Data management method and system for medical clinical research

Abstract

The present application relates to the field of data processing technology, and provides a data management method and system for medical clinical research. The method includes obtaining data during the clinical research process, and performing structured processing, integration, and collection on the data during the clinical research process to generate the clinical research database; obtaining a clinical research information set of a target disease in the clinical research database, and determining multiple target symptoms of the target disease based on the clinical research information set; for each target symptom, obtaining historical research information of the target disease based on big data, and determining a research potential coefficient of the target disease based on the historical research information; determining a research topic for the target disease based on each research potential coefficient; obtaining literature information related to the research topic in a literature database; and generating a research plan for the research topic based on the clinical research information set and the literature information. The method can efficiently select research topics for target diseases and can improve the effectiveness of research topics.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data management method and system for medical clinical research. Background Art

[0002] Medical clinical research is an indispensable part of the medical field. Through in-depth research on diseases and accumulation of clinical experimental data, medical clinical research provides important support for the development of new drugs and the formulation of disease treatment plans. However, there are many problems in the traditional management of medical clinical research data. In the process of selecting clinical research topics, it is difficult to efficiently and accurately evaluate the innovation, scientificity, and whether it meets clinical needs; the efficiency of clinical research program design is low; the large amount of research data generated during the research execution forms a "data island" effect, which is not integrated and fully mined, resulting in a huge waste of resources. These problems affect the research efficiency and the efficient use of research data. Summary of the invention

[0003] The present application provides a data management method and system for medical clinical research to solve the problems raised by the above-mentioned background technology.

[0004] In a first aspect, the present application provides a data management method for medical clinical research, comprising:

[0005] Acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom;

[0006] For each of the target pathologies, acquiring historical research information of the target pathologies based on big data, and determining a research potential coefficient of the target pathologies based on the historical research information;

[0007] Determining a research topic for the target disease based on each of the research potential coefficients;

[0008] Acquire target scientific research literature information related to the research topic in a scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information;

[0009] Conduct clinical research on the research topic based on the research plan, collect clinical research data related to the clinical research, and store the clinical research data in the clinical research information set.

[0010] In a second aspect, the present application provides a data management system for medical clinical research, comprising:

[0011] A first acquisition module is used to acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom;

[0012] A second acquisition module is used to acquire, for each of the target pathologies, historical research information of the target pathologies based on big data, and determine a research potential coefficient of the target pathologies based on the historical research information;

[0013] A determination module, for determining a research topic for the target disease based on each of the research potential coefficients;

[0014] A generation module, used to obtain target scientific research literature information related to the research topic in a scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information;

[0015] An execution module is used to conduct clinical research on the research topic based on the research plan, collect clinical research data related to the clinical research, and store the clinical research data in the clinical research information set.

[0016] The present application provides a data management method and system for medical clinical research. The method includes: obtaining a clinical research information set of a target disease in a clinical research database, and determining the target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom; for each target symptom, obtaining the historical research information of the target symptom based on big data, and determining the research potential coefficient of the target symptom based on the historical research information; determining the research topic of the target disease based on each research potential coefficient; obtaining the target scientific research literature information related to the research topic in the scientific research literature database, and generating the research plan of the research topic based on the clinical research information set and the target scientific research literature information; conducting clinical research on the research topic based on the research plan, collecting clinical research data related to the clinical research, and storing the clinical research data in the clinical research information set. The method, on the one hand, can efficiently, scientifically and accurately select research topics for the target disease, and on the other hand, fully integrates and mines the clinical research data, avoids the waste of clinical research data resources, and helps prevent a large amount of clinical research data from forming a "data island" effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0018] Figure 1 A flowchart of a data management method for medical clinical research provided in an embodiment of the present application;

[0019] Figure 2 A schematic block diagram of the structure of a data management system for medical clinical research provided in an embodiment of the present application;

