CN113871025A - Dermatological clinical special disease database construction method and system - Google Patents
Dermatological clinical special disease database construction method and system Download PDFInfo
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- CN113871025A CN113871025A CN202111052527.5A CN202111052527A CN113871025A CN 113871025 A CN113871025 A CN 113871025A CN 202111052527 A CN202111052527 A CN 202111052527A CN 113871025 A CN113871025 A CN 113871025A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/338—Presentation of query results
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Abstract
The invention discloses a dermatology clinical special disease database construction method and a system, wherein the method comprises the following steps: creating a dermatology-specific disease data model; creating an exchange data scheme; creating a clinical skin disease database project; collecting case basic archive data; performing a patient follow-up task; retrieving and deriving statistical application data; and optimizing the special disease database data model. The system function module comprises a data exchange module, a data management module, a data model management module, a special patient database project module, a case management module, a follow-up visit management module, a data application module and a system management module. The invention can solve the problems of low doctor data processing efficiency and long processing time; the classified storage and unified management of clinical data, special data, image data and follow-up data are realized; privacy protection, safe communication and effective supervision on data are realized; the scientific research capability and the working efficiency of dermatologists are improved, and the clinical scientific research value of data is shown.
Description
Technical Field
The invention relates to the field of medical instruments, in particular to a method and a system for constructing a clinical special disease database of a dermatology department.
Background
For a long time, clinical research is always a weak link in the development of medical science and technology in China. Due to insufficient clinical medical research deployment in China, the development of a scientific research innovation platform which is an important link in clinical medicine is almost blank, so that the problems of serious professional construction delay, high dispersion and lack of integration of clinical resources, insufficient clinical research innovation, low level and delayed overall development of a clinical research team are caused. Researchers in clinical research need to collate required data in a plurality of systems and paper materials in hospitals, and data correlation processing needs extremely high data analysis capacity and computer knowledge storage, which is not easy for clinicians. Clinical business data stored in the hospital system are greatly different from scientific research data structures, and the hospital system cannot be directly used for exporting data. The clinical data is mainly business data of event records related to medical activities and medical care and treatment intervention of patients. The scientific research data mainly focuses on diseases and treatment methods and disease conditions. And a series of problems that the application data flow is complicated, the data granularity is coarse, the data standard is difficult to unify, the data processing is inconvenient and the like result in extremely low utilization degree of the existing data. Data are stored through excel and other traditional tools, and preparation work in the early stage of research is time-consuming and labor-consuming. The image data of most cases in dermatology is the most important analysis basis in clinical research, and the image data is almost stored in a separate storage medium such as a detection device, a hard disk, and a memory card. The information contained in a single image after the single image is separated from the clinical data of a patient is limited, and the difficulty of clinical scientific research is undoubtedly improved for the centralized management of the image data. Therefore, how to provide a database system for clinical special diseases of dermatology to improve the use value of data, improve the clinical scientific research capability and promote the development of dermatology is a problem to be solved in the special construction aspect of dermatology.
Disclosure of Invention
In order to solve the defects and shortcomings of the technical problems, the invention provides a dermatological clinical special disease database construction method and system, which can solve the problems of low data processing efficiency and long processing time of doctors; the classified storage and unified management of clinical data, special data, image data and follow-up data are realized; privacy protection, safe communication and effective supervision on data are realized; the scientific research capability and the working efficiency of dermatologists are improved, and the clinical scientific research value of data is shown.
A dermatology clinical special disease database construction method comprises the following steps:
step one, a dermatological special disease data model is established, data elements are sorted out, the data elements are summarized into data groups according to categories, and unique identifiers are distributed to different data groups;
step two, establishing a data exchange scheme, determining a data source, and analyzing the correctness, integrity, consistency, completeness, effectiveness, timeliness and acquirability of the data;
step three, establishing a clinical skin special disease library project, and setting project responsible persons, group entry conditions, exclusion conditions, participants and participation mechanisms;
step four, collecting case basic archive data, matching according to the patient identifier, and loading the data in the hospital into a target database;
step five, executing a follow-up task of the patient, creating a follow-up question according to the scientific research of the specific disease and the requirement of a department after treatment, associating the follow-up question with a follow-up table, and creating a periodic follow-up plan by the follow-up table;
step six, retrieving, exporting, counting and applying data;
and seventhly, optimizing the data model of the special disease database, adjusting the data model according to the change of the data requirement, and automatically updating the data model under the condition of not changing data.
