Auxiliary diagnosis data processing method and middleware interaction platform

Abstract

The invention relates to an auxiliary diagnosis data processing method and a middleware interaction platform. The method is applied to a middleware interaction platform and comprises the following steps: in response to a middleware display page viewing request, displaying a middleware display page; the middleware display page comprises a sample list area, a sample information area and a detection result area; the detection result area comprises an auxiliary diagnosis analysis column, and under the condition that at least one target auxiliary diagnosis model is deployed on the middleware interaction platform, the auxiliary diagnosis analysis column comprises tabs of the deployed target auxiliary diagnosis models; and when selection of a detection sample in the sample list area is received, synchronously displaying sample information of the detection sample in the sample information area, and displaying an auxiliary diagnosis result of the deployed target auxiliary diagnosis model on the detection sample on a tab of each target auxiliary diagnosis model. In this way, the middleware display page is displayed in different regions, and the display space of the middleware display page can be fully and reasonably utilized.

Description

Auxiliary diagnostic data processing methods and middleware interaction platform Technical Field

[0001] This application relates to the field of medical testing technology, and in particular to an auxiliary diagnostic data processing method and middleware interaction platform. Background Technology

[0002] With the rapid development of medical testing technology and the advancement of science and technology, the number of medical testing instruments and auxiliary diagnostic models deployed by various medical institutions is increasing daily. These institutions manage instruments, models, and data uniformly through middleware interaction platforms such as Laboratory Information Systems (LIS) and data management systems. On the one hand, the middleware display page space utilization in traditional middleware interaction platforms is relatively inefficient; on the other hand, auxiliary diagnostic models are often manually deployed by after-sales service personnel, making timely updates or upgrades difficult and causing inconvenience to medical staff.

[0003] Therefore, a key issue is how to reasonably display multiple auxiliary diagnostic models on the middleware interaction platform and update the auxiliary diagnostic models in a timely manner to meet the needs of auxiliary diagnosis. Summary of the Invention

[0004] Therefore, it is necessary to provide an auxiliary diagnostic data processing method and middleware interaction platform to address the aforementioned technical problems.

[0005] In a first aspect, this application provides an auxiliary diagnostic data processing method, characterized in that it is applied to a middleware interaction platform, the method comprising:

[0006] In response to a request to view the middleware display page, the middleware display page is displayed. The middleware display page includes a sample list area, a sample information area, and a detection result area. The sample list area is used to display at least one detection sample. The sample information area is used to display the sample information of the selected detection sample. The detection result area includes an auxiliary diagnostic analysis column. When at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis column includes tabs for each deployed target auxiliary diagnostic model, and the tabs for the target auxiliary diagnostic models are used to display the auxiliary diagnostic results of the target auxiliary diagnostic models.

[0007] When an input event is received to select a test sample in the sample list area, the sample information of the test sample is displayed synchronously in the sample information area, and the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the test sample are displayed on the tab of each target auxiliary diagnostic model.

[0008] In one embodiment, the method further includes:

[0009] The display order of the target auxiliary diagnostic model's tabs is determined based on at least one of the probability values ​​in each of the auxiliary diagnostic results, the accuracy of the target auxiliary diagnostic model, the authentication status of the target auxiliary diagnostic model, and the matching degree of the target auxiliary diagnostic model; wherein, the matching degree is determined based on the type of input data of the target auxiliary diagnostic model and the type of detection data of the detection sample;

[0010] According to the display order, the tabs of the target auxiliary diagnostic model are displayed sequentially in the auxiliary diagnostic analysis section.

[0011] In one embodiment, the matching degree includes a first matching degree, a second matching degree, a third matching degree, and a fourth matching degree; the method further includes:

[0012] If the type of the input data of the target-assisted diagnostic model is the same as the type of the detection data of the detection sample, the first matching degree is determined;

[0013] If the type of the detection data includes the type of the input data and the type of the detection data is greater than the type of the input data, then the second matching degree is determined; the second matching degree is less than the first matching degree.

[0014] If the type of the input data includes the type of the detection data and the type of the detection data is less than the type of the input data, then the third matching degree is determined; the third matching degree is less than the second matching degree.

[0015] If the type of the detection data is completely different from the type of the input data, the fourth matching degree is determined; the fourth matching degree is less than the third matching degree; the display priority of the first matching degree, the second matching degree, the third matching degree, and the fourth matching degree decreases sequentially.

[0016] In one embodiment, the method further includes:

[0017] If the number of target auxiliary diagnostic models is greater than a first threshold, the target auxiliary diagnostic models whose display order is greater than a predetermined order value are either collapsed or not displayed; wherein, the display order being greater than the predetermined order value indicates that the probability value of the target auxiliary diagnostic model for the selected detection sample is less than a second threshold, the accuracy is less than a third threshold, the authentication status is unauthenticated, and the matching degree is less than a fourth threshold.

[0018] In one embodiment, the auxiliary diagnostic analysis panel includes a deployment model option;

[0019] In response to the selection of the deployment model option, a model deployment page is displayed; the model deployment page includes local deployment options.

[0020] In response to the selection of the local deployment option, the target auxiliary diagnostic model stored locally is deployed to the middleware interaction platform;

[0021] The tab for the newly deployed target auxiliary diagnostic model is displayed in the auxiliary diagnostic analysis section.

[0022] In one embodiment, the model deployment page includes a deployment identifier input option;

[0023] Receive a deployment identifier input based on the deployment identifier input option, download the corresponding target auxiliary diagnostic model from the sharing platform according to the deployment identifier, and deploy the target auxiliary diagnostic model corresponding to the deployment identifier to the middleware interaction platform;

[0024] The tab for the newly deployed target auxiliary diagnostic model is displayed in the auxiliary diagnostic analysis section.

[0025] In one embodiment, before deploying the target auxiliary diagnostic model corresponding to the deployment identifier on the middleware interaction platform, the method further includes:

[0026] Determine the compatibility between the target auxiliary diagnostic model to be deployed and the instrument connected to the middleware. If the compatibility is less than a fifth threshold, output the prompt information. The prompt information includes at least one of the matching degree or deployment prompt.

