System and method for detecting lupus anticoagulant based on conventional blood coagulation reagent

Abstract

The invention relates to a lupus anticoagulant detection system and method based on a conventional blood coagulation reagent, and relates to the technical field of detection.The lupus anticoagulant detection method comprises the steps that a user sample is collected, the conventional blood coagulation reagent is adopted for conducting anticoagulant detection on the user sample, and a detection standardization ratio is obtained; acquiring similar detection data in historical time, and performing fuzzy error analysis on the detection standardization ratio by adopting a historical detection standardization ratio set in the historical detection data to obtain a fuzzy error coefficient; calculating to obtain a reagent error correction coefficient according to the fuzzy error coefficient and the conventional blood coagulation reagent error; and according to the fuzzy error coefficient, acquiring anticoagulation treatment characteristic information of the user, analyzing to obtain a user error correction coefficient, combining with a reagent error correction coefficient, performing error correction calculation on the detection standardization ratio, and obtaining a correction detection standardization ratio interval as a detection result. The technical problems that lupus anticoagulant detection conditions are deficient and the detection efficiency is low in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to a lupus anticoagulant detection system and method based on conventional coagulation reagents. Background Art

[0002] Lupus anticoagulant is an antiphospholipid antibody. The general principle of lupus anticoagulant detection is that when lupus anticoagulant exists in plasma, it can competitively bind to phospholipids, resulting in prolonged phospholipid-dependent coagulation time (screening test). Adding excess phospholipids can correct the prolonged coagulation time (confirmatory test). Therefore, when the ratio of the coagulation time of the screening test and the confirmatory test is greater than a judgment threshold, lupus anticoagulant can be considered to be present. However, the current lupus anticoagulant detection mainly relies on imported reagents, resulting in a lack of testing conditions in primary laboratories and low testing efficiency. Summary of the invention

[0003] The present invention aims at the technical problems of insufficient lupus anticoagulant detection conditions and low detection efficiency in primary laboratories in the prior art, and proposes a lupus anticoagulant detection system and method based on conventional coagulation reagents.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a lupus anticoagulant detection system based on conventional coagulation reagents, comprising: an anticoagulation test module for collecting user samples, performing anticoagulation detection on the user samples using conventional coagulation reagents, and obtaining a detection standardized ratio; An error analysis module, used to obtain similar detection data in historical time according to the detection standardization ratio, use the historical detection standardization ratio set in the historical detection data, perform fuzzy error analysis on the detection standardization ratio, and obtain a fuzzy error coefficient; A reagent error analysis module, used for calculating a reagent error correction coefficient based on the fuzzy error coefficient and a conventional coagulation reagent error; The error correction module is used to collect the user's anticoagulant treatment characteristic information according to the fuzzy error coefficient, analyze and obtain the user error correction coefficient, combine the reagent error correction coefficient, perform error correction calculation on the detection standardized ratio, and obtain the corrected detection standardized ratio interval as the detection result.

[0005] In a second aspect, the present invention provides a method for detecting lupus anticoagulants based on conventional coagulation reagents, comprising: Collecting user samples, performing anticoagulation detection on the user samples using conventional coagulation reagents, and obtaining a detection standardized ratio; According to the detection standardization ratio, similar detection data in the historical time are obtained, and a historical detection standardization ratio set in the historical detection data is used to perform fuzzy error analysis on the detection standardization ratio to obtain a fuzzy error coefficient; Calculating a reagent error correction coefficient according to the fuzzy error coefficient and a conventional coagulation reagent error; According to the fuzzy error coefficient, the user's anticoagulant treatment characteristic information is collected, and the user error correction coefficient is obtained by analysis. In combination with the reagent error correction coefficient, the detection standardized ratio is subjected to error correction calculation to obtain the corrected detection standardized ratio interval as the detection result.

[0006] The beneficial effects of the present invention are as follows: the present invention adopts conventional coagulation reagents for anticoagulation detection, and evaluates the presence of lupus anticoagulants by calculating the detection standardized ratio, so that the detection does not need to rely on imported reagents, improves the accessibility of primary medical institutions, and improves the detection efficiency. In addition, fuzzy error analysis is performed through historical detection data, and reagent error correction coefficients and individualized user error correction coefficients are calculated. It can effectively identify error fluctuations that may exist in the detection process, thereby reducing detection errors caused by reagent batch differences and individual differences, and further improving the repeatability and reliability of the detection. By combining the reagent error correction coefficient and the user error correction coefficient, the method can perform error correction calculation on the detection standardized ratio, and finally obtain the corrected detection standardized ratio interval, further enhancing the robustness and credibility of the detection, ensuring that the detection results are more accurate, and providing more reliable data support for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic diagram of the structure of a lupus anticoagulant detection system based on conventional coagulation reagents provided by the present invention; Figure 2 A schematic flow chart of a lupus anticoagulant detection method based on conventional coagulation reagents provided by the present invention.

