Subject data processing method and system and electronic equipment
Through double-blind random grouping and weighted calculation of compliance levels, the problem of inaccurate analysis of physical examination data was solved, and a scientific evaluation of the effects of products such as health foods, health foods and cosmetics was achieved.
Patent Information
- Application Number
- CN202511097636.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-23
- Publication Date
- 2025-10-17
AI Technical Summary
The existing physical examination data analysis is not accurate enough, and its credibility is affected by the behavioral habits of the examinees, making it impossible to accurately evaluate the effectiveness of functional products such as health foods, health foods and cosmetics.
A double-blind random grouping method was used to divide the subjects into a trial group, a control group, and a blank group. Their physical examination data and sample usage data were obtained and analyzed. Different weight values were assigned according to the compliance level, and weighted calculations were performed to obtain more accurate sample effect data.
By referring to the compliance level, the accuracy of physical examination data analysis is improved, a more scientific product effect evaluation is obtained, and the problem of inaccurate physical examination data analysis is solved.
Smart Images
Figure CN120809085A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of big data, and in particular to a subject data processing method and system and electronic equipment. BACKGROUND
[0002] With the popularization of medical and health technology, people pay more and more attention to the physical condition of the body, and institutions such as schools and work units will regularly organize personnel for physical examination.
[0003] Correspondingly, a physical examination data management system will be provided for physical examination institutions such as hospitals or physical examination centers to enter, store and analyze the physical examination data of the examinees. However, detailed physical examination data cannot completely reflect the results of physical examination analysis. Since the different behavior habits of examinees will affect the acceptance of physical examination data, the prior art has the problem of inaccurate analysis of physical examination data. SUMMARY
[0004] The present application aims to provide a subject data processing method, system and electronic equipment to alleviate the problem of inaccurate analysis of physical examination data in the prior art.
[0005] In a first aspect, the present application provides a subject data processing method applied to a physical examination service data platform, which comprises:
[0006] S1. Double-blind random grouping of subjects, and storing the group to which each subject belongs; the group at least includes a trial group and a reference group, and the reference group includes one or more of a control group, a blank group and a positive reference group;
[0007] S2. Obtaining the first physical examination data of the subject; the first physical examination data includes height, weight, body fat ratio, blood pressure, blood oxygen, B-ultrasound single, and electrocardiogram;
[0008] S3. Obtaining the sample use data of the subject, which includes log data, picture data or video data, dividing the subject into multiple compliance levels based on the sample use data, and determining the weight value corresponding to each compliance level, wherein the weight value corresponding to the highest compliance level is 1, the weight value corresponding to the middle compliance level is a decimal between 0 and 1, and the weight value corresponding to the lowest compliance level is 0; and sending a reward to the specific sample use data uploaded by the subject;
[0009] S4. Obtaining the second physical examination data of the subject; the physical examination indexes included in the second physical examination data are consistent with the first physical examination data;
[0010] S5. Comparing the first physical examination data and the second physical examination data of the subjects in each compliance level to obtain the physical examination data difference values under different compliance levels; for different compliance levels, comparing each index of each subject in the first and second physical examination to obtain the physical examination data difference of each subject under different compliance levels;
[0011] S6. Weighting the physical examination data difference values under different compliance levels with corresponding weight values to obtain the compliance difference values;
[0012] S7. Obtaining the sample effect data according to the compliance difference values of different groups.
[0013] Preferably, at least three of the compliance levels are included.
[0014] Preferably, before the step of grouping the subjects by double-blind randomization and storing the group to which each subject belongs, the method further comprises:
[0015] Obtaining the identity information and physiological information of the subject;
[0016] Setting the target requirement of the physical examination project.
[0017] Preferably, the identity information includes one or more of name, identity information, occupation, fingerprint, and facial features.
[0018] Preferably, the physiological information includes one or more of gender, age, allergen, medical history, and physical feature index.