[0020] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0023] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] Medical clinical research is an indispensable part of the medical field. Medical clinical research provides important support for the development of new drugs and the formulation of disease treatment plans through in-depth research on diseases and accumulation of clinical experimental data. However, there are many problems in the traditional management of medical clinical research data. It is difficult to efficiently and accurately evaluate the innovation, scientificity, and whether it meets clinical needs in the process of clinical research topic selection; the efficiency of clinical research program design is low; the large amount of research data generated during the research execution forms a "data island" effect, which is not integrated and fully mined, resulting in a huge waste of resources, etc. These problems affect the research efficiency and the efficient use of research data. To this end, this application provides a data management method and system for medical clinical research to solve the above problems.

[0026] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] See also Figure 1 , Figure 1 A flow chart of a data management method for medical clinical research provided in an embodiment of the present application, such as Figure 1 As shown, the data management method for medical clinical research provided in the embodiment of the present application includes steps S100 to S500.

[0028] Step S100: Acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom.

[0029] Step S200: For each of the target pathologies, historical research information of the target pathologies is obtained based on big data, and a research potential coefficient of the target pathologies is determined based on the historical research information.

[0030] Step S300: determining a research topic for the target disease based on each of the research potential coefficients.

[0031] Step S400: Obtain target scientific research literature information related to the research topic in a scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information.

[0032] Step S500: Conduct clinical research on the research topic based on the research plan, collect clinical research data related to the clinical research, and store the clinical research data in the clinical research information set.

[0033] In this embodiment, as described in step S100 above, a clinical research information set of a target disease is obtained in a clinical research database, and target symptom information of the target disease is determined based on the clinical research information set. Specifically, first, an identification image and an identification code of the target symptom are obtained, and a target matrix is ​​generated based on the identification image and the identification code, and each patient information set in the clinical research database is decrypted based on the target matrix, and the patient information set that is successfully decrypted is determined to be the clinical research information set, and then, for each patient in the clinical research information set, the physiological health information of the patient is determined in the clinical research information set, and for each patient, based on the physiological health information corresponding to the patient, it is determined whether the target disease is the only disease of the patient, and for each patient, if the target disease is the only disease of the patient, the symptom of the patient is determined to be an intermediate target symptom, and finally, all the intermediate target symptom are removed to obtain the target symptom information. Wherein, the target disease refers to the name of the disease, such as hypertension, diabetes, myocardial infarction and cold, and the target symptom refers to the symptom of the disease, such as dizziness, cough, palpitation and shortness of breath. It can be understood that, before step S100, the method further includes acquiring data during the clinical research process, and performing structured processing, integration, and collection on the data during the clinical research process to generate the clinical research database.

[0034] As described in the above step S200, after obtaining the target pathology information, for each of the target pathologies, the historical research information of the target pathology is obtained based on big data, and the research potential coefficient of the target pathology is determined based on the historical research information. Specifically, for each of the target pathologies, first, the target scientific research literature information related to the target pathology is obtained in the scientific research literature database, and the target medical record information of the patient related to the target pathology is obtained in the patient medical information database of the target disease, and the historical research information is generated based on the target scientific research literature information and the target medical record information, and then the research potential coefficient of the target pathology is determined based on the historical research information. Wherein, the target scientific research literature information includes a plurality of target scientific research literatures, and the target medical record information includes a plurality of medical records, and each medical record corresponds to a patient.

[0035] As described in step S300, after obtaining a plurality of research potential coefficients, a research topic for the target disease is determined based on each of the research potential coefficients. Specifically, the relationship between the target disease corresponding to the maximum research potential coefficient among the research potential coefficients and the target disease is determined as the research topic.

[0036] As described in the above step S400, after obtaining the research topic, target scientific research literature information related to the research topic is obtained in the scientific research literature database, and a research plan for the research topic is generated based on the clinical research information set and the target scientific research literature information. Specifically, first, the target scientific research literature information related to the research topic is obtained in the scientific research literature database, and then the research plan for the research topic is generated based on the clinical research information set and the target scientific research literature information.

