The sequence feature processing unit 730 pre-stores a number of different gene recognition fragments. The sequence feature processing unit 730 identifies and marks the gene recognition fragments in the test data. When the cluster cluster meets the preset sequence feature extraction conditions, the patient characterization feature or animal characterization feature is generated according to the set of gene recognition fragments. By marking the gene recognition fragments to form corresponding characterization features, if there is a set of identical gene fragments, the match is considered successful. In this way, data extraction of test data in different formats can be completed, and then as long as there is a sufficient number of test data that meets the clustering conditions, the corresponding characterization features can be determined, and matching can be completed based on the characterization features.

[0024] The patient sample anchoring module 200 includes a reliability screening unit 210, an anchor clustering unit 220 and an anchor matching unit 230. First, due to the reasons mentioned above, if the diagnostic node is directly determined by patient sample clustering analysis, the obtained features will have a large deviation from the actual features, and the test data will be more difficult to associate. Therefore, the first step is to find patient samples that can be used as anchor diagnostic nodes, that is, patient samples with higher reliability and less affected and disturbed samples as anchors. The coordinates of the diagnostic nodes corresponding to the anchor samples in the model are the same in each expression layer. In this way, the non-anchor diagnostic nodes are located through the anchored diagnostic nodes, thereby associating the corresponding test data, and the reliability is higher. Specifically, first, reliability analysis needs to be performed on all patient samples to screen out patient samples with higher reliability. The specific method is as follows: The reliability screening unit 210 is configured with a reliability evaluation algorithm, which is used to calculate the reliability value of each patient sample and screen patient samples according to a pre-generated patient reliability threshold; The reliability assessment algorithm includes: ,in, To verify the reliability of the data, is the macro reliability corresponding to the test data, and the macro reliability is negatively correlated with the discrete degree corresponding to the test sub-item, that is, if the discrete degree of the corresponding test result in the test sub-item is higher, the value of the macro reliability is lower. For the The reliable weight corresponding to the influencing factor item related to the test data reflects the stability of the influencing factor item itself. For example, age, gender, exercise status, and eating status are all influencing factors, so the influencing factor item has a corresponding reliable weight. For the The controllable selection value corresponding to the influencing factor item related to the test data, the specific content of each influencing factor item actually collected has a corresponding controllable selection value, different age ranges have different controllable selection values, and the controllable selection value reflects the controllability of the selection content corresponding to the influencing factor item, is the total number of influencing factors related to the test data, For the The difference value corresponding to the micro-difference item related to the test data reflects the dispersion degree of the category corresponding to the micro-difference item. The micro-difference item corresponds to the dispersion degree of the test data itself. For example, if the change stability of blood sugar data is low, the corresponding dispersion degree is high, so the corresponding difference value is large, and the reliability is also large. is the total number of micro-difference items related to the test data, is the preset macro reliability weight, is the preset influencing factor weight, is the preset micro-difference weight, .

[0025] The anchor clustering unit 220 is configured with an anchor clustering condition. When the cluster of the screened patient sample at the corresponding diagnosis node meets the corresponding anchor clustering condition, the corresponding patient sample is anchored at the corresponding diagnosis node. The anchor matching unit 230 is used to generate patient characterization features according to the test data in the patient sample corresponding to the anchored diagnosis node; The anchor clustering condition includes a similarity sub-condition, a reliability sub-condition and a quantity sub-condition, and the tag clustering condition includes a similarity sub-condition, a reliability sub-condition and a quantity sub-condition. When the similarity sub-condition, the reliability sub-condition and the quantity sub-condition are all satisfied, the corresponding anchor clustering condition or tag clustering condition is deemed to be satisfied. The similarity sub-condition is that the similarity mean between the test data of the clusters is greater than a preset similarity benchmark; the similarity mean is the difference between the test data, for example, if the difference between the test data of different patients is smaller, the similarity mean is larger.

[0026] The reliability sub-condition is that the reliability value mean between the test data of the cluster cluster is greater than the preset reliability value benchmark; the reliability mean reflects the reliability of the test data itself, and the reliability of each test data is different. Its overall reliability can be determined by calculating the mean.