[0008] Reference numerals: anticoagulation test module 11 , error analysis module 12 , reagent error analysis module 13 , error correction module 14 . DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0010] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0011] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0012] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a lupus anticoagulant detection system based on conventional coagulation reagents, the system comprising an anticoagulation testing module 11 , an error analysis module 12 , a reagent error analysis module 13 and an error correction module 14 .

[0013] The anticoagulation test module 11 is used to perform: Collecting user samples, performing anticoagulation detection on the user samples using conventional coagulation reagents, and obtaining a detection standardized ratio; In the embodiment of the present application, a conventional coagulation reagent is first used to perform anticoagulation detection on the user's blood sample to obtain a detection standardized ratio. Among them, the detection by conventional coagulation reagent can reduce the difficulty of obtaining the detection reagent, thereby improving the accessibility and detection efficiency of anticoagulation detection in primary laboratories.

[0014] In the embodiment of the present application, the anticoagulation test module 11 is specifically used to perform: re-dissolving the prothrombin time assay reagent, diluting it with pure water according to a first ratio and a second ratio, and mixing it with a calcium chloride solution to obtain a confirmation reagent and a screening reagent; Collect user samples and normal samples, process them, and perform coagulation tests with confirmation reagents and screening reagents to obtain screening ratios and confirmation ratios; The ratio of the screening ratio and the confirmation ratio is calculated to obtain a detection standardized ratio.

[0015] In the embodiment of the present application, the user sample is first collected, that is, the whole blood specimen of the patient to be tested is collected by venous blood sampling. During the blood collection process, a sodium citrate (3.2%) anticoagulant tube is used to ensure that the blood sample maintains the stability of the coagulation system during coagulation testing, and avoids detection errors due to improper anticoagulant concentration or improper blood collection operation. After blood collection, the sample needs to be centrifuged within 1 hour, centrifuged at 2000g for 10 minutes, platelets are removed, and platelet-poor plasma is obtained, thereby reducing the interference of platelets on phospholipid-dependent coagulation reactions and improving the accuracy and repeatability of the test.

[0016] Then, a conventional coagulation reagent is used to perform anticoagulation detection on the user sample. The conventional coagulation reagent used in the embodiment of the present application is a prothrombin time (PT) assay reagent, which contains tissue factor and phospholipids and can be used as an activation medium for phospholipid-dependent coagulation reactions. In order to distinguish the competitive binding effect of lupus anticoagulant (LA) on phospholipids, the reagent needs to be diluted to different degrees to change the concentration of phospholipids in the reagent. This method dilutes the PT reagent according to a first ratio (specifically 1:5) and a second ratio (specifically 1:50), and mixes them with 0.025 mol / L calcium chloride solution in a ratio of 2:1, respectively, to form a confirmation reagent (high phospholipids) and a screening reagent (low phospholipids), respectively.

[0017] Furthermore, a normal blood sample, i.e., a blood sample of a user who has been determined to be negative for lupus anticoagulants, is collected and subjected to the above-mentioned centrifugation treatment as a normal sample. The user sample and the normal sample are mixed with the screening reagent and the confirmation reagent in a volume ratio of 1:2, and the coagulation time is measured on a fully automatic coagulation analyzer. By calculating the screening ratio of the user sample (i.e., the ratio of the coagulation time of the user sample and the screening reagent to the coagulation time of the normal sample and the screening reagent) and the confirmation ratio (the ratio of the coagulation time of the user sample and the confirmation reagent to the coagulation time of the normal sample and the confirmation reagent), the detection standardized ratio is further calculated, i.e., the ratio of the screening ratio to the confirmation ratio. The standardized ratio can reflect the effect of lupus anticoagulants on the phospholipid-dependent coagulation reaction, thereby providing a quantitative basis for the determination of lupus anticoagulants.

[0018] By using conventional coagulation reagents for testing, the accessibility and efficiency of testing can be improved.

[0019] In the embodiment of the present application, it is determined that the detection standardized ratio is greater than or equal to the threshold value or less than the threshold value, and a positive or negative detection judgment result can be obtained with high accuracy. Among them, the threshold value can be obtained by using the average detection standardized ratio of the lupus anticoagulant negative users of the test reagent in the above method as the threshold value.

[0020] However, the use of conventional coagulation reagents for testing may lead to errors in testing due to differences in different batches of conventional coagulation reagents and individual differences in coagulation treatments that users may undergo. Therefore, error analysis and correction are required to improve the accuracy of testing based on conventional coagulation reagents.

[0021] The error analysis module 12 is used to perform: S20: acquiring similar detection data in historical time according to the detection standardization ratio, using a historical detection standardization ratio set in the historical detection data, performing fuzzy error analysis on the detection standardization ratio, and obtaining a fuzzy error coefficient; In an embodiment of the present application, similar test data within a historical period is obtained based on the test standardization ratio, that is, past lupus anticoagulant (LA) test records stored in a laboratory information management system or database are called to ensure that the data covers a sufficient time span and sample diversity, thereby improving the reliability of error analysis.

