Data matching optimization system for medical plans of drug clinical trials

Abstract

The present invention relates to the technical field of intelligent medical systems, and in particular to a data matching optimization system for drug clinical trial medical schemes, comprising an intelligent model generation module, a literature retrieval module, an optimization module and a collaboration platform module. The data matching optimization system for drug clinical trial medical schemes of the present invention is ahead of similar products in terms of technical level. The present invention is provided with an optimization module, and by reallocating recommended parameters to targeted data matching optimization personnel, the state of data matching optimization of medical schemes can be effectively monitored, and the efficiency of data matching optimization of drug clinical trial medical schemes can be improved. Its practicality and innovation have been widely recognized by the industry. In the CRO industry, the system of the present invention has become an important tool for improving the quality and efficiency of data matching optimization of drug clinical trial medical schemes. By using the system, high-quality medical schemes can be efficiently completed, thereby accelerating the drug research and development process and bringing more suitable treatment schemes to patients.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent medical systems, and in particular to a data matching optimization system for medical programs of drug clinical trials. Background Art

[0002] Clinical trials are an important procedure for evaluating the safety and efficacy of new drugs. Data matching and optimization of clinical trial protocols are the first step in conducting clinical trials. They are also very complex and important. Good data matching and optimization of protocols help ensure the scientificity, compliance and feasibility of the trials. Therefore, improving the efficiency of data matching and optimization in drug clinical trial medicine is an urgent problem to be solved.

[0003] Prior art patent number: CN116403672A, the embodiments disclosed in the prior art relate to methods, devices and media for data matching optimization of medical reports. According to various embodiments, based on a first user input in an editing interface of a medical report, a target medical report model associated with the first user input is determined; the target medical report model is presented in the editing interface; based on a second user input for editing the target medical report model in the editing interface, the medical content associated with the text sequence indicated by the second user input is determined; and the medical content is presented in the editing interface. Through this scheme, the target medical report model can be determined and presented based on the first user input, and the medical content for editing the target medical report model can be determined and presented based on the second user input, thereby improving the efficiency and accuracy of the user's data matching optimization of medical reports. However, the problem of low efficiency of medical data matching optimization caused by the non-compliance of the intelligent generation of medical report models with standards is not solved. Summary of the invention

[0004] To this end, the present invention provides a data matching optimization system for a medical plan for a drug clinical trial, so as to overcome the problem of low efficiency of data matching optimization for a medical plan for a drug clinical trial in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides a data matching optimization system for a drug clinical trial medical plan, comprising:

[0006] A model generation module, which generates a model for generating a medical plan for a drug clinical trial by training personnel with characteristics of the drug clinical trial;

[0007] A literature retrieval module, which is connected to the model generation module and is used to retrieve literature based on the medical plan output by the drug clinical trial medical plan generation model;

[0008] The optimization module is connected to the model generation module and the literature retrieval module respectively, and is used to determine whether the data matching optimization of the medical scheme meets the standards according to the proportion of the number of differences in the drug clinical trials, and, when it is determined that it does not meet the standards, redetermine the number of vocabulary selections, or redetermine the order of sentence structure arrangement, or redetermine the number of corresponding logical relationships,

[0009] And, based on the literature retrieved by the literature retrieval module, the benchmark for the proportion of differences in the number of drug clinical trials is re-determined.

[0010] Furthermore, the optimization module preliminarily determines that the data matching optimization of the medical scheme does not meet the standards based on the proportion of indifferent numbers of drug clinical trials, and makes a secondary determination based on the retrieved literature; when it is determined that the data matching optimization of the medical scheme does not meet the standards, the reason for non-compliance is re-determined based on the difference in the proportion of indifferent numbers of drug clinical trials.

[0011] Furthermore, when the optimization module preliminarily determines that the data matching optimization of the medical scheme does not meet the standards, it re-determines the benchmark for the proportion of indifferent quantities in clinical trials of each drug based on the retrieved literature.