[0037] As described in the above step S500, after obtaining the research scheme, a clinical study is conducted on the research topic based on the research scheme, and clinical research data related to the clinical study is collected, and the clinical research data is stored in the clinical research information set. Specifically, first, a clinical study is conducted on the research topic based on the research scheme, and clinical research data related to the clinical study is collected throughout the entire process of studying the research topic, and then the clinical research data is stored in the clinical research information set.

[0038] The method provided in this embodiment, on the one hand, can efficiently, scientifically and accurately select research topics for the target disease; on the other hand, it fully integrates and mines the clinical research data, avoids the waste of clinical research data resources, and helps prevent a large amount of clinical research data from forming a "data island" effect.

[0039] In some embodiments, the clinical research database includes multiple patient information sets, each of which is provided with an encryption password, and obtaining the clinical research information set of the target disease in the clinical research database includes the following steps:

[0040] Acquire an identification image and an identification code of the target disease;

[0041] Segmenting the identification image based on a preset image segmentation rule to obtain a plurality of sub-images;

[0042] Input each of the sub-images into a preset image feature extraction model to obtain a feature vector corresponding to each of the sub-images; wherein the image feature extraction model includes an input layer, a feature extraction layer, a feature vector generation layer and an output layer;

[0043] Based on a preset hash algorithm, the hash values ​​corresponding to the feature vectors are calculated respectively to obtain multiple hash values; wherein each of the hash values ​​includes the same number of characters;

[0044] For each of the hash values, obtain a first target number corresponding to the hash value; wherein the first target number is the sum of the first number and the last number in the hash value; for example, if the hash value is S4HU7M2, then the first target number corresponding to the hash value is 6; for another example, if the hash value is U9QN3Y8, then the first target number corresponding to the hash value is 17;

[0045] Obtain a second target number corresponding to the identification code; wherein the second target number is the product of the first number and the last number in the identification code; for example, if the identification code is Q6P3MT6G, then the first target number is 36; for another example, if the identification code is KY53MP4G, then the first target number is 20;

[0046] For each of the first target numbers, compare the first target number with the second target number, and when the first target number is smaller than the second target number, determine the hash value corresponding to the first target number as the target hash value;

[0047] Arranging the target hash values ​​in order from top to bottom based on the first target numbers corresponding to the target hash values ​​to obtain an initial matrix; wherein, in the initial matrix, the first target numbers corresponding to the hash values ​​decrease in order from top to bottom;

[0048] Deleting a target matrix element from the initial matrix to obtain an element vacancy; wherein the identification code includes the target matrix element;

[0049] Inserting a specified matrix element into the element vacancy to obtain a target matrix; wherein the identification code does not include the specified matrix element;

[0050] Decryption processing is performed on each of the patient information sets based on the target matrix, and the patient information set that is successfully decrypted is determined to be the clinical research information set.

[0051] The method provided in this embodiment, on the one hand, improves the security of each patient information set by setting an encryption password for each patient information set, and protects the privacy of the patient while preventing the patient information set from being tampered with by unauthorized personnel. On the other hand, the target matrix is ​​generated through the identification image and identification code of the target disease, and each patient information set is decrypted based on the target matrix, which increases the difficulty of cracking the password set on the patient information set, further improves the security of each patient information set, and helps to improve the accuracy of the research results.

[0052] In some embodiments, determining the target condition information of the target disease based on the clinical research information set comprises the following steps:

[0053] For each patient in the clinical research information set, the physiological health information of the patient is determined in the clinical research information set; wherein, the physiological health information includes the symptoms of the disease suffered by the patient and the patient's condition. It can be understood that the disease suffered by the patient at least includes the target condition.

[0054] For each of the patients, determining whether the target disease is the only disease of the patient based on the physiological health information corresponding to the patient;

[0055] For each of the patients, if the target disease is the only disease of the patient, the disease of the patient is determined to be an intermediate target disease; wherein the intermediate target disease includes at least one; it can be understood that the target disease is the only disease of the patient means that the patient only suffers from the disease referred to by the target disease;

[0056] All the intermediate target symptoms are removed to obtain the target symptom information.