[0027] In one embodiment, the tab of the target auxiliary diagnostic model includes a status identifier; the status identifier is used to characterize the authentication status of the target auxiliary diagnostic model; the authentication status includes an authenticated status and an unauthenticated status; the authenticated status indicates that the target auxiliary diagnostic model has been verified by the R&D platform and the verification is qualified; the unauthenticated status indicates that the target auxiliary diagnostic model has not been verified by the R&D platform or the verification is unqualified.

[0028] In one embodiment, the detection result area includes a project result column; the project result column is used to display the detection data of the selected detection sample determined by the sample analyzer; before displaying the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the detection sample on the tab of each target auxiliary diagnostic model, the method further includes:

[0029] The sample information and / or the detection data of the selected test sample are input into the target auxiliary diagnostic model to obtain the auxiliary diagnostic result of the test sample.

[0030] Secondly, this application also provides a middleware interaction platform, which is connected to one or more sample analyzers; the platform includes:

[0031] The display module is used to respond to a middleware display page viewing request and display the middleware display page. The middleware display page includes a sample list area, a sample information area, and a detection result area. The sample list area is used to display at least one detection sample. The sample information area is used to display the sample information of the selected detection sample. The detection result area includes an auxiliary diagnostic analysis bar. When at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis bar includes tabs for each deployed target auxiliary diagnostic model, and the tabs for the target auxiliary diagnostic models are used to display the auxiliary diagnostic results of the target auxiliary diagnostic models.

[0032] The processing module is configured to, when receiving a selection of the test sample in the sample list area, simultaneously display the sample information of the test sample in the sample information area, and display the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the test sample in the tab of each target auxiliary diagnostic model.

[0033] Thirdly, this application also provides an electronic device, including a processor and a memory for storing a computer program of the processor; wherein the processor is configured to implement the auxiliary diagnostic data processing method described in any embodiment of this application when executing the computer program.

[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the auxiliary diagnostic data processing method described in any embodiment of this application.

[0035] In the aforementioned auxiliary diagnostic data processing method, the middleware interaction platform, through the middleware display page, enables unified management of test samples and deployed target auxiliary diagnostic models. When the middleware interaction platform receives a selection of test samples in the sample list area, it can simultaneously display sample information in the sample information area, and the auxiliary diagnostic results of each deployed target auxiliary diagnostic model for the test sample can be intuitively viewed in the test result area. Thus, by displaying test samples, sample information, and auxiliary diagnostic results in separate areas, the middleware display page can fully and rationally utilize its display space. The test result area allows for intuitive, convenient, and quick determination of the auxiliary diagnostic results of each target auxiliary diagnostic model for the test sample. Furthermore, the auxiliary diagnostic models can be deployed or upgraded in real time, with good update timeliness, thereby meeting the auxiliary diagnostic needs of different diseases in the test samples. Attached Figure Description

[0036] Figure 1 is a schematic diagram of the application environment of an auxiliary diagnostic data processing method according to an exemplary embodiment;

[0037] Figure 2 is a flowchart illustrating an auxiliary diagnostic data processing method according to an exemplary embodiment;

[0038] Figure 3 is a schematic diagram of a middleware display page according to an exemplary embodiment;

[0039] Figure 4 is a schematic diagram illustrating the display of a model deployment page according to an exemplary embodiment;

[0040] Figure 5 is a schematic diagram showing the detection result area according to an exemplary embodiment;

[0041] Figure 6 is a structural block diagram of a middleware interaction platform according to an exemplary embodiment;

[0042] Figure 7 is an internal structure diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "at least one" is used to indicate one or more; "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0045] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0046] In some embodiments, the auxiliary diagnostic data processing method provided in this application can be applied to a middleware interaction platform; the middleware interaction platform can be applied to an electronic device. The electronic device can be any mobile terminal or fixed terminal. The terminal can be a device that provides voice and / or data connectivity to the user. For example, the terminal can be an Internet of Things (IoT) terminal, such as a sensor device, a mobile phone (or "cellular" phone), and a computer with an IoT terminal, for example, a fixed, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted device. Portable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Alternatively, the terminal can also be a device from an unmanned aerial vehicle (UAV).

[0047] As shown in Figure 1, this application provides an auxiliary diagnostic data processing system, including: a middleware interaction platform, a sharing platform, a research and development platform, and a sample analyzer. The middleware interaction platform, the sharing platform, the research and development platform, and the instrument are interconnected. The sample analyzer includes instruments for various testing items, such as hematology analyzers, coagulation analyzers, or immunoassay analyzers. The middleware interaction platform includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement an auxiliary diagnostic data processing method.

[0048] In this embodiment of the application, the middleware interaction platform (Data Management System, DMS) refers to a data management platform or system that integrates Internet technology, database and information management technology.

[0049] In this embodiment of the application, the middleware interaction platform may include, but is not limited to, at least one of the following functions: laboratory information management, sample management, test result management, quality control management, and data analysis.

[0050] In this embodiment of the application, the R&D platform refers to an artificial intelligence cloud platform developed based on a machine learning framework; the R&D platform is used to train and optimize models based on the detection data obtained from the middleware interaction platform to obtain at least one auxiliary diagnostic model.

[0051] In this embodiment of the application, the sharing platform is used to provide users with a centralized space so that users can publish their personal model resources for other users to download, use or modify, or allow users to browse and obtain model resources published by other users.

[0052] In some embodiments, as shown in FIG2, this application provides an auxiliary diagnostic data processing method applied to a middleware interaction platform, wherein the middleware interaction platform is connected to one or more sample analyzers; the method includes the following steps:

[0053] S101, in response to a middleware display page viewing request, the middleware display page is displayed; the middleware display page includes a sample list area, a sample information area, and a detection result area; the sample list area is used to display at least one detection sample; the sample information area is used to display the sample information of the selected detection sample; the detection result area includes an auxiliary diagnostic analysis column, and when at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis column includes tabs for each deployed target auxiliary diagnostic model, and the tabs for the target auxiliary diagnostic models are used to display the auxiliary diagnostic results of the target auxiliary diagnostic models.

[0054] In this embodiment of the application, the sample list area is used to display at least one test sample and a simplified version of the sample information corresponding to the test sample; the sample information may include, but is not limited to, at least one of name, age, gender and status information.

[0055] In this embodiment, the sample information area is used to display detailed version of the sample information of the selected test sample; the sample information may also include at least one of the following: test item, date of birth, contact number, ward, bed number, sampling doctor, sampling time, test time, and rule details.