[0012] Further, when the optimization module determines that the data matching optimization of the medical scheme does not meet the standards, the optimization module determines the reason why the data matching optimization of the medical scheme does not meet the standards based on the difference between the proportion of the number of indifferences in the drug clinical trials and the preset first drug clinical trials The proportion of the number of indifferences does not meet the standards, wherein:

[0013] If the difference is less than or equal to a preset first difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical scheme does not meet the standard is a problem of the number of vocabulary selections, and redetermines the number of vocabulary selections;

[0014] If the difference is greater than a preset first difference benchmark and greater than a preset second difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical scheme does not meet the standard is a problem of the sentence structure arrangement order, and redefines the sentence structure arrangement order rule;

[0015] If the difference is greater than a preset second difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical plan does not meet the standards is the problem of the corresponding quantity of the logical relationship. The lack of training based on the intelligent model considering environmental factors leads to deviations in the generation of the intelligent model, and the proportion of training data for non-medical factors is redetermined.

[0016] Furthermore, the optimization module is provided with several correction methods for the drug clinical trial difference quantity ratio benchmark based on the difference between the number of retrieved documents and a preset quantity benchmark, and the correction range of each correction method is different.

[0017] Furthermore, the optimization module is provided with several adjustment methods for the number of vocabulary selections based on the difference between the proportion of indifference quantity in drug clinical trials and a preset second benchmark for the proportion of indifference quantity in drug clinical trials, and the adjustment range of the vocabulary selection number in each adjustment method is different.

[0018] Furthermore, the optimization module is provided with several adjustment methods for the sentence structure arrangement order rules based on the ratio of the proportion of indifference numbers in drug clinical trials to a preset first drug clinical trial indifference number proportion benchmark, and the adjustment range of each adjustment method for the sentence structure arrangement order rules is different.

[0019] Furthermore, the optimization module is provided with several adjustment methods for the proportion of training data volume of non-medical factors based on the ratio of the proportion of indifference quantity in drug clinical trials to a preset benchmark for the proportion of indifference quantity in second drug clinical trials, and the adjustment range of each adjustment method for the proportion of training quantity of non-medical factors is different; the proportion of training quantity of non-medical factors is the ratio of the number of trainings of non-medical factors to the total number of trainings of environmental factors.

[0020] Furthermore, the formula for the proportion of indifferent drug clinical trials used by the optimization module is:

[0021]

[0022] Among them, θ is the proportion of differences in drug clinical trials, m is the number of people whose symptoms match the medical plan drug clinical trial training; M is the total number of people participating in drug clinical trial training, and i represents the number of clinical trials of the same drug, i=1, 2, 3.

[0023] Further, the optimization module maintains and continues to monitor the data after the optimization module recommends the parameters to be updated.

[0024] Compared with the prior art, the beneficial effect of the present invention is that the present invention integrates modern computer technology, artificial intelligence and big data analysis, and provides a comprehensive and highly integrated solution for medical researchers. The core features include intelligent model generation module, literature retrieval module, optimization module and collaboration platform module. The data matching optimization system for medical plans for drug clinical trials of the present invention is ahead of similar products in terms of technical level, and its practicality and innovation have been widely recognized by the industry; in the CRO industry, the system of the present invention has become an important tool for improving the quality and efficiency of data matching optimization of medical plans for drug clinical trials; by using this system, the research team can complete high-quality medical plans faster, thereby accelerating the process of drug development and bringing more suitable treatment plans to patients. The present invention provides an optimization module in the data matching optimization system of the medical scheme for drug clinical trials. By reallocating recommended parameters to data matching optimization personnel, the status of the data matching optimization of the medical scheme can be effectively monitored, and the stability of the data matching optimization of the medical scheme for drug clinical trials can be ensured. At the same time, when the optimization module determines that the data matching optimization of the medical scheme does not meet the standards, it can redetermine the reasons for not meeting the standards and make corresponding allocation adjustments, thereby reducing the occurrence of misjudgments, saving time for finding reasons, and improving the efficiency of the data matching optimization of the medical scheme. Moreover, when it is preliminarily determined that the data matching optimization of the medical scheme does not meet the standards, a secondary judgment is made, thereby reducing the occurrence of situations where the actual situation does not meet the expectations, thereby further improving the efficiency of the data matching optimization of the medical scheme.