[0057] The method provided in this embodiment determines, for each patient, that the patient's symptom is an intermediate target symptom if the target symptom is the patient's only symptom, and removes all the intermediate target symptoms to obtain the target symptom information, thereby improving the accuracy of the target symptom information and facilitating improving the accuracy of the research results.

[0058] In some embodiments, the step of obtaining target scientific research literature information related to the target condition from a scientific research literature database comprises the following steps:

[0059] For each scientific research document in the scientific research document database, the scientific research document is input into a preset keyword extraction model to obtain keyword information of the scientific research document; wherein the keyword information includes at least one keyword; wherein the keyword extraction model includes an input layer, a feature extraction layer, a keyword extraction layer and an output layer;

[0060] For each of the scientific research documents, each keyword in the keyword information corresponding to the scientific research document is input into a preset semantic feature extraction model to obtain first semantic feature information; wherein the first semantic feature information includes at least one first semantic feature vector, and the semantic feature extraction model includes an input layer, a feature extraction layer, a feature vector generation layer and an output layer;

[0061] Inputting the target symptom into the semantic feature extraction model to obtain a second semantic feature vector;

[0062] For each of the scientific research documents, calculate the similarity between each of the first semantic feature vectors corresponding to the scientific research document and the second semantic feature vector, and compare each of the similarities with a preset similarity, and if any of the similarities is greater than the preset similarity, determine that the scientific research document is a target scientific research document;

[0063] The target scientific research document information is generated based on all the target scientific research documents corresponding to the target condition.

[0064] The method provided in this embodiment can efficiently and accurately obtain target scientific research literature information related to the target disease condition, which helps to improve the accuracy of the research results.

[0065] In some embodiments, determining the research potential coefficient of the target condition based on the historical research information comprises the following steps:

[0066] Acquire a first research coefficient based on the target scientific research document information; wherein the first research coefficient is the number of target scientific research documents in the target scientific research document information;

[0067] For each medical record of the target medical record information, a first symptom level and a second symptom level corresponding to the medical record are compared, and when the second symptom level is not lower than the first symptom level, the medical record is determined to be a target medical record; wherein the first symptom level is the symptom level corresponding to the target symptom of the patient corresponding to the medical record before receiving treatment, and the second symptom level is the symptom level corresponding to the target symptom of the patient corresponding to the medical record after receiving treatment, and the severity of the target symptom is proportional to the symptom level;

[0068] The ratio of the first number to the second number is used as a second research coefficient; wherein the first number is the number of the target medical records, and the second number is the number of all medical records in the target medical record information;

[0069] The ratio of the second research coefficient to the first research coefficient is used as the research potential coefficient.

[0070] The method provided in this embodiment, by calculating the research potential coefficient, helps to scientifically and rationally determine the research topics and the direction of investment of medical research resources, thereby avoiding the waste of medical research resources, improving the efficiency of medical research, and promoting the effective progress of medical research.

[0071] In some embodiments, before conducting a clinical study on the research topic based on the research plan, collecting clinical research data related to the clinical study, and storing the clinical research data in the clinical research information set, the method further includes the following steps:

[0072] In response to the researcher's input of research progress information, the research progress information is received, and the research progress information is sent to the terminal device of each patient in the clinical research information set, so that each patient can make suggestions on the research topic based on the research progress information; wherein the research progress information is the research progress information of the research topic;

[0073] Receive suggestions from each of the patients on the research topic, and optimize the research topic based on the suggestions.

