A cloud-based POCT data analysis method and system

Abstract

The present invention discloses a cloud-based POCT data analysis method and system, comprising: constructing an initial POCT data analysis model, the initial POCT data analysis model is used to analyze POCT data stored in the cloud according to POCT data analysis rules, and obtain disease analysis results based on the POCT data; generating POCT data analysis rules, the POCT data analysis rules are used to extract feature data for generating disease analysis results according to the POCT data; extracting features of the POCT data according to the POCT data analysis rules to obtain feature data; using the feature data as input data of the initial POCT data analysis model for model training to generate disease analysis results; the present invention can achieve data sharing of each clinic by transmitting the POCT test results to the cloud, so that patients and clinics can see the test results of all clinics, and big data analysis can be performed on all test data, so that the prone diseases in each area can be prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud medical data analysis, and in particular to a cloud-based POCT data analysis method and system. Background Art

[0002] With the development of medical technology, more and more POCT devices have emerged. POCT has the advantages of small space, easy use, high efficiency and high accuracy, and is generally low in price. It is of great significance for disease prevention, determining causes and preventing consequences, improving treatment effectiveness and reducing medical costs. It can meet the clinical testing needs of medical institutions at all levels and types, and is favored by grassroots medical institutions.

[0003] When it comes to the test data of existing POCT equipment, each medical institution usually conducts separate tests in its own medical equipment, and the data obtained can only be used by the institution itself.

[0004] For existing POCT devices, they are often limited to the device itself or a fixed medical institution for viewing, which will result in the test results not being recognized when switching medical institutions, and the need for repeated testing. In addition, the data is only stored locally, making it difficult to form shareable data for regional analysis of important diseases. Summary of the invention

[0005] In order to solve the existing technical problems, the present invention provides a cloud-based POCT data analysis method, comprising the following steps:

[0006] Construct an initial POCT data analysis model, which is used to analyze the POCT data stored in the cloud according to the POCT data analysis rules and obtain disease analysis results based on the POCT data;

[0007] Generate POCT data analysis rules, where the POCT data analysis rules are used to extract characteristic data for generating disease analysis results based on POCT data;

[0008] According to the POCT data analysis rules, feature extraction is performed on the POCT data to obtain feature data;

[0009] The characteristic data is used as input data of the initial POCT data analysis model for model training to generate disease analysis results.

[0010] Preferably, in the process of generating disease analysis results, based on the disease analysis results and according to expert experience, the POCT data analysis rules are modified to generate a POCT data analysis model for analyzing POCT data, wherein the POCT data analysis model is used to analyze the POCT data collected to the cloud to generate disease analysis results.

[0011] Preferably, real-time POCT data is collected, and real-time feature data is generated according to POCT data analysis rules;

[0012] Generate real-time disease analysis results based on real-time feature data through POCT data analysis model;

[0013] Performing a similarity analysis on the real-time feature data and the feature data, and obtaining a disease analysis result of the feature data having the highest similarity to the real-time feature data;

[0014] Obtaining real-time disease analysis results and distinguishing features of the disease analysis results, as well as distinguishing feature data corresponding to the distinguishing features;

[0015] Based on the distinguishing characteristic data, the recognition accuracy of the real-time characteristic data is judged through the POCT data analysis model.

[0016] Preferably, in the process of acquiring the distinguishing feature data, when the distinguishing feature is not empty, a distinguishing feature recognition rule is generated according to the distinguishing feature data, and the POCT data analysis rule is updated.

[0017] Preferably, in the process of acquiring distinguishing feature data, when the distinguishing feature is empty, a feature recognition rule is generated according to the feature data, and the POCT data analysis rule is updated.

[0018] Preferably, in the process of performing similarity analysis on the real-time feature data and the feature data, if the similarity is not greater than 90%, the real-time feature data is analyzed based on expert experience and the POCT data analysis rules are modified.

[0019] The present invention discloses a cloud-based POCT data analysis system, comprising:

[0020] Data acquisition module, used to collect POCT data and real-time POCT data;

[0021] The first data analysis module is used to analyze the POCT data stored in the cloud by building a POCT data analysis model according to the POCT data analysis rules, and obtain disease analysis results based on the POCT data;

[0022] A communication module, used to send POCT data to the cloud and collect POCT data from the cloud;

[0023] The second data analysis module is used to analyze the real-time POCT data through the POCT data analysis model and update the POCT data analysis rules.

[0024] Preferably, the POCT data analysis system further comprises:

[0025] A data processing module is used to generate a data comparison chart based on POCT data and real-time POCT data, and to perform feature annotation on the data comparison chart;

[0026] The display module is used to display POCT data, real-time POCT data, data comparison charts, disease analysis results, and feature annotation information.