[0025] Furthermore, the present invention can quickly determine the reasons for non-compliance with the standards by determining whether the data matching optimization of the medical plan meets the standards, and can quickly make a secondary determination when it is initially determined that the data matching optimization of the medical plan does not meet the standards, thereby reducing the occurrence of situations where the actual medical data matching optimization plan is inconsistent with the expected medical data matching optimization plan, and improving the efficiency of data matching optimization of medical plans for drug clinical trials.

[0026] Furthermore, the present invention can quickly re-determine whether the data matching optimization of the medical plan meets the standards through the number of retrieved documents, and when it is determined that the data matching optimization of the medical plan does not meet the standards, it can quickly redetermine the benchmark for the proportion of the number of indifferences in clinical trials of each drug, thereby improving the accuracy of data matching optimization of medical plans for drug clinical trials.

[0027] Furthermore, the present invention can quickly determine the number of vocabulary choices, sentence structure arrangement rules and the proportion of training data of non-medical factors of the recommended data matching optimization module through the reasons why the data matching optimization of the medical plan does not meet the standards, thereby saving the time for finding the reasons and the adjustment time of the corresponding modules, and improving the efficiency of data matching optimization of the medical plan for drug clinical trials.

[0028] Furthermore, the present invention can accurately determine whether the data matching optimization of the medical plan meets the standards by modifying the benchmark of the proportion of the number of indifferences in each drug clinical trial, thereby improving the accuracy of the data matching optimization of the medical plan for the drug clinical trial of the present invention.

[0029] Furthermore, the present invention further reduces the occurrence of data matching optimization of medical plans that are inconsistent with expectations by adjusting the number of vocabulary selections in the optimization module, adjusting the sentence structure order rules, and adjusting the proportion of training quantity of non-medical factors, thereby effectively improving the efficiency of data matching optimization of medical plans for drug clinical trials of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A structural block diagram of a data matching optimization system for a medical plan for a drug clinical trial of the present invention;

[0031] Figure 2 A flow chart for determining whether the data matching optimization of the medical scheme of the present invention meets the standards;

[0032] Figure 3 A secondary determination flow chart for whether the data matching optimization of the medical scheme of the present invention meets the standards;

[0033] Figure 4 A flow chart for determining reasons for non-compliance with standards for data matching optimization of the medical scheme of the present invention. DETAILED DESCRIPTION

[0034] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0035] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0036] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] It should be noted that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical test data and corresponding historical test results of the system of the present invention in the six months before this test. The system of the present invention comprehensively determines the values ​​of the preset parameter benchmarks for this test based on the results of the cumulative tests in the previous six months before this test. It can be understood by those skilled in the art that the determination method of the system of the present invention for a single parameter mentioned above can be to select the value with the highest proportion as the preset benchmark parameter according to the data distribution, use weighted summation to use the obtained value as the preset benchmark, use the value of each historical data as the preset benchmark parameter or other selection methods, as long as the system of the present invention can clearly define the different specific situations in the single determination process through the obtained values.

[0038] See also Figure 1 As shown, it is a structural block diagram of the data matching optimization system of the medical scheme of the drug clinical trial of the present invention. The data matching optimization system of the medical scheme of the drug clinical trial of the present invention includes:

[0039] a model generation module, which generates a model for generating a medical protocol for a drug clinical trial by training personnel with characteristics of the drug clinical trial, wherein the embodiment is based on the drug name, disease area, trial stage, total number of people participating in the drug clinical trial, and number of people matching the drug symptoms in the drug clinical trial;

[0040] A literature retrieval module, which is connected to the model generation module and is used to retrieve literature based on the medical plan output by the drug clinical trial medical plan generation model;

[0041] The optimization module is connected to the model generation module and the literature retrieval module respectively, and is used to determine whether the data matching optimization of the medical scheme meets the standards according to the proportion of the number of differences in the drug clinical trials, and, when it is determined that it does not meet the standards, redetermine the number of vocabulary selections, or redetermine the order of sentence structure arrangement, or redetermine the number of corresponding logical relationships,

[0042] And, based on the literature retrieved by the literature retrieval module, the benchmark for the proportion of differences in the number of drug clinical trials is re-determined.

