WO2019219035A1 - Vital sign data processing method and system based on cloud platform - Google Patents
Vital sign data processing method and system based on cloud platform Download PDFInfo
- Publication number
- WO2019219035A1 WO2019219035A1 PCT/CN2019/087109 CN2019087109W WO2019219035A1 WO 2019219035 A1 WO2019219035 A1 WO 2019219035A1 CN 2019087109 W CN2019087109 W CN 2019087109W WO 2019219035 A1 WO2019219035 A1 WO 2019219035A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- data
- vital sign
- analysis
- real
- time
- Prior art date
Links
Images
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
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
-
- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Definitions
- the present application relates to the field of medical cloud computing, and in particular, to a cloud platform-based vital sign data processing method and system.
- Vital signs monitoring equipment including bedside multi-parameter monitor, respiratory function monitor, intracranial pressure monitor, fetal heart monitor is the hospital intensive care unit (ICU), as well as cardiology, respiratory, neurosurgery, The main equipment of the ICU in emergency department, obstetrics and gynaecology, etc., is used to monitor the vital signs of patients in real time, which plays an important role in saving the lives of patients.
- ICU hospital intensive care unit
- cardiology respiratory
- respiratory neurosurgery
- the main equipment of the ICU in emergency department, obstetrics and gynaecology, etc. is used to monitor the vital signs of patients in real time, which plays an important role in saving the lives of patients.
- ICU custodial services In the United States, there are millions of ICU custodial services per year. China is the country with the largest number of ICUs in the world. There are millions of types of vital signs monitoring devices in hospitals across the country, and they are growing rapidly.
- the Clinical Information System (CIS) of the intensive care unit has been able to reduce the labor intensity of the ICU, but it does not solve the problem of data analysis and data management of users' vital signs, and requires users to have expensive hospital information.
- HIS Hospital Information System
- data on vital signs monitoring equipment of lower-level hospitals are generally sent to higher-level hospitals remotely, helping ICU doctors in lower-level hospitals to analyze and diagnose vital signs data and guide clinical medical work.
- ICU doctors in lower-level hospitals to analyze and diagnose vital signs data and guide clinical medical work.
- a large number of ICU patient data in lower-level hospitals are concentrated in the upper-level hospitals, it will bring unbearable pressure to the higher-level hospitals, and it is difficult to achieve scale.
- the global vital sign monitoring device already has a variety of data communication interfaces, but each manufacturer has its own communication protocol and data format, which are not compatible with each other.
- Some existing technologies can solve the problem of data receiving services of other manufacturers' devices. However, the problem of different data formats of different manufacturers is not solved, and multiple processing softwares need to be set up, and the data storage format is matched, and the efficiency is obviously low.
- the information module and the communication module are connected to the vital sign data source module, and the communication module is connected to the Internet to send the data to the cloud computing module and the cloud database.
- the technology is in each device and the cloud platform of each manufacturer. Between the two, the intermediate processing link has been added, not only the complexity and cost of each device have increased significantly, but also the reliability has decreased.
- the present application aims to provide a cloud platform-based vital sign data processing method and system for solving the problem that the hospital lacks the ability to analyze and interpret vital signs data, and to solve the data format of different manufacturers' vital signs monitoring equipment, Communication protocols are incompatible with each other and it is difficult to concentrate and efficiently deal with them.
- a cloud platform-based vital sign data processing method including the following steps:
- Step S1 acquiring a plurality of user data in real time; the user data includes vital sign monitoring device ID code, patient information, and vital sign data;
- Step S2 pre-processing each acquired user data and performing unified encapsulation and storing the data into the vital sign database;
- step S3 the vital sign data in the vital sign database is read in real time, and the deep learning framework of the distributed parallel computing is used for analysis and screening processing, and the analysis screening result is obtained, and a data analysis report is generated.
- step S2
- the patient business serial number and the preprocessed vital sign data are uniformly encapsulated and stored in the vital sign database.
- the user information, clinical information, and data information are linked by the service serial number, and the device ID is bidirectionally mapped and converted, thereby solving the problem of identifying different patients of the same device (the same bed), and establishing a reliable and efficient internal data query and data interaction. Relationship with external logic to meet internal data query and interaction with external data.
- the real-time reading of the vital sign data in the vital sign database and the use of the distributed parallel computing deep learning framework for analysis and screening processing are implemented by an online real-time data analysis processing method and a deep learning framework based on the Spark engine:
- the Spark distributed parallel computing deep learning framework reads the vital sign data in the vital sign data. According to the set micro-batch processing interval, the Spark engine creates multiple tasks in parallel, triggering Spark Streaming to cut the data by type. It is divided into RDD data sets, and the central model of the corresponding type is controlled to calculate and process the type data.
- the central model is divided into two categories, one is to analyze and calculate the shape, rhythm and rate of the waveform data, and the other is to analyze and calculate the amplitude of the numerical data;
- the central model includes a second-order differential calculation tool and / or logic analysis tools, real-time calculation and analysis of the shape, rhythm, rate, and value of vital sign data, classification and labeling of waveforms, statistical summarization of logarithmic values, real-time analysis and screening of abnormal data beyond the benchmark.
- the central model calculation process finds abnormal data exceeding the set reference, the abnormal data feature is analyzed, the duration is calculated, and the abnormal data attribute is marked.
- the abnormal data is generated into a real-time data analysis report, an abnormal event warning is issued to the user, and the real-time data analysis report is sent to the user.
- the deep learning framework based on Spark engine has the advantages of distributed, high throughput and self-learning. It realizes real-time analysis and processing of massive vital signs data, solves the problem of lack of ability of hospitals to analyze and interpret vital signs data, and timely discovers abnormal data and supports medical care. Rapid response interventions improve the quality of medical care and work efficiency.
- each type of central model is trained and optimized in real time to obtain a new central model of the type data.
- the central model applies quantitative and qualitative vital sign data for training optimization, which can further improve the analysis and calculation accuracy of the central model, and thus effectively improve the data processing efficiency of the cloud platform, reduce the frequent equipment false alarm events during the vital sign monitoring process, and reduce Medical staff's labor intensity and work pressure.
- the dynamic data analysis report is automatically generated, and the dynamic data analysis report is sent to the user according to the patient service serial number.
- the vital signs monitoring equipment lacks the summary data of the electronic data in the application process.
- the medical staff can analyze and diagnose the patient's condition, evaluate the clinical treatment effect, adjust the treatment plan, and effectively improve the user's Medical quality and work efficiency.
- a cloud platform-based vital sign data processing system including: a cloud platform data communication subsystem and a cloud platform data support subsystem; the cloud platform data communication subsystem includes a data communication module and a data pre- a processing module; the cloud platform data support subsystem comprises a message bus module, a data storage module, and a real-time analysis processing module;
- the message bus module is configured to connect data transmission between the control data communication module, the data preprocessing module, the real-time analysis processing module, and the data storage module;
- the data communication module is configured to receive a plurality of user data in real time, and is also used for data interaction with the user, and the received data is transmitted to the data preprocessing module, where the user data includes a vital sign monitoring terminal device ID. Coding, patient information and vital signs data;
- the data pre-processing module includes a system coding table, configured to perform pre-processing on each acquired user data, and perform unified encapsulation and then store the data in the vital sign database;
- the data storage module includes a vital sign database, a file database, a business information database, and a cache database for data storage and calling;
- the real-time analysis processing module includes a deep learning framework for distributed parallel computing, which is used for real-time reading vital sign data in the vital sign database, performing analysis and screening processing, generating a data analysis report, and transmitting the analysis report to the user. Save to the file database.
- the data preprocessing module is configured to:
- the patient business serial number and the preprocessed vital sign data are uniformly encapsulated and stored in the vital sign database.
- the user information, clinical information, and data information are linked by the service serial number, and the device ID is bidirectionally mapped and converted, thereby solving the problem of identifying different patients of the same device (the same bed), and establishing a reliable and efficient internal data query and data interaction. Relationship with external logic to meet internal data query and interaction with external data.