Further, the data in the first step includes patient identification data, demographic data, treatment data, medical history data, diagnosis data, medication data, examination data, inspection data, treatment data, image data, operation data, and the like, wherein the data value range relates to a national medical informatization standard or an international medical informatization standard of standardized terms, and a coding corresponding relationship is established to ensure standardization, accuracy and sharing of the data.
Furthermore, data of special attributes which need to be defined separately can be stored in the model according to an entity-attribute-value mode.
Further, three mapping rules are sorted out according to the analysis in the step two:
according to the rule I, data are directly mapped to a database of a disease-specific data model without any cleaning;
according to a second rule, data needs to be converted into a special disease data model once;
and thirdly, calculating and storing the data into a target database for the second time.
Furthermore, data are loaded in a full loading mode according to the mapping rule in the first loading, and subsequently loaded data are loaded in an incremental loading mode.
Further, processing the long text data into an entity by using an NLP module, cleaning historical data, and then sorting and labeling; and extracting characteristic values to establish a data model, and putting the data model into a data training model to perform fine adjustment on the model.
Furthermore, in the third step, the participants include researchers, research managers, data collectors and department managers; the participant authority comprises an operation authority and a data authority, and is used for protecting the data use safety.
Furthermore, after the data are automatically loaded according to the examination in the step four, a special data set template is found, and special data are input according to the medical history data set field.
Further, in the fifth step, the follow-up plan is associated with the early warning item, when adverse reaction or serious illness occurs, the patient feeds back the content to trigger the early warning item, and service prompt after early warning triggering is automatically displayed.
Preferably, in the sixth step, the searching includes code searching and text searching; the export data is in an xsl and csv format; the statistical data is visualized quantitative analysis of the data of the special disease database according to a working dimension, a case dimension, a follow-up dimension and a time dimension.
Further, the system constructed according to the construction method of the dermatology clinical special disease database comprises the following functional modules:
the data exchange module is used for realizing data exchange such as data acquisition, cleaning, conversion, loading and the like with a hospital system and comprises a data standard library;
the data management module is used for managing source data, including data source tracing, data error and loading task management;
the data model management module is used for carrying out centralized management on data elements and data groups in the patient database data model, and comprises data standards, data structures, data codes and model modification;
the special patient database item module is used for managing special patient database item information, daily operation, data authority, data supervision and the like;
the case management module is used for carrying out advanced retrieval on the archive data and the image data of the case;
the follow-up visit management module is used for creating and managing a follow-up visit plan, so that a doctor can conveniently supervise the extra-hospital activities of the patient;
the data application module is used for performing medical application on data, including advanced retrieval, patient view and advanced export;
and the system management module is used for managing the user, the authority, the organization and the system configuration of the system.
The invention has the beneficial effects that:
1. the problems of low data processing efficiency and long processing time of doctors are solved;
2. the classified storage and unified management of clinical data, special data, image data and follow-up data are realized;
3. privacy protection, safe communication and effective supervision on data are realized;
4. the scientific research capability and the working efficiency of dermatologists are improved, and the clinical scientific research value of data is shown.
Drawings
FIG. 1 is a flow chart of the construction method of the present invention;
fig. 2 is a schematic diagram of functional modules of the database system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a method for constructing a database of clinical specialty diseases of dermatology, which comprises the following steps:
firstly, a dermatological special disease data model is created, patient identification data, demographic data, treatment data, medical history data, diagnosis data, medication data, examination data, inspection data, treatment data, image data, operation data and the like are comprehensively recorded in all clinical event data accumulated by a patient during the period of receiving diagnosis and treatment by combining the data requirements of dermatology on a special disease database, data elements are sorted out, unique identifications are distributed for different data elements, and each data element is ensured to have a definite position. Then, data value range, data type, data unit, accurate digit, data option, data remark and data abbreviation of the data element are listed. The data value domain relates to a reference national medical informatization standard or an international medical informatization standard of a standardized term, and a coding corresponding relation is established to ensure the standardization, the accuracy and the sharing of data; the data types can include text type, numeric type, multiple selection type, single selection type, score type, date type, time type, date time type, Boolean type; the digital type comprises a data range and a data storage digit; the date type includes a start date, an execution date, and an end date; the time type comprises a starting time, an execution time and an ending time; the time-of-day type includes a start date and time, an execution date and time, and an end date and time. And finally, summarizing the data elements into data groups according to categories in combination with scientific research purposes, assigning different unique identifiers for the data groups by referring to national health industry standards, and adding patient identification data and dates to the different data groups to ensure that the relationship between the data is established by people and time. And the universal attribute is reserved for the source of the data, so that the uniqueness and the authenticity of the data are ensured. For data needing to define special attributes separately, the data can be stored in a model according to an entity-attribute-value mode.
