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WO2018120668A1 - Système et procédé d'association et de stockage de mégadonnées médicales - Google Patents

Système et procédé d'association et de stockage de mégadonnées médicales Download PDF

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Publication number
WO2018120668A1
WO2018120668A1 PCT/CN2017/088351 CN2017088351W WO2018120668A1 WO 2018120668 A1 WO2018120668 A1 WO 2018120668A1 CN 2017088351 W CN2017088351 W CN 2017088351W WO 2018120668 A1 WO2018120668 A1 WO 2018120668A1
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WIPO (PCT)
Prior art keywords
patient
data
medical
information table
medical data
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Ceased
Application number
PCT/CN2017/088351
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English (en)
Chinese (zh)
Inventor
张贯京
葛新科
王海荣
高伟明
张红治
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E-Techno Information Technologies Co Ltd
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E-Techno Information Technologies Co Ltd
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Publication of WO2018120668A1 publication Critical patent/WO2018120668A1/fr
Anticipated expiration legal-status Critical
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Classifications

    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS

Definitions

  • the present invention relates to the technical field of medical informationization, and in particular, to a medical big data association storage system and method.
  • the main object of the present invention is to provide a medical big data associated storage system and method, which aims to solve the problem that the existing medical data storage system performs distributed storage of massive medical data and affects the efficiency of medical data analysis and processing. Problem solution
  • the present invention provides a medical big data associative storage system, which runs in a cloud server, and the cloud server establishes a communication connection with a plurality of medical data sources through a communication network, and connects through a database and a large Data storage warehouse connection, the system includes:
  • a data collection module configured to collect raw medical data of each patient from a plurality of medical data sources
  • a data cleaning module configured to perform cleaning conversion processing on each patient's original medical data to obtain each patient's Standardize medical data
  • a data extraction module configured to extract identity information, vital sign data, and historical visit information of each patient from each patient's standardized medical data, and generate an identity for each patient according to the identity information of each patient. number;
  • a data association module configured to associate each patient's identification number with the patient's corresponding vital sign data and establish a patient vital information table, and associate each patient's identification number with the patient's respective history. Correlate the visit information and establish a patient visit information form;
  • a data storage module configured to store the patient sign information table in a first partition database in a big data storage warehouse, and store the patient visit information table in a second partition database in the big data storage warehouse in.
  • the data collection module collects the original medical data of each patient from a plurality of medical data sources by: setting a execution time and execution period of a constant script, and following the script
  • the execution of the daytime and execution cycle collects raw medical data from each patient from different medical data sources.
  • the manner in which the data cleaning module performs the cleaning conversion process on the raw medical data of each patient is: using the ETL data filtering conversion component to remove meaningless words in the original medical data, and one of the original medical data is The different forms of the word are converted to the same form, and the duplicated data in the original medical data is deleted.
  • the header field of the patient vital sign information table stores an identity identification number of each patient
  • the content field of the patient vital sign information table stores vital sign data corresponding to each patient
  • the patient visit information table The header field stores the identification number of each patient
  • the content field of the patient vital information table stores historical visit information corresponding to each patient.
  • the vital sign data includes a patient's height data, body weight data, blood pressure data, pulse data, heart rate data, blood oxygen data, and blood glucose data
  • the historical medical information includes a history of the patient, history, history Visiting hospitals, historical clinics, and historical electronic medical records.
  • the present invention also provides a medical big data association storage method, which is applied to a cloud server, wherein the cloud server establishes a communication connection with a plurality of medical data sources through a communication network, and connects with a big data storage warehouse through a database connection.
  • the medical big data association storage method includes the steps of:
  • the step of collecting raw medical data of each patient from a plurality of medical data sources comprises the following steps:
  • Raw medical data for each patient is collected from different medical data sources in accordance with the execution time and execution cycle of the programmer script.
  • the step of performing the cleaning conversion process on the raw medical data of each patient comprises the following steps: removing the meaningless words in the original medical data by using the ETL data filtering conversion component; Different forms of words are converted to the same form; delete duplicate data from raw medical data
  • the header field of the patient vital sign information table stores an identity identification number of each patient
  • the content field of the patient vital sign information table stores vital vital sign data corresponding to each patient
  • the patient is
  • the header field of the medical information table stores the identification number of each patient
  • the content field of the patient vital information table stores historical medical visit information corresponding to each patient.
  • the vital sign data includes a patient's height data, body weight data, blood pressure data, pulse data, heart rate data, blood oxygen data, and blood glucose data
  • the historical medical information includes a history of the patient, history, history Visiting hospitals, historical clinics, and historical electronic medical records.
  • the medical big data associated storage system and method of the present invention adopts the above technical solutions, and the technical effects brought by: collecting medical data in different medical data sources, and Data is cleaned and converted to obtain standardized medical data, making medical data collection more comprehensive and accurate.
  • each patient's identification number is used as a relationship between the vital information table and the medical information table, and the patient's vital information table and the medical information table are stored in different partition databases of the big data storage warehouse to reduce
  • the system load improves data processing efficiency, avoids conflicts in accessing medical data, speeds up the reading and processing of medical data, and improves medical service level and patient satisfaction.