- the real-time analysis processing module reads the vital sign data in the vital sign database in real time, and performs online analysis and processing by using an online real-time data analysis processing method and a deep learning framework based on the Spark engine;
- the Spark distributed parallel computing deep learning framework reads the vital sign data in the vital sign data. According to the set micro-batch processing interval, the Spark engine creates multiple tasks in parallel, triggering the Spark stream to divide the data into RDD data by type. The collection, while controlling the central model of the corresponding type, performs an analysis screening process on the type of data.
- the central model is divided into two categories, one is to analyze and calculate the shape, rhythm and rate of the waveform data, and the other is to analyze and calculate the amplitude of the numerical data;
- the central model includes a second-order differential calculation tool and / or logic analysis tools, real-time calculation and analysis of the shape, rhythm, rate, and value of vital sign data, classification and labeling of waveforms, statistical summarization of logarithmic values, real-time analysis and screening of abnormal data beyond the benchmark.
- the real-time analysis processing module analyzes the abnormal data feature, calculates the duration, and marks the abnormal data attribute.
- the real-time analysis processing module when the abnormal data is found, the real-time analysis processing module generates a real-time data analysis report of the abnormal data, issues an abnormal event warning to the user, and sends the real-time data analysis report to the user.
- the deep learning framework based on Spark engine has the advantages of distributed, high throughput and self-learning, realizing the real-time processing of massive vital signs data, solving the problem of lack of ability of hospital to analyze and interpret vital signs data, and timely discovering abnormal data, prompting medical staff Rapid response intervention to improve medical quality and work efficiency.
- the real-time analysis processing module uses the vital sign data after the analysis and screening process in the vital sign database, and performs real-time training on each type of central model to obtain a new central model of the type data.
- the real-time analysis and processing module trains and optimizes the quantitative and qualitative vital sign data after the analysis and processing of the central model application, which can further improve the analysis and calculation accuracy of the central model, thereby effectively improving the data processing efficiency of the cloud platform and reducing vital signs. Frequent equipment alarm events during the monitoring process reduce the labor intensity and work pressure of medical staff.
- the real-time analysis processing module integrates the whole vitality data of each user after the analysis and screening process, automatically generates a dynamic data analysis report, and stores the data in a file database, and sends the dynamic data analysis report according to the patient service serial number. To the user.
- the vital signs monitoring equipment lacks the summary data of the electronic data in the application process.
- the medical staff can analyze and diagnose the patient's condition, evaluate the clinical treatment effect, adjust the treatment plan, and effectively improve the user's Medical quality and work efficiency.
- FIG. 1 is a flowchart of a method for processing vital sign data based on a cloud platform in an embodiment of the present application
- FIG. 2 is a structural diagram of a vital sign data processing system based on a cloud platform in an embodiment of the present application
- 3 is a deep learning framework based on Spark distributed parallel computing in the embodiment of the present application.
- a specific embodiment of the present application discloses a cloud platform-based vital sign data processing method, as shown in FIG. 1 , including the following steps:
- Step S1 acquiring a plurality of user data in real time; the user data includes vital sign monitoring terminal device ID code, patient information, and vital sign data;
- Step S2 pre-processing each acquired user data and uniformly encapsulating the data into the vital sign database
- step S3 the vital sign data in the vital sign database is read in real time, and the deep learning framework of the distributed parallel computing is used for analysis and screening processing, and the analysis screening result is obtained, and the data analysis report is generated.
- the method processes and processes massive vital signs data (including raw alarm event data of the device) in real time, screens abnormal data, and prompts abnormal data warning to the user, prompts the medical staff to quickly respond to the intervention, and generates a data analysis report.
- the user can receive, read and download and download and print, as a medical basis, effectively reduce equipment false alarm events, improve the user's medical quality and work efficiency, and reduce the labor intensity and work pressure of medical staff.
- step S1 data interaction is performed by interacting with the vital sign monitoring terminal device (exemplarily, the vital sign monitoring device may be a multi-parameter monitoring device, a respiratory function monitor, an intracranial pressure monitor, a fetal heart monitor, etc.) User data.
- the vital sign monitoring terminal device exemplarily, the vital sign monitoring device may be a multi-parameter monitoring device, a respiratory function monitor, an intracranial pressure monitor, a fetal heart monitor, etc.
- the received vital sign data is parsed, classified, and the data format is standardized, and the original alarm event flag of the device is retained; the parsing refers to extracting data (including timestamps and numerical values) from the received data packet, and the classification refers to according to the data protocol.
- the parsed vital sign data is classified according to the parameter type; the data format normalization process is converted into the cloud platform vital sign data standard format by digital change, rate conversion, and re-encoding;
- the patient business serial number and the processed vital sign data are uniformly encapsulated and stored in the vital sign database.
- the service serial number is associated with user information, clinical information, and data information, and is associated with the vitality monitoring device ID, and bidirectional mapping is converted, thereby establishing a reliable and efficient internal and external logical relationship of data query and data interaction. It solves the problem of identifying different patients on the same device (same bed), meeting the internal data query of the system, and the need to interact with external data.
- the data format standardization process solves the problem that the data formats of the devices of different manufacturers are different and difficult to be processed centrally, and the data processing efficiency of the cloud platform is improved.
- step S3 the vital sign data in the vital sign database and the original device alarm data included in the vital sign database are read in real time, and the real-time analysis and screening process is performed by using the deep learning framework of the distributed parallel computing, and the online process is adopted.
- Real-time data analysis and processing and deep learning framework based on Spark engine you can also use one of the common Storm, Flink, and Samza frameworks, as shown in Figure 3, which has distributed, high-throughput, self-learning Advantages, greatly improve the real-time processing speed of massive vital signs data, and at the same time review the parameters, waveforms and marks of the original alarm event data of the equipment, effectively reduce false alarm events and reduce the labor intensity and work pressure of medical staff.
- the deep learning framework of the Spark distributed parallel computing reads the vital sign data in the vital sign data and the original alarm data of the included device, according to the set micro-batch processing interval (0.01 seconds or more), the Spark engine. Create multiple tasks in parallel, trigger the Spark stream to divide the data into RDD data sets by type, and control the central model of the corresponding type to analyze and process the type data.
- the central model can be divided into two categories, one for the waveform class. The shape, rhythm and rate of the data are analyzed and calculated, and the other type is used to analyze and calculate the amplitude of the numerical data.
- the central model includes a second-order differential calculation tool and/or logic analysis tool to calculate and analyze the shape and rhythm of the vital sign data in real time. , rate, value, classification and labeling of waveforms, statistical summation of logarithmic values, real-time analysis and screening of abnormal data beyond the benchmark.
- the vital sign data is divided into a waveform class and a numerical class; wherein the waveform type vital sign data includes: total heart rate, ECG interval, QRS time limit, ST segment shape, QT interval, total respiratory time , respiratory wave interval, pulse volume peak-to-valley value, intracranial pressure peak-to-valley value, end-tidal carbon dioxide partial pressure peak-to-peak value, end-tidal carbon dioxide partial pressure wave interval; numerical vital sign data including: non-invasive/inclusive Systolic blood pressure and diastolic blood pressure, pulse rate, blood oxygen saturation, body temperature value, fetal heart rate; non-invasive cardiac output, stroke rate, cardiac index, total peripheral resistance value; airway pressure value of respiratory mechanics, Airway flow value, airway volume value.
- the waveform type vital sign data includes: total heart rate, ECG interval, QRS time limit, ST segment shape, QT interval, total respiratory time , respiratory wave interval, pulse volume peak-to-valley value, intracranial pressure peak-to-valley value,
- the central model calculation analysis process finds abnormal data exceeding the set reference, analyzes the abnormal data feature, marks, calculates the duration, and marks the abnormal data attribute; meanwhile, generates the real-time data analysis report of the abnormal data, and sends the data to the user.
- the abnormal event is alerted, and the real-time data analysis report is sent to the user; it should be noted that the benchmark uses the diagnostic criteria of internationally-used vital sign data.
- Abnormal data characteristics include: tachycardia, bradycardia, flutter, frequent premature beats, cardiac arrest, RonT, QT interval prolongation, ST segment elevation/depression, apnea, hypopnea, and rapid breathing.