[0027] The quantity sub-condition is that the number of test data of the cluster is greater than the preset quantity benchmark. The quantity benchmark requires that the number of test data is large enough and the generated characterization features are more accurate. If the above three sub-conditions cannot be met, it means that the diagnostic node is not suitable as an anchored diagnostic node. After finding a diagnostic node suitable for anchoring, the characteristics of the patient sample can be evolved through animal samples. There are differences between animal samples and patient samples, and the specific expression content of each numerical value may be different, but generally speaking, they are regular and correlated. Therefore, when the reliability of patient samples is low, the deduction can be achieved based on the anchored diagnostic node through the collection of animal test data.

[0028] The animal sample labeling module 300 includes a labeling clustering unit 310 and a labeling matching unit 320. The labeling clustering unit 310 is configured with a labeling clustering condition. When the clustering cluster of the animal sample at the corresponding diagnostic node meets the corresponding labeling clustering condition, the corresponding animal sample is labeled at the corresponding diagnostic node. The labeling matching unit 320 is used to generate animal characterization features based on the inspection data of the animal sample corresponding to the labeled diagnostic node. The method of clustering feature extraction has been described above and will not be repeated here. The labeling clustering unit 310 and the label matching unit 320 are to configure corresponding animal characterization features at all diagnostic nodes.

[0029] The evolution estimation module 400 includes an evolution configuration unit 410, an evolution training unit 420, and an evolution estimation unit 430; the evolution configuration unit 410 is configured with evolution estimation sub-models corresponding to different inspection items. The evolutionary training unit 420 is used to obtain the patient characterization features and animal characterization features belonging to the same diagnostic node to establish an evolutionary sample, and train the corresponding evolutionary inference sub-model based on the evolutionary sample. The purpose of the evolutionary inference sub-model is to discover the regular relationship between the patient characterization features and the animal characterization features based on the same diagnostic node, so as to infer the corresponding result value based on the diagnostic node of the unknown patient characterization features through the animal characterization features. This evolutionary inference sub-model first constructs a benchmark expression with variable parameters based on experience, and then introduces the animal characterization features of the anchored diagnostic node, corrects the variable parameters through deviations, and completes the construction of each inference sub-model through machine learning.

[0030] The evolutionary inference unit 430 generates corresponding evolutionary characterization features according to the animal characterization features of other diagnostic nodes based on the evolutionary inference sub-model as the patient characterization features of the diagnostic node; the evolutionary characterization features are inferred patient characterization features. When the reliability of patient samples is not high, the patient characterization features corresponding to the diagnostic node can be inferred to associate subsequent test data.

[0031] The model correction module 500 is configured with a reliable evaluation unit 510, a vector configuration unit 520 and a distance configuration unit 530. The reliability evaluation unit 510 is used to generate dynamic reliability parameters of the identification line. The reliability evaluation unit is configured with a transfer function table. The transfer function table is pre-configured. Different evolution elements correspond to unreliability adjustment parameters. The transfer function table stores a number of reliability adjustment parameters. Each reliability adjustment parameter is indexed by an evolution element. When the evolution inference module 400 generates an evolution characterization feature, the evolution element is generated according to the original fit and target fit of the evolution characterization feature. The original fit reflects the fit between the generated evolution characterization feature and the animal characterization feature, that is, the fit relationship between the evolution characterization feature and the animal characterization feature. The target fit reflects the fit between the evolution characterization feature and the patient sample corresponding to the diagnosis node, that is, the matching relationship between the evolution characterization feature and the patient sample with low reliability. The dynamic reliability parameter is generated according to the reliability adjustment parameter. The reliability value of the patient characterization feature corresponding to the diagnosis node pointed to by the identification line is calculated according to the dynamic reliability parameter. In this way, the reliability value of the patient characterization feature corresponding to each diagnosis node can be calculated through the identification line, specifically, the reliability value of the starting diagnosis node multiplied by the corresponding dynamic reliability parameter, so that the reliability can be transferred.

[0032] The vector configuration unit is used to generate a vector for identifying a connection. The vector configuration unit includes obtaining a disease course migration value and a test migration value between two diagnosis nodes. The vector for identifying a connection is expressed as ,in, Indicates the number The diagnostic nodes and numbers are A vector of identification lines between diagnostic nodes, For the number The diagnostic nodes and numbers are The disease course migration value between the diagnosis nodes, For the number The diagnostic nodes and numbers are The test migration value between the diagnostic nodes, the disease course migration value reflects the disease course relationship between the two diagnostic nodes, that is, whether the two diagnostic nodes occur with correlation, such as occurring at an interval of time or at the same time. The test migration value reflects the characterization feature difference between the two diagnostic nodes. Each characterization feature is pre-associated with a characterization feature value. The characterization feature value is a weighted sum of the feature complexity and feature accuracy corresponding to the data. In theory, the more complex and accurate it is, the higher the correlation with other nodes may be. Therefore, in vertical vectorization, the characterization feature difference is the difference between the characterization feature values ​​between the diagnostic nodes.