[0022] According to the historical detection standardized ratios in the historical time, the centralized distribution level of the detection standardized ratios when positive or negative results appear is analyzed, and then the error range between the current detection standardized ratio and the centralized distribution level is analyzed to calculate the fuzzy error coefficient. Since this error is only used for reference analysis, it is convenient for the experimenter to make further detection judgments, so the fuzzy error analysis is carried out to provide support for subsequent error correction.

[0023] The error analysis module 12 is specifically used to perform: According to the detection standardization ratio, similar detection data in the historical time is obtained, and a historical detection standardization ratio set is extracted; Randomly selecting a first centroid detection normalization ratio from the historical detection normalization ratio set, and calculating and obtaining a first centroid cost; Continue to iterate the centroid selection to obtain the centroid detection normalization ratio with the minimum centroid cost; The error amplitude between the detection normalized ratio and the centroid detection normalized ratio is calculated as a fuzzy error coefficient.

[0024] In the embodiment of the present application, it is determined that the detection standardization ratio is greater than or equal to a threshold value or less than a threshold value to obtain a detection judgment result, specifically a preliminary judgment of positive or negative. Then, in the detection data within the historical time, similar detection data is obtained, for example, the detection data finally determined to be positive or negative is obtained, and the detection standardization ratio therein is extracted to obtain a historical detection standardization ratio set of similar detection results.

[0025] In order to reflect the centralized distribution level of the detection standardized ratios of the same type of detection results, the embodiment of the present application selects appropriate centroid data points in the historical detection standardized ratio set to reflect the centralized distribution point of the detection standardized ratios.

[0026] A historical detection standardized ratio is randomly selected in the historical detection standardized ratio set as the first centroid detection standardized ratio, and the first centroid cost is calculated. The smaller the first centroid cost, the more suitable the first centroid detection standardized ratio is as the centroid data point of the historical detection standardized ratio set.

[0027] In the embodiment of the present application, randomly selecting a first centroid detection normalization ratio from the historical detection normalization ratio set and calculating a first centroid cost includes: Randomly selecting a historical detection normalization ratio from the historical detection normalization ratio set as the first centroid detection normalization ratio; The difference between other historical detection normalized ratios and the first centroid detection normalized ratio is calculated, and the sum is calculated to obtain the first centroid cost.

[0028] In the embodiment of the present application, a historical detection normalization ratio is randomly selected from the historical detection normalization ratio set as the first centroid detection normalization ratio.

[0029] Further, the difference amplitude between other historical detection standardized ratios in the historical detection standardized ratio set and the first centroid detection standardized ratio is calculated. For example, the absolute value of the difference between each other historical detection standardized ratio and the first centroid detection standardized ratio is calculated, and then the ratio of the absolute value to the first centroid detection standardized ratio is calculated as the difference amplitude.

[0030] Calculate the sum of the difference amplitudes of all other historical detection standardized ratios and the first centroid detection standardized ratio as the first centroid cost. The larger the first centroid cost, the greater the overall difference between the first centroid detection standardized ratio and other historical detection standardized ratios, and the less suitable it is as a centroid data point, and it cannot reflect the centralized distribution level within the historical detection standardized ratio set. Conversely, the smaller the first centroid cost, the more it can reflect the centralized distribution level within the historical detection standardized ratio set.

[0031] Based on the same steps, continue to iterate the random selection of centroids and calculate the centroid cost until convergence. For example, after randomly selecting 100 centroid detection standardization ratios, it converges and outputs the detection standardization ratio with the smallest centroid cost as the final selected centroid detection standardization ratio, which can reflect the concentration distribution level of historical detection standardization ratios of similar detection results. The accurate detection standardization ratios are relatively close and distributed near the centroid point. The closer the current detection standardization ratio is to the centroid detection standardization ratio, the more accurate the detection result is. Conversely, the greater the difference between the current detection standardization ratio and the centroid detection standardization ratio, the more inaccurate the detection result is.

[0032] Calculate the error range of the current detection standardized ratio and the centroid detection standardized ratio, for example, calculate the ratio of the absolute value of the difference between the two and the centroid detection standardized ratio, as the fuzzy error coefficient. The fuzzy error coefficient reflects the deviation of the current detection standardized ratio from the center of the detection standardized ratio of the same test result. The larger the fuzzy error coefficient, the greater the probability of error of the detection standardized ratio of the current coagulation test.

[0033] By selecting the centroid point in the historical detection standardization ratio set of similar detection results, the error between the current detection standardization ratio and the detection standardization ratio of similar detection results can be quantified, and then the possible error coefficient of the current detection standardization ratio can be obtained for subsequent error correction.

