Clinical data intelligent acquisition method and system and storage medium

Abstract

The invention provides a clinical data intelligent collection method and system and a storage medium, and belongs to the technical field of data analysis, and the method specifically comprises the steps: determining the change conditions of clinical data of different dimensions in a treatment process, determining a correlation coefficient between the clinical data of different dimensions and the disease type in combination with a correlation condition between the clinical data of different dimensions and the disease severity of the disease type, and obtaining a historical collection data volume of the clinical data of different dimensions in historical patients with different disease severity; and the acquisition strategies of the clinical data of different dimensions are determined in combination with the correlation coefficients of the clinical data of different dimensions and the disease types, so that the reliability of the acquisition result of the clinical data is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and in particular, relates to a clinical data intelligent collection method, system and storage medium. Background Art

[0002] Reliable collection of clinical data is crucial to assist doctors in making diagnoses. The following technical problems exist in the existing technical solutions when collecting clinical data: For different types of diseases, there are large differences in the number of patients in hospitals. Therefore, using the same collection strategy for diseases with a small number of patients and diseases with a large number of patients will result in a smaller amount of clinical data collected for diseases with a small number of patients, making it difficult to meet the doctors' needs for auxiliary diagnosis.

[0003] In response to the above technical problems, the present invention provides a clinical data intelligent collection method, system and storage medium. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a method for intelligently collecting clinical data is provided.

[0005] A clinical data intelligent collection method, specifically comprising: S1 determines the disease type corresponding to the patient based on the patient's diagnosis data, and proceeds to the next step when the historical collected data of the disease type meets the requirements based on the historical patient data of the disease type and the collected data of the historical patients' clinical data in different dimensions; S2 determines historically similar patients of the patient through the patient's diagnostic data, and when it is determined that the patient does not belong to a patient of a specific collection type by using the collection data of clinical data of the historically similar patients in different dimensions, proceeds to the next step; S3: determining the changes of clinical data of different dimensions during the treatment process, and determining the correlation coefficient between the clinical data of different dimensions and the disease type in combination with the correlation between the clinical data of different dimensions and the severity of the disease type; S4 obtains the amount of historically collected clinical data of different dimensions in historical patients with different disease severity, and determines the collection strategy of clinical data of different dimensions in combination with the correlation coefficient between the clinical data of different dimensions and the disease type.

[0006] In the second aspect, the present invention provides a clinical data management system, which is applied to the above-mentioned clinical data intelligent collection method, including a knowledge reading module, a database design module, a data collection module, a scientific research project configuration module, a voice data verification module, a data quality management module, a data push module, a permission management module, and a scientific research management module.

[0007] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the above-mentioned method for intelligent collection of clinical data.

[0008] The beneficial effects of the present invention are: 1. Determine whether the historical collected data of the disease type meets the requirements based on the historical patient data of the disease type and the collected clinical data of historical patients in different dimensions, and realize the screening of disease types with less data volume in the history of collected data. And through the adjustment of differentiated clinical data collection strategies, ensure the adequacy of data of disease types with less data volume, and also lay the foundation for clinical guidance of doctors.

[0009] 2. The collection strategy of clinical data of different dimensions is determined based on the amount of historically collected data from historical patients with different disease severity and the correlation coefficient with the disease type. This not only takes into account the dimension of the amount of historically collected data to achieve dynamic adjustment of the clinical data collection method, but also takes into account the correlation with the disease type, thereby ensuring the reliable collection of clinical data with a small amount of historically collected data and a high degree of correlation with the disease type. This not only reduces the waste of storage space, but also ensures the reference value of clinical data.

[0010] A further technical solution is that the disease type corresponding to the patient is determined based on the diagnosed disease type corresponding to the patient's diagnostic data.

[0011] A further technical solution is that the historical patient data is determined based on the analysis results of the historical patient database of the disease type.