[0056] In this embodiment, the detection result area is used to display the detection item information of the detection sample and / or the model information of at least one deployed target-assisted diagnostic model.

[0057] In this embodiment of the application, the target auxiliary diagnostic model refers to the model that has been deployed / to be deployed to the middleware interaction platform selected from at least one auxiliary diagnostic model.

[0058] In this embodiment of the application, the auxiliary diagnostic model is used to indicate a model that assists in disease diagnosis by collecting patient test data (such as medical images, medical records, laboratory test results, etc.) and using algorithms such as machine learning and deep learning to analyze and process this data.

[0059] In this embodiment, tabs are labels used to identify and distinguish different pages or functions in a webpage or application; these labels may have custom names so that users can more easily understand and navigate, allowing users to quickly switch between different pages or views in the same window or interface.

[0060] In some embodiments, the detection results area further includes a navigation bar; the navigation bar includes a project results tab, a research project tab, an AI (Artificial Intelligence) analysis tab, a more project tab, and a retest & history tab. When a selection operation is received for a target tab in the navigation bar, the corresponding content of the target tab is displayed in the detection results area. For example, as shown in Figure 3, when a click operation is received for the AI ​​analysis tab in the navigation bar, a background color is added to the AI ​​analysis tab to indicate to the user that the AI ​​analysis tab is selected; the auxiliary diagnostic analysis bar corresponding to the AI ​​analysis tab is displayed in the detection results area.

[0061] In one embodiment, the Project Results tab, Research Project tab, AI Analysis tab, More Projects tab, and Retest & History tab can be tabs at the same level, while the Target Auxiliary Diagnostic Model tab is a next-level tab of the AI ​​Analysis tab. S102, when a selection of a test sample is received in the Sample List area, the sample information of the test sample is simultaneously displayed in the Sample Information area, and the auxiliary diagnostic results of the deployed Target Auxiliary Diagnostic Model for the test sample are displayed on the tabs of each Target Auxiliary Diagnostic Model.

[0062] In one embodiment, when the middleware interaction platform receives an input event to select any test sample in the sample list area, it simultaneously displays the sample information of the selected test sample in the sample information area and the auxiliary diagnostic model tabs of each target auxiliary diagnostic model in the auxiliary diagnostic analysis column corresponding to the AI ​​analysis tab, displaying the auxiliary diagnostic results of each deployed target auxiliary diagnostic model for the selected test sample. For example, as shown in Figure 3, when a user wants to view the auxiliary diagnostic results of test sample number 007, they can click the eighth enable switch corresponding to the sample option for test sample number 007. After the user selects the sample option corresponding to test sample number 007, the display style of the sample option is as shown in Figure 3, that is, a background color is added to the eighth enable switch corresponding to the sample option to indicate to the user that the sample option is selected; simultaneously, the sample information of test sample number 007 is displayed in the sample information area, and the auxiliary diagnostic results of each deployed target auxiliary diagnostic model in the auxiliary diagnostic analysis column corresponding to the AI ​​analysis tab are displayed.

[0063] In the aforementioned auxiliary diagnostic data processing method, the middleware interaction platform, through the middleware display page, enables unified management of test samples and deployed target auxiliary diagnostic models. When the middleware interaction platform receives a selection of test samples in the sample list area, it can simultaneously display sample information in the sample information area, and the auxiliary diagnostic results of each deployed target auxiliary diagnostic model for the test sample can be intuitively viewed in the test result area. Thus, by displaying test samples, sample information, and auxiliary diagnostic results in separate areas, the middleware display page can fully and rationally utilize its display space. The test result area allows for intuitive, convenient, and quick determination of the auxiliary diagnostic results of each target auxiliary diagnostic model for the test sample. Furthermore, the auxiliary diagnostic models can be deployed or upgraded in real time, with good update timeliness, thereby meeting the auxiliary diagnostic needs of different diseases in the test samples.

[0064] In some embodiments, the method further includes:

[0065] The display order of the target auxiliary diagnostic model's tabs is determined based on at least one of the probability values ​​in each of the auxiliary diagnostic results, the accuracy of the target auxiliary diagnostic model, the authentication status of the target auxiliary diagnostic model, and the matching degree of the target auxiliary diagnostic model; wherein, the matching degree is determined based on the type of input data of the target auxiliary diagnostic model and the type of detection data of the detection sample;

[0066] According to the display order, the tabs of the target auxiliary diagnostic model are displayed sequentially in the auxiliary diagnostic analysis section.

[0067] In this embodiment of the application, the probability value indicates the likelihood that the sample may have a certain disease.

[0068] In this embodiment of the application, accuracy is one of the important indicators for evaluating model performance; accuracy is used to indicate the proportion of samples correctly predicted by the model to the total number of samples.

[0069] In this embodiment of the application, the authentication status may include an authenticated status and an unauthenticated status.

[0070] In this embodiment, the detection data may include, but is not limited to, at least one of sample detection item data, sample early warning data, and sample diagnostic data. For example, sample detection item data may indicate data for blood cell, immune, or coagulation tests.

[0071] In some embodiments, the type of input data can be determined based on the type of the target auxiliary diagnostic model. For example, if the target auxiliary diagnostic model is used to assist in the diagnosis of coagulation-related diseases, then the type of input data can be determined to be a coagulation-related data type.

[0072] In some embodiments, the type of test data can be determined based on the source of the test data. For example, if the test data of the selected test sample comes from a coagulation test, or if the test data of the selected test sample comes from a coagulation analyzer, then the type of test data can be determined to be a coagulation-related data type.

[0073] In some embodiments, since there can be multiple target auxiliary models deployed in the middleware interaction platform, in order to optimize the display of the tabs for target auxiliary diagnostic models, the display order of the tabs for target auxiliary diagnostic models can be determined based on at least one of the probability values ​​in each auxiliary diagnostic result, the accuracy of each target auxiliary diagnostic model, the authentication status of the target auxiliary diagnostic model, and the matching degree between the input data of the target auxiliary diagnostic model and the detection data of the detection sample; as shown in Figure 3, the middleware interaction platform displays the tabs for target auxiliary diagnostic models in the auxiliary diagnostic column according to the display order.