[0043] When the data matching optimization system of the medical scheme of the drug clinical trial is running, the intelligent model generation module allocates the corresponding structure in the intelligent model to the data matching optimization personnel according to the drug type, disease field and trial stage; the structure includes a document retrieval module and an optimization module, the document retrieval module is connected to the intelligent model generation module, and adopts big data analysis technology to intelligently retrieve the retrieved documents in authoritative databases at home and abroad and record the number of retrieved documents; the optimization module is connected to the intelligent model generation module, and adopts artificial intelligence technology to reallocate the recommended parameters to the data matching optimization personnel, and the recommended parameters include the number of vocabulary selections, the order of sentence structure arrangement and the number of corresponding logical relationships; then, the optimization module is respectively connected to the intelligent model generation module and the document retrieval module, and the intelligent model generation ... The document retrieval module is connected to the document retrieval module to perform quality assessment on the medical scheme in the data matching optimization of the scheme in real time and calculate the proportion of indifference in the number of drug clinical trials. Based on the proportion of indifference in the number of drug clinical trials, it is determined whether the data matching optimization of the medical scheme meets the standards and the corresponding recommended parameters are reallocated and adjusted. If it is determined that the data matching optimization of the medical scheme does not meet the standards, the corresponding recommended parameters are re-determined; if it is preliminarily determined that the data matching optimization of the medical scheme does not meet the standards and the secondary determination does not meet the standards, the proportion benchmark of indifference in the number of clinical trials of each drug is re-determined; finally, the collaboration platform module is connected to the optimization module to support multi-person online collaboration, and personnel from different regions, departments and professional backgrounds jointly complete the data matching optimization of the medical scheme.

[0044] See also Figure 2 As shown, it is a flow chart for determining whether the data matching optimization of the medical scheme of the present invention meets the standard. The optimization module of the present invention is provided with a first drug clinical trial number ratio and a second drug clinical trial number ratio. Whether the data matching optimization of the medical scheme meets the standard is determined based on the number ratio of drug clinical trials, wherein:

[0045] If the proportion of indifferent numbers of drug clinical trials is less than or equal to a preset first benchmark for the proportion of indifferent numbers of drug clinical trials, the optimization module determines that the data matching optimization of the medical scheme does not meet the standards, and redetermines the reasons for not meeting the standards based on the difference in the proportion of indifferent numbers of drug clinical trials;

[0046] If the proportion of indifferent clinical trials of drugs is greater than the preset first proportion of indifferent clinical trials of drugs and is less than or equal to the second proportion of indifferent clinical trials of drugs, the optimization module preliminarily determines that the data matching optimization of the medical scheme does not meet the standards and makes a secondary determination based on the number of retrieved documents;

[0047] If the proportion of indifference in the number of drug clinical trials is greater than a preset second drug clinical trial proportion benchmark, the optimization module determines that the data matching optimization of the medical scheme meets the standards and continues monitoring.

[0048] Specifically, the proportion of indifferent results in the drug clinical trials described in the embodiment is used as the evaluation value of the model generation module, and the proportion of indifferent results in the drug clinical trials is obtained by calculation using a formula.

[0049] See also Figure 3 As shown, it is a secondary determination flow chart of whether the data matching optimization of the medical scheme of the present invention meets the standard. The optimization module of the present invention is provided with a reference quantity benchmark. When the data matching optimization of the medical scheme is initially determined not to meet the standard, a secondary determination is made based on the retrieved references whether the data matching optimization of the medical scheme meets the standard, wherein:

[0050] If the number of retrieved documents is less than or equal to the preset document quantity benchmark, the optimization module determines that the data matching optimization of the medical scheme does not meet the standard and redetermines the proportion benchmark of the number of indifferent drug clinical trials;

[0051] If the number of retrieved documents is greater than a preset document number benchmark, the optimization module determines for the second time that the data matching optimization of the medical scheme meets the standard and continues monitoring.