[0012] A further technical solution is that the method for determining the collection strategy of the clinical data is: Dividing the historical patients into different disease severity intervals according to their disease severity, determining the data sufficiency coefficients of the different disease severity intervals based on the ratio of the historical collected data volume of the clinical data of the specific dimensions of the different disease severity intervals to the preset collected data volume, and determining the comprehensive data sufficiency coefficients of the clinical data of the specific dimensions in combination with the preset weight values ​​of the different disease severity intervals; Determine the data collection requirement coefficient of the clinical data of the specific dimension based on the correlation coefficient between the clinical data of the specific dimension and the disease type and the average value of the comprehensive data sufficiency coefficient; The data collection requirement coefficient is used to determine the collection strategy of the clinical data of the specific dimension.

[0013] A further technical solution is that the collection strategy of the clinical data includes no collection, full collection and partial collection, wherein the partial collection collects the clinical data according to a preset interval duration.

[0014] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0017] Figure 1 It is a flow chart of a method for intelligent collection of clinical data; Figure 2 is a flow chart of a method for determining a correlation coefficient between clinical data and said disease type; Figure 3 It is a flow chart of the method for determining the collection strategy of clinical data. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification. Embodiment 1

[0019] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for intelligent collection of clinical data is provided, which specifically includes: S1 determines the disease type corresponding to the patient based on the patient's diagnosis data, and proceeds to the next step when the historical collected data of the disease type meets the requirements based on the historical patient data of the disease type and the collected data of the historical patients' clinical data in different dimensions; Furthermore, the disease type corresponding to the patient is determined based on the diagnosed disease type corresponding to the patient's diagnostic data.

[0020] Specifically, the historical patient data is determined based on the analysis results of the historical patient database of the disease type.

[0021] It is understood that historically collected data to determine the type of disease meets the requirements, including: Based on the historical patient data of the disease type, determine the number of historical patients of the disease type in the historical patient database, and determine the reliability coefficient of the number of cases of the disease type through the number of historical patients; Based on the collected data of the clinical data of the historical patients in different dimensions, determine the data volume of the collected data of the clinical data of the disease type in different dimensions, and determine the data reliability coefficient of the disease type based on the data volume of the collected data of the clinical data in different dimensions; Determine the reliability coefficients of collected data of different historical patients based on the collected data of clinical data of different historical patients in different dimensions, and determine the reliability coefficients of patient data using the reliability coefficients of collected data of different historical patients; The comprehensive reliability coefficient of the historically collected data of the disease type is determined by the patient data reliability coefficient, the data reliability coefficient and the case quantity reliability coefficient, and whether the historically collected data of the disease type meets the requirements is determined based on the comprehensive reliability coefficient.

[0022] It should be noted that when the comprehensive reliability coefficient of the historically collected data of the disease type is within a preset coefficient range, it is determined that the historically collected data of the disease type meets the requirements.

[0023] Furthermore, when the historically collected data of the disease type does not meet the requirements, all clinical data of the patient are collected and processed.

[0024] It should also be noted that the historical data collected to determine the disease type meets the requirements, including: Based on the historical patient data of the disease type, determine the number of historical patients of the disease type in the historical patient database, and judge whether the number of historical patients is less than the preset number of patients. If so, determine that the historical collected data of the disease type does not meet the requirements. If not, proceed to the next step. Determine the reliability coefficients of the collected data of different historical patients based on the collected data of clinical data of different historical patients in different dimensions, and determine the data reliable patients in the historical patient database using the reliability coefficients of the collected data of different historical patients, and judge whether the number of data reliable patients in the historical patient database is less than the preset number of reliable patients, if so, determine that the historical collected data of the disease type does not meet the requirements, if not, proceed to the next step; The comprehensive reliability coefficient of the historically collected data of the disease type is determined by the number of patients with reliable data of the disease type and the number of historical patients, and whether the historically collected data of the disease type meets the requirements is determined based on the comprehensive reliability coefficient.