[0074] For example, the probability value output by the first target auxiliary diagnostic model is 96%, the probability value output by the second target auxiliary diagnostic model is 57%, and the probability value output by the third target auxiliary diagnostic model is 60%. The middleware interaction platform can sort the tabs of each target auxiliary diagnostic model according to the probability value from largest to smallest to determine the display order; according to the display order, the tabs corresponding to the first target auxiliary diagnostic model, the third target auxiliary diagnostic model, and the second target auxiliary diagnostic model are displayed in sequence.

[0075] For example, the matching degree of the auxiliary diagnostic model for target A is the first matching degree, the matching degree of the auxiliary diagnostic model for target B is the second matching degree, and the matching degree of the auxiliary diagnostic model for target C is the third matching degree; the first matching degree is greater than the second matching degree, which is greater than the third matching degree; the middleware interaction platform can sort the matching degrees from largest to smallest or smallest to largest to determine the display order; for example, according to the display order of matching degrees from largest to smallest, the tabs corresponding to the auxiliary diagnostic model for target A, the auxiliary diagnostic model for target B, and the auxiliary diagnostic model for target C are displayed in sequence.

[0076] For example, an authenticated status indicates that the target auxiliary diagnostic model has relatively high reliability; conversely, an unauthenticated status indicates that the target auxiliary diagnostic model has relatively low reliability. The middleware interaction platform can sort the models according to their authentication status and prioritize displaying target auxiliary diagnostic models with an authenticated status.

[0077] In some embodiments, the middleware interaction platform can assign weighted weights to the probability value of the assisted diagnostic result, the accuracy of the target assisted diagnostic model, the authentication status of the target assisted diagnostic model, and the matching degree between the input data of the target assisted diagnostic model and the detection data of the detection sample, respectively, based on the importance or influence of these factors on the assisted diagnosis. The middleware interaction platform scores the target assisted diagnostic model sequentially based on the accuracy, probability value, authentication status, and matching degree, obtaining a first score, a second score, a third score, and a fourth score. Based on the first score and its corresponding weighted weight, the second score and its corresponding weighted weight, the third score and its corresponding weighted weight, and the fourth score and its corresponding weighted weight, the middleware interaction platform determines the target score of the target assisted diagnostic model. The middleware interaction platform sequentially determines the target score corresponding to each target assisted diagnostic model and sorts them according to the target scores from largest to smallest or smallest to largest to determine the display order.

[0078] In this embodiment, the middleware interaction platform sorts the tabs of each target auxiliary diagnostic model, and can prioritize displaying the auxiliary diagnostic results that are more accurate for the current test sample, so that users can determine the auxiliary diagnostic results more intuitively, conveniently and quickly.

[0079] In some embodiments, the matching degree includes a first matching degree, a second matching degree, a third matching degree, and a fourth matching degree; the method further includes:

[0080] If the type of the input data of the target-assisted diagnostic model is the same as the type of the detection data of the detection sample, the first matching degree is determined;

[0081] If the type of the detection data includes the type of the input data and the type of the detection data is greater than the type of the input data, then the second matching degree is determined; the second matching degree is less than the first matching degree.

[0082] If the type of the input data includes the type of the detection data and the type of the detection data is less than the type of the input data, then the third matching degree is determined; the third matching degree is less than the second matching degree.

[0083] If the type of the detection data is completely different from the type of the input data, the fourth matching degree is determined; the fourth matching degree is less than the third matching degree; the display priority of the first matching degree, the second matching degree, the third matching degree, and the fourth matching degree decreases sequentially.

[0084] In some embodiments, when the probability value of the auxiliary diagnostic result, the accuracy of the model, and the authentication status are consistent, the display order priority of the target auxiliary diagnostic model is positively correlated with the matching degree; the greater the matching degree of the target auxiliary diagnostic model, the higher the display order priority and the earlier the display order.

[0085] In one embodiment, if the type of the test data includes coagulation-related data types, and the type of the input data is also coagulation-related data types, and the type of the test data is the same as the type of the input data, then the overlap is high, and the matching degree is the first matching degree. If the type of the test data includes biochemical-related data types, coagulation-related data types, and immune-related data types, and the type of the input data is coagulation-related data types, and the type of the test data is more than the type of the input data, then the overlap is moderate, and the matching degree is the second matching degree, which is less than the first matching degree. If the type of the test data includes biochemical-related data types, and the type of the input data includes both biochemical-related data types and immune-related data types, and the type of the test data is less than the type of the input data, then the overlap is low, and the matching degree is the third matching degree, which is less than the second matching degree. If the type of the test data is coagulation-related data types, and the type of the input data is immune-related data types, then the overlap is zero, and the matching degree is the fourth matching degree, which is less than the third matching degree.

[0086] In this embodiment, the degree of matching can be accurately determined based on the overlap between the type of input data of the target auxiliary diagnostic model and the type of detection data of the detection sample, thereby identifying the target auxiliary diagnostic model with a high degree of matching.

[0087] In some embodiments, the method further includes:

[0088] If the number of target auxiliary diagnostic models is greater than a first threshold, the target auxiliary diagnostic models whose display order is greater than a predetermined order value are either collapsed or not displayed; wherein, the display order being greater than the predetermined order value indicates that the probability value of the target auxiliary diagnostic model for the selected detection sample is less than a second threshold, the accuracy is less than a third threshold, the authentication status is unauthenticated, and the matching degree is less than a fourth threshold.

[0089] For example, if the auxiliary diagnostic analysis section in the middleware display page supports displaying tabs for five target auxiliary diagnostic models, then the first threshold can be 4, 5, or 6, etc.

[0090] For example, the second threshold can be any numerical value used to measure the probability of the target-assisted diagnostic model output being low. For instance, the second threshold could be 20%, 21%, or 30%, etc.

[0091] For example, the third threshold can be any value used to measure the lower accuracy of the target-assisted diagnostic model. For instance, the third threshold could be 30%, 45%, or 50%, etc.

[0092] For example, the fourth threshold can be any value used to measure the relatively low matching degree between the input data of the target auxiliary diagnostic model and the detection data of the test sample. For example, the matching degree ranges from 0 to 1, and the fourth threshold can be 0.1, 0.2, or 0.02, etc. In one embodiment, the middleware interaction platform can determine the matching degree between the input data of the target auxiliary diagnostic model and the detection data of the test sample based on the overlap between the type of the input data of the target auxiliary diagnostic model and the type of the detection data of the test sample. As shown in Figure 3, if the matching degree is less than the second preset threshold, the middleware interaction platform can determine that the detection data of the test sample does not match the input data of the A target auxiliary diagnostic model. The auxiliary diagnostic result of the A target auxiliary diagnostic model can be that the detection data does not support analysis, and the tab of the A target auxiliary diagnostic model can be arranged to the end, collapsed at the end, or not displayed.