[0019] Preferably, the step of grouping the subjects by double-blind randomization comprises:
[0020] According to the target requirement of the project, screening the subjects meeting the target requirement, and grouping the subjects meeting the target requirement by double-blind randomization.
[0021] Preferably, during the execution of the above steps, strict quality control is performed, the process and results are monitored and checked, and personnel responsible for the quality control link are arranged for random inspection and verification.
[0022] In a second aspect, the present application further provides a subject data processing system applied to a physical examination service data platform, the system comprising:
[0023] A grouping module for grouping the subjects by double-blind randomization and storing the group to which each subject belongs;
[0024] A physical examination data module for obtaining the first physical examination data of the subjects;
[0025] The compliance module is configured to obtain sample use data of the subjects, divide the subjects into a plurality of compliance levels based on the sample use data, and determine a weight value corresponding to each compliance level;
[0026] The physical examination data module is further configured to obtain second physical examination data of the subjects;
[0027] The comparison module is configured to compare the first physical examination data and the second physical examination data of the subjects in each compliance level, and obtain physical examination data differences under different compliance levels;
[0028] The calculation module is configured to weight the physical examination data differences under different compliance levels with the corresponding weight values, and obtain compliance differences;
[0029] The analysis module is configured to obtain sample effect data according to the compliance differences of different groups.
[0030] In a third aspect, the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor implements the steps of the above method when executing the computer program.
[0031] The present application has the following beneficial technical effects:
[0032] The subject data processing method provided by the present application can be applied to analyze the influence of a sample of an efficacy product for improving the state of the human body, such as a health food, a health care food or a cosmetic, on the physical condition of a subject. First, the subjects are randomly divided into groups in a double-blind manner, such as a test group, a blank group, a control group, a positive reference group, etc., and the group to which each subject belongs is stored. Then, the first physical examination data of the subjects is obtained, which is the initial physical condition of the subjects, i.e., the physical condition before using the sample. In a period of time thereafter, the subjects in the test group will insist on using the sample, the subjects in the control group will insist on using a placebo sample, and the subjects in the blank group will not use the sample. During this period, the sample use data of the subjects needs to be continuously obtained, the subjects are divided into a plurality of compliance levels based on the sample use data, and a weight value corresponding to each compliance level is determined. The sample use data can reflect the degree of persistence of the subjects in using the sample, i.e., the compliance level. Different weight values are assigned to different compliance levels, so that the physical examination data of the subjects who insist on using the sample has a higher reference value in subsequent physical examination data analysis.
[0033] Then, the second physical examination data of the subjects is acquired, and then the first physical examination data and the second physical examination data of the subjects in each compliance level are compared to obtain physical examination data differences under different compliance levels, and the physical examination data differences under different compliance levels are weighted with corresponding weight values to obtain compliance differences, and sample effect data is obtained according to the compliance differences of different groups. The influence effect of the sample on the physical condition of the human body can be obtained through the compliance differences between the trial group and the reference group (including one or more groups of the control group, the blank group and the positive reference group). The physical examination data is analyzed by referring to the compliance level, and more accurate product sample effects can be obtained, thereby relieving the problem that the analysis of the physical examination data is not accurate enough in the prior art.
[0034] Correspondingly, the subject data processing system, the electronic device and the computer readable storage medium provided by the present application also have the above technical effects. BRIEF DESCRIPTION OF DRAWINGS
[0035] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.
[0036] Figure 1 The flow chart of the subject data processing method provided by the first embodiment of the present application;
[0037] Figure 2 The schematic diagram of the subject data processing system provided by the second embodiment of the present application. DETAILED DESCRIPTION
[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the present application will be described clearly and completely below in combination with the drawings. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.
[0039] The terms "include" and "have" and any variations thereof mentioned in the embodiments of the present application are intended to cover the non-exclusive inclusion. For example, the process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally includes other steps or units not listed, or optionally includes other steps or units inherent to the process, method, product or device.