- the peak value of the partial pressure wave rises/falls, the fetal heart rate increases/decreases, the non-invasive cardiac output decreases, and the respiratory mechanics value increases/decreases.
- the method further comprises using the quantitative and qualitative vital sign data after analysis and screening in the vital sign database, and training and optimizing each type of central model in real time to obtain the type data.
- New central model used to improve the efficiency and accuracy of the central model analysis screening process.
- the central model uses real-time training and optimization of quantitative and qualitative vital signs data after analysis and screening, further improving the analytical calculation accuracy of the central model and improving the efficiency of data service of the cloud platform.
- biometric data of each user of the analysis and screening process is integrated to generate a dynamic data analysis report, and is sent to the user according to the patient service serial number.
- the user can read the data value corresponding to the vital sign database according to the dynamic data analysis report template content, including: the whole process of dynamic electrocardiogram data, dynamic blood pressure data, respiratory data, blood oxygen saturation data, and invasive blood pressure data. , intracranial pressure data, end-tidal carbon dioxide data, body temperature data, fetal heart rate data comprehensive analysis and calculation, waveform classification marks, waveform patterns, etc.;
- the method may further include the steps of: providing the dynamic data analysis report to the user for reading and downloading, downloading and printing according to the patient business serial number.
- the medical staff can analyze and diagnose the disease state of the patient and evaluate the clinical treatment effect. Making or adjusting medical plan decisions can effectively improve the quality of medical care and work efficiency of users, and reduce the workload of medical staff.
- a second embodiment of the present application discloses a cloud platform-based vital sign data processing system, as shown in FIG. 2, including: a cloud platform data communication subsystem, a cloud platform data support subsystem, and a cloud platform data communication subsystem.
- the utility model comprises a data communication module and a data preprocessing module;
- the cloud platform data support subsystem comprises a message bus module, a data storage module and a real-time analysis processing module.
- the data communication module is configured to receive a plurality of user data and data interaction in real time, and deliver the received data to a data preprocessing module, wherein the user data includes a vital sign monitoring terminal device ID code, patient information, and vital signs. data;
- the above data communication module supports a plurality of communication protocols, including: TCP/IP protocol, instant communication protocol, HL7 protocol, DICOM protocol, multimedia communication protocol, device manufacturer communication protocol, automatic identification of user identity and device ID coding, Establish a network connection, receive data, and send it to the data preprocessing module.
- TCP/IP protocol instant communication protocol
- HL7 protocol high definition protocol
- DICOM protocol multimedia communication protocol
- device manufacturer communication protocol automatic identification of user identity and device ID coding
- Establish a network connection receive data, and send it to the data preprocessing module.
- the business service surface is effectively expanded to meet the data interaction of various vital vitality monitoring devices and external systems of the user, including the hospital information management system (HIS) and the intensive care clinical information system ( CIS), and the application programming interface (API) of the platform of the medical examination organization, health management organization, and insurance organization.
- API application programming interface
- the data preprocessing module is configured to preprocess each acquired user data and perform unified encapsulation and then store the data in the vital sign database; specifically:
- the patient business serial number and the preprocessed vital sign data are uniformly encapsulated and sent to the vital sign database for reading, invoking, calculating analysis, and retrieving statistics.
- the service serial number includes time stamp, patient information, user information, device information, and quantity counter; the parsing is to extract data (including timestamp and numerical value) from the received data packet, and the classification is parsed according to the data protocol.
- the vital sign data is classified into a data type; the data format normalization process is converted into a vital sign data standard format of the embodiment by using a digital bit change, a rate conversion, and a re-encoding method.
- the data format standardization process solves the problem that the data format of each manufacturer is incompatible.
- the unified data format is adopted to improve the operating efficiency of the cloud platform; the service serial number and the vital sign monitoring device ID maintain bidirectional mapping conversion. It establishes reliable and efficient internal and external logical relationships of data query and data interaction, satisfies the requirements of internal data query and interaction with external data, and solves the problem of identifying different patients of the same device (same bed).
- the message bus module is used for connecting data transmission between the control data communication module, the data preprocessing module, the data real-time analysis processing module, and the data storage module;
- the data storage module includes a vital sign database, a file database, a business information database, and a cache database for data storage and invoking; the data storage module integrates the advantages of various databases and data storage service systems, and solves the massive vital signs of the system.
- the vital sign database belongs to a structured database for storing vital sign data after unified encapsulation; providing massive storage capacity and real-time query capability, featuring high concurrency, low latency, and flexible support;
- the file database belongs to the object storage database, and is used for storing the vital sign data analysis report file, the clinical information file, the patient information file, the video image file, and the medical tool data file generated by the system;
- the business information database belongs to a relational database for storing structured business data and business logic relationship data; based on the relationship model, it has the advantages of maintaining data consistency;
- the cache database belongs to a non-relational database. It acts as a cache for data exchange and state preservation between modules. It is also used to cache the query results of the data storage module, which reduces the number of database accesses and improves the response speed of the system.
- the real-time analysis processing module includes a deep learning framework for distributed parallel computing, which is used for real-time reading of vital sign data in the vital sign database for processing, generating a data analysis report, and transmitting the analysis report to the user, and simultaneously storing it in a file database; As shown in Figure 4, specifically:
- Real-time reading of vital sign data of the vital sign database (including the original alarm data of the device), and using the central model of the vital sign data of the Spark engine-based deep learning framework for screening processing, obtaining analysis screening processing results, and abnormal data Attributes, and the data processed by the analysis and screening is stored in the vital signs database.
- the real-time analysis processing module adopts a deep learning framework based on distributed parallel computing, and can be one of the general Spark, Storm, Flink, and Samza frameworks.
- the deep learning framework of the Spark distributed parallel computing reads the vital sign data in the vital sign data, and according to the set micro-batch processing interval (0.01 seconds or more), the Spark engine creates multiple tasks in parallel, triggering Spark.
- the stream divides the data into RDD data sets by type, and controls the central model of the corresponding type to analyze and process the type data; the central model includes second-order difference calculation tools and/or logic analysis tools to calculate and analyze vital sign data in real time. Morphology, rhythm, rate, and numerical values are used to classify the waveforms, statistically summarize the logarithmic values, and analyze the abnormal data beyond the baseline in real time.
- the vital sign data is divided into waveform class and numerical class; specifically, the waveform type vital sign data includes: total heart beat, ECG interval, QRS time limit, ST segment shape, QT interval, full breathing Total number, respiratory wave interval, pulse volume peak-to-valley value, intracranial pressure peak-to-valley value, end-tidal carbon dioxide partial pressure peak-to-peak value, end-tidal carbon dioxide partial pressure wave interval; numerical vital sign data including: non-invasive Systolic and diastolic blood pressure, pulse rate, blood oxygen saturation, body temperature, fetal heart rate in non-invasive blood pressure; stroke volume, cardiac index, total peripheral resistance value of non-invasive cardiac output; airway pressure value of respiratory mechanics , airway flow value, airway volume value.
- the above abnormal data features include: tachycardia, bradycardia, fluttering, frequent premature beats, stop, RonT, QT interval prolongation, ST segment elevation/depression, apnea, hypopnea, and rapid breathing.
- the partial pressure wave rises/falls, the fetal heart rate increases/decreases, the non-invasive cardiac output decreases, and the respiratory mechanics value increases/decreases.
- the central model is divided into two categories, one is to calculate and analyze the shape, rhythm and rate of waveform data, and the other is to calculate and analyze the amplitude of numerical data; when the central model finds that it exceeds the set reference
- the abnormal data characteristics are analyzed, the abnormal event duration is calculated, and the abnormal data attribute is marked;
- the real-time analysis processing module generates the real-time data analysis report of the abnormal data, stores it in the file database, and simultaneously sends it to the user.
- the real-time analysis and processing module uses the quantitative and qualitative vital sign data after analysis and screening in the vital sign database to train and optimize each type of central model in real time. Get a new central model of this type of data.
- the real-time analysis processing module of the embodiment may further integrate the whole life vital sign data of each user subjected to the analysis screening process, generate a dynamic data analysis report, and store the data in the file database, and send the dynamic data analysis report according to the patient service serial number. To the user.