[0052] See also Figure 4 As shown, it is a flow chart for determining the reason why the data matching optimization of the medical scheme of the present invention does not meet the standards. The optimization module of the present invention is provided with a first proportion difference benchmark and a second proportion difference benchmark. When it is determined that the data matching optimization of the medical scheme does not meet the standards, the reason why the data matching optimization of the medical scheme does not meet the standards is determined based on the difference between the proportion of the number of indifferent clinical trials of drugs and the preset first proportion benchmark of the number of indifferent clinical trials of drugs, wherein:

[0053] If the difference in proportion is less than or equal to a preset first difference in proportion benchmark, the optimization module determines that the reason why the data matching optimization of the medical scheme does not meet the standard is a problem of the number of vocabulary selections, and redetermines the number of vocabulary selections;

[0054] If the difference in proportion is greater than a preset first proportion difference benchmark and greater than a preset second proportion difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical scheme does not meet the standard is a problem of the sentence structure arrangement order, and redefines the sentence structure arrangement order rule;

[0055] If the difference in proportion is greater than a preset second proportion difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical plan does not meet the standards is the problem of the corresponding quantity of the logical relationship. The lack of training based on the intelligent model considering environmental factors leads to deviations in the generation of the intelligent model, and the proportion of training data for non-medical factors is re-determined; the corresponding quantity of the logical relationship described in the embodiment represents the symptoms corresponding to the performance of the drug in the medical plan of this clinical trial, and the correspondence between the drug and the symptom is logically expressed, and the quantity corresponding to the drug and the symptom represents the corresponding quantity of the logical relationship.

[0056] Specifically, the optimization module of the present invention is provided with a first document quantity difference benchmark and a second document quantity difference benchmark, and based on the difference between the number of retrieved documents and the preset quantity benchmark, several correction methods for the proportion benchmark of the number of indifferences in clinical trials of each drug are provided, wherein:

[0057] If the difference is less than or equal to the preset first document quantity difference benchmark, the optimization module uses the first correction coefficient α1 to correct the preset indifferent quantity proportion benchmark of each drug clinical trial to a corresponding value;

[0058] If the difference is greater than the preset first literature quantity difference benchmark and less than or equal to the preset second literature quantity difference benchmark, the optimization module uses the second correction coefficient α2 to correct the preset indifferent quantity proportion benchmark of each drug clinical trial to the corresponding value;

[0059] If the difference is greater than the preset second literature quantity difference benchmark, the optimization module uses the third correction coefficient α3 to correct the preset indifferent quantity proportion benchmark of each drug clinical trial to a corresponding value;

[0060] Among them, the indifference quantity ratio benchmark of each drug clinical trial includes a preset first drug clinical trial indifference quantity ratio benchmark and a preset second drug clinical trial indifference quantity ratio benchmark; the correction formula adopted in the present invention is, T=t×αn, wherein, t is the preset drug clinical trial indifference quantity ratio benchmark before correction, T is the preset drug clinical trial indifference quantity ratio benchmark after correction, n is 1, 2, 3..., αn is the correction coefficient of the preset indifference quantity ratio of each drug clinical trial, and this embodiment adopts, α1=0.99; α2=0.97; α3=0.95; the present invention can reduce the occurrence of deviations in the preset drug clinical trial indifference quantity ratio benchmark caused by too few retrieved documents through the correction of the preset indifference quantity ratio benchmark of each drug clinical trial, thereby further improving the efficiency of data matching optimization of medical schemes.

[0061] Specifically, the optimization module of the present invention is provided with a first difference benchmark of the number of vocabulary selections and a second difference benchmark of the number of vocabulary selections, and is provided with several adjustment methods for the number of vocabulary selections based on the difference between the proportion of the number of indifferent drug clinical trials and the preset second proportion benchmark of the number of indifferent drug clinical trials, wherein:

[0062] If the difference is less than or equal to a preset first difference benchmark, the optimization module uses a first adjustment coefficient β1 to adjust the number of vocabulary selections to a corresponding value;

[0063] If the difference is greater than a preset first difference benchmark and less than or equal to a preset second difference benchmark, the optimization module uses a second adjustment coefficient β2 to adjust the number of vocabulary selections to a corresponding value;

[0064] If the difference is greater than a preset second difference benchmark, the optimization module uses a third adjustment coefficient β3 to adjust the number of vocabulary selections to a corresponding value;

[0065] The adjustment formula adopted by the present invention is Q=q×βn, where q is the number of preset vocabulary selections before adjustment, Q is the number of preset vocabulary selections after adjustment, n is 1, 2, 3..., βn is the adjustment coefficient of the preset vocabulary selection number, and this embodiment adopts β1=1.12; β2=1.16; β3=1.19. The more vocabulary selections in the intelligent model of the present invention, the closer to the expected result.