[0025] It should also be noted that determining whether the historically collected data of the disease type meets the requirements includes steps S11-S13, specifically: S11 determines the number of historical patients of the disease type in the historical patient database based on the historical patient data of the disease type S12 determines the amount of collected data of clinical data of the disease type in different dimensions based on the collected data of clinical data of the historical patients in different dimensions, determines the reliability coefficients of collected data of different historical patients based on the collected data of clinical data of different historical patients in different dimensions, obtains the number of patients with reliable data in the historical patient database, and determines the distribution reliability coefficient in combination with the reliability coefficients of collected data of different reliable data patients; S13 determines the amount of collected data of clinical data of the disease type in different dimensions based on the collected data of clinical data of the historical patients in different dimensions, determines the data reliability coefficient of the disease type based on the amount of collected data of clinical data in different dimensions, determines the reliability coefficient of the number of cases of the disease type through the number of historical patients, determines the patient data reliability coefficient using the reliability coefficients of collected data of different historical patients, determines the comprehensive reliability coefficient of the historical collected data of the disease type through the average of the patient data reliability coefficient, the data reliability coefficient and the case number reliability coefficient, and determines whether the historical collected data of the disease type meets the requirements based on the comprehensive reliability coefficient.

[0026] S2 determines historically similar patients of the patient through the patient's diagnostic data, and when it is determined that the patient does not belong to a patient of a specific collection type by using the collection data of clinical data of the historically similar patients in different dimensions, proceeds to the next step; Specifically, the method for determining the patient's historically similar patients is: Determine the similarity coefficient between the patient's diagnostic data and the historical patient's diagnostic data by using the similarity coefficients in different dimensions with the patient's diagnostic data; Historically similar patients to the patient are determined based on the diagnostic data similarity coefficient.

[0027] It is understandable that determining that the patient does not belong to a patient of a specific collection type specifically includes: Determine the data volume of the clinical data collected by the historically similar patients in different dimensions based on the collected data of the clinical data of the historically similar patients in different dimensions, and determine the reliability coefficients of the collected data of different historically similar patients according to the data volume; Determine the patient with reliable data among the historical similar patients according to the reliability coefficient of the collected data, and use the patient as the similar reliable patient; Whether the acquisition type of the patient belongs to a specific acquisition type is determined according to the number of the similar reliable patients.

[0028] Further, when the number of the similar reliable patients is not within a preset number range, it is determined that the collection type of the patient does not belong to a specific collection type.

[0029] It should also be noted that determining that the patient does not belong to a patient of a specific collection type includes steps S21-S23, specifically: S21: obtaining the number of historically similar patients of the patient, determining the data volume of the clinical data collected of the historically similar patients in different dimensions based on the collected data of the clinical data of the historically similar patients in different dimensions, and determining the reliability coefficients of the collected data of different historically similar patients according to the data volume; S22 determines the patient with reliable data among the historical similar patients according to the collected data reliability coefficient, and uses the patient as the similar reliable patient; S23 determines the comprehensive reliability coefficient of the similar reliable patients according to the number of the similar reliable patients and the collection data reliability coefficients of different similar reliable patients, and uses the comprehensive reliability coefficient to determine whether the collection type of the patient belongs to a specific collection type.

[0030] S3: determining the changes of clinical data of different dimensions during the treatment process, and determining the correlation coefficient between the clinical data of different dimensions and the disease type in combination with the correlation between the clinical data of different dimensions and the severity of the disease type; Specifically, Figure 2 As shown, the method for determining the correlation coefficient between the clinical data and the disease type is: Determine the change curve of the clinical data of the specific dimension during the treatment process of different historical patients based on the change of the clinical data of the specific dimension during the treatment process, and determine the data change amount of the clinical data of the specific dimension according to the change curve of the clinical data of the specific dimension during the treatment process of different historical patients; Determine the distribution aggregation of the clinical data of a specific dimension in different disease severity intervals according to the distribution of the clinical data of a specific dimension in different disease severity intervals, and determine the disease severity correlation of the clinical data of a specific dimension based on the distribution aggregation of the clinical data of a specific dimension in different disease severity intervals; The correlation coefficient between the clinical data of the specific dimension and the disease type is determined by the data variation of the clinical data of the specific dimension and the correlation between the disease severity.