And step two, establishing an exchange data scheme, determining the hospital data source of the basic data of the patient according to the requirement in the step one, and analyzing the correctness, integrity, consistency, completeness, effectiveness, timeliness and acquirability of the data. Wherein, the correctness indicates whether the data correctly represents a real or verifiable source; integrity refers to whether data is complete in a certain dimension; consistency refers to the definition or understanding of whether data is consistent; completeness refers to whether all required data is missing; validity refers to whether the data is within an acceptable range; timeliness refers to whether data is complete in the required time; the availability indicates whether the data is easy to obtain, easy to understand, and easy to use. The mapping rules analyzed and sorted out can be divided into three types: one is that the data is directly mapped to a database of a disease-specific data model without any cleaning; the second type is that data needs to be converted into a special disease data model once; and the third type is that the data needs secondary calculation and is stored in a target database. The method comprises the following steps that a loading task is created for one type of data, and the loading task is divided into timed loading and real-time loading according to the timeliness degree of the data; the second kind of data is processed according to methods corresponding to data conversion and coding, wherein the data conversion comprises blank filling data, unified semantics, data deduplication, dirty data deletion, image file renaming and the like, and the coding correspondence refers to that the source data coding after sorting is correspondingly related to the data in the special data model and is used for correctly loading the data into a target database; the three types of data refer to digital data which needs to be calculated and has unified units. The first loading is carried out by importing data in a full loading mode according to the mapping rule, and the subsequent loading data is imported in an incremental loading mode. And rolling back the data with errors in the loading process in a log record mode or importing the rest data into the target database in a mobile phone loading mode. And processing the long text data into an entity by using an NLP module, cleaning historical data, then sorting and labeling, and referring to an original data cleaning process in a cleaning process. The data entity needs to be divided into character ranges and corresponding relations. And then extracting characteristic values to establish a data model, and putting the data training model into fine adjustment of the model to prevent the under-fitting problem.
And step three, establishing a clinical skin special disease library project, and setting project responsible persons, group entry conditions, exclusion conditions, participants and participation mechanisms. Wherein, the special disease database participator adds the cases meeting the grouping condition in batch by the patient name, case number and identification number, or retrieves the case result according to the retrieval condition combination to export the special disease database. The participator has the operation authority and the data authority, and the data can be set to be visible, visible in an organization and visible in a whole database, so that the use safety of the data is protected. The research types of the special disease database comprise prospective and retrospective, and the special disease database project configures a special disease database data model, and the addition is completed by selecting data elements and data groups.
And step four, collecting the basic archive data of the case. And storing case data by using the special database project data model generated in the third step, wherein the case data comprises patient identification data, demographic data, treatment data, medical history data, diagnosis data, medication data, inspection data, treatment data, image data, operation data and the like. And loading basic archive data after initial filing, and automatically importing the data into the patient archive through the interface by the data exchange scheme established in the step two, wherein the data comprise demographic data, inspection data, medication data and the like. According to the matching of the patient identifiers, the data in the hospital is loaded into a target database, the data identifiers are automatically generated, and the data of the clinical diagnosis and treatment part are integrated. After the data is automatically loaded, a special data set template is found, and special data is input according to the medical history data set field. The special data group template refers to a special template configured for data needing to be acquired about a special disease in a data model, and is composed of a special data element and a special data group and used for acquiring special disease data.
And step five, executing a follow-up task of the patient. And creating follow-up questions according to the requirements of the department after the scientific research and treatment of the special diseases, wherein the follow-up questions comprise follow-up questions, follow-up answers, follow-up prompts, follow-up feedback, requisite option configuration and the like, and are associated with a follow-up table. The follow-up table is a table integrating follow-up questions set in a certain dimension, such as a quality of life questionnaire, a psychological condition evaluation table, a disease rehabilitation condition table and the like, and is associated with a periodic follow-up plan. And creating a periodic follow-up plan by the follow-up table, wherein the periodic follow-up plan comprises a follow-up plan theme, the follow-up table, a follow-up period, follow-up push time, a follow-up responsible person, an early warning item and the like, the periodic follow-up plan generates follow-up tasks according to the follow-up push time and the follow-up table, and pushes follow-up messages to the patient under a timer according to the condition of the patient. And the patient feeds back on the service receiving platform, selects the follow-up form, fills in the content and then sends the content to the sending end. The early warning item is associated with the follow-up plan, when adverse reaction or disease aggravation problems are indicated, the patient feeds back content to trigger the early warning item, service prompt after the early warning is triggered is automatically displayed, and the follow-up responsible person association relationship prompts an early warning statistical item for a doctor to supervise the outdoor activities of the patient.