[0066] Specifically, the optimization module of the present invention is provided with a first ratio benchmark and a second ratio benchmark for the sentence structure arrangement order rule, and is provided with several adjustment methods for the sentence structure arrangement order rule based on the ratio of the proportion of the number of indifferent drug clinical trials to the preset first proportion benchmark of the number of indifferent drug clinical trials, wherein:

[0067] If the ratio is less than or equal to a preset first ratio benchmark, the optimization module uses the first rule to adjust the sentence structure arrangement order rule to a corresponding value;

[0068] If the ratio is greater than a preset first ratio benchmark and less than or equal to a preset second ratio benchmark, the optimization module uses a second rule to adjust the sentence structure arrangement order rule to a corresponding value;

[0069] If the ratio is greater than a preset second ratio benchmark, the optimization module uses a third specification to adjust the sentence structure arrangement order rule to a corresponding value;

[0070] In this example, the first rule is to arrange the order according to: keyword 1 + keyword 2 + keyword 3; the second rule is to arrange the order according to: keyword 2 + keyword 3 + keyword 1; the third rule is to arrange the order according to: keyword 3 + keyword 1 + keyword 2. The structure of the keywords is arranged in a sentence structure that can be combined and preset in the intelligent module.

[0071] Specifically, the optimization module is provided with a first ratio benchmark and a second ratio benchmark of the training quantity ratio of non-medical factors, and is provided with several adjustment methods for the training data quantity ratio of non-medical factors based on the ratio of the quantity ratio of indifferent drug clinical trials to the preset second quantity ratio benchmark of indifferent drug clinical trials, wherein:

[0072] If the ratio is less than or equal to a preset first ratio benchmark, the optimization module uses the first ratio adjustment coefficient γ1 to adjust the training quantity ratio of non-medical factors to a corresponding value;

[0073] If the ratio is greater than a preset first ratio benchmark and less than or equal to a preset second ratio benchmark, the optimization module uses a second ratio adjustment coefficient γ2 to adjust the ratio of the training data volume of non-medical factors to a corresponding value;

[0074] If the ratio is greater than a preset second ratio benchmark, the optimization module uses a third ratio adjustment coefficient γ3 to adjust the ratio of the training data volume of non-medical factors to a corresponding value;

[0075] The adjustment formula adopted by the present invention is R=r×γn, wherein r is the proportion of the training data volume of the preset non-medical factors before adjustment, R is the proportion of the training data volume of the preset non-medical factors after adjustment, n is 1, 2, 3..., γn is the adjustment coefficient of the proportion of the training data volume of the preset non-medical factors, and this embodiment adopts γ1=0.92; γ2=0.96; γ3=0.98; this embodiment adopts an artificial intelligence model for model training, and the training based on environmental factors lacks the use of the type of training objects to expand the calculation of the proportion of the training data volume of non-medical factors; the proportion of the training quantity of non-medical factors is the ratio of the training quantity of non-medical factors to the total number of training quantity of environmental factors.

[0076] Specifically, the formula for the proportion of indifferent drug clinical trials used by the optimization module is:

[0077]

[0078] Among them, θ is the proportion of the number of people with no difference in drug clinical trials, m is the number of people with no difference in medical scheme participating in drug clinical trial training; M is the total number of people participating in drug clinical trial training, i represents the number of clinical trials of the same drug, i=1, 2, 3; the number of people with no difference in drug clinical trial training represents the sum of the people whose symptoms participating in drug clinical trial training are not different from those in the model generation module.

[0079] Specifically, the optimization module of the present invention maintains and continues to monitor the data after the parameters recommended by the optimization module are updated.