[0031] Furthermore, the value range of the correlation coefficient between the clinical data of the specific dimension and the disease type is between 0 and 1, wherein the higher the correlation coefficient between the clinical data of the specific dimension and the disease type, the higher the degree of correlation between the clinical data of the specific dimension and the disease type.

[0032] It is understandable that the data change amount of the clinical data of a specific dimension is determined according to the change curve of the clinical data of a specific dimension of different historical patients during the treatment process, specifically including: Determining the average change curve of the clinical data of the specific dimension during the treatment process according to the change curve of the clinical data of the specific dimension during the treatment process of different historical patients; The data variation of the clinical data of the specific dimension is determined by using the average value of the variation rates of different points in the average variation curve.

[0033] S4 obtains the amount of historically collected clinical data of different dimensions in historical patients with different disease severity, and determines the collection strategy of clinical data of different dimensions in combination with the correlation coefficient between the clinical data of different dimensions and the disease type.

[0034] Specifically, Figure 3 As shown, the method for determining the collection strategy of the clinical data is: Dividing the historical patients into different disease severity intervals according to their disease severity, determining the data sufficiency coefficients of the different disease severity intervals based on the ratio of the historical collected data volume of the clinical data of the specific dimensions of the different disease severity intervals to the preset collected data volume, and determining the comprehensive data sufficiency coefficients of the clinical data of the specific dimensions in combination with the preset weight values ​​of the different disease severity intervals; Determine the data collection requirement coefficient of the clinical data of the specific dimension based on the correlation coefficient between the clinical data of the specific dimension and the disease type and the average value of the comprehensive data sufficiency coefficient; The data collection requirement coefficient is used to determine the collection strategy of the clinical data of the specific dimension.

[0035] Furthermore, the collection strategy of the clinical data includes no collection, full collection and partial collection, wherein the partial collection collects the clinical data according to a preset interval time.

[0036] It should also be noted that the method for determining the clinical data collection strategy includes steps S41-S42, specifically: S41: obtaining the correlation coefficient between the clinical data of the specific dimension and the disease type, obtaining the historical data volume of the clinical data of the specific dimension, dividing the historical patients into different disease severity intervals according to the disease severity of the historical patients, and determining the data sufficiency coefficients of different disease severity intervals based on the ratio of the historical data volume of the clinical data of the specific dimension in different disease severity intervals to the preset data volume; Steps S411-S414 in the above step S41 are specifically: S411: obtaining the correlation coefficient between the clinical data of the specific dimension and the disease type, and determining whether the correlation coefficient between the clinical data of the specific dimension and the disease type is less than the minimum limit value of the correlation coefficient; if so, determining the collection strategy of the clinical data by using the collection strategy of not collecting; if not, proceeding to the next step; S412 determines whether the correlation coefficient between the clinical data of the specific dimension and the disease type is greater than a preset correlation coefficient, if so, proceeds to the next step, if not, proceeds to step S42; S413: obtaining the historical data volume of the clinical data of the specific dimension, determining whether the historical data volume of the clinical data of the specific dimension is less than the minimum data volume limit value, and if so, determining the collection strategy of the clinical data by adopting the preset collection strategy, and if not, proceeding to the next step; S414 divides the historical patients into different disease severity intervals according to their disease severity, determines the data sufficiency coefficients of different disease severity intervals based on the ratio of the historical collected data volume of clinical data of specific dimensions of different disease severity intervals to the preset collected data volume, and determines whether there is a disease severity interval whose data sufficiency coefficient does not meet the requirements. If so, adopts the preset collection strategy to determine the collection strategy of the clinical data; if not, proceeds to step S42.