  • FIG. 1 is a block diagram of a preferred embodiment of a medical big data associative storage system of the present invention
  • FIG. 2 is a flow chart of a preferred embodiment of the medical big data association storage method of the present invention.
  • FIG. 1 is an application environment framework of a preferred embodiment of the medical big data associative storage system of the present invention.
  • the medical big data associated storage system 10 is applied and runs in the cloud server 1, and the cloud server 1 is connected to the plurality of medical data sources 2 through the communication network 3 (two in FIG. 1 as an example).
  • Description Establish a communication connection and connect to the big data storage repository 4 via the database connection 5.
  • the communication network 3 can be a wired communication network or a wireless communication network.
  • the communication network 3 is preferably a wireless communication network, including but not limited to, a GSM network, a GPRS network, a CDMA network, a TD-SCDMA network, a WiMAX network, a TD-LTE network, an FDD-LTE network, and the like. transporting network.
  • the database connection 5 can be an Open Database Connectivity (ODBC) and a Java Data Base Connectivity (JDBC).
  • the cloud server 1 is a cloud platform or a server in the cloud platform, and the data transmission capability, the data storage capability, and the data processing capability of the cloud server 1 can be quickly collected from different medical data sources 2 Different raw medical data.
  • the medical data source 2 stores the original medical data of the patient, and may be a hospital information system that generates clinical data, such as a HIS system, an EMR, a LIS, a PACS system, or any suitable medical center, private clinic, emergency center, and the like.
  • the big data storage repository 4 includes a first partition database 41 and a second partition database 42, a first partition database 41 for storing patient sign information tables, and a second partition database 42 for storing patient visit information tables.
  • the patient sign information table is used to store vital sign data of patients collected from different medical data sources 2 for storing historical visit data of patients collected from different medical data sources 2.
  • the cloud server 1 includes, but is not limited to, a medical big data associated storage system 10, a communication unit 11, a storage unit 12, and a processing unit 13.
  • the communication unit 11 is a wired communication interface or a wireless communication interface, for example, a communication interface supporting communication technologies such as GSM, GPRS, WCDMA, CDMA, TD-S CDMA, WiMAX TD-LTE ⁇ FDD-LTE.
  • the storage unit 12 can be a read only memory unit ROM, an EEPROM, a flash memory unit F LASH or a solid hard disk.
  • the processing unit 13 may be a central processing unit (CPU), a microcontroller (MCU), a data processing chip, or an information processing unit having a data processing function.
  • the medical big data associated storage system 10 includes, but is not limited to, a data collection module 101, a data cleaning module 102, a data extraction module 103, a data association module 104, and a data storage module 105.
  • the module referred to in the present invention refers to a module that can be executed by the processing unit 13 of the cloud platform server 1 and is capable of A series of computer program instruction segments that perform fixed functions are stored in the storage unit 12 of the cloud platform server 1.
  • the data collection module 101 is configured to collect raw medical data for each patient from a plurality of different medical data sources 2.
  • the generation and collection of the patient's original medical data usually comes from the clinical data generated by the hospital information system, such as HIS system, EMR, LIS, PACS system, but with the development of the Internet of Things, the patient's original medical data can also come from Any suitable clinical business system, such as a medical center, private clinic, and emergency center.
  • the data collection module 101 is specifically configured to set an execution time and an execution period of a programmer script, and collect original medical data from different medical data sources according to execution time and execution cycle of the programmer script. .
  • the data cleaning module 102 is configured to perform a cleaning conversion process on the raw medical data of each patient to obtain standardized medical data for each patient.
  • the data cleaning module 102 needs to utilize ETL (extract, Transform (loading), loading (lo ad)
  • ETL extract, Transform (loading), loading (lo ad)
  • the data filtering and transformation component cleans and converts the collected raw medical data to obtain standardized medical data, thereby ensuring the accuracy of medical data and saving storage for medical data storage. space.
  • the manner in which the data cleaning module 102 performs the cleaning conversion process on the raw medical data of each patient is: using the ETL data filtering conversion component to remove meaningless words in the original medical data, and using a word in the original medical data.
  • the different forms are converted to the same form, and the processing of duplicate data in the original medical data is deleted.
  • the data extraction module 103 is configured to extract identity information, vital sign data, and historical visit information of each patient from the standardized medical data of each patient, and generate one for each patient according to the identity information of each patient.
  • Identification number ID
  • the vital sign data includes data such as height data, body weight data, blood pressure data, pulse data, heart rate data, blood oxygen data, blood glucose data, and the like of the patient.
  • the historical medical treatment information includes data such as a patient's historical visit, a historical hospital, a historical medical department, and a historical electronic medical record.
  • the data association module 104 is configured to associate each patient's identification number with the patient's corresponding vital sign data and establish a patient sign information table, and associate each patient's identification number with the patient.
  • the historical visit information is linked and a patient visit information form is created.
  • the header field of the patient vital sign information table stores an identification number of each patient
  • the content field of the patient vital sign information table stores vital sign data corresponding to each patient
  • the table of the patient medical information table The header field stores the identity identification number of each patient
  • the content field of the patient vital sign information table stores historical medical visit information corresponding to each patient.
  • the data storage module 105 is configured to store the patient vital information information table in the first partition database 41 in the big data storage warehouse 4, and store the patient visit information table in the second partition in the big data storage warehouse 4.
  • database 42 Since each patient's identification number is unique, each patient's identification number is used as a relationship between the patient's vital information table and the patient's medical information table, and the patient's physical information table and the patient's medical information table are stored separately.