[0034] The reagent error analysis module 13 is used to perform: S30: Calculate and obtain a reagent error correction coefficient according to the fuzzy error coefficient and the conventional coagulation reagent error.

[0035] In the embodiments of the present application, due to the composition errors of conventional coagulation reagents due to differences in reagent batches, changes in the detection environment, etc., the detection standardization ratio obtained by the detection may also have errors.

[0036] According to the above fuzzy error coefficient and the conventional coagulation reagent error obtained by analysis, the reagent error correction coefficient is calculated for subsequent correction of the detection standardized ratio. The conventional coagulation reagent error is the error amplitude of the detection standardized ratio caused by the detection of different batches of conventional coagulation reagents, which can be obtained by testing different batches of conventional coagulation reagents on the same user sample and calculating the error in the historical time.

[0037] The reagent error analysis module 13 is specifically used to perform: Obtaining a detection standardized ratio error set for anticoagulation detection using the conventional coagulation reagent; Calculating the mean of the detection standardized ratio error set to obtain a conventional coagulation reagent error; The reagent error correction coefficient is calculated based on the fuzzy error coefficient and the conventional coagulation reagent error.

[0038] In the embodiment of the present application, a detection standardized ratio error set for anticoagulation detection using conventional coagulation reagents is first obtained. The detection standardized ratio error set includes a deviation set of detection standardized ratios obtained after different batches of conventional coagulation reagents are tested on the same blood sample under the same test conditions. For example, the detection standardized ratio obtained after the sample is tested using one batch of conventional coagulation reagents is X, and the detection standardized ratio obtained after the same sample is tested using another batch is Y, then the detection standardized ratio error is |XY| / X. In this way, the detection standardized ratio error set is acquired.

[0039] Furthermore, the mean of all the detection standardized ratio errors in the detection standardized ratio error set is calculated as the conventional coagulation reagent error, that is, the average error size of the detection standardized ratio errors caused by testing different batches of conventional coagulation reagents.

[0040] Further, according to the fuzzy error coefficient and the conventional coagulation reagent error, the reagent error correction coefficient is calculated, wherein the fuzzy error coefficient is multiplied by the conventional coagulation reagent error as the reagent error correction coefficient. The larger the current fuzzy error coefficient is, the larger the error of the detection standardized ratio is, and the larger the error of the detection standardized ratio caused by the reagent batch is with a high probability, and the larger the reagent error correction coefficient is. On the contrary, the smaller the error of the detection standardized ratio caused by the reagent batch is, the smaller the reagent error correction coefficient is. For example, if the fuzzy error coefficient is 10% and the conventional coagulation reagent error is 10%, the reagent error correction coefficient calculated is 1%.

[0041] The reagent error correction coefficient is used for subsequent error correction of the detection standardized ratio to eliminate the error of the detection standardized ratio caused by changes in reagents from different batches, and to obtain the range of the detection standardized ratio that is not affected by the error caused by the reagent change, which is used for reference by experimenters and subsequent detection verification.

[0042] The error correction module 14 is used to perform: S40: According to the fuzzy error coefficient, the user's anticoagulation treatment characteristic information is collected, and the user error correction coefficient is obtained by analysis. In combination with the reagent error correction coefficient, the error correction calculation is performed on the detection standardized ratio to obtain the corrected detection standardized ratio interval as the detection result.

[0043] In the embodiment of the present application, in addition to the errors caused by different batches of reagents, the anticoagulant therapy performed by the user will also affect the coagulation test results, resulting in errors in the detection of the standardized ratio. Therefore, the anticoagulant therapy characteristic information of the user is collected, and the user error correction coefficient for the error in the detection of the standardized ratio caused by the anticoagulant therapy is analyzed based on the fuzzy error coefficient.

[0044] Furthermore, the reagent error correction coefficient and the user error correction coefficient caused by the characteristics of different batches of reagents and individual users are combined to perform error correction calculation on the detection standardization ratio obtained by the current test, and obtain the corrected detection standardization ratio range, which reflects the actual distribution range of the detection standardization ratio after error correction, and is used by experimenters to conduct further testing or judgment, etc., to improve detection accuracy.

[0045] The error correction module 14 is specifically used to perform: Collecting the user's anticoagulation therapy parameters and anticoagulation therapy time as anticoagulation therapy characteristic information; Using integrated learning, training a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, wherein the number of paths of the integrated anticoagulation detection error analysis path is an integrated number; The fuzzy error coefficient is used and multiplied by the integration quantity to obtain the number of selected paths; Randomly selecting the anticoagulation detection error analysis paths of the selected number of paths in the user anticoagulation detection error analyzer, inputting the anticoagulation treatment characteristic information, obtaining a user error correction coefficient set, and calculating the mean to obtain the user error correction coefficient; According to the user error correction coefficient and the reagent error correction coefficient, the error correction coefficient is calculated, and the positive and negative error correction calculations are performed on the detection standardized ratio to obtain a corrected detection standardized ratio interval as a detection result.