[0027] The present invention discloses the following technical effects:

[0028] The present invention enables data sharing among each clinic by transmitting the POCT test results to the cloud, so that patients and clinics can see the test results of all clinics. Big data analysis can be performed on all the test data, and prone diseases in each area can be prevented. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1 A flow chart of the method described in the present invention;

[0031] Figure 2 This is a process framework diagram described in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0033] like Figure 1-2 As shown, the present invention provides a cloud-based POCT data analysis method, comprising the following steps:

[0034] Construct an initial POCT data analysis model, which is used to analyze the POCT data stored in the cloud according to the POCT data analysis rules and obtain disease analysis results based on the POCT data;

[0035] Generate POCT data analysis rules, where the POCT data analysis rules are used to extract characteristic data for generating disease analysis results based on POCT data;

[0036] According to the POCT data analysis rules, feature extraction is performed on the POCT data to obtain feature data;

[0037] The characteristic data is used as input data of the initial POCT data analysis model for model training to generate disease analysis results.

[0038] Further preferably, in the process of generating disease analysis results, based on the disease analysis results and according to expert experience, the POCT data analysis rules are modified to generate a POCT data analysis model for analyzing POCT data, wherein the POCT data analysis model is used to analyze the POCT data collected to the cloud to generate disease analysis results.

[0039] Further preferably, real-time POCT data is collected, and real-time feature data is generated according to POCT data analysis rules;

[0040] Generate real-time disease analysis results based on real-time feature data through POCT data analysis model;

[0041] Performing a similarity analysis on the real-time feature data and the feature data, and obtaining a disease analysis result of the feature data having the highest similarity to the real-time feature data;

[0042] Obtaining real-time disease analysis results and distinguishing features of the disease analysis results, as well as distinguishing feature data corresponding to the distinguishing features;

[0043] Based on the distinguishing characteristic data, the recognition accuracy of the real-time characteristic data is judged through the POCT data analysis model.

[0044] Further preferably, in the process of acquiring the distinguishing feature data, when the distinguishing feature is not empty, a distinguishing feature recognition rule is generated according to the distinguishing feature data, and the POCT data analysis rule is updated.

[0045] Further preferably, in the process of acquiring the distinguishing feature data, when the distinguishing feature is empty, a feature recognition rule is generated according to the feature data, and the POCT data analysis rule is updated.

[0046] Further preferably, in the process of performing similarity analysis on the real-time feature data and the feature data, if the similarity is not greater than 90%, the real-time feature data is analyzed based on expert experience and the POCT data analysis rules are modified.

[0047] The present invention discloses a cloud-based POCT data analysis system, comprising:

[0048] Data acquisition module, used to collect POCT data and real-time POCT data;

[0049] The first data analysis module is used to analyze the POCT data stored in the cloud by building a POCT data analysis model according to the POCT data analysis rules, and obtain disease analysis results based on the POCT data;

[0050] A communication module, used to send POCT data to the cloud and collect POCT data from the cloud;

[0051] The second data analysis module is used to analyze the real-time POCT data through the POCT data analysis model and update the POCT data analysis rules.

[0052] Further preferably, the POCT data analysis system further includes:

[0053] A data processing module is used to generate a data comparison chart based on POCT data and real-time POCT data, and to perform feature annotation on the data comparison chart;

[0054] The display module is used to display POCT data, real-time POCT data, data comparison charts, disease analysis results, and feature annotation information.

[0055] The expert experience mentioned in the present invention is used to represent the doctor's diagnosis and treatment experience on POCT data. It is obtained by collecting at least 10 experts, and analyzing the features of the same POCT data by each expert, selecting features, and then discussing with experts. The expert experience has a strong technical guidance role.

[0056] The POCT data mentioned in the present invention is bound by account binding, which is convenient for data tracing. The POCT data analysis rules are set in the cloud. When the data is transmitted to the cloud for data storage, the characteristic data of the data is automatically identified, and the identification result is obtained through the data analysis model to obtain the diagnosis result. In this process, different combinations of POCT data and some POCT data with special circumstances will appear. Therefore, an adaptive system is designed to help the intelligent identification of POCT data. The system designed by the present invention includes a correction process. The purpose is to adjust the identification method of POCT data through the correction function when the identification result has a large gap with the existing case result after the POCT data is identified, thereby helping the system to identify different POCT data and enhance the accuracy of data identification. In addition, by continuously performing correction iterations, the identification and analysis capabilities of POCT data are enhanced, and the system is endowed with a certain self-learning ability. In addition, by collecting big data and identifying account information through the cloud, big data analysis can be performed on the disease situation in the region, which is helpful for the reasonable allocation of medical supplies. In addition, for public health emergencies, corresponding and data support can be obtained in the first time, providing technical support for rapid diagnosis and rapid coordination of medical supplies.