- the dynamic data analysis report generated by the real-time analysis processing module includes: full-range dynamic electrocardiogram data, dynamic blood pressure data, respiratory data, blood oxygen saturation data, invasive blood pressure data, intracranial pressure data, and exhalation. Carbon dioxide partial pressure data, body temperature data, fetal heart rate data, non-invasive cardiac output data, comprehensive analysis and calculation of respiratory mechanics data, waveform classification markers, waveform patterns, and their trend graphs, histograms, scatter plots, variability diagram.
- the real-time data analysis report includes: abnormal ECG data, abnormal blood pressure data, abnormal respiratory data, abnormal blood oxygen saturation data, abnormal intracranial pressure data, abnormal end-tidal carbon dioxide partial pressure data, abnormal body temperature, abnormal fetal heart rate data Abnormal non-invasive cardiac output data, real-time computational analysis of abnormal respiratory mechanics data, waveform classification markers, abnormal waveform graphics, and trend graphs.
- the dynamic data analysis report of the embodiment solves the problem that the vital sign monitoring device lacks the summary and analysis record of the electronic data in the application process, and at the same time, as the data basis of the clinical medical treatment, the clinical treatment effect and the patient state can be evaluated, and Adjust medical plan decision-making; real-time data analysis report solves the problem of lack of electronic abnormal data analysis report record when abnormal event occurs, and serves as the basis of abnormal event data to support rapid intervention of medical staff; the above-mentioned electronic data analysis report effectively improves medical treatment Quality and work efficiency reduce the labor intensity and workload of medical staff. Users can also send request instructions to the cloud platform, perform search queries, statistical analysis, and review and summarize clinical experience.
- the method and system for processing vital signs based on the cloud platform can be implemented and executed on a public cloud or a private cloud, and can be implemented by using a cloud server, a database, and an application service system, and implemented in the form of a cluster.
- the functionality of the modules involved in the system can be implemented and executed on a public cloud or a private cloud, and can be implemented by using a cloud server, a database, and an application service system, and implemented in the form of a cluster.
- the computer readable storage medium is a magnetic disk, an optical disk, a read-only storage memory, or a random storage memory.
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
A vital sign data processing method and system based on a cloud platform, which belong to the field of medical cloud computing, solve the problem of a user not having the ability to analyze and process vital sign data, and also solve the problems of data formats and communication protocols of vital sign monitoring devices from different manufactures being incompatible with one another and being difficult to integrate and efficiently process. The method comprises: acquiring user data in real time for preprocessing and uniform encapsulation, and storing same in a vital sign database; applying a learning framework based on distributed parallel computing to the reading and analyzing of massive vital sign data in real time; screening abnormal data; automatically generating an electronic data analysis report; and sending, in a timely manner, an event abnormity prewarning to a user to prompt a healthcare professional to intervene quickly, thereby improving medical quality and working efficiency, reducing false alarm events of a device, and reducing the labor intensity and working pressure of the healthcare professional. Applying quantitative and qualitative data in a vital signal database to train and optimize a central model in real time further improves the data processing efficiency of a cloud platform.
Description
本申请涉及医疗云计算领域,尤其涉及一种基于云平台的生命体征数据处理方法及系统。The present application relates to the field of medical cloud computing, and in particular, to a cloud platform-based vital sign data processing method and system.
生命体征监护设备包括床边多参数监护仪、呼吸功能监测仪、颅内压监测仪、胎心监护仪是医院重症监护病房(Intensive care unit,ICU),以及心内科、呼吸科、神经外科、急诊科、妇产科等专科ICU的主要设备,用于实时监测患者的生命体征数据,对挽救患者的生命具有重要的作用。在美国,每年ICU监护服务数百万例患者,中国是全球ICU数量最多的国家,全国医院中现有数百万台各种类型的生命体征监护设备,并在快速增长之中。Vital signs monitoring equipment including bedside multi-parameter monitor, respiratory function monitor, intracranial pressure monitor, fetal heart monitor is the hospital intensive care unit (ICU), as well as cardiology, respiratory, neurosurgery, The main equipment of the ICU in emergency department, obstetrics and gynaecology, etc., is used to monitor the vital signs of patients in real time, which plays an important role in saving the lives of patients. In the United States, there are millions of ICU custodial services per year. China is the country with the largest number of ICUs in the world. There are millions of types of vital signs monitoring devices in hospitals across the country, and they are growing rapidly.
全球生命体征监护设备存在的问题是,每台设备每天产生数百MB数据,但是没有长时间数据存储能力,设备误报警率极高,不能生成电子化数据分析报告,需要医院ICU医护人员人工实时分析甄别数据,手工转抄相关信息数据,全球范围的医院ICU医护人员长期处于高负荷状态,疲惫不堪。另外,医院长期缺少有经验的ICU医护人员,无力应对复杂繁重的生命体征数据处理工作,直接影响到医疗质量。近年来出现的重症监护病房临床信息系统(Clinical Information System,CIS),能够减轻ICU部分劳动强度,但是它没有解决用户生命体征数据分析和数据管理的难题,同时需要用户具有价格不菲的医院信息管理系统(Hospital Information System,HIS)的支持,现有技术中一般将下级医院生命体征监护设备数据远程发送到上级医院,帮助下级医院ICU医生分析诊断生命体征数据,指导临床医疗工作。但是,当大量的下级医院ICU患者数据全部集中到上级医院处理,将会给上级医院带来难以承受的巨大压力,很难实现规模化。The problem with global vital sign monitoring equipment is that each device generates hundreds of MB of data every day, but there is no long-term data storage capability, the device false alarm rate is extremely high, and it is impossible to generate an electronic data analysis report, which requires the hospital ICU medical staff to manually real-time. Analysis of the identification data, manual transfer of relevant information and data, the global hospital ICU medical staff for a long period of high load, exhausted. In addition, the hospital lacks experienced ICU medical staff for a long time, unable to cope with the complicated and heavy vital signs data processing work, directly affecting the quality of medical care. In recent years, the Clinical Information System (CIS) of the intensive care unit has been able to reduce the labor intensity of the ICU, but it does not solve the problem of data analysis and data management of users' vital signs, and requires users to have expensive hospital information. Supported by the Hospital Information System (HIS). In the prior art, data on vital signs monitoring equipment of lower-level hospitals are generally sent to higher-level hospitals remotely, helping ICU doctors in lower-level hospitals to analyze and diagnose vital signs data and guide clinical medical work. However, when a large number of ICU patient data in lower-level hospitals are concentrated in the upper-level hospitals, it will bring unbearable pressure to the higher-level hospitals, and it is difficult to achieve scale.
全球的生命体征监护设备已经具有多种数据通讯接口,但是每个厂商都有自己的通讯协议和数据格式,互相不兼容。有的现有技术中可以解决其他厂家设备数据接收服务问题,但是,没有解决各厂家不同的数据格式问题,需要设立多个处理软件,以及匹配数据存储格式,效率明显低下。同时还有许多厂家通讯协议不支持输入患者信 息,该技术无法保证识别同一台设备(同一床位)不同患者的问题,在医院中,同一台设备往往要服务许多患者。还有的现有技术,在生命体征数据源模块配接了信息处理模块和通讯模块,通讯模块与Internet相连将数据发送给云计算模块和云端数据库,该技术在各厂家每台设备与云平台之间,增加了中间处理环节,不仅每台设备所带来的复杂程度和成本明显上升,同时可靠性下降。The global vital sign monitoring device already has a variety of data communication interfaces, but each manufacturer has its own communication protocol and data format, which are not compatible with each other. Some existing technologies can solve the problem of data receiving services of other manufacturers' devices. However, the problem of different data formats of different manufacturers is not solved, and multiple processing softwares need to be set up, and the data storage format is matched, and the efficiency is obviously low. At the same time, there are many manufacturers' communication protocols that do not support the input of patient information. This technology cannot guarantee the identification of different patients in the same device (same bed). In hospitals, the same device often has to serve many patients. In the prior art, the information module and the communication module are connected to the vital sign data source module, and the communication module is connected to the Internet to send the data to the cloud computing module and the cloud database. The technology is in each device and the cloud platform of each manufacturer. Between the two, the intermediate processing link has been added, not only the complexity and cost of each device have increased significantly, but also the reliability has decreased.