[0046] In the embodiment of the present application, the user's anticoagulant therapy parameters and anticoagulant therapy time are first collected as anticoagulant therapy characteristic information. The anticoagulant therapy parameters include the type of anticoagulant drug used (such as warfarin, heparin, direct oral anticoagulants DOACs), the dosage used, and the anticoagulant therapy time includes the time from the last anticoagulant therapy to the present.

[0047] Furthermore, ensemble learning is adopted to train a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, wherein the number of paths of the integrated anticoagulation detection error analysis path is the integrated number. The user anticoagulation detection error analyzer is constructed by ensemble learning, and the user anticoagulation detection error analyzer can analyze the error influence coefficient of the user's anticoagulation treatment on the current detection standardized ratio according to the user's anticoagulation treatment characteristic information, and the integrated multiple anticoagulation detection error analysis paths can improve the analysis accuracy and the training efficiency of each anticoagulation detection error analysis path.

[0048] In the embodiment of the present application, integrated learning is adopted to train a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, including: According to the anticoagulation test data record, a sample anticoagulation treatment characteristic information set is collected, and the error coefficient of the detection standardized ratio under different sample anticoagulation treatment characteristic information is collected, and marked as a sample user error correction coefficient set; Dividing the sample anticoagulation treatment feature information set and the sample user error correction coefficient set according to the integration quantity to obtain a plurality of user error training data; The plurality of user error training data are respectively used to train an integrated number of anticoagulation detection error analysis paths based on machine learning to obtain a user anticoagulation detection error analyzer.

[0049] In the embodiment of the present application, the method trains the user anticoagulation detection error analyzer through integrated learning to accurately evaluate the impact of the user's anticoagulation treatment characteristics on the detection standardized ratio, perform error correction, and improve the accuracy and individual adaptability of the detection.

[0050] First, based on the anticoagulation test data records of multiple users in the past, the sample anticoagulation treatment characteristic information of each user is collected to obtain the sample anticoagulation treatment characteristic information set. Then, the detection standardized ratios of these users and the detection standardized ratios of these users when they have not undergone anticoagulation treatment or the anticoagulation treatment has been over for a long time (such as half a year) are collected. The difference amplitude of the detection standardized ratios in the two states is calculated as the error coefficient of the detection standardized ratio under the influence of the user's sample anticoagulation treatment characteristic information, which is marked as the sample user error correction coefficient set.

[0051] Then, according to the integrated number of the integrated anticoagulation detection error analysis path, for example, 10, the sample anticoagulation treatment feature information set and the sample user error correction coefficient set are randomly divided into multiple sets of data with the same data volume as multiple sets of user error training data. Among them, the sample anticoagulation treatment feature information is used as the input data of the training, and the sample user error correction coefficient is used as the output data of the training.

[0052] Furthermore, multiple user error training data are used respectively, and based on integrated machine learning, an integrated number of anticoagulation detection error analysis paths are trained to obtain a user anticoagulation detection error analyzer. The training data of each anticoagulation detection error analysis path is different, and the performance of each anticoagulation detection error analysis path is different. Integrating multiple anticoagulation detection error analysis paths to perform error analysis caused by anticoagulation therapy can improve the accuracy.

[0053] Taking the training process of one of the anticoagulation detection error analysis paths as an example, illustratively, based on the feedforward neural network in machine learning, an anticoagulation detection error analysis path is constructed, which includes an input layer, an output layer and a hidden layer, and the dimension of the input layer and the hidden layer is 1, corresponding to the sample anticoagulation treatment feature information and the sample user error correction coefficient. During the training process, the sample anticoagulation treatment feature information in the user error training data is input, the output user error correction coefficient is obtained, the error with the corresponding real sample user error correction coefficient is calculated, and the loss is calculated using the mean square error loss function, and the network parameters of the anticoagulation detection error analysis path are randomly adjusted to reduce the loss until the requirements are met, for example, the loss is less than 5%, and iterative training of multiple sets of training data is performed until the overall loss meets the requirements, the training is completed, and the trained anticoagulation detection error analysis path is obtained.

[0054] The integrated number of anticoagulation detection error analysis paths that have been trained are combined to obtain a user anticoagulation detection error analyzer.

[0055] In the embodiment of the present application, the larger the fuzzy error coefficient is, the larger the possible error of the detection standardized ratio is, and more anticoagulation detection error analysis paths are selected for error analysis to improve the accuracy of error analysis. Conversely, if the smaller the fuzzy error coefficient is, the smaller the possible error of the detection standardized ratio is, and the error correction amplitude of the detection standardized ratio is smaller, then more anticoagulation detection error analysis paths are selected for error analysis to save processing power.

[0056] Specifically, the fuzzy error coefficient is used, multiplied by the integrated number and rounded up to obtain the number of selected paths. The larger the fuzzy error coefficient, the larger the number of selected paths. For example, if the fuzzy error coefficient is 15%, the number of selected paths is 15%*10 and rounded up to 2.