[0074] In this embodiment, first, in response to the researcher's input of the research progress information, the research progress information is received, and the research progress information is sent to the terminal devices of each patient in the clinical research information set, so that each patient can make suggestions on the research topic based on the research progress information. It can be understood that after obtaining the research topic, the researcher will collect literature related to the research topic according to the research topic, and design a research plan for the research topic based on the literature and the data related to the research topic in the clinical research database, and conduct clinical trials based on the research plan to collect clinical research progress information. At a specified time point, the researcher inputs the clinical research progress information into the terminal to send the clinical research progress information to the terminal devices of each patient in the clinical research information set, wherein the clinical research progress information includes information such as the test plan, test data and test results. Specifically, after the researcher inputs the research progress information on the terminal, in response to the researcher's input of the research progress information, the research progress information is received, and the research progress information is sent to the terminal devices of each patient in the clinical research information set, so that each patient can make suggestions on the research topic based on the research progress information.

[0075] Then, the suggestions on the research topic proposed by each patient are received, and the research topic is optimized based on the suggestions. Specifically, the research progress information is sent to the terminal device of each patient in the form of a link. After the terminal device of each patient receives the link, the terminal device of each patient will remind each patient to open the link so that the patient can view the research progress information and make suggestions on the research topic based on the research progress information. The reminder is in the form of voice prompts, and each patient inputs the suggestions through the terminal device.

[0076] The method provided in this embodiment improves the patient's participation in the research topic and helps to improve the reliability of the research results.

[0077] See also Figure 2 , Figure 2 A schematic block diagram of the structure of a data management system 100 for medical clinical research provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the data management system 100 for medical clinical research includes:

[0078] The first acquisition module 110 is used to acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom.

[0079] The second acquisition module 120 is used to acquire the historical research information of the target pathology based on big data for each target pathology, and determine the research potential coefficient of the target pathology based on the historical research information.

[0080] The determination module 130 is used to determine the research topic of the target disease based on each of the research potential coefficients.

[0081] The generation module 140 is used to obtain target scientific research literature information related to the research topic in the scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information.

[0082] The execution module 150 is used to conduct a clinical study on the research topic based on the research plan, collect clinical research data related to the clinical research, and store the clinical research data in the clinical research information set.

[0083] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned data management method embodiment of medical clinical research, and will not be repeated here.

[0084] The medical clinical research data management system 100 provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 3 The system is run on the terminal device 200 shown.

[0085] See also Figure 3 , Figure 3 The terminal device 200 provided in the embodiment of the present application is a schematic block diagram of the structure, wherein the terminal device 200 includes a processor 201 and a memory 202, wherein the processor 201 and the memory 202 are connected via a system bus 203, wherein the memory 202 may include a non-volatile storage medium and an internal memory.

[0086] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 201, the processor 201 can execute any of the above-mentioned data management methods for medical clinical research.

[0087] The processor 201 is used to provide computing and control capabilities to support the operation of the entire terminal device 200.

[0088] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned data management methods for medical clinical research.

[0089] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the terminal device 200 involved in the scheme of the present application. The specific terminal device 200 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0090] It should be understood that the processor 201 may be a central processing unit (CPU), and the processor 201 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0091] In some embodiments, the processor 201 is used to run a computer program stored in the memory to implement the following steps:

[0092] Acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom;

[0093] For each of the target pathologies, acquiring historical research information of the target pathologies based on big data, and determining a research potential coefficient of the target pathologies based on the historical research information;

[0094] Determining a research topic for the target disease based on each of the research potential coefficients;

[0095] Acquire target scientific research literature information related to the research topic in a scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information;

[0096] Conduct clinical research on the research topic based on the research plan, collect clinical research data related to the clinical research, and store the clinical research data in the clinical research information set.

[0097] It should be noted that those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the terminal device 200 described above can refer to the corresponding process of the aforementioned data management method for medical clinical research, and will not be repeated here.

[0098] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the data management method for medical clinical research provided in the embodiment of the present application.

[0099] The computer-readable storage medium may be an internal storage unit of the terminal device 200 in the aforementioned embodiment, such as a hard disk or memory of the terminal device 200. The computer-readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the terminal device 200.