And step six, retrieving and exporting the statistical application data. The retrieval refers to selecting data element values, combination of logical operators and occurrence combination conditions for retrieving patient cases, wherein coded retrieval is searched according to values, text retrieval is searched according to keywords, and precise and fuzzy search is carried out. The derivation means that the derivation full data, the first data, the last data, the data group range and the data element range are selected according to the retrieval result, and the derivation format is an xsl format and a csv format. The statistics refers to the visual quantitative analysis of the data of the special disease database according to the working dimension, the case dimension, the follow-up dimension and the time dimension, and comprises the analysis of key data on the median of follow-up time, the distribution of case birth dates, the distribution of case ages, the statistics of follow-up times, the proportion of cases to male and female and the analysis of key data according to bar charts, line charts, pie charts and trend charts under the day, week, month, quarter and year. The application means that the data provides help for doctors according to an image view, a follow-up view, a medical history view and a medical process view.
And seventhly, optimizing the special disease database data model. And adjusting the data model according to the change of the data requirement, editing the data value range, the data type, the data unit, the accurate digit, the data option, the data remark and the data abbreviation of the new data element in the special disease database, and adding the new data element into the data group. And adjusting and modifying the value range of the data element on the original data group. And under the condition of not changing the data, the data model is automatically updated.
Further, as shown in fig. 2, the system constructed according to the construction method of the database of dermatology clinical speciality comprises the following functional modules:
the data exchange module is used for realizing data exchange such as data acquisition, cleaning, conversion, loading and the like with a hospital system and comprises a data standard library;
the data management module is used for managing source data, including data source tracing, data error and loading task management;
the data model management module is used for carrying out centralized management on data elements and data groups in the patient database data model, and comprises data standards, data structures, data codes and model modification;
the special patient database item module is used for managing special patient database item information, daily operation, data authority, data supervision and the like;
the case management module is used for carrying out advanced retrieval on the archive data and the image data of the case;
the follow-up visit management module is used for creating and managing a follow-up visit plan, so that a doctor can conveniently supervise the extra-hospital activities of the patient;
the data application module is used for performing medical application on data, including advanced retrieval, patient view and advanced export;
and the system management module is used for managing the user, the authority, the organization and the system configuration of the system.
The present invention is capable of other embodiments, and various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention.
Claims (10)
1. A dermatology clinical special disease database construction method is characterized by comprising the following steps:
step one, a dermatological special disease data model is established, data elements are sorted out, the data elements are summarized into data groups according to categories, and unique identifiers are distributed to different data groups;
step two, establishing a data exchange scheme, determining a data source, and analyzing the correctness, integrity, consistency, completeness, effectiveness, timeliness and acquirability of the data;
step three, establishing a clinical skin special disease library project, and setting project responsible persons, group entry conditions, exclusion conditions, participants and participation mechanisms;
step four, collecting case basic archive data, matching according to the patient identifier, and loading the data in the hospital into a target database;
step five, executing a follow-up task of the patient, creating a follow-up question according to the scientific research of the specific disease and the requirement of a department after treatment, associating the follow-up question with a follow-up table, and creating a periodic follow-up plan by the follow-up table;
step six, retrieving, exporting, counting and applying data;
and seventhly, optimizing the data model of the special disease database, adjusting the data model according to the change of the data requirement, and automatically updating the data model under the condition of not changing data.
2. The dermatological clinical specialty database construction method of claim 1, wherein: the data in the first step comprises patient identification data, demographic data, treatment data, medical history data, diagnosis data, medication data, examination data, inspection data, treatment data, image data, operation data and the like, wherein the data value range relates to a reference national medical informatization standard or an international medical informatization standard of standardized terms, a coding corresponding relation is established, and the standardization, the accuracy and the sharing of the data are ensured.
3. The dermatological clinical specialty database construction method of claim 2, wherein: for data needing to define special attributes separately, the data can be stored in a model according to an entity-attribute-value mode.
4. The database construction method for clinical specialties in dermatology according to claim 1, wherein three mapping rules are collated according to the analysis in the second step:
according to the rule I, data are directly mapped to a database of a disease-specific data model without any cleaning;
according to a second rule, data needs to be converted into a special disease data model once;
and thirdly, calculating and storing the data into a target database for the second time.
5. The dermatological clinical specialty database construction method of claim 4, wherein: the first loading is carried out by importing data in a full loading mode according to the mapping rule, and the subsequent loading data is imported in an incremental loading mode.