[0057] In the user anticoagulation detection error analyzer, the anticoagulation detection error analysis path of the selected path number is randomly selected, and the anticoagulation treatment characteristic information of the current user is respectively input into the anticoagulation detection error analysis path of the selected path number, and the user error correction coefficient of the output selected path number is obtained as the user error correction coefficient set. Then, multiple outputs are integrated, for example, to calculate the mean of the user error correction coefficient set, and the user error correction coefficient is obtained. By integrating multiple outputs, it is more accurate. The user error correction coefficient reflects the magnitude of the error of the detection standardized ratio caused by the user's anticoagulation treatment characteristic information.

[0058] Further, according to the user error correction coefficient and the reagent error correction coefficient, the total error correction coefficient of the detection standardized ratio error caused by the reagent batch and the user characteristics is calculated, reflecting the magnitude of the detection error caused by the two dimensions. For example, the sum of the user error correction coefficient and the reagent error correction coefficient is calculated as the error correction coefficient. For example, if the reagent error correction coefficient is 1% and the user error correction coefficient is 3%, the error correction coefficient is 4%.

[0059] Then, the error correction coefficient is used to perform positive and negative error correction calculations on the detection standardized ratio obtained by the test, for example, the detection standardized ratio * (1 ± error correction coefficient) is calculated to obtain the endpoint values ​​of the two corrected detection standardized ratios, and the corrected detection standardized ratio interval is constructed as the detection result after detection and error correction.

[0060] The correction detection standardized ratio interval reflects the actual distribution interval of the detection standardized ratio caused by the error, which can be used by the experimenter for judgment or further detection. For example, if the correction detection standardized ratio interval is all greater than or equal to the above threshold, it can be judged as positive. If part of the correction detection standardized ratio interval is greater than or equal to the above threshold and part is less than the threshold, further detection can be performed.

[0061] By integrating the errors caused by different batches of reagents and the errors caused by the user's anticoagulant treatment characteristics, the accuracy and reliability of the test results can be improved, compensating for the disadvantage that the accuracy of conventional coagulation reagents is lower than that of imported reagents.

[0062] The lupus anticoagulant detection system based on conventional coagulation reagents provided in the embodiment of the present invention has at least the following technical effects: The present invention adopts conventional coagulation reagents to perform anticoagulation detection, and evaluates the presence of lupus anticoagulants by calculating the detection standardized ratio, so that the detection does not need to rely on imported reagents, improves the accessibility of primary medical institutions, and improves the detection efficiency. It also performs fuzzy error analysis through historical detection data, calculates the reagent error correction coefficient and the individualized user error correction coefficient, and can effectively identify the error fluctuations that may exist in the detection process, thereby reducing the detection errors caused by reagent batch differences and individual differences, and then improves the repeatability and reliability of the detection. By combining the reagent error correction coefficient and the user error correction coefficient, the method can perform error correction calculation on the detection standardized ratio, and finally obtain the corrected detection standardized ratio interval, further enhancing the robustness and credibility of the detection, ensuring that the test results are more accurate, and providing more reliable data support for clinical diagnosis.

[0063] Embodiment 2, as Figure 2 As shown, the invention concept is the same as that of the lupus anticoagulant detection system based on conventional coagulation reagents in Example 1. The embodiment of the present invention further provides a lupus anticoagulant detection method based on conventional coagulation reagents. The explanation of the lupus anticoagulant detection system based on conventional coagulation reagents in Example 1 is also applicable to a lupus anticoagulant detection method based on conventional coagulation reagents, which comprises: S10: Collect user samples, and use conventional coagulation reagents to perform anticoagulation detection on the user samples to obtain a detection standardized ratio; S20: acquiring similar detection data in historical time according to the detection standardization ratio, using a historical detection standardization ratio set in the historical detection data, performing fuzzy error analysis on the detection standardization ratio, and obtaining a fuzzy error coefficient; S30: Calculating a reagent error correction coefficient according to the fuzzy error coefficient and a conventional coagulation reagent error; S40: According to the fuzzy error coefficient, the user's anticoagulation treatment characteristic information is collected, and the user error correction coefficient is obtained by analysis. In combination with the reagent error correction coefficient, the error correction calculation is performed on the detection standardized ratio to obtain the corrected detection standardized ratio interval as the detection result.

[0064] Further, collecting user samples, using conventional coagulation reagents to perform anticoagulation detection on the user samples, and obtaining a detection standardized ratio, including: re-dissolving the prothrombin time assay reagent, diluting it with pure water according to a first ratio and a second ratio, and mixing it with a calcium chloride solution to obtain a confirmation reagent and a screening reagent; Collect user samples and normal samples, process them, and perform coagulation tests with confirmation reagents and screening reagents to obtain screening ratios and confirmation ratios; The ratio of the screening ratio and the confirmation ratio is calculated to obtain a detection standardized ratio.