[0057] Example 1: The present invention adds a module (Bluetooth and wifi) to the POCT device. Through the communication module, the detection data of the POCT device is uploaded to the cloud database. By performing data analysis and data association in the cloud, the associated information is used for consultation in other medical institutions, thereby realizing data sharing among medical institutions. In the subsequent work, regional analysis can be performed for the detection of a certain disease. The specific implementation process is as follows: Figure 2 As shown:

[0058] exist Figure 2 The present invention divides the process into 4 steps:

[0059] Step 1: The patient enters the clinic for treatment;

[0060] Step 2: Doctors diagnose and treat, and patients undergo POCT testing;

[0061] Step 3: The POCT device transmits the data back to the database;

[0062] Step 4: Conduct big data analysis.

[0063] The present invention enables data sharing among each clinic by transmitting the POCT test results to the cloud, so that patients and clinics can see the test results of all clinics. Big data analysis can be performed on all the test data, and prone diseases in each area can be prevented.

[0064] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0065] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A cloud-based POCT data analysis method, characterized in that: The following steps are involved: Constructing an initial POCT data analysis model, wherein the initial POCT data analysis model is used to analyze the POCT data stored in the cloud according to the POCT data analysis rules to obtain disease analysis results based on the POCT data; Generating the POCT data analysis rule, wherein the POCT data analysis rule is used to extract characteristic data for generating the disease analysis result based on the POCT data; According to the POCT data analysis rule, feature extraction is performed on the POCT data to obtain the feature data; Using the characteristic data as input data of the initial POCT data analysis model to perform model training and generate the disease analysis result; In the process of generating the disease analysis result, based on the disease analysis result, the POCT data analysis rule is modified according to expert experience to generate a POCT data analysis model for analyzing the POCT data, wherein the POCT data analysis model is used to analyze the POCT data collected to the cloud to generate the disease analysis result; Collecting real-time POCT data, and generating real-time feature data according to the POCT data analysis rules; Generate real-time disease analysis results through the POCT data analysis model according to the real-time feature data; Performing a similarity analysis on the real-time feature data and the feature data, and obtaining the disease analysis result of the feature data having the highest similarity to the real-time feature data; Acquire the real-time disease analysis result and the distinguishing features of the disease analysis result, and distinguishing feature data corresponding to the distinguishing features; According to the distinguishing characteristic data, the recognition accuracy of the real-time characteristic data is determined through the POCT data analysis model.

2. A cloud-based POCT data analysis method according to claim 1, characterized in that: In the process of acquiring the distinguishing feature data, when the distinguishing feature is not empty, a distinguishing feature recognition rule is generated according to the distinguishing feature data, and the POCT data analysis rule is updated.

3. A cloud-based POCT data analysis method according to claim 2, characterized in that: In the process of acquiring the distinguishing feature data, when the distinguishing feature is empty, a feature recognition rule is generated according to the feature data, and the POCT data analysis rule is updated.

4. A cloud-based POCT data analysis method according to claim 3, characterized in that: In the process of performing similarity analysis on the real-time feature data and the feature data, if the similarity is not greater than 90%, the real-time feature data is analyzed based on the expert experience, and the POCT data analysis rule is modified.

5. A cloud-based POCT data analysis system, characterized in that: include: Data acquisition module, used to collect POCT data and real-time POCT data; A first data analysis module is used to analyze the POCT data stored in the cloud by constructing a POCT data analysis model according to POCT data analysis rules, and obtain disease analysis results based on the POCT data; A communication module, used for sending the POCT data to the cloud and collecting the POCT data from the cloud; A second data analysis module, configured to analyze the real-time POCT data through the POCT data analysis model and update the POCT data analysis rules; The POCT data analysis system also includes: A data processing module, used to generate a data comparison chart according to the POCT data and the real-time POCT data, and perform feature annotation on the data comparison chart; A display module, used to display the POCT data, the real-time POCT data, the data comparison chart, the disease analysis results, and feature annotation information; The system collects real-time POCT data and generates real-time feature data according to the POCT data analysis rules; Generate real-time disease analysis results through the POCT data analysis model according to the real-time feature data; Performing a similarity analysis on the real-time feature data and the feature data, and obtaining the disease analysis result of the feature data having the highest similarity to the real-time feature data; Acquire the real-time disease analysis result and the distinguishing features of the disease analysis result, and distinguishing feature data corresponding to the distinguishing features; According to the distinguishing characteristic data, the recognition accuracy of the real-time characteristic data is determined through the POCT data analysis model.

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