[0093] In this embodiment, the middleware interaction platform can delay or hide the tabs of target auxiliary diagnostic models that are not important to the current detection sample, thus saving space in the auxiliary diagnostic analysis column.

[0094] In some embodiments, the auxiliary diagnostic analysis section includes deployment model options;

[0095] In response to the selection of the deployment model option, a model deployment page is displayed; the model deployment page includes local deployment options.

[0096] In response to the selection of the local deployment option, the target auxiliary diagnostic model stored locally is deployed to the middleware interaction platform;

[0097] The tab for the newly deployed target auxiliary diagnostic model is displayed in the auxiliary diagnostic analysis section.

[0098] In this embodiment, the display style of the deployment model option, local deployment option, and deployment identifier input option, refresh option, delete option, and view details option in other embodiments can be any suitable form, color, or text style.

[0099] In this embodiment of the application, the selection operation may include, but is not limited to, at least one of the following operations: clicking, long pressing, sliding, and dragging.

[0100] In one embodiment, the auxiliary diagnostics section in the middleware display page includes a first enable switch corresponding to the deployment model option; selecting the first enable switch will navigate to the model deployment page. For example, as shown in Figure 3, the deployment model option is displayed as a plus sign; when a user wants to deploy a new target auxiliary diagnostic model, they can click the first enable switch corresponding to the deployment model option to display the model deployment page.

[0101] In one embodiment, as shown in Figure 4, which is a schematic diagram of the model deployment page, the local deployment option in Figure 4 is displayed in a folder shape. When a user wants to deploy a locally stored third-party target auxiliary diagnostic model to the middleware interaction platform, or when a network communication failure occurs and the user wants to deploy a locally stored target auxiliary diagnostic model downloaded from the shared platform, they can click the first enable switch corresponding to the deployment model option, select and deploy the target auxiliary diagnostic model to the middleware interaction platform, and display a tab for the newly deployed target auxiliary diagnostic model in the auxiliary analysis section.

[0102] In this embodiment, the middleware interaction platform can deploy the target auxiliary diagnosis to be deployed to the middleware interaction platform through local deployment, which can eliminate the interference caused by network factors. Even in offline state, it can meet the requirements of real-time deployment of the model and ensure the timeliness of subsequent identification of the detection sample based on the target auxiliary diagnosis model.

[0103] In some embodiments, the model deployment page includes a deployment identifier input option;

[0104] Receive a deployment identifier input based on the deployment identifier input option, download the corresponding target auxiliary diagnostic model from the sharing platform according to the deployment identifier, and deploy the target auxiliary diagnostic model corresponding to the deployment identifier to the middleware interaction platform;

[0105] The tab for the newly deployed target auxiliary diagnostic model is displayed in the auxiliary diagnostic analysis section.

[0106] In some embodiments, when the middleware interaction platform receives the selection operation of the deployment identifier input option, it receives the deployment identifier input based on the deployment identifier input option and sends a deployment request to the sharing platform; the deployment request includes the deployment identifier of the target auxiliary diagnostic model to be deployed; the middleware interaction platform downloads the target auxiliary diagnostic model corresponding to the deployment identifier from the sharing platform and deploys the target auxiliary diagnostic model to the middleware interaction platform.

[0107] In this embodiment of the application, the deployment request is used to instruct a request to download and deploy the target auxiliary diagnostic model.

[0108] In this embodiment of the application, the deployment identifier indicates an identifier used to identify the model resource.

[0109] In some embodiments, the deployment identifier may include, but is not limited to, at least one of graphic identifiers, color identifiers, and string identifiers; wherein, the string identifier may include, but is not limited to, at least one of text identifiers, number identifiers, and letter identifiers.

[0110] In one embodiment, as shown in Figure 4, the deployment identifier input option is displayed as a box shape. Under normal network communication conditions, when a user wants to download and deploy a target auxiliary diagnostic model from the sharing platform, they can copy the deployment code of the target auxiliary diagnostic model to be deployed from the sharing platform, and then click the deployment identifier input option on the model deployment page of the middleware interaction platform and enter the deployment code. This will download and deploy the target auxiliary diagnostic model corresponding to the deployment code from the sharing platform to the server connected to the middleware interaction platform. Consequently, the middleware interaction platform will automatically refresh and load the target auxiliary diagnostic model corresponding to the deployment code, and simultaneously display a tab for the newly added target auxiliary diagnostic model in the auxiliary analysis section.

[0111] In the field of traditional medical testing technology, the auxiliary diagnostic models used for disease auxiliary diagnosis in the middleware interaction platforms of various medical institutions are mostly fixed and few in number, which is difficult to meet the auxiliary diagnostic needs of a wide variety of diseases. Furthermore, when it is necessary to update or upgrade the auxiliary diagnostic models in the middleware interaction platform, the entire middleware interaction platform needs to be updated or upgraded, which is complex and time-consuming.

[0112] In contrast, in this embodiment of the application, by inputting a deployment identifier through the deployment identifier input option of the middleware interaction platform, a deployment request can be sent to the sharing platform; thereby, the target auxiliary diagnostic model can be downloaded and deployed from the sharing platform to the middleware interaction platform. The operation is simple and quick. Furthermore, new target auxiliary diagnostic models can be deployed in real time according to auxiliary diagnostic needs, which can meet the auxiliary diagnostic needs of a variety of diseases and realize the sharing of model resources in the medical field.

[0113] In some embodiments, before deploying the target auxiliary diagnostic model corresponding to the deployment identifier on the middleware interaction platform, the method further includes:

[0114] Determine the compatibility between the target auxiliary diagnostic model to be deployed and the instrument connected to the middleware. If the compatibility is less than a fifth threshold, output a prompt message. The prompt message includes at least one of the compatibility or deployment prompt.

[0115] In some embodiments, the middleware interaction platform can determine the compatibility between the target-assisted diagnostic model and the instrument connected to the middleware interaction platform based on at least one of the instrument information, the bound discipline information, and the location information of the bound instrument.