[0065] Further, according to the detection standardization ratio, similar detection data in the historical time is obtained, and the historical detection standardization ratio set in the historical detection data is used to perform fuzzy error analysis on the detection standardization ratio to obtain the fuzzy error coefficient, including: According to the detection standardization ratio, similar detection data in the historical time is obtained, and a historical detection standardization ratio set is extracted; Randomly selecting a first centroid detection normalization ratio from the historical detection normalization ratio set, and calculating and obtaining a first centroid cost; Continue to iterate the centroid selection to obtain the centroid detection normalization ratio with the minimum centroid cost; The error amplitude between the detection normalized ratio and the centroid detection normalized ratio is calculated as a fuzzy error coefficient.

[0066] Further, randomly selecting a first centroid detection normalization ratio from the historical detection normalization ratio set, and calculating and obtaining a first centroid cost, includes: Randomly selecting a historical detection normalization ratio from the historical detection normalization ratio set as the first centroid detection normalization ratio; The difference between other historical detection normalized ratios and the first centroid detection normalized ratio is calculated, and the sum is calculated to obtain the first centroid cost.

[0067] Further, according to the fuzzy error coefficient and the conventional coagulation reagent error, the reagent error correction coefficient is calculated, including: Obtaining a detection standardized ratio error set for anticoagulation detection using the conventional coagulation reagent; Calculating the mean of the detection standardized ratio error set to obtain a conventional coagulation reagent error; The reagent error correction coefficient is calculated based on the fuzzy error coefficient and the conventional coagulation reagent error.

[0068] Further, according to the fuzzy error coefficient, the anticoagulation treatment characteristic information of the user is collected, the user error correction coefficient is obtained by analysis, and the detection standardized ratio is calculated by error correction in combination with the reagent error correction coefficient to obtain the corrected detection standardized ratio interval as the detection result, including: Collecting the user's anticoagulation therapy parameters and anticoagulation therapy time as anticoagulation therapy characteristic information; Using integrated learning, training a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, wherein the number of paths of the integrated anticoagulation detection error analysis path is an integrated number; The fuzzy error coefficient is used and multiplied by the integration quantity to obtain the number of selected paths; Randomly selecting the anticoagulation detection error analysis paths of the selected number of paths in the user anticoagulation detection error analyzer, inputting the anticoagulation treatment characteristic information, obtaining a user error correction coefficient set, and calculating the mean to obtain the user error correction coefficient; According to the user error correction coefficient and the reagent error correction coefficient, the error correction coefficient is calculated, and the positive and negative error correction calculations are performed on the detection standardized ratio to obtain a corrected detection standardized ratio interval as a detection result.

[0069] Furthermore, integrated learning is adopted to train a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, including: According to the anticoagulation test data record, a sample anticoagulation treatment characteristic information set is collected, and the error coefficient of the detection standardized ratio under different sample anticoagulation treatment characteristic information is collected, and marked as a sample user error correction coefficient set; Dividing the sample anticoagulation treatment feature information set and the sample user error correction coefficient set according to the integration quantity to obtain a plurality of user error training data; The plurality of user error training data are respectively used to train an integrated number of anticoagulation detection error analysis paths based on machine learning to obtain a user anticoagulation detection error analyzer.

[0070] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0071] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to the flow diagrams and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flow diagram and / or block diagram, as well as the combination of flows and / or blocks in the flow diagram and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the flow diagram and / or block diagram. Figure 1 flow or flows and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the flow Figure 1 flow or flows and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the flow Figure 1 flow or flows and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

Claims

1. A lupus anticoagulant detection system based on conventional coagulation reagents, characterized in that: The system comprises: The anticoagulation test module is used to collect user samples, perform anticoagulation detection on the user samples using conventional coagulation reagents, and obtain a detection standardized ratio; An error analysis module, used to obtain similar detection data in historical time according to the detection standardization ratio, use the historical detection standardization ratio set in the historical detection data, perform fuzzy error analysis on the detection standardization ratio, and obtain a fuzzy error coefficient; A reagent error analysis module, used for calculating a reagent error correction coefficient based on the fuzzy error coefficient and a conventional coagulation reagent error; The error correction module is used to collect the user's anticoagulant treatment characteristic information according to the fuzzy error coefficient, analyze and obtain the user error correction coefficient, combine the reagent error correction coefficient, perform error correction calculation on the detection standardized ratio, and obtain the corrected detection standardized ratio interval as the detection result.