[0100] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data management method for medical clinical research, characterized in that: include: Acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom; For each of the target pathologies, acquiring historical research information of the target pathologies based on big data, and determining a research potential coefficient of the target pathologies based on the historical research information; Determining a research topic for the target disease based on each of the research potential coefficients; Acquire target scientific research literature information related to the research topic in a scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information; Conducting clinical research on the research topic based on the research plan, collecting clinical research data related to the clinical research, and storing the clinical research data in the clinical research information set; Wherein, the historical research information of the target disease condition is obtained based on big data, including: Acquire target scientific research literature information related to the target condition in a scientific research literature database; Acquiring target medical record information of a patient associated with the target condition in a patient medical information database of the target condition; Generate the historical research information based on the target scientific research literature information and the target medical record information; The determining of the research potential coefficient of the target condition based on the historical research information comprises: Acquire a first research coefficient based on the target scientific research document information; wherein the first research coefficient is the number of target scientific research documents in the target scientific research document information; For each medical record of the target medical record information, a first symptom level and a second symptom level corresponding to the medical record are compared, and when the second symptom level is not lower than the first symptom level, the medical record is determined to be a target medical record; wherein the first symptom level is the symptom level corresponding to the target symptom of the patient corresponding to the medical record before receiving treatment, and the second symptom level is the symptom level corresponding to the target symptom of the patient corresponding to the medical record after receiving treatment; The ratio of the first number to the second number is used as a second research coefficient; wherein the first number is the number of the target medical records, and the second number is the number of all medical records in the target medical record information; The ratio of the second research coefficient to the first research coefficient is used as the research potential coefficient.

2. The data management method for medical clinical research according to claim 1, characterized in that: The clinical research database includes a plurality of patient information sets, each of which is provided with an encryption password. The step of obtaining the clinical research information set of the target disease in the clinical research database includes: Acquire an identification image and an identification code of the target disease; Segmenting the identification image based on a preset image segmentation rule to obtain a plurality of sub-images; Inputting each of the sub-images into a preset image feature extraction model to obtain a feature vector corresponding to each of the sub-images; Based on a preset hash algorithm, the hash values ​​corresponding to the feature vectors are calculated respectively to obtain multiple hash values; wherein each of the hash values ​​includes the same number of characters; For each of the hash values, obtain a first target number corresponding to the hash value; wherein the first target number is the sum of the first number and the last number in the hash value; Obtaining a second target number corresponding to the identification code; wherein the second target number is the product of the first number and the last number in the identification code; For each of the first target numbers, compare the first target number with the second target number, and when the first target number is smaller than the second target number, determine the hash value corresponding to the first target number as the target hash value; Arranging the target hash values ​​in order from top to bottom based on the first target numbers corresponding to the target hash values ​​to obtain an initial matrix; wherein, in the initial matrix, the first target numbers corresponding to the hash values ​​decrease in order from top to bottom; Deleting a target matrix element from the initial matrix to obtain an element vacancy; wherein the identification code includes the target matrix element; Inserting a specified matrix element into the element vacancy to obtain a target matrix; wherein the identification code does not include the specified matrix element; Decryption processing is performed on each of the patient information sets based on the target matrix, and the patient information set that is successfully decrypted is determined to be the clinical research information set.

3. The data management method for medical clinical research according to claim 1, characterized in that: The determining target symptom information of the target disease based on the clinical research information set comprises: For each patient in the clinical research information set, determining physiological health information of the patient in the clinical research information set; For each of the patients, determining whether the target disease is the only disease of the patient based on the physiological health information corresponding to the patient; For each of the patients, if the target disease is the only disease of the patient, determining the disease condition of the patient as an intermediate target disease condition; wherein the intermediate target disease condition includes at least one; All the intermediate target symptoms are removed to obtain the target symptom information.