[0116] In this embodiment of the application, the bound instrument refers to the relevant instrument bound to the middleware interaction platform; the bound instrument may include, but is not limited to, at least one of sample analyzers, blood cell analyzers, flow cytometers, chemiluminescence analyzers, and enzyme immunoassay analyzers.

[0117] In this embodiment, the application of multiple testing disciplines or technologies bound to the middleware interaction platform is described; the bound testing disciplines may include, but are not limited to, at least two of biochemistry, immunology, microbiology and molecular diagnostics.

[0118] In this embodiment, the location information indicates the geographical location of the middleware interaction platform. For example, the location information may be Province A, City B, District C; or, the location information may be 50°N, 45°E.

[0119] In one embodiment, if the target auxiliary diagnostic model to be deployed is compatible with the biochemical project, and the instrument bound to the middleware interaction platform is a blood cell analyzer, then the compatibility between the target auxiliary diagnostic model and the instrument connected to the middleware interaction platform can be determined to be 0, and the middleware interaction platform can automatically determine not to deploy the target auxiliary diagnostic model.

[0120] In one embodiment, the middleware interaction platform determines a first candidate fit based on the instrument information of the bound instrument; a second candidate fit based on the bound subject information; a third candidate fit based on the location information of the middleware interaction platform; and a fourth binding fit based on the time information of the middleware interaction platform. The middleware interaction platform determines corresponding weights based on the importance of the instrument information, bound subject information, location information, and time information of the bound instrument. The middleware interaction platform determines a first value by multiplying the first candidate fit by its corresponding weight; a second value by multiplying the second candidate fit by its corresponding weight; and so on, determining the third and fourth values ​​respectively. The sum of the first to fourth values ​​determines the fit between the target-assisted diagnostic model and the instrument connected to the middleware interaction platform.

[0121] In this embodiment, the deployment prompt is used to remind the user that the fit is relatively moderate or low, and that confirmation is needed on whether to deploy the target auxiliary diagnostic model.

[0122] In this embodiment, the middleware interaction platform can measure the compatibility between the target auxiliary diagnostic model and the instruments connected to the middleware interaction platform in various ways, thereby accurately assessing the necessity of deploying the target auxiliary diagnostic model. Alternatively, it can subsequently determine the display order of the target auxiliary diagnostic model's tab based on the compatibility, improving the intelligence of the middleware interaction platform's model deployment and enhancing the user experience. When the compatibility between the target auxiliary diagnostic model and the instruments connected to the middleware interaction platform is less than a fifth threshold, the user can be reminded to reconfirm whether to deploy the target auxiliary diagnostic model, reducing the occurrence of misdeployment.

[0123] In some embodiments, the tab of the target auxiliary diagnostic model includes a status identifier; the status identifier is used to characterize the authentication status of the target auxiliary diagnostic model; the authentication status includes an authenticated status and an unauthenticated status; the authenticated status indicates that the target auxiliary diagnostic model has been verified by the R&D platform and the verification is qualified; the unauthenticated status indicates that the target auxiliary diagnostic model has not been verified by the R&D platform or the verification is unqualified.

[0124] For example, as shown in Figure 5, the status indicator is displayed in a style that combines a sun shape with text.

[0125] In this embodiment of the application, by displaying a status indicator on the tab of the target auxiliary diagnostic model, users can accurately determine the authentication status of the target auxiliary diagnostic model, thereby determining the reliability and credibility of the target auxiliary diagnostic model.

[0126] In some embodiments, the tab of the target-assisted diagnostic model further includes at least one of a refresh option, a delete option, and a view details option;

[0127] In response to the selection operation corresponding to the refresh option, the target auxiliary diagnostic model corresponding to the refresh option is refreshed according to the refresh option;

[0128] In response to the selection operation corresponding to the deletion option, the target auxiliary diagnostic model corresponding to the deletion option is deleted;

[0129] In response to the selection of the "View Details" option, the details page corresponding to the target auxiliary diagnostic model is displayed according to the "View Details" option.

[0130] In one embodiment, to facilitate unified management of deployed target auxiliary diagnostic models, each target auxiliary diagnostic model tab in the middleware display page includes a fourth enable switch for the refresh option, a fifth enable switch for the delete option, and a sixth enable switch for the view details option. Selecting the fourth enable switch refreshes the deployed target auxiliary diagnostic model corresponding to the target auxiliary diagnostic model tab; selecting the fifth enable switch deletes the deployed target auxiliary diagnostic model from the middleware interaction platform and simultaneously deletes the target auxiliary diagnostic model tab; selecting the sixth enable switch redirects to the target auxiliary diagnostic model's details page. For example, as shown in Figure 3, the refresh option is displayed as a circular rotating arrow, the delete option as a trash can shape, and the view details option as an "i" shape. When a user wants to refresh a target auxiliary diagnostic model, they can click the fourth enable switch corresponding to the refresh option; after the user selects the refresh option, the middleware interaction platform refreshes the target auxiliary diagnostic model in the target auxiliary diagnostic model tab.

[0131] In this embodiment, the target-assisted diagnostic model tab allows for a direct view of the target-assisted diagnostic model's auxiliary diagnostic results for the currently selected test sample. Users can also choose whether to refresh the deployed target-assisted diagnostic model, delete the target-assisted diagnostic model, or view the detailed information of the target-assisted diagnostic model, thus meeting the management needs of the deployed target-assisted diagnostic model.

[0132] The following provides specific examples in conjunction with any of the above embodiments:

[0133] Specific Example 1: This application provides an exemplary auxiliary diagnostic data processing method, applied to a middleware interaction platform, including:

[0134] 1) The middleware interaction platform responds to the middleware display page viewing request and displays the middleware display page.

[0135] In one optional embodiment, the middleware display page includes a sample list area, a sample information area, and a test result area; the test result area includes an auxiliary diagnostic analysis column. When at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis column includes tabs for each deployed target auxiliary diagnostic model, and the tabs for the target auxiliary diagnostic models are used to display the auxiliary diagnostic results of the target auxiliary diagnostic models.

[0136] 2) When the middleware interaction platform receives an input event for selecting a sample in the sample list area, it simultaneously displays the sample information of the sample in the sample information area, and displays the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the sample in the tab of each target auxiliary diagnostic model.

