CN112349364A - Method for acquiring clinical data - Google Patents
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Abstract
The invention discloses a method for acquiring clinical data, and particularly relates to the field of medical clinical information acquisition. The network side of the medical network establishes a clinical data service system for integrating and updating different clinical topics in a centralized manner, obtains clinical data service schemes aiming at different clinical topics on the basis of integrating or updating clinical data, processes the clinical data service related requests and obtains corresponding responses carrying the clinical data service schemes under the corresponding clinical topics, thereby conveniently and easily providing clinical data services under the required clinical topics for users under unified specifications.
Description
Technical Field
The invention relates to the technical field of medical clinical information acquisition, in particular to a method for acquiring clinical data.
Background
In order to solve the problems of the manual transcription of clinical data and the alternate transcription of other personnel still existing in the current clinical trial research, an EDC (electronic data capture system/data management system) system (clinical trial electronic data collection system/data management system) appears in the market, and the EDC system is platform software for collecting and transmitting the clinical trial data, so that the real-time collection of the clinical data and the data storage of the clinical data are conveniently realized by clinicians and researchers, paperless office work is facilitated, and the time cost of clinical evaluation is greatly reduced. The EDC system, because of its significant advantages, has been widely adopted in clinical trials in many countries to replace traditional paper case report forms and hand-written data collection modalities.
The acquisition of clinical test data is the core content in the clinical research of the medicine, and the real, accurate, timely and standard data acquisition can obviously improve the quality of clinical tests and shorten the research period. In the traditional mode, clinical trials mainly rely on paper case report forms to complete the data acquisition and management process, and the paper CRF (case report forms) has a long data acquisition and management period, which can slow down the clinical research process. The EDC system adopts the electronic case report form to replace the paper case report form to collect and manage the clinical test data, the platform user can complete the real-time checking and modifying updating of the data, and the design of the clinical test and the collection, arrangement, analysis and statistics of the clinical data by the clinical scientific research expert are facilitated. At present, all EDC systems existing in the market can complete the acquisition and entry of clinical case information and clinical data information after logging in the system by means of terminals such as computers, and the like, so that the filling operation of personal information of a subject is inconvenient. The bit stream concept uses several geometric shapes corresponding to binary system to represent literal numerical information, and can be automatically read by means of image input equipment or photoelectric scanning equipment to implement automatic information processing. However, the whole information acquisition system and method are not perfect, have certain use defects, and cannot be comprehensively popularized and used by users.
Disclosure of Invention
In order to overcome the above defects in the prior art, an embodiment of the present invention provides a method for acquiring clinical data, and the technical problem to be solved by the present invention is: how to solve the problem that the information processing amount of a doctor is large and the work progress is influenced because the filling operation of the personal information of a subject cannot be performed during the acquisition of the existing clinical test data.
In order to achieve the purpose, the invention provides the following technical scheme: a method for acquiring clinical data comprises an integration platform, a database and a cloud platform;
the integration platform is used for transmitting clinical data to the database in real time, sending the received request related to the clinical data service acquisition to the cloud platform, and receiving a clinical data service result returned by the cloud platform;
the database is used for integrating and storing the clinical data transmitted from the integration platform;
the cloud platform specifically comprises: the cloud platform is used for extracting stored clinical data corresponding to clinical topics from the database, performing quality control and standardization processing on the extracted clinical data corresponding to the topics, and generating clinical data service schemes corresponding to the clinical topics; after receiving a clinical data service related request, acquiring a clinical data service scheme corresponding to a clinical theme, and returning the clinical data service scheme to the integration platform;
the subject database is used for determining the unique identification code related to the clinical subject and extracting the clinical data set corresponding to the unique identification code from the database;
the data quality control and standardization module consists of a data quality control unit and a data standardization unit and is used for performing quality control and standardization processing on a clinical data set under a clinical theme extracted from a theme library;
the service scheme generation module is used for generating clinical data service schemes according to the clinical data sets aiming at all clinical topics, and comprises a data grouping device, a model generator, a model selector and a comprehensive risk prompt module, wherein the data grouping device is used for grouping the clinical data sets subjected to quality control and standardization processing after verification and standardization according to clinical data service types; the model generator is used for modeling the clinical data set grouped according to the clinical service types to obtain modeled clinical data service schemes of different clinical service types; a model selector for identifying a modeled clinical data schema corresponding to a clinical service type using different model identifications according to a requested clinical topic, and providing the schema to the data service module; the comprehensive risk prompting module is used for acquiring the corresponding abnormal data statistical table corresponding to the modeled clinical data service schemes of different clinical service types, acquiring comprehensive risk prompting information according to the acquired corresponding abnormal data statistical table and providing the comprehensive risk prompting information to the data service module;