[0037] S42 uses the data sufficiency coefficients of different disease severity intervals and preset weight values ​​to determine the comprehensive data sufficiency coefficient of the clinical data of the specific dimension, determines the data collection requirement coefficient of the clinical data of the specific dimension based on the correlation coefficient between the clinical data of the specific dimension and the disease type and the average value of the comprehensive data sufficiency coefficient, and determines the collection strategy of the clinical data of the specific dimension through the data collection requirement coefficient. Embodiment 2

[0038] In the second aspect, the present invention provides a clinical data management system, which is applied to the above-mentioned clinical data intelligent collection method, including a knowledge reading module, a database design module, a data collection module, a scientific research project configuration module, a voice data verification module, a data quality management module, a data push module, a permission management module, and a scientific research management module.

[0039] Before entering step S12, the following steps are also included: Based on the historical patient data of the disease type, determine the number of historical patients of the disease type in the historical patient database, and judge whether the number of historical patients is less than the preset number of patients. If so, determine that the historical collected data of the disease type does not meet the requirements. If not, enter step S12.

[0040] Optionally, the above step S12 includes steps S121-S123, specifically: S121 determines the amount of collected clinical data of the disease type in different dimensions based on the collected clinical data of the historical patients in different dimensions, and determines whether there is clinical data whose amount of collected data does not meet the requirements. If yes, proceed to the next step; if not, proceed to step S12; S122 determines the reliability coefficients of the collected data of different historical patients based on the collected data of clinical data of different historical patients in different dimensions, and uses the reliability coefficients of the collected data of different historical patients to determine whether there is a patient with reliable data in the historical patient database, if so, proceeds to the next step, if not, determines that the historical collected data of the disease type does not meet the requirements; S123 obtains the number of data reliable patients in the historical patient database, and determines the distribution reliability coefficient in combination with the collection data reliability coefficients of different data reliable patients to determine whether the distribution reliability coefficient meets the requirements. If so, proceeds to step S12; if not, it is determined that the historical collection data of the disease type does not meet the requirements.

[0041] Optionally, the above step S13 includes steps S131-S132, specifically: S131 determines the amount of collected clinical data of the disease type in different dimensions based on the collected clinical data of the historical patient in different dimensions, determines the data reliability coefficient of the disease type based on the amount of collected clinical data in different dimensions, and judges whether the data reliability coefficient of the disease type meets the requirements. If so, proceed to step S131. If not, determine that the historical collected data of the disease type does not meet the requirements. S132 determines the reliability coefficient of the number of cases of the disease type through the number of historical patients, determines the patient data reliability coefficient using the reliability coefficients of the collected data of different historical patients, determines the comprehensive reliability coefficient of the historical collected data of the disease type through the average value of the patient data reliability coefficient, the data reliability coefficient and the case number reliability coefficient, and determines whether the historical collected data of the disease type meets the requirements based on the comprehensive reliability coefficient. Embodiment 3

[0042] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the above-mentioned method for intelligent collection of clinical data.

[0043] Optionally, before entering step S21, the following steps need to be determined: Based on the collected data of clinical data of similar historical patients in different dimensions, it is determined whether there is clinical data whose data volume does not meet the requirements. If so, it is determined that the patient belongs to a patient of a specific collection type. If not, step S21 is entered.

[0044] Optionally, the above step S21 includes steps S211-S213, which are specifically: S211 obtains the number of historically similar patients of the patient, and determines whether the number of historically similar patients of the patient is less than a preset number threshold. If so, proceeds to the next step; if not, proceeds to step S22; S212 determines the data volume of the clinical data collected by the historically similar patients in different dimensions based on the collected data of the clinical data of the historically similar patients in different dimensions, and determines the reliability coefficients of the collected data of different historically similar patients according to the data volume, and judges whether the average values ​​of the reliability coefficients of the collected data of different historically similar patients meet the requirements, if yes, proceeds to the next step, if no, determines that the patient belongs to a patient of a specific collection type; S213 determines whether there is a patient with reliable data among the historical similar patients according to the reliability coefficient of the collected data. If so, proceed to step S22; if not, determine that the patient belongs to a patient of a specific collection type.

[0045] Optionally, the above step S22 includes the following contents: According to the reliability coefficient of the collected data, the data reliable patients among the historical similar patients are determined, and they are used as similar reliable patients to determine whether the number of the similar reliable patients meets the requirements. If so, proceed to step S23; if not, determine that the patient belongs to a patient of a specific collection type.