[0137] In one optional embodiment, the display order of the target auxiliary diagnostic model tabs is determined based on at least one of the probability values ​​in each auxiliary diagnostic result, the accuracy of the target auxiliary diagnostic model, and the matching degree between the target auxiliary diagnostic model and the instrument connected to the middleware interaction platform; and the tabs of each target auxiliary diagnostic model are displayed sequentially according to the display order.

[0138] In the aforementioned auxiliary diagnostic data processing method, the middleware interaction platform, through the middleware display page, enables unified management of test samples and deployed target auxiliary diagnostic models. When the middleware interaction platform receives a selection of test samples in the sample list area, it can simultaneously display sample information in the sample information area, and the auxiliary diagnostic results of each deployed target auxiliary diagnostic model for the test sample can be intuitively viewed in the test result area. Thus, by displaying test samples, sample information, and auxiliary diagnostic results in separate areas, the middleware display page can fully and rationally utilize its display space. The test result area allows for intuitive, convenient, and quick determination of the auxiliary diagnostic results of each target auxiliary diagnostic model for the test sample. Furthermore, the auxiliary diagnostic models can be deployed or upgraded in real time, with good update timeliness, thereby meeting the auxiliary diagnostic needs of different diseases in the test samples.

[0139] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0140] Based on the same inventive concept, this application also provides a middleware interaction platform for implementing the above-described auxiliary diagnostic data processing method. The solution provided by this platform is similar to the implementation scheme described in the above method; therefore, the specific limitations of the one or more middleware interaction platform embodiments provided below can be found in the limitations of the auxiliary diagnostic data processing method described above, and will not be repeated here.

[0141] In some embodiments, as shown in Figure 6, which is a structural block diagram of a middleware interaction platform; the middleware interaction platform is connected to one or more sample analyzers; the platform includes:

[0142] Display module 110 is used to display a middleware display page in response to a middleware display page viewing request. The middleware display page includes a sample list area, a sample information area, and a detection result area. The sample list area is used to display at least one detection sample. The sample information area is used to display the sample information of the selected detection sample. The detection result area includes an auxiliary diagnostic analysis bar. When at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis bar includes tabs for each deployed target auxiliary diagnostic model. The tabs for each target auxiliary diagnostic model are used to display the auxiliary diagnostic results of the target auxiliary diagnostic model.

[0143] The processing module 120 is configured to, when receiving an input event that selects the test sample in the sample list area, simultaneously display the sample information of the test sample in the sample information area, and display the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the test sample in the tabs of each target auxiliary diagnostic model.

[0144] In some embodiments, the platform further includes:

[0145] The determining module is used to determine the display order of the tabs of the target auxiliary diagnostic model based on at least one of the probability values ​​in each of the auxiliary diagnostic results, the accuracy of the target auxiliary diagnostic model, the authentication status of the target auxiliary diagnostic model, and the matching degree of the target auxiliary diagnostic model; wherein, the matching degree is determined based on the type of input data of the target auxiliary diagnostic model and the type of detection data of the detection sample;

[0146] The display module 110 is used to sequentially display the tabs of the target auxiliary diagnostic model in the auxiliary diagnostic analysis column according to the display order.

[0147] In some embodiments, the matching degree includes a first matching degree, a second matching degree, a third matching degree, and a fourth matching degree; the determining module is configured to perform the following steps:

[0148] If the type of the input data of the target-assisted diagnostic model is the same as the type of the detection data of the detection sample, the first matching degree is determined;

[0149] If the type of the detection data includes the type of the input data and the type of the detection data is greater than the type of the input data, then the second matching degree is determined; the second matching degree is less than the first matching degree.

[0150] If the type of the input data includes the type of the detection data and the type of the detection data is less than the type of the input data, then the third matching degree is determined; the third matching degree is less than the second matching degree.

[0151] If the type of the detection data is completely different from the type of the input data, the fourth matching degree is determined; the fourth matching degree is less than the third matching degree; the display priority of the first matching degree, the second matching degree, the third matching degree, and the fourth matching degree decreases sequentially.

[0152] In some embodiments, the auxiliary diagnostic analysis panel includes a deployment model option; the display model 110 is used to perform the following steps:

[0153] In response to the selection of the deployment model option, a model deployment page is displayed; the model deployment page includes local deployment options.

[0154] In response to the selection of the local deployment option, the target auxiliary diagnostic model stored locally is deployed to the middleware interaction platform;

[0155] The tab for the newly deployed target auxiliary diagnostic model is displayed in the auxiliary diagnostic analysis section.

[0156] In some embodiments, the model deployment page includes a deployment identifier input option; the processing module 120 is configured to receive a deployment identifier input based on the deployment identifier input option, download the corresponding target auxiliary diagnostic model from the sharing platform according to the deployment identifier, and deploy the target auxiliary diagnostic model corresponding to the deployment identifier to the middleware interaction platform;

[0157] The display model 110 is used to display a tab for the newly deployed target auxiliary diagnostic model in the auxiliary diagnostic analysis column.

[0158] In some embodiments, before deploying the target auxiliary diagnostic model corresponding to the deployment identifier to the middleware interaction platform, the display module 110 is used to determine the compatibility between the target auxiliary diagnostic model to be deployed and the instrument connected to the middleware, and output a prompt message if the compatibility is less than a fifth threshold; the prompt message includes at least one of the compatibility or deployment prompt.

[0159] In some embodiments, the tab of the target auxiliary diagnostic model includes a status identifier; the status identifier is used to characterize the authentication status of the target auxiliary diagnostic model; the authentication status includes an authenticated status and an unauthenticated status; the authenticated status indicates that the target auxiliary diagnostic model has been verified by the R&D platform and the verification is qualified; the unauthenticated status indicates that the target auxiliary diagnostic model has not been verified by the R&D platform or the verification is unqualified.

[0160] In some embodiments, the detection result area includes a project result column; the project result column is used to display the detection data of the selected detection sample determined by the sample analyzer; before the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the detection sample are displayed on the tab of each target auxiliary diagnostic model, the processing module 120 is used to input the sample information and / or the detection data of the selected detection sample into the target auxiliary diagnostic model to obtain the auxiliary diagnostic results of the detection sample.