the data service module is used for receiving a clinical data service related request sent by the integration platform, acquiring a clinical data service scheme corresponding to a requested clinical theme, carrying the clinical data service scheme in a clinical data service related response and returning the clinical data service scheme to the integration platform;
the specific collection steps are as follows:
s1: the integration platform is communicated with user equipment to obtain clinical data;
s2: integrating the clinical data obtained in the step S1 by the database, and setting a unique identification code for the clinical data according to a set coding rule;
s3: after the integration is completed in step S2, a clinical data set corresponding to the unique identification code is formed for storage; the unique identification code can be a patient identification, the unique identification code is used as a main identification code, and each medical operation and each time point of a clinical path related in a medical process under the same set clinical theme are used as sub-association keys to associate clinical data, so that a clinical data set of the set clinical theme corresponding to the unique identification code is formed;
s4: the subject database extracts a clinical data set corresponding to the unique identification code from the database, the quality control and standardization processing is carried out on the clinical data set under the clinical subject extracted by the subject database through the data quality control and standardization module, then a clinical data service scheme is generated according to the clinical data set through the service scheme generation module, after the clinical data service related request sent by the integration platform is received by the data service module, the clinical data service scheme corresponding to the requested clinical subject is obtained and carried in the clinical data service related response, and the clinical data service scheme is returned to the integration platform;
s5: the data quality control unit is used for carrying out correctness verification on the clinical data set under the clinical theme extracted from the theme library and carrying out abnormal data warning and marking; according to the particularity of clinical data, judging a normal threshold value which is suitable for the clinical data according to the main body identification code and the sub-association key identification of each subject bank by the abnormal data; the correctness is checked by judging whether the data null value of the data extraction result of the subject database is in a preset null value threshold range or not and the data distribution condition, and if the data null value duty ratio is larger than the set null value threshold range, marking; if the difference between the data dispersion of the theme base data extraction result and a preset value is not within a set difference threshold range, identifying abnormal data;
s6: the data standardization unit is used for standardizing the clinical data set under the clinical theme extracted from the theme library and mainly comprises a data standardization sub-module, a data binning sub-module and an abnormal data processing sub-module, and the data standardization sub-module is used for carrying out normalization processing on the clinical data set under the clinical theme extracted from the theme library so as to avoid index contribution degree difference caused by different dimensions;
s7: performing self-adaptive binning on the clinical data set under the theme, and performing reprocessing by using the binning method according to the information entropy obtained by grouping results each time until the information entropy obtained by binning results reaches a preset entropy value; the abnormal data processing sub-module removes or supplements the abnormal data marked by the data quality control unit, and generates an abnormal data statistical table for the extraction result of the clinical data set under the clinical theme extracted by the theme library 301;
s8: the data service module receives a clinical data service related request sent by the integration platform, acquires a clinical data service scheme corresponding to a requested clinical theme from the model selector, acquires corresponding comprehensive risk prompt information from the comprehensive risk prompt module, carries the comprehensive risk prompt information in a clinical data service related response, and returns the comprehensive risk prompt information to the integration platform; if the integration platform receives the clinical data from the user equipment in real time, the clinical data set corresponding to the unique identification code after the integration of the database is updated in real time; the subsequent cloud platform processes the clinical data set, and the obtained clinical data service scheme is also updated in real time;
s9: setting a unique identification code for clinical data according to a set coding rule, setting a main identification code for a belonged clinical theme, and performing association of the clinical data by taking each medical operation and each time point of a clinical path related in a medical process as a sub-association key under the same clinical theme to form a clinical data set of the set clinical theme corresponding to the unique identification code.
In a preferred embodiment, the integrated platform is formed by medical acquisition systems of various service types, including but not limited to computer application systems (HIS), examination information management systems (LIS), electronic medical record systems (EMR), image archiving and communication systems (PACS), nursing systems and medical logistics systems for information management and online operations in medical management and medical activities.