4. The data management method for medical clinical research according to claim 1, characterized in that: The step of obtaining target scientific research literature information related to the target disease condition from a scientific research literature database includes: For each scientific research document in the scientific research document database, input the scientific research document into a preset keyword extraction model to obtain keyword information of the scientific research document; wherein the keyword information includes at least one keyword; For each of the scientific research documents, input each keyword in the keyword information corresponding to the scientific research document into a preset semantic feature extraction model to obtain first semantic feature information; wherein the first semantic feature information includes at least one first semantic feature vector; Inputting the target symptom into the semantic feature extraction model to obtain a second semantic feature vector; For each of the scientific research documents, calculate the similarity between each of the first semantic feature vectors corresponding to the scientific research document and the second semantic feature vector, and compare each of the similarities with a preset similarity, and if any of the similarities is greater than the preset similarity, determine that the scientific research document is a target scientific research document; The target scientific research document information is generated based on all the target scientific research documents corresponding to the target condition.

5. The data management method for medical clinical research according to claim 1, characterized in that: The research topics for the target disease determined based on each of the research potential coefficients include: The relationship between the target symptom corresponding to the maximum research potential coefficient among the research potential coefficients and the target disease is determined as the research topic.

6. The data management method for medical clinical research according to claim 1, before obtaining the clinical research information set of the target disease in the clinical research database and determining the target symptom information of the target disease based on the clinical research information set, the method further comprises: Acquire data during the clinical research process, and perform structured processing, integration, and collection on the data during the clinical research process to generate the clinical research database.

7. The data management method for medical clinical research according to claim 1, characterized in that: Before conducting a clinical study on the research topic based on the research plan, collecting clinical research data related to the clinical study, and storing the clinical research data in the clinical research information set, the method further includes: In response to the researcher's input of research progress information, the research progress information is received, and the research progress information is sent to the terminal device of each patient in the clinical research information set, so that each patient can make suggestions on the research topic based on the research progress information; wherein the research progress information is the research progress information of the research topic; Receive suggestions from each of the patients on the research topic, and optimize the research topic based on the suggestions.

8. A data management system for medical clinical research, characterized in that: include: A first acquisition module is used to acquire a clinical research information set of a target disease in a clinical research database, and determine target symptom information of the target disease based on the clinical research information set; wherein the target symptom information includes at least one target symptom; A second acquisition module is used to acquire, for each of the target pathologies, historical research information of the target pathologies based on big data, and determine a research potential coefficient of the target pathologies based on the historical research information; A determination module, for determining a research topic for the target disease based on each of the research potential coefficients; A generation module, used to obtain target scientific research literature information related to the research topic in a scientific research literature database, and generate a research plan for the research topic based on the clinical research information set and the target scientific research literature information; An execution module, configured to conduct a clinical study on the research topic based on the research plan, collect clinical research data related to the clinical research, and store the clinical research data in the clinical research information set; Wherein, the historical research information of the target disease condition is obtained based on big data, including: Acquire target scientific research literature information related to the target condition in a scientific research literature database; Acquiring target medical record information of a patient associated with the target condition in a patient medical information database of the target condition; Generate the historical research information based on the target scientific research literature information and the target medical record information; The determining of the research potential coefficient of the target condition based on the historical research information comprises: Acquire a first research coefficient based on the target scientific research document information; wherein the first research coefficient is the number of target scientific research documents in the target scientific research document information; For each medical record of the target medical record information, a first symptom level and a second symptom level corresponding to the medical record are compared, and when the second symptom level is not lower than the first symptom level, the medical record is determined to be a target medical record; wherein the first symptom level is the symptom level corresponding to the target symptom of the patient corresponding to the medical record before receiving treatment, and the second symptom level is the symptom level corresponding to the target symptom of the patient corresponding to the medical record after receiving treatment; The ratio of the first number to the second number is used as a second research coefficient; wherein the first number is the number of the target medical records, and the second number is the number of all medical records in the target medical record information; The ratio of the second research coefficient to the first research coefficient is used as the research potential coefficient.

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