[0161] Each module in the aforementioned middleware interaction platform can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the operations corresponding to each module. In one embodiment, an electronic device is provided, which can be a terminal, and its internal structure diagram is shown in Figure 7. The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a method bus. The processor of the electronic device provides computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores operating methods and computer programs. The internal memory provides an environment for the operation of the operating methods and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used for wired or wireless communication with external terminals; wireless communication can be achieved through WIFI, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a model sharing platform. The display screen of the electronic device can be an LCD screen or an e-ink screen. The input device of the electronic device can be a touch layer covering the display screen, or buttons, trackballs or touchpads set on the casing of the electronic device, or external keyboards, touchpads or mice, etc.

[0162] Those skilled in the art will understand that the structure shown in Figure 7 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0163] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0164] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and / or features involved in this application are all information, data and / or features authorized by the user or fully authorized by all parties.

[0165] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0166] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for processing auxiliary diagnostic data, characterized in that, An application is made to a middleware interaction platform, which is connected to one or more sample analyzers. The method includes: responding to a middleware display page viewing request, displaying a middleware display page; the middleware display page includes a sample list area, a sample information area, and a detection result area; the sample list area is used to display at least one detection sample; the sample information area is used to display the sample information of the selected detection sample; the detection result area includes an auxiliary diagnostic analysis bar, and when at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis bar includes tabs for each deployed target auxiliary diagnostic model, and the target auxiliary diagnostic model tabs are used to display the auxiliary diagnostic results of the target auxiliary diagnostic model; when a selection of a detection sample in the sample list area is received, the sample information of the detection sample is simultaneously displayed in the sample information area, and the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the detection sample are displayed in the tabs of each target auxiliary diagnostic model.

2. The method according to claim 1, characterized in that, The method further includes: determining the display order of the target auxiliary diagnostic model's tabs based on at least one of the probability values ​​in each of the auxiliary diagnostic results, the accuracy of the target auxiliary diagnostic model, the authentication status of the target auxiliary diagnostic model, and the matching degree of the target auxiliary diagnostic model; wherein the matching degree is determined based on the type of input data of the target auxiliary diagnostic model and the type of detection data of the detection sample; and displaying the target auxiliary diagnostic model's tabs sequentially in the auxiliary diagnostic analysis column according to the display order.

3. The method according to claim 2, characterized in that, The matching degree includes a first matching degree, a second matching degree, a third matching degree, and a fourth matching degree; the method further includes: if the type of the input data of the target auxiliary diagnostic model is the same as the type of the detection data of the detection sample, determining the first matching degree; if the type of the detection data includes the type of the input data and the types of the detection data are more than the types of the input data, determining the second matching degree; the second matching degree is less than the first matching degree; if the type of the input data includes the type of the detection data and the types of the detection data are less than the types of the input data, determining the third matching degree; the third matching degree is less than the second matching degree; if the type of the detection data is completely different from the type of the input data, determining the fourth matching degree; the fourth matching degree is less than the third matching degree; the display priority of the first matching degree, the second matching degree, the third matching degree, and the fourth matching degree decreases sequentially.

4. The method according to claim 2, characterized in that, The method further includes: when the number of target auxiliary diagnostic models is greater than a first threshold, collapsing or not displaying the target auxiliary diagnostic models whose display order is greater than a predetermined order value; wherein, the display order being greater than the predetermined order value indicates that the probability value of the target auxiliary diagnostic model for the selected detection sample is less than a second threshold, the accuracy is less than a third threshold, the authentication status is an unauthenticated status, and the matching degree is less than a fourth threshold.

5. The method according to claim 1, characterized in that, The auxiliary diagnostic analysis panel includes a deployment model option; in response to selecting the deployment model option, a model deployment page is displayed; the model deployment page includes a local deployment option; in response to selecting the local deployment option, the target auxiliary diagnostic model stored locally is deployed to the middleware interaction platform. The tab for the newly deployed target auxiliary diagnostic model is displayed in the auxiliary diagnostic analysis section.

6. The method according to claim 1, characterized in that, The model deployment page includes a deployment identifier input option; it receives a deployment identifier input based on the deployment identifier input option, downloads the corresponding target auxiliary diagnostic model from the sharing platform according to the deployment identifier, and deploys the target auxiliary diagnostic model corresponding to the deployment identifier to the middleware interaction platform; and displays a tab for the newly deployed target auxiliary diagnostic model in the auxiliary diagnostic analysis column.

7. The method according to claim 6, characterized in that, Before deploying the target auxiliary diagnostic model corresponding to the deployment identifier to the middleware interaction platform, the method further includes: determining the compatibility between the target auxiliary diagnostic model to be deployed and the instrument connected to the middleware, and outputting a prompt message if the compatibility is less than a fifth threshold; the prompt message includes at least one of the compatibility or deployment prompt.

8. The method according to claim 1, characterized in that, The tab of the target auxiliary diagnostic model includes a status identifier; the status identifier is used to characterize the authentication status of the target auxiliary diagnostic model; the authentication status includes an authenticated status and an unauthenticated status; the authenticated status indicates that the target auxiliary diagnostic model has been verified by the R&D platform and the verification is qualified; the unauthenticated status indicates that the target auxiliary diagnostic model has not been verified by the R&D platform or the verification is unqualified.

9. The method according to claim 1, characterized in that, The detection result area includes a project result column; the project result column is used to display the detection data of the selected detection sample determined by the sample analyzer; before displaying the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the detection sample on the tab of each target auxiliary diagnostic model, the method further includes: inputting the sample information and / or the detection data of the selected detection sample into the target auxiliary diagnostic model to obtain the auxiliary diagnostic results of the detection sample.

10. A middleware interaction platform, characterized in that, The middleware interaction platform is connected to one or more sample analyzers; the platform includes: a display module, used to display the middleware display page in response to a middleware display page viewing request; the middleware display page includes a sample list area, a sample information area, and a test result area; the sample list area is used to display at least one test sample; the sample information area is used to display the sample information of the selected test sample; the test result area includes an auxiliary diagnostic analysis column, and when at least one target auxiliary diagnostic model is deployed on the middleware interaction platform, the auxiliary diagnostic analysis column includes tabs for each deployed target auxiliary diagnostic model, and the target auxiliary diagnostic model tabs are used to display the auxiliary diagnostic results of the target auxiliary diagnostic model; a processing module, used to simultaneously display the sample information of the test sample in the sample information area and display the auxiliary diagnostic results of the deployed target auxiliary diagnostic model for the test sample in the tabs of each target auxiliary diagnostic model when a selection of the test sample in the sample list area is received.

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