In a preferred embodiment, the integration platform has a communication connection relationship with a user device.
In a preferred embodiment, the theme base is configured to perform theme base data extraction on a clinical data set stored in the database and corresponding to a unique identification code, and perform extraction according to various set data usage requirements during extraction, where the various data usage requirements are obtained through the integration platform and are extracted one by one based on the identification codes corresponding to the respective requirements, and if the identification codes are extracorporeal circulation and intraoperative blood transfusion respectively, the extraction result of the theme base is the extracorporeal circulation blood theme database.
In a preferred embodiment, in step S2, a main body identification code is set for the affiliated clinical topic, each medical operation and each time point of the clinical route involved in the medical procedure under the same clinical topic are used as sub-association keys to perform association of clinical data, a clinical data set corresponding to the unique identification code of the set clinical topic is formed, and if the clinical data exists in the form of information tables, the clinical data is integrated according to association rules between the information tables.
In a preferred embodiment, the normalization scheme in step S6 includes, but is not limited to: the linear function is standardized, the mean value is standardized, and a normalization processing scheme matched with the data service demand response is adopted; and the data binning submodule is used for extracting clinics from the topic library according to the set endpoint event type.
In a preferred embodiment, the type of the endpoint event set in the step S7 by the preset entropy value may be from a data service module, including but not limited to: adverse events, cost prediction, risk of morbidity, clinical diagnosis and treatment routes.
The invention has the technical effects and advantages that:
the network side of the medical network establishes a clinical data service system for integrating and updating different clinical topics in a centralized manner, obtains clinical data service schemes aiming at different clinical topics on the basis of integrating or updating clinical data, processes the clinical data service related requests and obtains corresponding responses carrying the clinical data service schemes under the corresponding clinical topics, thereby conveniently and easily providing clinical data services under the required clinical topics for users under unified specifications.
Drawings
FIG. 1 is a schematic diagram of the overall system of the present invention.
Fig. 2 is a system diagram of a cloud platform structure according to the present invention.
The reference signs are: the system comprises a 10 integration platform, a 20 database, a 30 cloud platform, a 301 subject library, a 302 data quality control and standardization module, a 303 service scheme generation module and a 304 data service module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for acquiring clinical data, which comprises an integration platform 10, a database 20 and a cloud platform 30;
the integrated platform 10 is used for transmitting clinical data to the database 20 in real time, sending a received request related to clinical data service acquisition to the cloud platform 30, and receiving a clinical data service result returned by the cloud platform 30, wherein the integrated platform 10 is composed of medical acquisition systems of various service types, including but not limited to a computer application system (HIS), an inspection information management system (LIS), an electronic medical record system (EMR), an image archiving and communication system (PACS), a nursing system and a medical logistics system, which perform information management and online operation in medical management and medical activities;
the database 20 is used for integrating and storing clinical data transmitted from the integration platform 10;
the cloud platform 30 specifically includes: the cloud platform 30 is configured to extract stored clinical data corresponding to clinical topics from the database 20, perform quality control and standardization processing on the extracted clinical data corresponding to the topics, and generate clinical data service schemes corresponding to the clinical topics; after receiving the clinical data service related request, obtaining the clinical data service scheme corresponding to the clinical topic, and returning the clinical data service scheme to the integration platform 10;
the subject database 301 is configured to determine a unique identification code related to a clinical subject, extract a clinical data set corresponding to the unique identification code from the database 20, extract the data of the subject database 301 from the clinical data set corresponding to the unique identification code stored in the database 20, and extract the data according to various set data use requirements during extraction, where the various data use requirements are obtained through the integration platform 10 and are extracted one by one based on the identification codes corresponding to the various requirements, and if the identification codes are extracorporeal circulation and intraoperative blood transfusion respectively, the extraction result of the subject database 301 is the extracorporeal circulation blood use subject database 20;
the data quality control and standardization module 302 consists of a data quality control unit and a data standardization unit and is used for performing quality control and standardization processing on a clinical data set under a clinical theme extracted from the theme library 301;
the service scheme generation module 303 is configured to generate a clinical data service scheme according to the clinical data set for each clinical topic, and the service scheme generation module 303 includes a data grouper, a model generator, a model selector, and a comprehensive risk prompt module, where the data grouper is configured to group clinical data sets subjected to quality control and standardization after verification and standardization according to clinical data service types; the model generator is used for modeling the clinical data set grouped according to the clinical service types to obtain modeled clinical data service schemes of different clinical service types; a model selector for identifying a modeled clinical data schema corresponding to a clinical service type using different model identifications according to a requested clinical topic, and providing the schema to the data service module 304; the comprehensive risk prompting module is used for acquiring the corresponding abnormal data statistical table corresponding to the modeled clinical data service schemes of different clinical service types, acquiring comprehensive risk prompting information according to the acquired corresponding abnormal data statistical table, and providing the comprehensive risk prompting information to the data service module 304;