6. The dermatological clinical specialty database construction method of claim 3, wherein: processing the long text data into an entity by using an NLP module, cleaning historical data, and then sorting and labeling; and extracting characteristic values to establish a data model, and putting the data model into a data training model to perform fine adjustment on the model.
7. The dermatological clinical specialty database construction method of claim 1, wherein: in the third step, the participants comprise researchers, research managers, data collectors and department managers; the participant authority comprises an operation authority and a data authority, and is used for protecting the data use safety.
8. The dermatological clinical specialty database construction method of claim 1, wherein: and after the data are automatically loaded according to the fourth step, finding out a special data set template, and inputting special data according to the medical history data set field.
9. The dermatological clinical specialty database construction method of claim 1, wherein: and step five, associating the follow-up plan with the early warning item, and when adverse reaction or disease aggravation problems occur, feeding back content to the patient to trigger the early warning item, and automatically displaying service prompts after early warning triggering.
10. The system for constructing the database for clinical specialities in dermatology according to any one of claims 1 to 9, comprising the following functional modules:
the data exchange module is used for realizing data exchange such as data acquisition, cleaning, conversion, loading and the like with a hospital system and comprises a data standard library;
the data management module is used for managing source data, including data source tracing, data error and loading task management;
the data model management module is used for carrying out centralized management on data elements and data groups in the patient database data model, and comprises data standards, data structures, data codes and model modification;
the special patient database item module is used for managing special patient database item information, daily operation, data authority, data supervision and the like;
the case management module is used for carrying out advanced retrieval on the archive data and the image data of the case;
the follow-up visit management module is used for creating and managing a follow-up visit plan, so that a doctor can conveniently supervise the extra-hospital activities of the patient;
the data application module is used for performing medical application on data, including advanced retrieval, patient view and advanced export;
and the system management module is used for managing the user, the authority, the organization and the system configuration of the system.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115691735A (en) * | 2022-11-02 | 2023-02-03 | 广州医科大学 | Multi-mode data management method and system based on special data of chronic obstructive pulmonary disease |
| CN117219214A (en) * | 2023-11-07 | 2023-12-12 | 江苏法迈生医学科技有限公司 | Data management method of clinical scientific research integrated information platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108538395A (en) * | 2018-04-02 | 2018-09-14 | 上海市儿童医院 | A kind of construction method of general medical disease that calls for specialized treatment data system |
| CN109785918A (en) * | 2018-12-29 | 2019-05-21 | 南京海泰医疗信息系统有限公司 | A kind of data collection system and method applied to clinical research |
| CN110335647A (en) * | 2019-06-21 | 2019-10-15 | 上海市精神卫生中心（上海市心理咨询培训中心） | A kind of clinical data standards system and standardized data acquisition method |
| CN111863267A (en) * | 2020-07-08 | 2020-10-30 | 首都医科大学附属北京天坛医院 | Data information acquisition method, data analysis device and storage medium |
| CN112164469A (en) * | 2020-10-14 | 2021-01-01 | 杭州卓健信息科技有限公司 | Clinical scientific research data acquisition management system |
-
2021
- 2021-09-08 CN CN202111052527.5A patent/CN113871025A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108538395A (en) * | 2018-04-02 | 2018-09-14 | 上海市儿童医院 | A kind of construction method of general medical disease that calls for specialized treatment data system |
| CN109785918A (en) * | 2018-12-29 | 2019-05-21 | 南京海泰医疗信息系统有限公司 | A kind of data collection system and method applied to clinical research |
| CN110335647A (en) * | 2019-06-21 | 2019-10-15 | 上海市精神卫生中心（上海市心理咨询培训中心） | A kind of clinical data standards system and standardized data acquisition method |
| CN111863267A (en) * | 2020-07-08 | 2020-10-30 | 首都医科大学附属北京天坛医院 | Data information acquisition method, data analysis device and storage medium |
| CN112164469A (en) * | 2020-10-14 | 2021-01-01 | 杭州卓健信息科技有限公司 | Clinical scientific research data acquisition management system |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115691735A (en) * | 2022-11-02 | 2023-02-03 | 广州医科大学 | Multi-mode data management method and system based on special data of chronic obstructive pulmonary disease |
| CN117219214A (en) * | 2023-11-07 | 2023-12-12 | 江苏法迈生医学科技有限公司 | Data management method of clinical scientific research integrated information platform |
| CN117219214B (en) * | 2023-11-07 | 2024-02-20 | 江苏法迈生医学科技有限公司 | Data management method of clinical scientific research integrated information platform |
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