发明内容Summary of the invention
鉴于上述的分析,本申请旨在提供一种基于云平台的生命体征数据处理方法及系统,用以解决医院缺乏分析解读生命体征数据能力的问题,以及解决不同制造商生命体征监护设备数据格式、通讯协议互不兼容,难以集中、高效处理的问题。In view of the above analysis, the present application aims to provide a cloud platform-based vital sign data processing method and system for solving the problem that the hospital lacks the ability to analyze and interpret vital signs data, and to solve the data format of different manufacturers' vital signs monitoring equipment, Communication protocols are incompatible with each other and it is difficult to concentrate and efficiently deal with them.
本申请的目的主要是通过以下技术方案实现的:The purpose of this application is mainly achieved by the following technical solutions:
一方面,提供了一种基于云平台的生命体征数据处理方法,包括以下步骤:In one aspect, a cloud platform-based vital sign data processing method is provided, including the following steps:
步骤S1,实时获取多个用户数据;所述用户数据包括生命体征监护设备ID编码、患者信息和生命体征数据;Step S1: acquiring a plurality of user data in real time; the user data includes vital sign monitoring device ID code, patient information, and vital sign data;
步骤S2,对获取的每一用户数据进行预处理并进行统一封装后存入生命体征数据库;Step S2: pre-processing each acquired user data and performing unified encapsulation and storing the data into the vital sign database;
步骤S3,实时读取上述生命体征数据库中的生命体征数据,并利用分布式并行计算的深度学习框架进行分析筛查处理,得到分析筛查结果,生成数据分析报告。In step S3, the vital sign data in the vital sign database is read in real time, and the deep learning framework of the distributed parallel computing is used for analysis and screening processing, and the analysis screening result is obtained, and a data analysis report is generated.
本申请有益效果如下:The beneficial effects of the application are as follows:
1、通过对海量生命体征数据实时分析处理,解决了医院缺乏分析解读生命体征数据能力的问题,提高了医疗质量和工作效率。1. Through the real-time analysis and processing of massive vital signs data, the problem of lack of ability to analyze and interpret vital signs data in hospitals is solved, and medical quality and work efficiency are improved.
2、通过对设备原始报警事件数据实时分析计算,有效减少监护过程中频发的误报警事件,提高了异常事件预警的准确性,降低了医护人员劳动强度和工作压力。2. Real-time analysis and calculation of the original alarm event data of the equipment, effectively reducing the false alarm events frequently occurring during the monitoring process, improving the accuracy of the abnormal event warning, and reducing the labor intensity and work pressure of the medical staff.
在上述方案的基础上,本申请还做了如下改进:Based on the above scheme, the application also made the following improvements:
进一步,在步骤S2中,Further, in step S2,
基于系统编码表规则,将生命体征监护设备ID编码和患者信息绑定,生成患者业务流水号,并与所述设备ID编码保持双向映射转换;Binding the vital sign monitoring device ID code and the patient information according to the system coding table rule, generating a patient service serial number, and maintaining bidirectional mapping conversion with the device ID code;
对接收的生命体征数据进行解析、分类、数据格式标准化处理,保留设备原始报警事件标志;Parsing, classifying, and normalizing the data format of the received vital signs data, and retaining the original alarm event flag of the device;
将患者业务流水号和经过预处理的生命体征数据进行统一封装,存入生命体征数 据库。The patient business serial number and the preprocessed vital sign data are uniformly encapsulated and stored in the vital sign database.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
通过业务流水号关联用户信息、临床信息、数据信息,并与设备ID双向映射转换,解决了识别同一台设备(同一病床)不同患者的问题,同时建立了可靠高效的数据查询、数据交互的内部和外部逻辑关系,满足系统内部数据查询,以及与外部数据交互的需求。The user information, clinical information, and data information are linked by the service serial number, and the device ID is bidirectionally mapped and converted, thereby solving the problem of identifying different patients of the same device (the same bed), and establishing a reliable and efficient internal data query and data interaction. Relationship with external logic to meet internal data query and interaction with external data.
进一步,实时读取上述生命体征数据库中的生命体征数据,并利用分布式并行计算的深度学习框架进行分析筛查处理,是采用在线实时数据分析处理方式及基于Spark引擎的深度学习框架实现:Further, the real-time reading of the vital sign data in the vital sign database and the use of the distributed parallel computing deep learning framework for analysis and screening processing are implemented by an online real-time data analysis processing method and a deep learning framework based on the Spark engine:
通过Spark分布式并行计算的深度学习框架读取生命体征数据中的生命体征数据,按照设置的微批处理间隔时间,Spark引擎并行创建多个任务,触发Spark流(Spark Streaming)将数据按类型切分为RDD数据集合,同时控制相应类型的中央模型对该类型数据进行计算处理。The Spark distributed parallel computing deep learning framework reads the vital sign data in the vital sign data. According to the set micro-batch processing interval, the Spark engine creates multiple tasks in parallel, triggering Spark Streaming to cut the data by type. It is divided into RDD data sets, and the central model of the corresponding type is controlled to calculate and process the type data.
进一步,所述中央模型分为两类,一类对波形类数据的形态、节律、速率进行分析计算,另一类对数值型数据幅值进行分析计算;中央模型内置包括二阶差分计算工具和/或逻辑分析工具,实时计算分析生命体征数据的形态、节律、速率、数值,对波形进行分类标记、对数值进行统计归纳,实时分析筛查超出基准的异常数据。Further, the central model is divided into two categories, one is to analyze and calculate the shape, rhythm and rate of the waveform data, and the other is to analyze and calculate the amplitude of the numerical data; the central model includes a second-order differential calculation tool and / or logic analysis tools, real-time calculation and analysis of the shape, rhythm, rate, and value of vital sign data, classification and labeling of waveforms, statistical summarization of logarithmic values, real-time analysis and screening of abnormal data beyond the benchmark.
进一步,当所述中央模型计算处理发现超出设定基准的异常数据时,分析异常数据特征,计算持续时间,标记异常数据属性。Further, when the central model calculation process finds abnormal data exceeding the set reference, the abnormal data feature is analyzed, the duration is calculated, and the abnormal data attribute is marked.
进一步,在发现异常数据时,将异常数据生成实时数据分析报告,向用户发出异常事件预警,并将实时数据分析报告发送给用户。Further, when abnormal data is found, the abnormal data is generated into a real-time data analysis report, an abnormal event warning is issued to the user, and the real-time data analysis report is sent to the user.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
基于Spark引擎的深度学习框架具有分布式、高吞吐量、自学习的优势,实现海量生命体征数据的实时分析处理,解决了医院缺乏分析解读生命体征数据能力的问题,及时发现异常数据,支持医护人员快速反应干预,提高了医疗质量和工作效率。The deep learning framework based on Spark engine has the advantages of distributed, high throughput and self-learning. It realizes real-time analysis and processing of massive vital signs data, solves the problem of lack of ability of hospitals to analyze and interpret vital signs data, and timely discovers abnormal data and supports medical care. Rapid response interventions improve the quality of medical care and work efficiency.
进一步,使用所述生命体征数据库中经过分析筛查处理后的生命体征数据,实时对每类中央模型进行训练优化,得到该类型数据新的中央模型。Further, using the vital sign data after the analysis and screening process in the vital sign database, each type of central model is trained and optimized in real time to obtain a new central model of the type data.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
中央模型应用定量定性的生命体征数据进行训练优化,能够进一步提高中央模型 的分析计算准确度,进而,有效提高云平台的数据处理效率,减少生命体征监护过程中频发的设备误报警事件,降低医护人员劳动强度和工作压力。The central model applies quantitative and qualitative vital sign data for training optimization, which can further improve the analysis and calculation accuracy of the central model, and thus effectively improve the data processing efficiency of the cloud platform, reduce the frequent equipment false alarm events during the vital sign monitoring process, and reduce Medical staff's labor intensity and work pressure.