[0033] The distance configuration unit 530 is used to generate the length of the identification line. The distance configuration unit 530 performs a correlation analysis on all patient samples corresponding to two diagnosis nodes between the identification lines of each expression layer, and is configured with a correlation mapping function, and the length of the corresponding identification line is calculated by the correlation mapping function. The higher the correlation, the shorter the length of the identification line calculated by the corresponding correlation mapping function.

[0034] The model correction module 500 also includes a similarity learning unit 540, which is used to obtain new patient samples, match the corresponding diagnostic nodes according to the diagnostic results of the patient samples, and extract the test data in the patient samples, and compare the corresponding patient characterization features and the test data to generate deviation correction information, the deviation correction information includes an association correction index, a characterization deviation index, and a characterization reliability index, and the deviation correction information is substituted into the evolution inference module 400 to correct the evolution inference sub-model. The deviation correction information is the deviation correction evolution inference sub-model of the actual results through the three values ​​of the association correction index, the characterization deviation index, and the characterization reliability index, so that the characterization features obtained by the evolution inference sub-model are close to the patient samples, and abnormal test data can be excluded in time.

[0035] The result analysis module 600 includes a data acquisition unit 610, a data matching unit 620, a result positioning unit 630 and a result output unit 640. The data acquisition unit 610 is used to acquire actual test information, which includes test data corresponding to different test items. If there is new test information, the corresponding test data is matched by acquiring the test information.

[0036] The data matching unit 620 matches the corresponding test data according to the patient's characterization features, so that the corresponding diagnosis node can be found.

[0037] The result positioning unit 630 determines the estimated coordinates of the measured test information in the multivariate correction model according to the matching results of the test data, and the result output unit 640 generates and outputs an analysis map according to the estimated coordinates. The result positioning unit 630 determines the closest diagnostic node coordinates as relative sub-coordinates at each expression layer by matching the test data with the patient representation data, and calculates the estimated coordinates of the corresponding measured test information according to the preset coordinate mean formula. , ,in, is the abscissa value of the estimated coordinate, is the ordinate value of the estimated coordinate, For the The horizontal coordinate value of the relative sub-coordinate in the expression layer, For the The ordinate value of the relative sub-coordinate in the expression layer, For the The reliability weight of the test data in the expression layer, For the The reliability weight of the matched diagnosis node in the expression layer, For the The similarity weights of the test data and the corresponding patient representation features in the expression layer, is the total number of expression layers that match the test information and satisfy the constraints , , , , , ,in, is the preset reliable weight parameter, is the preset similarity weight parameter, For the The reliability value of the test data in the expression layer, is the sum of the reliability values ​​of the test data of all expression layers, For the The reliability value of the diagnosis node matched by the expression layer, is the sum of the reliability values ​​of the matched diagnosis nodes in all expression layers, For the The similarity value between the test data and the corresponding patient representation features in the expression layer, It is the sum of the similarity values ​​of all test data and the corresponding patient characterization features. Because different test data will find different nodes, this solution can determine the comprehensive coordinates, determine the specific location, and then generate the judgment result based on the coordinates as the analysis result.

[0038] Of course, the above are only typical examples of the present invention. In addition, the present invention may also have many other specific implementations. All technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention.