the data service module 304 is configured to, after receiving a clinical data service related request sent by the integration platform 10, obtain a clinical data service scheme corresponding to a requested clinical topic, carry the clinical data service scheme in a clinical data service related response, and return the clinical data service scheme to the integration platform 10;
the specific collection steps are as follows:
s1: the integration platform 10 and the user equipment have a communication connection relationship, and perform communication to acquire clinical data;
s2: the database 20 integrates the clinical data obtained in step S1, sets a unique identification code for the clinical data according to a set coding rule, sets a main identification code for the affiliated clinical topic, associates the clinical data with each time point of each medical operation and clinical route involved in the medical procedure as a sub-association key under the same clinical topic, forms a clinical data set of the set clinical topic corresponding to the unique identification code, and integrates according to an association rule between each information table if the clinical data exists in the form of information tables;
s3: after the integration is completed in step S2, a clinical data set corresponding to the unique identification code is formed for storage; the unique identification code can be a patient identification, the unique identification code is used as a main identification code, and each medical operation and each time point of a clinical path related in a medical process under the same set clinical theme are used as sub-association keys to associate clinical data, so that a clinical data set of the set clinical theme corresponding to the unique identification code is formed;
s4: the subject database 301 extracts a clinical data set corresponding to the unique identification code from the database 20, performs quality control and standardization processing on the clinical data set under the clinical subject extracted from the subject database 301 through the data quality control and standardization module 302, generates a clinical data service scheme according to the clinical data set through the service scheme generation module 303, receives a clinical data service related request sent by the integration platform 10 through the data service module 304, obtains the clinical data service scheme corresponding to the requested clinical subject, carries the clinical data service related request in a clinical data service related response, and returns the clinical data service related response to the integration platform 10;
s5: the data quality control unit performs correctness verification and abnormal data warning and marking on the clinical data set under the clinical theme extracted from the theme library 301; according to the particularity of clinical data, the abnormal data judges that a normal threshold value which is suitable for the clinical data is identified according to a main body identification code and a sub-association key of each subject database 301; the correctness is checked by judging whether the data null value of the data extraction result of the subject database 301 is in the preset null value threshold range or not and the data distribution condition, and if the data null value duty ratio is larger than the set null value threshold range, marking; if the difference between the data dispersion of the data extraction result of the subject database 301 and a preset value is not within the range of a set difference threshold, marking abnormal data;
s6: the data normalization unit is used for normalizing the clinical data set under the clinical topic extracted from the topic library 301, and the normalization scheme includes but is not limited to: the linear function is standardized, the mean value is standardized, and a normalization processing scheme matched with the data service demand response is adopted; the data binning submodule is used for extracting clinics from the topic library 301 according to the set end point event type so as to avoid index contribution degree difference caused by different dimensions;
s7: the subject clinical data set is subjected to adaptive binning, the binning method performs reprocessing according to the information entropy obtained by each binning result until the information entropy obtained by the binning result reaches a preset entropy value, and the set endpoint event type can be from the data service module 304, including but not limited to: adverse events, cost prediction, morbidity risk, clinical diagnosis and treatment path; the abnormal data processing sub-module removes or supplements the abnormal data marked by the data quality control unit, and generates an abnormal data statistical table for the extraction result of the clinical data set under the clinical theme extracted by the theme library 301;
s8: the data service module 304 receives a clinical data service related request sent by the integration platform 10, acquires a clinical data service scheme corresponding to a requested clinical topic from the model selector, acquires corresponding comprehensive risk prompt information from the comprehensive risk prompt module, carries the comprehensive risk prompt information in a clinical data service related response, and returns the response to the integration platform 10; if the integration platform 10 receives the clinical data from the user device in real time, the database 20 is updated in real time after integration with the corresponding set of clinical data of the unique identification code; the subsequent processing of the clinical data set by the cloud platform 30 to obtain a clinical data service scheme is also updated in real time;
s9: setting a unique identification code for clinical data according to a set coding rule, setting a main identification code for a belonged clinical theme, and performing association of the clinical data by taking each medical operation and each time point of a clinical path related in a medical process as a sub-association key under the same clinical theme to form a clinical data set of the set clinical theme corresponding to the unique identification code.