进一步,将经过分析筛查处理的每一用户全程生命体征数据进行整合,自动生成动态数据分析报告,根据患者业务流水号,将动态数据分析报告发送给用户。Further, the entire life vital sign data of each user after the analysis and screening process is integrated, the dynamic data analysis report is automatically generated, and the dynamic data analysis report is sent to the user according to the patient service serial number.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
解决了生命体征监护设备在应用过程中缺少电子化数据总结分析记录的问题,通过动态数据分析报告,医护人员可以对患者病情状态进行分析诊断,评估临床治疗效果,调整治疗方案,有效提高用户的医疗质量和工作效率。It solves the problem that the vital signs monitoring equipment lacks the summary data of the electronic data in the application process. Through the dynamic data analysis report, the medical staff can analyze and diagnose the patient's condition, evaluate the clinical treatment effect, adjust the treatment plan, and effectively improve the user's Medical quality and work efficiency.
另一方面,还提供了一种基于云平台的生命体征数据处理系统,包括:云平台数据通信子系统、云平台数据支持子系统;所述云平台数据通信子系统包括数据通信模块、数据预处理模块;所述云平台数据支持子系统包括消息总线模块、数据存储模块、实时分析处理模块;On the other hand, a cloud platform-based vital sign data processing system is provided, including: a cloud platform data communication subsystem and a cloud platform data support subsystem; the cloud platform data communication subsystem includes a data communication module and a data pre- a processing module; the cloud platform data support subsystem comprises a message bus module, a data storage module, and a real-time analysis processing module;
所述消息总线模块用于连接控制数据通信模块、数据预处理模块、实时分析处理模块、数据存储模块之间的数据传输;The message bus module is configured to connect data transmission between the control data communication module, the data preprocessing module, the real-time analysis processing module, and the data storage module;
所述数据通信模块用于实时接收多个用户数据,还用于和用户之间的数据交互,并将接收的数据传递给所述数据预处理模块,所述用户数据包括生命体征监护终端设备ID编码、患者信息和生命体征数据;The data communication module is configured to receive a plurality of user data in real time, and is also used for data interaction with the user, and the received data is transmitted to the data preprocessing module, where the user data includes a vital sign monitoring terminal device ID. Coding, patient information and vital signs data;
所述数据预处理模块包括系统编码表,用于对获取的每一用户数据进行预处理并进行统一封装后存入生命体征数据库;The data pre-processing module includes a system coding table, configured to perform pre-processing on each acquired user data, and perform unified encapsulation and then store the data in the vital sign database;
所述数据存储模块包括生命体征数据库、文件数据库、业务信息数据库、缓存数据库,用于数据存储、调用;The data storage module includes a vital sign database, a file database, a business information database, and a cache database for data storage and calling;
所述实时分析处理模块包括分布式并行计算的深度学习框架,用于实时读取生命体征数据库中的生命体征数据,进行分析筛查处理,生成数据分析报告,并将分析报告发送给用户,同时存入文件数据库。The real-time analysis processing module includes a deep learning framework for distributed parallel computing, which is used for real-time reading vital sign data in the vital sign database, performing analysis and screening processing, generating a data analysis report, and transmitting the analysis report to the user. Save to the file database.
采用本申请的有益效果如下:The beneficial effects of using the present application are as follows:
1、通过对海量生命体征数据实时分析处理,解决了医院缺乏分析解读生命体征数据能力的问题,提高了医疗质量和工作效率。1. Through the real-time analysis and processing of massive vital signs data, the problem of lack of ability to analyze and interpret vital signs data in hospitals is solved, and medical quality and work efficiency are improved.
2、通过对设备原始报警事件数据进行实时分析计算,有效减少监护过程中频发的误报警事件,提高了异常事件预警的准确性,降低了医护人员劳动强度和工作压力。2. Real-time analysis and calculation of the original alarm event data of the equipment, effectively reducing the false alarm events frequently occurring during the monitoring process, improving the accuracy of the abnormal event warning, and reducing the labor intensity and work pressure of the medical staff.
进一步,所述数据预处理模块用于:Further, the data preprocessing module is configured to:
基于系统编码表规则将生命体征监护终端设备ID编码和患者信息绑定,生成业务流水号,并与所述设备ID编码双向映射转换;Binding the vital sign monitoring terminal device ID code and the patient information according to the system coding table rule, generating a service serial number, and performing bidirectional mapping conversion with the device ID code;
对接收的生命体征数据进行解析、分类、数据格式标准化处理,保留设备原始报警事件标志;Parsing, classifying, and normalizing the data format of the received vital signs data, and retaining the original alarm event flag of the device;
将患者业务流水号和经过预处理的生命体征数据进行统一封装,存入生命体征数据库。The patient business serial number and the preprocessed vital sign data are uniformly encapsulated and stored in the vital sign database.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
通过业务流水号关联用户信息、临床信息、数据信息,并与设备ID双向映射转换,解决了识别同一台设备(同一病床)不同患者的问题,同时建立了可靠高效的数据查询、数据交互的内部和外部逻辑关系,满足系统内部数据查询,以及与外部数据交互的需求。The user information, clinical information, and data information are linked by the service serial number, and the device ID is bidirectionally mapped and converted, thereby solving the problem of identifying different patients of the same device (the same bed), and establishing a reliable and efficient internal data query and data interaction. Relationship with external logic to meet internal data query and interaction with external data.
进一步,所述实时分析处理模块实时读取生命体征数据库中的生命体征数据,并利用在线实时数据分析处理方式及基于Spark引擎的深度学习框架进行分析计算处理;Further, the real-time analysis processing module reads the vital sign data in the vital sign database in real time, and performs online analysis and processing by using an online real-time data analysis processing method and a deep learning framework based on the Spark engine;
通过Spark分布式并行计算的深度学习框架读取生命体征数据中的生命体征数据,按照设置的微批处理间隔时间,Spark引擎并行创建多个任务,触发Spark流将数据按类型切分为RDD数据集合,同时控制相应类型的中央模型对该类型数据进行分析筛查处理。The Spark distributed parallel computing deep learning framework reads the vital sign data in the vital sign data. According to the set micro-batch processing interval, the Spark engine creates multiple tasks in parallel, triggering the Spark stream to divide the data into RDD data by type. The collection, while controlling the central model of the corresponding type, performs an analysis screening process on the type of data.
进一步,所述中央模型分为两类,一类对波形类数据的形态、节律、速率进行分析计算,另一类对数值型数据幅值进行分析计算;中央模型内置包括二阶差分计算工具和/或逻辑分析工具,实时计算分析生命体征数据的形态、节律、速率、数值,对波形进行分类标记、对数值进行统计归纳,实时分析筛查超出基准的异常数据。Further, the central model is divided into two categories, one is to analyze and calculate the shape, rhythm and rate of the waveform data, and the other is to analyze and calculate the amplitude of the numerical data; the central model includes a second-order differential calculation tool and / or logic analysis tools, real-time calculation and analysis of the shape, rhythm, rate, and value of vital sign data, classification and labeling of waveforms, statistical summarization of logarithmic values, real-time analysis and screening of abnormal data beyond the benchmark.
进一步,所述实时分析处理模块在中央模型计算处理发现超出设定基准的异常数据时,分析异常数据特征,计算持续时间,标记异常数据属性。Further, when the central model calculation processing finds abnormal data exceeding the set reference, the real-time analysis processing module analyzes the abnormal data feature, calculates the duration, and marks the abnormal data attribute.
进一步,所述实时分析处理模块在发现异常数据时,将异常数据生成实时数据分析报告,向用户发出异常事件预警,并将实时数据分析报告发送给用户。Further, when the abnormal data is found, the real-time analysis processing module generates a real-time data analysis report of the abnormal data, issues an abnormal event warning to the user, and sends the real-time data analysis report to the user.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
基于Spark引擎的深度学习框架具有分布式、高吞吐量、自学习的优势,实现海 量生命体征数据的实时处理,解决了医院缺乏分析解读生命体征数据能力的问题,及时发现异常数据,提示医护人员快速反应干预,提高医疗质量和工作效率。The deep learning framework based on Spark engine has the advantages of distributed, high throughput and self-learning, realizing the real-time processing of massive vital signs data, solving the problem of lack of ability of hospital to analyze and interpret vital signs data, and timely discovering abnormal data, prompting medical staff Rapid response intervention to improve medical quality and work efficiency.