[0040] The current physical examination data cannot fully reflect the analysis result of physical examination, and the analysis of the physical examination data is not accurate due to the different behavior habits of the physical examination person, and thus the existing technology has the problem of inaccurate analysis of the physical examination data.
[0041] Meanwhile, the qualification certification of various health foods, health care foods, cosmetics and other efficacy products has gradually developed and popularized, and according to the requirements of national regulations, clinical trials for product efficacy verification will be organized, and thus a scientific and reliable evaluation process, management method and management tool for product use effect are needed.
[0042] In view of the above problems, the embodiment of the present application provides a subject data processing method applied to a physical examination service data platform.
[0043] Embodiment one:
[0044] As shown in Figure 1 The subject data processing method provided by the embodiment of the present application comprises the following steps:
[0045] S1: double-blind random grouping of subjects, and storing the group to which each subject belongs.
[0046] The group arrangement in this embodiment will be divided into a trial group and a reference group according to the project requirements, and the reference group can include one or more groups of a control group, a blank group and a positive reference group, that is, from a minimum of two groups to a maximum of four groups. Among them, the subjects in the trial group use the efficacy product verified by the project, the subjects in the control group use a placebo product, the subjects in the blank group do not use any product, and the subjects in the positive reference group use other similar efficacy products.
[0047] Double-blind random grouping is a medical judgment standard, which means that when the subjects are divided into two groups or three groups, they are randomly assigned, and the subjects do not know which group they belong to; at the same time, the staff do not know whether the samples they distribute are real samples or placebos. Therefore, the subjects are "blind" to their own group, and the staff are "blind" to the samples they distribute, so as to realize double-blind random grouping.
[0048] S2: obtaining the first physical examination data of the subject.
[0049] The embodiment of the present application can be applied to analyze the influence of the efficacy product sample of health food, health care food or cosmetic, etc. on the physical condition of the subject, and first obtains and stores the first physical examination data of the subject, which is the initial physical condition of the subject, that is, the physical condition before using the sample.
[0050] The first physical examination data can include height, weight, body fat ratio, blood pressure, blood oxygen, B-ultrasound, electrocardiogram and the like. The categories of physical examination indexes can be determined according to actual needs.
[0051] S3: Obtain sample use data of the subject, divide the subject into multiple compliance levels based on the sample use data, and determine a weight value corresponding to each compliance level.
[0052] After the first physical examination, the subjects are provided with samples. In a period of time, the subjects in the trial group insist on using the samples, the subjects in the control group use placebo products, the subjects in the blank group do not use any products, and the subjects in the positive reference group use other similar efficacy products.
[0053] During this period, the sample use data of the subjects needs to be continuously obtained. The sample use data can generally include log data, picture data or video data, etc. The sample use data can reflect the degree of persistence of the subjects in using the samples, that is, the compliance level. Different compliance levels are assigned different weight values, so that the physical examination data of the subjects who insist on using the samples more have higher reference value in subsequent physical examination data analysis.
[0054] To ensure that the final analysis result has high accuracy, at least three compliance levels should be included, for example, level one, level two and level three. In one possible implementation, the weight value corresponding to the highest compliance level (level one) is 1, the weight value corresponding to the middle compliance level (level two) is a decimal between 0 and 1, and the weight value corresponding to the lowest compliance level (level three) is 0 (equivalent to removing the physical examination data of the lowest compliance level).
[0055] In order to encourage the subjects to better insist on using the samples, rewards can also be sent to the subjects for uploading specific sample use data, for example, uploading photos of taking the samples for 7 consecutive days. Rewards can be issued in the mobile application of the subjects to encourage them to actively participate in feedback.
[0056] S4: Obtain the second physical examination data of the subject.
[0057] The physical examination indexes included in the second physical examination data should be consistent with those in the first physical examination data, so as to facilitate comparison of each index.