As shown in fig. 1-2, the embodiment specifically is: the method comprises the steps of acquiring original clinical data by adopting a clinical test electronic data desensitization acquisition management method, processing the original clinical data based on a block chain technology, and inputting the processed data into an electronic data capture system (EDC); the EDC receives an original clinical data calling request, restores the original clinical data after data processing based on the block chain technology, and sends the restored original clinical data to a data request end; the method is suitable for the scene of safe data storage and sharing of EDC, provides data safety service for clinical data statistics, analysis, inspection and sharing, and ensures the completeness of data.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. A method of acquiring clinical data, comprising: the system comprises an integration platform (10), a database (20) and a cloud platform (30);
the integration platform (10) is used for transmitting clinical data to the database (20) in real time, sending the received clinical data service acquisition related request to the cloud platform (30), and receiving a clinical data service result returned by the cloud platform (30);
the database (20) is used for integrating and storing clinical data transmitted from the integration platform (10);
the cloud platform (30) specifically comprises: the cloud platform (30) is used for extracting stored clinical data corresponding to clinical topics from the database (20), performing quality control and standardization processing on the extracted clinical data corresponding to the topics, and generating clinical data service schemes corresponding to the clinical topics; after receiving the clinical data service related request, acquiring a clinical data service scheme corresponding to a clinical theme, and returning the clinical data service scheme to the integration platform (10);
the subject database (301) is used for determining the unique identification code related to the clinical subject, and extracting a clinical data set corresponding to the unique identification code from the database (20);
the data quality control and standardization module (302) consists of a data quality control unit and a data standardization unit and is used for performing quality control and standardization processing on a clinical data set under a clinical theme extracted from the theme library (301);
the service scheme generation module (303) is used for generating clinical data service schemes according to the clinical data sets aiming at all clinical topics, and the service scheme generation module (303) comprises a data grouping device, a model generator, a model selector and a comprehensive risk prompt module, wherein the data grouping device is used for grouping the clinical data sets subjected to quality control and standardization processing after verification and standardization according to clinical data service types; the model generator is used for modeling the clinical data set grouped according to the clinical service types to obtain modeled clinical data service schemes of different clinical service types; a model selector for identifying a modeled clinical data schema for a corresponding clinical service type using different model identifications according to a requested clinical topic, for provision to a data service module (304); the comprehensive risk prompting module is used for acquiring the corresponding abnormal data statistical table corresponding to the modeled clinical data service schemes of different clinical service types, acquiring comprehensive risk prompting information according to the acquired corresponding abnormal data statistical table, and providing the comprehensive risk prompting information to the data service module (304);
the data service module (304) is configured to, after receiving a clinical data service related request sent by the integration platform (10), obtain a clinical data service scheme corresponding to a requested clinical topic, carry the clinical data service scheme in a clinical data service related response, and return the clinical data service scheme to the integration platform (10);
the specific collection steps are as follows:
s1: the integration platform (10) is communicated with user equipment to acquire clinical data;
s2: the database (20) integrates the clinical data obtained in the step S1, and sets a unique identification code for the clinical data according to a set coding rule;
s3: after the integration is completed in step S2, a clinical data set corresponding to the unique identification code is formed for storage; the unique identification code can be a patient identification, the unique identification code is used as a main identification code, and each medical operation and each time point of a clinical path related in a medical process under the same set clinical theme are used as sub-association keys to associate clinical data, so that a clinical data set of the set clinical theme corresponding to the unique identification code is formed;
s4: the subject database (301) extracts a clinical data set corresponding to the unique identification code from the database (20), the quality control and standardization processing is carried out on the clinical data set under the clinical subject extracted from the subject database (301) through the data quality control and standardization module (302), then a clinical data service scheme is generated according to the clinical data set through the service scheme generation module (303), and after a clinical data service related request sent by the integration platform (10) is received by the data service module (304), the clinical data service scheme corresponding to the requested clinical subject is obtained and carried in a clinical data service related response and returned to the integration platform (10);