  • the access to medical data is avoided, conflicts are generated, and the reading and processing speed of the massive medical data is accelerated, thereby improving the medical service level and patient satisfaction.
  • the present invention also provides a medical big data association storage method.
  • FIG. 2 is a flow chart of a preferred embodiment of the medical big data association storage method of the present invention.
  • the medical big data association storage method includes the following steps:
  • Step S21 Collecting raw medical data of each patient from different medical data sources; specifically, the data collecting module 101 collects raw medical data of each patient from a plurality of different medical data sources 2.
  • the generation and collection of the patient's original medical data usually comes from the clinical data generated by the hospital information system, such as HIS system, EMR, LIS, PACS system, but with the development of the Internet of Things, the patient's original medical data can also come from Any suitable clinical business system, such as a medical center, private clinic, and emergency center.
  • the step of the data acquisition module 101 performing the cleaning conversion process on the raw medical data of each patient includes the steps of: setting an execution time and an execution period of a programmer script according to the execution of the script. Inter- and execution cycles collect raw medical data from different medical data sources.
  • Step S22 performing clean conversion processing on the raw medical data of each patient to obtain standardized medical data for each patient; specifically, the data cleaning module 102 performs cleaning conversion processing on each patient's original medical data to obtain each patient. Standard medical data.
  • the data cleaning module 102 needs to utilize ETL (extract, Transformation The data filtering conversion component performs the cleaning conversion processing on the collected raw medical data to obtain the standardized medical data, thereby ensuring the accuracy of the medical data and saving the storage space for the storage of the medical data.
  • the step of performing the cleaning conversion process on the raw medical data of each patient by the data cleaning module 102 includes the steps of: removing the meaningless word in the original medical data by using the ETL data filtering conversion component, and using a word in the original medical data
  • the different forms are converted to the same form, and the processing of duplicate data in the original medical data is deleted.
  • Step S23 extracting identity information and vital sign data of each patient from the standardized medical data of each patient; specifically, the data extraction module 103 extracts identity information of each patient from the standardized medical data of each patient. , vital signs data and historical visit information.
  • the vital sign data of the patient includes data such as height data, body weight data, blood pressure data, pulse data, heart rate data, blood oxygen data, and blood glucose data of the patient.
  • the patient's historical visit information includes data such as the patient's historical visit, historical hospital, historical medical department, and historical electronic medical records.
  • step S24 generating an identity identification number for each patient according to the identity information of each patient; specifically, the data extraction module 103 generates an identity identification number for each patient according to the identity information of each patient, as each The unique identity of the patient.
  • Step S25 associating the identification number of each patient with the vital sign data of the patient and establishing a patient vital information table, and associating the identification number of each patient with the historical medical information of the patient and Establishing a patient visit information table; specifically, the data association module 104 associates each patient's identification number with the patient's respective vital sign data and establishes a patient sign information table, and identifies each patient's identification number and The patient's respective historical visit information is correlated and a patient visit information form is established.
  • the header field of the patient vital sign information table stores the identity identification number of each patient
  • the content field of the patient vital sign information table stores vital sign data corresponding to each patient
  • the patient visit information table The header field stores the identification number of each patient
  • the content field of the patient vital information table stores a historical visit corresponding to each patient.
  • step S26 storing the patient vital sign information table in the first partition database in the big data storage warehouse, and storing the patient visit information table in the second partition database in the big data storage warehouse; specifically, The data storage module 105 stores the patient vital information table in the first partition database 41 in the big data storage repository 4, and stores the patient visit information table in the second partition database 42 in the big data storage repository 4.
  • the identification number of each patient is unique, the identification number of each patient is used as a relationship between the patient's vital information table and the patient's medical information table, and the patient's vital information table and the patient's medical information are displayed.
  • the table branches are stored in different partition databases of the big data storage warehouse 4, thereby enhancing data structured storage and avoiding conflicts in accessing medical data, speeding up the reading and processing speed of the medical data by the cloud server 1 Improve medical service levels and patient satisfaction.
  • the medical big data associated storage system and method of the present invention collects medical data in different medical data sources 2, and performs medical cleaning and conversion processing to obtain standardized medical data, thereby making medical data collection more comprehensive. more precise.
  • each patient's identification number is used as a relationship between the vital information table and the medical information table, and the patient's vital information table and the medical information table are stored in different partition databases of the big data storage warehouse to reduce
  • the system load improves data processing efficiency, avoids conflicts in accessing medical data, speeds up the reading and processing of medical data, and improves medical service level and patient satisfaction.
  • the medical big data associated storage system and method of the present invention adopts the above technical solutions, and the technical effects brought by: collecting medical data in different medical data sources, and Data is cleaned and converted to obtain standardized medical data, making medical data collection more comprehensive and accurate.
  • each patient's identification number is used as a relationship between the vital information table and the medical information table, and the patient's vital information table and the medical information table are stored in different partition databases of the big data storage warehouse to reduce
  • the system load improves data processing efficiency, avoids conflicts in accessing medical data, speeds up the reading and processing of medical data, and improves medical service level and patient satisfaction.