[0058] S5: Compare the first physical examination data and the second physical examination data of the subjects in each compliance level, and obtain the physical examination data difference under different compliance levels.
[0059] For different compliance levels, compare each index in the first and second physical examinations of each subject, and obtain the physical examination data difference of each subject under different compliance levels.
[0060] For example, for the analysis of weight loss effect, taking the weight data as the analysis object, the results obtained by the step are as follows: the first level physical examination data difference (weight loss value, unit: kg) is 8, the second level physical examination data difference is 5, and the third level physical examination data difference is 1.
[0061] S6: The physical examination data differences under different compliance levels are weighted with corresponding weight values to obtain a compliance difference.
[0062] For example, the weight value corresponding to the first level is 1, the weight value corresponding to the second level is 0.6, and the weight value corresponding to the third level is 0. The above-mentioned physical examination data differences are weighted and calculated (8*1+5*0.6+1*0) ÷ (1+0.6+0) = 6.875, and the compliance difference is obtained as 6.875.
[0063] In contrast, if the calculation method of the prior art is used, the weight values of the compliance are not considered, and the average of the same three sets of physical examination data differences 8, 5 and 1 is obtained as 4.667. It can be seen that the existing calculation result is greatly affected by the physical examination data with low reference value, and will greatly affect the analysis of the sample effect.
[0064] S7: Obtain sample effect data according to the compliance differences of different groups.
[0065] The compliance difference between the test group and the reference group (one or more groups of control group, blank group, and positive reference group) can obtain the influence effect of the sample on the human body condition. The embodiment of the present application can obtain more accurate product sample effect by analyzing the physical examination data by referring to the compliance level, thereby relieving the problem that the analysis of the physical examination data is not accurate in the prior art.
[0066] In a possible implementation, the step S1 can further include the following steps before the step S1:
[0067] Identity information and physiological information of the subject are obtained. The identity information can include one or more of name, identity information, occupation, fingerprint and facial features; the physiological information can include one or more of gender, age, allergen, medical history and physical feature indicators (such as obesity, anemia, etc.).
[0068] In a possible implementation, the step S1 can further include the following steps before the step S1:
[0069] The target demand of the physical examination project is set. Correspondingly, the double-blind random grouping of the subjects in the step S1 is specifically as follows: according to the set target demand of the project, the subjects meeting the target demand are screened out, and the subjects meeting the standard are double-blind grouped.
[0070] During the implementation of each step, strict quality control is needed, the process and results are monitored, checked and arranged for random inspection and verification by the personnel responsible for the quality control link to ensure the accuracy of the information and data, so as to finally ensure the integrity and effectiveness of the test process.
[0071] Embodiment two:
[0072] As shown in Figure 2 Embodiments of the present application provide a subject data processing system, applied to a physical examination service data platform, the system comprises:
[0073] A grouping module 1 is configured to randomly group subjects in a double-blind manner and store the group to which each subject belongs.
[0074] A physical examination data module 2 is configured to obtain first physical examination data of the subjects.
[0075] A compliance module 3 is configured to obtain sample use data of the subjects, divide the subjects into a plurality of compliance levels based on the sample use data, and determine a weight value corresponding to each compliance level.
[0076] The physical examination data module 2 is further configured to obtain second physical examination data of the subjects.
[0077] A comparison module 4 is configured to compare the first physical examination data and the second physical examination data of the subjects in each compliance level to obtain physical examination data differences under different compliance levels.
[0078] A calculation module 5 is configured to weight the physical examination data differences under different compliance levels with corresponding weight values to obtain compliance differences.
[0079] An analysis module 6 is configured to obtain sample effect data according to the compliance differences of different groups.
[0080] Embodiment three:
[0081] Embodiments of the present application provide an electronic device, comprising a memory and a processor, the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the steps of the method provided in the above embodiment one or embodiment two.