2. The lupus anticoagulant detection system based on conventional coagulation reagents according to claim 1, characterized in that: Collecting user samples, performing anticoagulation detection on the user samples using conventional coagulation reagents, and obtaining a detection standardized ratio, including: re-dissolving the prothrombin time assay reagent, diluting it with pure water according to a first ratio and a second ratio, and mixing it with a calcium chloride solution to obtain a confirmation reagent and a screening reagent; Collect user samples and normal samples, process them, and perform coagulation tests with confirmation reagents and screening reagents to obtain screening ratios and confirmation ratios; The ratio of the screening ratio and the confirmation ratio is calculated to obtain a detection standardized ratio.

3. The lupus anticoagulant detection system based on conventional coagulation reagents according to claim 1, characterized in that: According to the detection standardization ratio, similar detection data in historical time is obtained, and a historical detection standardization ratio set in the historical detection data is used to perform fuzzy error analysis on the detection standardization ratio to obtain a fuzzy error coefficient, including: According to the detection standardization ratio, similar detection data in the historical time is obtained, and a historical detection standardization ratio set is extracted; Randomly selecting a first centroid detection normalization ratio from the historical detection normalization ratio set, and calculating and obtaining a first centroid cost; Continue to iterate the centroid selection to obtain the centroid detection normalization ratio with the minimum centroid cost; The error amplitude between the detection normalized ratio and the centroid detection normalized ratio is calculated as a fuzzy error coefficient.

4. The lupus anticoagulant detection system based on conventional coagulation reagents according to claim 3, characterized in that: Randomly selecting a first centroid detection normalization ratio in the historical detection normalization ratio set, and calculating a first centroid cost, including: Randomly selecting a historical detection normalization ratio from the historical detection normalization ratio set as the first centroid detection normalization ratio; The difference between other historical detection normalized ratios and the first centroid detection normalized ratio is calculated, and the sum is calculated to obtain the first centroid cost.

5. The lupus anticoagulant detection system based on conventional coagulation reagents according to claim 1, characterized in that: According to the fuzzy error coefficient and the conventional coagulation reagent error, the reagent error correction coefficient is calculated, including: Obtaining a detection standardized ratio error set for anticoagulation detection using the conventional coagulation reagent; Calculating the mean of the detection standardized ratio error set to obtain a conventional coagulation reagent error; The reagent error correction coefficient is calculated based on the fuzzy error coefficient and the conventional coagulation reagent error.

6. The lupus anticoagulant detection system based on conventional coagulation reagents according to claim 1, characterized in that: According to the fuzzy error coefficient, the anticoagulation treatment characteristic information of the user is collected, the user error correction coefficient is obtained by analysis, and the detection standardized ratio is calculated by error correction in combination with the reagent error correction coefficient to obtain a corrected detection standardized ratio interval as a detection result, including: Collecting the user's anticoagulation therapy parameters and anticoagulation therapy time as anticoagulation therapy characteristic information; Using integrated learning, training a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, wherein the number of paths of the integrated anticoagulation detection error analysis path is an integrated number; The fuzzy error coefficient is used and multiplied by the integration quantity to obtain the number of selected paths; Randomly selecting the anticoagulation detection error analysis paths of the selected number of paths in the user anticoagulation detection error analyzer, inputting the anticoagulation treatment characteristic information, obtaining a user error correction coefficient set, and calculating the mean to obtain the user error correction coefficient; According to the user error correction coefficient and the reagent error correction coefficient, the error correction coefficient is calculated, and the positive and negative error correction calculations are performed on the detection standardized ratio to obtain a corrected detection standardized ratio interval as a detection result.

7. The lupus anticoagulant detection system based on conventional coagulation reagents according to claim 6, characterized in that: Ensemble learning is used to train a user anticoagulation detection error analyzer including an integrated anticoagulation detection error analysis path, including: According to the anticoagulation test data record, a sample anticoagulation treatment characteristic information set is collected, and the error coefficient of the detection standardized ratio under different sample anticoagulation treatment characteristic information is collected, and marked as a sample user error correction coefficient set; Dividing the sample anticoagulation treatment feature information set and the sample user error correction coefficient set according to the integration quantity to obtain a plurality of user error training data; The plurality of user error training data are respectively used to train an integrated number of anticoagulation detection error analysis paths based on machine learning to obtain a user anticoagulation detection error analyzer.

8. A method for detecting lupus anticoagulants based on conventional coagulation reagents, characterized in that: The method comprises: Collecting user samples, performing anticoagulation detection on the user samples using conventional coagulation reagents, and obtaining a detection standardized ratio; According to the detection standardization ratio, similar detection data in the historical time are obtained, and a historical detection standardization ratio set in the historical detection data is used to perform fuzzy error analysis on the detection standardization ratio to obtain a fuzzy error coefficient; Calculating a reagent error correction coefficient according to the fuzzy error coefficient and a conventional coagulation reagent error; According to the fuzzy error coefficient, the user's anticoagulant treatment characteristic information is collected, and the user error correction coefficient is obtained by analysis. In combination with the reagent error correction coefficient, the detection standardized ratio is subjected to error correction calculation to obtain the corrected detection standardized ratio interval as the detection result.

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