Claims

1. A T2DM test data association system based on an adaptive multivariate correction model, characterized in that: Model building module, patient sample anchoring module, animal sample labeling module, evolution inference module and model correction module; The model building module is used to build a multivariate correction model, the multivariate correction model includes a plurality of diagnosis nodes, each diagnosis node corresponds to a diagnosis result item, and an identification line is formed between the diagnosis nodes, and the identification line reflects the association relationship between the diagnosis nodes. The multivariate correction model includes a plurality of expression layers, each expression layer corresponds to a test item setting, and the expression layer is provided with a patient characterization feature corresponding to the diagnosis node; The patient sample anchoring module includes a reliability screening unit, an anchor clustering unit and an anchor matching unit. The reliability screening unit is configured with a reliability evaluation algorithm, which is used to calculate the reliability value of each patient sample and screen the patient samples according to the pre-generated patient reliability threshold. The anchor clustering unit is configured with an anchor clustering condition. When the cluster cluster of the screened patient sample at the corresponding diagnosis node meets the corresponding anchor clustering condition, the corresponding patient sample is anchored to the corresponding diagnosis node. The anchor matching unit is used to generate patient characterization features according to the test data in the patient sample corresponding to the anchored diagnosis node; The animal sample marking module includes a marking clustering unit and a marking matching unit. The marking clustering unit is configured with a marking clustering condition. When the cluster of the animal sample at the corresponding diagnosis node meets the corresponding marking clustering condition, the corresponding animal sample is marked at the corresponding diagnosis node. The marking matching unit is used to generate an animal characterization feature according to the inspection data of the animal sample corresponding to the marked diagnosis node; The evolutionary inference module includes an evolutionary configuration unit, an evolutionary training unit, and an evolutionary inference unit; the evolutionary configuration unit is configured with an evolutionary inference sub-model corresponding to different test items, the evolutionary training unit is used to obtain patient characterization features and animal characterization features belonging to the same diagnostic node to establish an evolutionary sample, and train the corresponding evolutionary inference sub-model according to the evolutionary sample, and the evolutionary inference unit generates corresponding evolutionary characterization features according to the animal characterization features of other diagnostic nodes according to the evolutionary inference sub-model as the patient characterization features of the diagnostic node; The model correction module is configured with a reliable evaluation unit, a vector configuration unit and a distance configuration unit. The reliable evaluation unit is used to generate dynamic reliability parameters of the identification line, the vector configuration unit is used to generate the vector of the identification line, and the distance configuration unit is used to generate the length of the identification line.

2. The T2DM test data association system based on the adaptive multivariate correction model as claimed in claim 1, characterized in that: It also includes a result analysis module, which includes a data acquisition unit, a data matching unit, a result positioning unit and a result output unit. The data acquisition unit is used to acquire measured test information, and the measured test information includes test data corresponding to different test items. The data matching unit matches the corresponding test data according to the patient's characterization characteristics. The result positioning unit determines the estimated coordinates of the measured test information in the multivariate correction model according to the matching results of the test data. The result output unit generates an analysis map according to the estimated coordinates and outputs it.

3. The T2DM test data association system based on the adaptive multivariate correction model according to claim 2, characterized in that: The result positioning unit determines the closest diagnostic node coordinates as relative sub-coordinates at each expression layer by matching the patient representation data with the test data, and calculates the estimated coordinates corresponding to the measured test information according to the preset coordinate mean formula. , ,in, is the abscissa value of the estimated coordinate, is the ordinate value of the estimated coordinate, For the The horizontal coordinate value of the relative sub-coordinate in the expression layer, For the The ordinate value of the relative sub-coordinate in the expression layer, For the The reliability weight of the test data in the expression layer, For the The reliability weight of the matched diagnosis node in the expression layer, For the The similarity weights of the test data and the corresponding patient representation features in the expression layer, is the total number of expression layers that match the test information and satisfy the constraints , , , , , ,in, is the preset reliable weight parameter, is the preset similarity weight parameter, For the The reliability value of the test data in the expression layer, is the sum of the reliability values ​​of the test data of all expression layers, For the The reliability value of the diagnosis node matched by the expression layer, is the sum of the reliability values ​​of the matched diagnosis nodes in all expression layers, For the The similarity value between the test data and the corresponding patient representation features in the expression layer, It is the sum of the similarity values ​​of all test data and the corresponding patient characterization features.

4. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: It also includes a data processing module, which includes a quantitative feature processing unit, an image feature processing unit and a sequence feature processing unit, and the expression layer includes a serum test expression layer, a stool test expression layer, a urine test expression layer, an eye pattern recognition expression layer, a pulse test expression layer and a gene test expression layer; The quantitative feature processing unit is used to process the test data whose data type is a numerical value. The quantitative feature processing unit is pre-configured with a test mapping function corresponding to different test sub-items, and re-assigns the test data according to the test mapping function. When the cluster to which it belongs meets the preset quantitative feature extraction condition, the patient characterization feature or animal characterization feature is generated according to the mapping rule between the test data after the different test sub-items are re-assigned; The image feature processing unit is used to process the inspection data whose data type is an image. After graying the image, the image processing unit generates a binarization threshold according to the grayscale mean value corresponding to the image to binarize the image. When the cluster to which it belongs meets the preset image feature extraction condition, the element shape graphic in the inspection sub-item is extracted as the patient characterization feature or the animal characterization feature; The sequence feature processing unit pre-stores a number of different gene identification fragments. The sequence feature processing unit identifies and marks the gene identification fragments in the test data. When the cluster meets the preset sequence feature extraction conditions, the patient characterization feature or animal characterization feature is generated according to the set of gene identification fragments.

5. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: The reliability evaluation algorithm includes ,in, To verify the reliability of the data, is the macro reliability corresponding to the test data, and the macro reliability is negatively correlated with the discrete degree corresponding to the test sub-item. For the a reliable weight corresponding to an influencing factor item related to the test data, wherein the reliable weight reflects the stability of the influencing factor item itself, For the a controllable selection value corresponding to an influencing factor item related to the inspection data, wherein the controllable selection value reflects the controllability of the selection content corresponding to the influencing factor item, is the total number of influencing factors related to the test data, For the a difference value corresponding to a micro-difference item related to the test data, wherein the difference value reflects the dispersion degree of the category corresponding to the micro-difference item, is the total number of micro-difference items related to the test data, is the preset macro reliability weight, is the preset influencing factor weight, is the preset micro-difference weight, .

6. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: The anchor clustering condition includes a similarity sub-condition, a reliability sub-condition and a quantity sub-condition, and the tag clustering condition includes a similarity sub-condition, a reliability sub-condition and a quantity sub-condition. When the similarity sub-condition, the reliability sub-condition and the quantity sub-condition are all satisfied, the corresponding anchor clustering condition or tag clustering condition is deemed to be satisfied; The similarity sub-condition is that the similarity mean between the test data of the clusters is greater than a preset similarity benchmark; The reliability sub-condition is that the mean of the reliability values ​​between the test data of the clusters is greater than a preset reliability value benchmark; The quantity sub-condition is that the quantity of the inspection data of the cluster is greater than a preset quantity benchmark.

7. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: The vector configuration unit includes obtaining the disease course migration value and the test migration value between two diagnosis nodes. The vector expression of the identification connection line is: ,in, Indicates the number The diagnostic nodes and numbers are A vector of identification lines between diagnostic nodes, For the number The diagnostic nodes and numbers are The disease course migration value between the diagnosis nodes, For the number The diagnostic nodes and numbers are The test migration value between the diagnostic nodes, the disease course migration value reflects the disease course relationship between the two diagnostic nodes, the test migration value reflects the characterization feature difference between the two diagnostic nodes, each characterization feature is pre-associated with a characterization feature value, and the characterization feature difference is the difference between the characterization feature values ​​between the diagnostic nodes.

8. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: The reliability evaluation unit is configured with a transfer function table, which stores a number of reliability adjustment parameters. Each reliability adjustment parameter is indexed by an evolution element. When the evolution inference module generates an evolution characterization feature, the evolution element is generated according to the original fit and target fit of the evolution characterization feature. The original fit reflects the degree of fit between the generated evolution characterization feature and the animal characterization feature, and the target fit reflects the degree of fit between the evolution characterization feature and the patient sample corresponding to the diagnostic node. The dynamic reliability parameter is generated according to the reliability adjustment parameter, and the reliability value of the patient characterization feature corresponding to the diagnostic node pointed to by the identification line is calculated according to the dynamic reliability parameter.

9. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: The model correction module also includes a similarity learning unit, which is used to obtain new patient samples, match corresponding diagnostic nodes according to the diagnostic results of the patient samples, and extract test data in the patient samples, and compare the corresponding patient characterization features and test data to generate deviation correction information. The deviation correction information includes an association correction index, a characterization deviation index, and a characterization reliability index. The deviation correction information is substituted into the evolutionary inference module to correct the evolutionary inference sub-model.

10. The T2DM test data association system based on the adaptive multivariate correction model according to claim 1, characterized in that: The distance configuration unit performs a correlation analysis on all patient samples corresponding to two diagnosis nodes between the identification lines of each expression layer, and is configured with a correlation mapping function, through which the length of the corresponding identification line is calculated.

Images