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Abstract

La présente invention concerne un système et un procédé d'association et de stockage de mégadonnées médicales. Le procédé comprend les étapes suivantes consistant à : collecter des données médicales originales de chaque patient depuis une pluralité de différentes sources de données médicales (S21) ; réaliser un traitement de nettoyage et de conversion sur les données médicales originales pour obtenir des données médicales standard de chaque patient (S2) ; extraire des informations d'identité et des données de signes vitaux liées à chaque patient à partir des données médicales standard de chaque patient (S23) ; générer un numéro d'identification d'identité pour chaque patient selon les informations d'identité liées à chaque patient (S24) ; associer le numéro d'identification d'identité de chaque patient aux données de signes vitaux du patient et établir une table d'informations de signes de patient, et associer le numéro d'identification d'identité de chaque patient à des informations historiques d'examen liées au patient et établir une table d'informations d'examen de patient (S25) ; et stocker la table d'informations de signes de patient dans une première base de données de partitions d'un entrepôt de stockage de mégadonnées et stocker la table d'informations d'examen de patient dans une seconde base de données de partitions de l'entrepôt de stockage de mégadonnées (S26). Au moyen de la collecte de données médicales depuis différentes sources de données médicales et de la réalisation d'une association et d'un stockage, l'exhaustivité et la précision de la collecte des données médicales et l'efficacité de traitement des données médicales sont améliorées.
PCT/CN2017/088351 2016-12-31 2017-06-15 Système et procédé d'association et de stockage de mégadonnées médicales Ceased WO2018120668A1 (fr)