[0046] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0047] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A clinical data intelligent collection method, characterized in that: Specifically include: Determine the disease type corresponding to the patient based on the patient's diagnosis data, and determine the historical patient data of the disease type and the historical patient clinical data collection data of different dimensions when the historical collection data of the disease type meets the requirements, then proceed to the next step; When determining similar historical patients of the patient through the patient's diagnostic data and determining that the patient does not belong to a patient of a specific collection type through the collection data of clinical data of similar historical patients in different dimensions, proceed to the next step; Determine the changes of clinical data of different dimensions during the treatment process, and determine the correlation coefficient between the clinical data of different dimensions and the disease type based on the correlation between the clinical data of different dimensions and the severity of the disease type; Obtain the amount of historical data collected of clinical data of different dimensions in historical patients with different disease severity, and determine the collection strategy of clinical data of different dimensions based on the correlation coefficient between clinical data of different dimensions and disease types.

2. The clinical data intelligent collection method according to claim 1, characterized in that: The disease type corresponding to the patient is determined according to the diagnosed disease type corresponding to the patient's diagnostic data.

3. The clinical data intelligent collection method according to claim 1, characterized in that: The historical patient data is determined based on the analysis results of the historical patient database of the disease type.

4. The clinical data intelligent collection method according to claim 1, characterized in that: Determine that the historical data collected for the disease type meets the requirements, including: Based on the historical patient data of the disease type, determine the number of historical patients of the disease type in the historical patient database, and judge whether the number of historical patients is less than the preset number of patients. If so, determine that the historical collected data of the disease type does not meet the requirements. If not, proceed to the next step. Determine the reliability coefficients of the collected data of different historical patients based on the collected data of clinical data of different historical patients in different dimensions, and determine the data reliable patients in the historical patient database using the reliability coefficients of the collected data of different historical patients, and judge whether the number of data reliable patients in the historical patient database is less than the preset number of reliable patients, if so, determine that the historical collected data of the disease type does not meet the requirements, if not, proceed to the next step; The comprehensive reliability coefficient of the historically collected data of the disease type is determined by the number of patients with reliable data of the disease type and the number of historical patients, and whether the historically collected data of the disease type meets the requirements is determined based on the comprehensive reliability coefficient.

5. The clinical data intelligent collection method according to claim 1, characterized in that: When the historically collected data of the disease type does not meet the requirements, all clinical data of the patient are collected and processed.

6. The clinical data intelligent collection method according to claim 1, characterized in that: The method for determining the patient's history of similar patients is: Determine the similarity coefficient between the patient's diagnostic data and the historical patient's diagnostic data by using the similarity coefficients in different dimensions with the patient's diagnostic data; Historically similar patients to the patient are determined based on the diagnostic data similarity coefficient.

7. The clinical data intelligent collection method according to claim 1, characterized in that: Determining that the patient does not belong to a specific collection type includes: Determine the data volume of the clinical data collected by the historically similar patients in different dimensions based on the collected data of the clinical data of the historically similar patients in different dimensions, and determine the reliability coefficients of the collected data of different historically similar patients according to the data volume; Determine the patient with reliable data among the historical similar patients according to the reliability coefficient of the collected data, and use the patient as the similar reliable patient; Whether the acquisition type of the patient belongs to a specific acquisition type is determined according to the number of the similar reliable patients.

8. The clinical data intelligent collection method according to claim 7, characterized in that: When the number of the similar reliable patients is not within a preset number range, it is determined that the collection type of the patient does not belong to a specific collection type.

9. A clinical data management system, applied to a clinical data intelligent collection method according to any one of claims 1 to 8, characterized in that: It includes knowledge reading module, database design module, data collection module, scientific research project configuration module, voice data verification module, data quality management module, data push module, authority management module and scientific research management module.

10. A computer storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute a clinical data intelligent collection method according to any one of claims 1 to 8.

Images