[0082] The subject data processing system and the electronic device provided by the embodiments of the present application have the same technical features as the subject data processing method provided by the above embodiments, so they can also solve the same technical problems and achieve the same technical effects.
[0083] Corresponding to the above method, the embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium stores computer executable instructions, when the computer executable instructions are called and run by a processor, the computer executable instructions cause the processor to run the steps of the above method.
[0084] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementation manners, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.
[0085] For another example, the division of the units is only a logical function division, and in actual implementation, there can be another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some communication interfaces, units or components, which can be electrical, mechanical or other forms.
[0086] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.
[0087] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the prior art that essentially contributes or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.
[0088] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can make modifications or easily think of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed by the present application, or make equivalent replacements to some of the technical features. These modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application. They should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims
1. A method for processing subject data, characterized in that: Applied to a physical examination service data platform, the method includes: S1. Perform double-blind randomization of the subjects and store the group to which each subject belongs; the groups include at least a trial group and a reference group, and the reference group includes one or more of a control group, a blank group, and a positive reference group; S2. Obtain the subject's first physical examination data; the first physical examination data includes height, weight, body fat ratio, blood pressure, blood oxygen, B-ultrasound single, electrocardiogram; S3. Obtaining the subject's sample usage data, including log data, image data, or video data, categorizing the subject into multiple compliance levels based on the sample usage data, and determining a weight value corresponding to each compliance level, where the highest compliance level corresponds to a weight value of 1, intermediate compliance levels correspond to weight values that are decimals between 0 and 1, and the lowest compliance level corresponds to a weight value of 0. A reward is issued for specific sample usage data uploaded by the subject; S4. Obtain the subject's second physical examination data; the second physical examination data includes physical examination indicators consistent with the first physical examination data; S5. Compare the first and second physical examination data of subjects at each compliance level to obtain the difference in physical examination data at different compliance levels; for different compliance levels, compare the various indicators of each subject in the two physical examinations to obtain the difference in physical examination data for each subject at different compliance levels; S6. Weighting the differences in the physical examination data at different compliance levels with corresponding weight values to obtain a compliance difference; S7. Obtain sample effect data based on the difference in compliance among different groups.
2. The method according to claim 1, characterized in that At least three of said compliance levels are included.
3. The method according to claim 1, characterized in that Before the step of double-blind randomization of the subjects and storing the group to which each subject belongs, the method further includes: Obtain the subject's identity and physiological information; Set the target requirements for this physical examination project.
4. The method according to claim 3, characterized in that Identity information includes one or more of name, identity information, occupation, fingerprint and facial features.
5. The method according to claim 3, characterized in that Physiological information includes one or more of gender, age, allergies, medical history, and physical characteristic indicators.
6. The method according to claim 3, characterized in that The steps for double-blind randomization of subjects include: According to the target requirements of the project, subjects who meet the target requirements are screened out and randomized into double-blind groups.
7. The method according to claim 1, characterized in that During the execution of each of the above steps, strict quality control is carried out to monitor and check the process and results, and personnel responsible for quality control are arranged to conduct random inspections and verifications.
8. A subject data processing system, characterized in that: Applied to the physical examination service data platform, the system includes: The grouping module is used to perform double-blind random grouping of subjects and store the group to which each subject belongs; Physical examination data module, used to obtain the subject's first physical examination data; a compliance module, configured to obtain sample usage data of a subject, classify the subject into a plurality of compliance levels based on the sample usage data, and determine a weight value corresponding to each compliance level; The physical examination data module is also used to obtain the subject's second physical examination data; A comparison module is used to compare the first physical examination data and the second physical examination data of subjects at different compliance levels to obtain the difference in physical examination data at different compliance levels; A calculation module, configured to weight the differences in the physical examination data at different compliance levels with corresponding weight values to obtain a compliance difference; The analysis module is used to obtain sample effect data based on the compliance differences of different groups.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.