Applications Claiming Priority (2)

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CN201611267777.X 2016-12-31
CN201611267777.XA CN106815337A (zh) 2016-12-31 2016-12-31 医疗大数据关联存储系统及方法

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CN110442638A (zh) * 2019-07-23 2019-11-12 万达信息股份有限公司 一种医疗数据的可视化信息生成方法及系统
CN113537529A (zh) * 2021-07-30 2021-10-22 国能铁路装备有限责任公司 货车轴承关联数据生成方法、装置和计算机设备
CN113938539B (zh) * 2021-12-17 2022-03-04 中国人民解放军总医院 医疗数据的溯源方法和系统
CN114417033A (zh) * 2022-01-14 2022-04-29 联仁健康医疗大数据科技股份有限公司 影像数据的处理系统、方法、电子终端及存储介质
CN119166057B (zh) * 2024-09-02 2025-07-22 深圳市第二人民医院(深圳市转化医学研究院) 医疗检测设备的数据存储方法、系统及应用

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050158767A1 (en) * 2003-12-19 2005-07-21 Haskell Robert E. System for managing healthcare data including genomic and other patient specific information
CN1845115A (zh) * 2006-05-15 2006-10-11 赵达 医疗数据处理系统及处理方法
CN101782935A (zh) * 2009-01-19 2010-07-21 中国人民解放军第二军医大学 一种中医电子病历系统和病历数据解析处理方法
CN102054032A (zh) * 2010-12-22 2011-05-11 广州市慧通计算机有限公司 一种医疗数据信息处理方法及其系统
CN104392132A (zh) * 2014-11-24 2015-03-04 蔡志明 一种基于云计算的医疗数据存储系统
CN106815337A (zh) * 2016-12-31 2017-06-09 深圳市易特科信息技术有限公司 医疗大数据关联存储系统及方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050158767A1 (en) * 2003-12-19 2005-07-21 Haskell Robert E. System for managing healthcare data including genomic and other patient specific information
CN1845115A (zh) * 2006-05-15 2006-10-11 赵达 医疗数据处理系统及处理方法
CN101782935A (zh) * 2009-01-19 2010-07-21 中国人民解放军第二军医大学 一种中医电子病历系统和病历数据解析处理方法
CN102054032A (zh) * 2010-12-22 2011-05-11 广州市慧通计算机有限公司 一种医疗数据信息处理方法及其系统
CN104392132A (zh) * 2014-11-24 2015-03-04 蔡志明 一种基于云计算的医疗数据存储系统
CN106815337A (zh) * 2016-12-31 2017-06-09 深圳市易特科信息技术有限公司 医疗大数据关联存储系统及方法

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112582056A (zh) * 2020-12-21 2021-03-30 曙光星云信息技术（北京）有限公司 一种基于大数据技术的区域医疗信息管理平台
CN112863626A (zh) * 2021-03-08 2021-05-28 北京冠新医卫软件科技有限公司 多平台相似医疗数据去除方法、装置及设备
CN114974565A (zh) * 2022-05-17 2022-08-30 上海市第四人民医院 新冠患者诊疗阶段精细化管控方法及系统
CN115033566A (zh) * 2022-05-23 2022-09-09 西安交通大学医学院第一附属医院 一种多源异构医疗大数据融合方法及系统
CN115757645A (zh) * 2022-12-19 2023-03-07 重庆忽米网络科技有限公司 基于云端分区存储和共享积分的工业设备数据共享方法
CN117272395A (zh) * 2023-11-21 2023-12-22 江西曼荼罗软件有限公司 一种患者医疗数据处理方法及系统
CN117272395B (zh) * 2023-11-21 2024-01-26 江西曼荼罗软件有限公司 一种患者医疗数据处理方法及系统

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