s5: the data quality control unit carries out correctness verification and abnormal data warning and marking on a clinical data set under a clinical theme extracted from the theme library (301); according to the particularity of clinical data, the abnormal data judges that a normal threshold value which is suitable for the clinical data is identified according to a main body identification code and a sub-association key of each subject library (301);
s6: the clinical data set under the clinical theme extracted from the theme library (301) is standardized by a data standardization unit, and mainly comprises a data standardization sub-module, a data binning sub-module and an abnormal data processing sub-module, wherein the data standardization sub-module is used for carrying out normalization processing on the clinical data set under the clinical theme extracted from the theme library (301);
s7: performing self-adaptive binning on the clinical data set under the theme, and performing reprocessing by using the binning method according to the information entropy obtained by grouping results each time until the information entropy obtained by binning results reaches a preset entropy value; the abnormal data processing sub-module removes or supplements the value of the abnormal data marked by the data quality control unit, and generates an abnormal data statistical table for the extraction result of the clinical data set under the clinical topic extracted by the topic library (301);
s8: the data service module (304) receives a clinical data service related request sent by the integration platform (10), acquires a clinical data service scheme corresponding to a requested clinical theme from the model selector, acquires corresponding comprehensive risk prompt information from the comprehensive risk prompt module, carries the comprehensive risk prompt information in a clinical data service related response, and returns the comprehensive risk prompt information to the integration platform (10);
s9: setting a unique identification code for clinical data according to a set coding rule, setting a main identification code for a belonged clinical theme, and performing association of the clinical data by taking each medical operation and each time point of a clinical path related in a medical process as a sub-association key under the same clinical theme to form a clinical data set of the set clinical theme corresponding to the unique identification code.
2. A method of clinical data acquisition as claimed in claim 1, wherein: the integrated platform (10) is composed of medical acquisition systems of various service types, including but not limited to a computer application system (HIS) for information management and online operation in medical management and medical activities, an examination information management system (LIS), an electronic medical record system (EMR), an image archiving and communication system (PACS), a nursing system and a medical logistics system.
3. A method of clinical data acquisition as claimed in claim 1, wherein: the integration platform (10) and the user equipment have a communication connection relation.
4. A method of clinical data acquisition as claimed in claim 1, wherein: the subject database (301) is used for extracting the data of the subject database (301) from the clinical data set which is stored in the database (20) and corresponds to the unique identification code, and extracting the data according to various set data use requirements during extraction, wherein the various data use requirements are obtained through the integration platform (10), the identification codes corresponding to the requirements are extracted one by one, if the identification codes are extracorporeal circulation and intraoperative blood transfusion respectively, the extraction result of the subject database (301) is the extracorporeal circulation blood subject database (20).
5. A method of clinical data acquisition as claimed in claim 1, wherein: in step S2, a main body identification code is set for the clinical topic to which the subject belongs, and each time point of each medical operation and clinical route related to the medical procedure under the same clinical topic is used as a sub-association key to associate the clinical data, so as to form a clinical data set with a corresponding unique identification code of the set clinical topic, and if the clinical data exists in the form of an information table, the clinical data set is integrated according to an association rule between the information tables.
6. A method of clinical data acquisition as claimed in claim 1, wherein: the normalization scheme in step S6 includes but is not limited to: the linear function is standardized, the mean value is standardized, and a normalization processing scheme matched with the data service demand response is adopted; and the data binning submodule is used for extracting clinics from the topic library (301) according to the set endpoint event type.
7. A method of clinical data acquisition as claimed in claim 1, wherein: the type of endpoint event for the preset entropy setting in step S7 may come from the data service module (304), including but not limited to: adverse events, cost prediction, risk of morbidity, clinical diagnosis and treatment routes.
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| CN113674868A (en) * | 2021-08-24 | 2021-11-19 | 联仁健康医疗大数据科技股份有限公司 | Method, device, equipment and storage medium for acquiring clinical research data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113674868A (en) * | 2021-08-24 | 2021-11-19 | 联仁健康医疗大数据科技股份有限公司 | Method, device, equipment and storage medium for acquiring clinical research data |
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