[0080] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data matching optimization system for drug clinical trial medical plan, characterized in that: include: A model generation module, which generates a model for generating a medical plan for a drug clinical trial by training personnel with characteristics of the drug clinical trial; A literature retrieval module, which is connected to the model generation module and is used to retrieve literature based on the medical plan output by the drug clinical trial medical plan generation model; The optimization module is connected to the model generation module and the literature retrieval module respectively, and is used to determine whether the data matching optimization of the medical scheme meets the standards according to the proportion of the number of differences in the drug clinical trials, and, when it is determined that it does not meet the standards, redetermine the number of vocabulary selections, or redetermine the order of sentence structure arrangement, or redetermine the number of corresponding logical relationships, and, re-determining the benchmark for the proportion of differences in the number of drug clinical trials based on the literature retrieved by the literature retrieval module; The optimization module preliminarily determines that the data matching optimization of the medical scheme does not meet the standards based on the proportion of the number of indifferent drug clinical trials, and makes a secondary determination based on the retrieved literature; when it is determined that the data matching optimization of the medical scheme does not meet the standards, the reason for not meeting the standards is re-determined based on the difference in the proportion of the number of indifferent drug clinical trials; When the optimization module preliminarily determines that the data matching optimization of the medical scheme does not meet the standards, and then re-determines the proportion benchmark of the number of indifferent clinical trials of each drug based on the retrieved literature when the data matching optimization of the medical scheme does not meet the standards; The formula for the proportion of indifferent drug clinical trials used by the optimization module is: Among them, θ is the proportion of differences in drug clinical trials, m is the number of people whose symptoms match those in the medical scheme drug clinical trial training; M is the total number of people participating in drug clinical trial training, i represents the number of clinical trials of the same drug, i = 1, 2, 3; When the optimization module determines that the data matching optimization of the medical scheme does not meet the standards, the optimization module determines the reason why the data matching optimization of the medical scheme does not meet the standards based on the difference between the proportion of the number of indifferences in the drug clinical trials and the preset first drug clinical trials The proportion of the number of indifferences in the benchmark is determined, wherein: If the difference is less than or equal to a preset first difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical scheme does not meet the standard is a problem of the number of vocabulary selections, and redetermines the number of vocabulary selections; If the difference is greater than a preset first difference benchmark and greater than a preset second difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical scheme does not meet the standard is a problem of the sentence structure arrangement order, and redefines the sentence structure arrangement order rule; If the difference is greater than a preset second difference benchmark, the optimization module determines that the reason why the data matching optimization of the medical plan does not meet the standards is the problem of the corresponding quantity of the logical relationship. The lack of training based on the intelligent model considering environmental factors leads to deviations in the generation of the intelligent model, and the proportion of training data for non-medical factors is redetermined.

2. The data matching optimization system for drug clinical trial medical plan according to claim 1, characterized in that: The optimization module is provided with several correction methods for the drug clinical trial difference quantity ratio benchmark based on the difference between the number of retrieved documents and the preset quantity benchmark, and the correction range of each correction method is different.

3. The data matching optimization system for drug clinical trial medical scheme according to claim 2, characterized in that: The optimization module is provided with several adjustment methods for the number of vocabulary selections based on the difference between the proportion of indifference quantity in drug clinical trials and a preset second drug clinical trial indifference quantity proportion benchmark, and the adjustment range of the vocabulary selection number in each adjustment method is different.

4. The data matching optimization system for drug clinical trial medical scheme according to claim 3, characterized in that: The optimization module is provided with several adjustment methods for the sentence structure arrangement order rules based on the ratio of the proportion of indifference numbers in drug clinical trials to a preset first drug clinical trial indifference number proportion benchmark, and the adjustment range of each adjustment method for the sentence structure arrangement order rules is different.

5. The data matching optimization system for drug clinical trial medical plan according to claim 4, characterized in that: The optimization module is provided with several adjustment methods for the proportion of training data volume of non-medical factors based on the ratio of the proportion of indifference quantity in drug clinical trials to a preset benchmark for the proportion of indifference quantity in second drug clinical trials, and the adjustment range of each adjustment method for the proportion of training quantity of non-medical factors is different; the proportion of training quantity of non-medical factors is the ratio of the number of training of non-medical factors to the total number of training of environmental factors.

6. The data matching optimization system for drug clinical trial medical scheme according to claim 5, characterized in that: The optimization module persists and continues to monitor the data after the optimization module recommends the parameters to be updated.

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