进一步,所述实时分析处理模块使用生命体征数据库中经过分析筛查处理后的生命体征数据,实时对每类中央模型进行训练优化得到该类型数据新的中央模型。Further, the real-time analysis processing module uses the vital sign data after the analysis and screening process in the vital sign database, and performs real-time training on each type of central model to obtain a new central model of the type data.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
实时分析处理模块对中央模型应用经过分析筛查处理后的定量定性的生命体征数据进行训练优化,能够进一步提高中央模型的分析计算准确度,进而,有效提高云平台的数据处理效率,减少生命体征监护过程中频发的设备误报警事件,降低医护人员劳动强度和工作压力。The real-time analysis and processing module trains and optimizes the quantitative and qualitative vital sign data after the analysis and processing of the central model application, which can further improve the analysis and calculation accuracy of the central model, thereby effectively improving the data processing efficiency of the cloud platform and reducing vital signs. Frequent equipment alarm events during the monitoring process reduce the labor intensity and work pressure of medical staff.
进一步,所述实时分析处理模块将经过分析筛查处理的每一用户的全程生命体征数据进行整合,自动生成动态数据分析报告,并存入文件数据库,根据患者业务流水号将动态数据分析报告发送给用户。Further, the real-time analysis processing module integrates the whole vitality data of each user after the analysis and screening process, automatically generates a dynamic data analysis report, and stores the data in a file database, and sends the dynamic data analysis report according to the patient service serial number. To the user.
采用上述进一步方案的有益效果是:The beneficial effects of using the above further solution are:
解决了生命体征监护设备在应用过程中缺少电子化数据总结分析记录的问题,通过动态数据分析报告,医护人员可以对患者病情状态进行分析诊断,评估临床治疗效果,调整治疗方案,有效提高用户的医疗质量和工作效率。It solves the problem that the vital signs monitoring equipment lacks the summary data of the electronic data in the application process. Through the dynamic data analysis report, the medical staff can analyze and diagnose the patient's condition, evaluate the clinical treatment effect, adjust the treatment plan, and effectively improve the user's Medical quality and work efficiency.
本申请中,上述各技术方案之间还可以相互组合,以实现更多的优选组合方案。本申请的其他特征和优点将在随后的说明书中阐述,并且,部分优点可从说明书中变得显而易见,或者通过实施本申请而了解。本申请的目的和其他优点可通过说明书、权利要求书以及附图中所特别指出的内容中来实现和获得。In the present application, the above various technical solutions can also be combined with each other to achieve more preferred combinations. Other features and advantages of the present application will be set forth in the description which follows. The objectives and other advantages of the present invention are realized and attained by the description and the appended claims.
附图仅用于示出具体实施例的目的,而并不认为是对本申请的限制,在整个附图中,相同的参考符号表示相同的部件。The drawings are only for the purpose of illustrating the embodiments, and are not intended to
图1为本申请实施例中基于云平台的生命体征数据处理方法流程图;1 is a flowchart of a method for processing vital sign data based on a cloud platform in an embodiment of the present application;
图2为本申请实施例中基于云平台的生命体征数据处理系统结构图;2 is a structural diagram of a vital sign data processing system based on a cloud platform in an embodiment of the present application;
图3为本申请实施例中基于Spark分布式并行计算的深度学习框架;3 is a deep learning framework based on Spark distributed parallel computing in the embodiment of the present application;
图4为本申请实施例中实时数据分析处理流程图;4 is a flowchart of real-time data analysis processing in the embodiment of the present application;
下面结合附图来具体描述本申请的优选实施例,其中,附图构成本申请一部分,并与本申请的实施例一起用于阐释本申请的原理,并非用于限定本申请的范围。The preferred embodiments of the present application are described in detail below with reference to the accompanying drawings, wherein the accompanying drawings illustrate,
本申请的一个具体实施例,公开了一种基于云平台的生命体征数据处理方法,如图1所示,包括以下步骤:A specific embodiment of the present application discloses a cloud platform-based vital sign data processing method, as shown in FIG. 1 , including the following steps:
步骤S1,实时获取多个用户数据;所述用户数据包括生命体征监护终端设备ID编码、患者信息和生命体征数据;Step S1: acquiring a plurality of user data in real time; the user data includes vital sign monitoring terminal device ID code, patient information, and vital sign data;
步骤S2,对获取的每一用户数据进行预处理并进行统一封装,存入生命体征数据库;Step S2: pre-processing each acquired user data and uniformly encapsulating the data into the vital sign database;
步骤S3,实时读取上述生命体征数据库中的生命体征数据,利用分布式并行计算的深度学习框架进行分析筛查处理,得到分析筛查结果,生成数据分析报告。In step S3, the vital sign data in the vital sign database is read in real time, and the deep learning framework of the distributed parallel computing is used for analysis and screening processing, and the analysis screening result is obtained, and the data analysis report is generated.
与现有技术相比,该方法通过实时分析处理海量生命体征数据(包括设备原始报警事件数据),筛查异常数据,及时向用户发出异常数据预警,提示医护人员快速反应干预,生成数据分析报告,用户可以接收、阅读浏览和下载打印,作为医疗依据,有效减少设备误报警事件,提高了用户医疗质量和工作效率,降低医护人员劳动强度和工作压力。Compared with the prior art, the method processes and processes massive vital signs data (including raw alarm event data of the device) in real time, screens abnormal data, and prompts abnormal data warning to the user, prompts the medical staff to quickly respond to the intervention, and generates a data analysis report. The user can receive, read and download and download and print, as a medical basis, effectively reduce equipment false alarm events, improve the user's medical quality and work efficiency, and reduce the labor intensity and work pressure of medical staff.
在步骤S1中,通过与生命体征监护终端设备(示例性地,生命体征监护设备可以为多参数监护设备、呼吸功能监测仪、颅内压监测仪、胎心监测仪等)进行数据交互,获取用户数据。In step S1, data interaction is performed by interacting with the vital sign monitoring terminal device (exemplarily, the vital sign monitoring device may be a multi-parameter monitoring device, a respiratory function monitor, an intracranial pressure monitor, a fetal heart monitor, etc.) User data.
需要说明的是,为了解决识别同一台设备(同一病床)不同患者的问题,同时解决数据查询、数据交互的寻址识别问题;还包括以下步骤:It should be noted that in order to solve the problem of identifying different patients of the same device (same bed), and solving the problem of addressing and identifying data query and data interaction; the following steps are also included:
基于系统编码表规则,将生命体征监护设备ID编码和患者信息绑定,生成业务流水号,并与所述设备ID编码双向映射转换;Binding the vital sign monitoring device ID code and the patient information according to the system coding table rule, generating a service serial number, and bidirectional mapping conversion with the device ID code;
对接收的生命体征数据进行解析、分类、数据格式标准化处理,同时保留设备原始报警事件标志;解析是指从接收的数据包中抽取数据(包括时间戳、数值),分类是指根据数据协议将上述解析的生命体征数据按参数类型进行分类;数据格式标准化处理是采用数位变化、码率转换、再编码方式转换为所述云平台生命体征数据标准格式;The received vital sign data is parsed, classified, and the data format is standardized, and the original alarm event flag of the device is retained; the parsing refers to extracting data (including timestamps and numerical values) from the received data packet, and the classification refers to according to the data protocol. The parsed vital sign data is classified according to the parameter type; the data format normalization process is converted into the cloud platform vital sign data standard format by digital change, rate conversion, and re-encoding;
将患者业务流水号和经过处理的生命体征数据进行统一封装,存入生命体征数据 库。The patient business serial number and the processed vital sign data are uniformly encapsulated and stored in the vital sign database.
需要强调的是,业务流水号关联用户信息、临床信息、数据信息,并与生命体征监护设备ID保持对应关联,双向映射转换,从而建立了可靠、高效的数据查询、数据交互的内部外部逻辑关系,解决了识别同一台设备(同一病床)不同患者的问题,满足系统内部数据查询,以及与外部数据交互的需求。数据格式标准化处理,解决了各厂家设备数据格式不同,难以集中处理的问题,提高了云平台数据处理效率。It should be emphasized that the service serial number is associated with user information, clinical information, and data information, and is associated with the vitality monitoring device ID, and bidirectional mapping is converted, thereby establishing a reliable and efficient internal and external logical relationship of data query and data interaction. It solves the problem of identifying different patients on the same device (same bed), meeting the internal data query of the system, and the need to interact with external data. The data format standardization process solves the problem that the data formats of the devices of different manufacturers are different and difficult to be processed centrally, and the data processing efficiency of the cloud platform is improved.
需要说明的是,步骤S3中,实时读取上述生命体征数据库中的生命体征数据及所包含的设备原始报警数据,并利用分布式并行计算的深度学习框架进行实时分析筛查处理,采用了在线实时数据分析处理方式及基于Spark引擎的深度学习框架(还可以采用通用的Storm、Flink、Samza框架的其中一种),如图3所示,该框架具有分布式、高吞吐量、自学习的优势,极大地提高了海量生命体征数据实时处理速度,同时对设备原始报警事件数据的参数、波形、标记进行复核计算,有效减少误报警事件,降低医护人员劳动强度和工作压力。It should be noted that, in step S3, the vital sign data in the vital sign database and the original device alarm data included in the vital sign database are read in real time, and the real-time analysis and screening process is performed by using the deep learning framework of the distributed parallel computing, and the online process is adopted. Real-time data analysis and processing and deep learning framework based on Spark engine (you can also use one of the common Storm, Flink, and Samza frameworks), as shown in Figure 3, which has distributed, high-throughput, self-learning Advantages, greatly improve the real-time processing speed of massive vital signs data, and at the same time review the parameters, waveforms and marks of the original alarm event data of the equipment, effectively reduce false alarm events and reduce the labor intensity and work pressure of medical staff.
具体来说,通过Spark分布式并行计算的深度学习框架读取生命体征数据中的生命体征数据及所包含的设备原始报警数据,按照设置的微批处理间隔时间(大于等于0.01秒),Spark引擎并行创建多个任务,触发Spark流将数据按类型切分为RDD数据集合,同时控制相应类型的中央模型对该类型数据进行分析筛查处理;中央模型可以分为两类,一类对波形类数据的形态、节律、速率进行分析计算,另一类对数值型数据幅值进行分析计算,中央模型内置包括二阶差分计算工具和/或逻辑分析工具,实时计算分析生命体征数据的形态、节律、速率、数值,对波形进行分类标记、对数值进行统计归纳,实时分析筛查超出基准的异常数据。Specifically, the deep learning framework of the Spark distributed parallel computing reads the vital sign data in the vital sign data and the original alarm data of the included device, according to the set micro-batch processing interval (0.01 seconds or more), the Spark engine. Create multiple tasks in parallel, trigger the Spark stream to divide the data into RDD data sets by type, and control the central model of the corresponding type to analyze and process the type data. The central model can be divided into two categories, one for the waveform class. The shape, rhythm and rate of the data are analyzed and calculated, and the other type is used to analyze and calculate the amplitude of the numerical data. The central model includes a second-order differential calculation tool and/or logic analysis tool to calculate and analyze the shape and rhythm of the vital sign data in real time. , rate, value, classification and labeling of waveforms, statistical summation of logarithmic values, real-time analysis and screening of abnormal data beyond the benchmark.
需要说明的是,生命体征数据分为波形类和数值类;其中,波形类生命体征数据包括:全程总心博、心电波间期、QRS时限、ST段形态、QT间期,全程呼吸总次数、呼吸波间期,脉搏容积波峰谷值,颅内压波峰谷值,呼气末二氧化碳分压波峰谷值、呼气末二氧化碳分压波间期;数值类生命体征数据包括:全程无创/有创血压中的收缩压和舒张压、脉率、血氧饱和度、体温值、胎心率;无创心排量的心搏量、心脏指数、总外周阻力值;呼吸力学的气道压力值、气道流量值、气道容积值。It should be noted that the vital sign data is divided into a waveform class and a numerical class; wherein the waveform type vital sign data includes: total heart rate, ECG interval, QRS time limit, ST segment shape, QT interval, total respiratory time , respiratory wave interval, pulse volume peak-to-valley value, intracranial pressure peak-to-valley value, end-tidal carbon dioxide partial pressure peak-to-peak value, end-tidal carbon dioxide partial pressure wave interval; numerical vital sign data including: non-invasive/inclusive Systolic blood pressure and diastolic blood pressure, pulse rate, blood oxygen saturation, body temperature value, fetal heart rate; non-invasive cardiac output, stroke rate, cardiac index, total peripheral resistance value; airway pressure value of respiratory mechanics, Airway flow value, airway volume value.
进一步,当中央模型计算分析处理发现超出设定基准的异常数据时,分析异常数据特征,并进行标记,计算持续时间,标记异常数据属性;同时,将异常数据生成实 时数据分析报告,向用户发出异常事件预警,并将实时数据分析报告发送给用户;需要说明的是:所述基准采用了国际通用的生命体征数据的诊断标准。Further, when the central model calculation analysis process finds abnormal data exceeding the set reference, analyzes the abnormal data feature, marks, calculates the duration, and marks the abnormal data attribute; meanwhile, generates the real-time data analysis report of the abnormal data, and sends the data to the user. The abnormal event is alerted, and the real-time data analysis report is sent to the user; it should be noted that the benchmark uses the diagnostic criteria of internationally-used vital sign data.
异常数据特征包括:心动过速、心动过缓、扑动颤动、频发早搏、心脏停博、RonT、QT间期延长、ST段抬高/压低,呼吸暂停、呼吸过缓、呼吸过快,血氧饱和度升高/下降、收缩压和舒张压升高/下降、平均动脉压升高/下降,脉搏容积波峰值升高/下降,颅内压波峰值升高/下降,呼气末二氧化碳分压波峰值升高/下降,胎心率升高/下降,无创心排量下降,呼吸力学值升高/下降。Abnormal data characteristics include: tachycardia, bradycardia, flutter, frequent premature beats, cardiac arrest, RonT, QT interval prolongation, ST segment elevation/depression, apnea, hypopnea, and rapid breathing. Increase/decrease in blood oxygen saturation, increase/decrease in systolic and diastolic blood pressure, increase/decrease in mean arterial pressure, increase/decrease in peak volume of pulse volume, increase/decrease in peak intracranial pressure wave, end-tidal carbon dioxide The peak value of the partial pressure wave rises/falls, the fetal heart rate increases/decreases, the non-invasive cardiac output decreases, and the respiratory mechanics value increases/decreases.
为了提高中央模型分析筛查处理的效率和准确度,该方法还包括使用生命体征数据库中分析筛查处理后的定量定性的生命体征数据,实时对每类中央模型进行训练优化,得到该类型数据新的中央模型。In order to improve the efficiency and accuracy of the central model analysis screening process, the method further comprises using the quantitative and qualitative vital sign data after analysis and screening in the vital sign database, and training and optimizing each type of central model in real time to obtain the type data. New central model.
通过Spark分布式并行计算的深度学习框架实现了海量生命体征数据的分析处理,满足用户生命体征数据分析解读的需求,提高了医疗质量和工作效率。中央模型使用经过分析筛查处理后的定量定性的生命体征数据实时训练优化,进一步提高中央模型的分析计算准确度,提高了云平台数据服务效率。Through the deep learning framework of Spark distributed parallel computing, the analysis and processing of massive vital signs data is realized, which satisfies the needs of user vital sign data analysis and interpretation, and improves medical quality and work efficiency. The central model uses real-time training and optimization of quantitative and qualitative vital signs data after analysis and screening, further improving the analytical calculation accuracy of the central model and improving the efficiency of data service of the cloud platform.
进一步,将经过分析筛查处理的每一用户全程生命体征数据进行整合,生成动态数据分析报告,并根据患者业务流水号发送给用户。Further, the biometric data of each user of the analysis and screening process is integrated to generate a dynamic data analysis report, and is sent to the user according to the patient service serial number.
需要说明的是,用户可以根据动态数据分析报告模板内容读取生命体征数据库对应的数据数值,包括:全程的动态心电数据、动态血压数据、呼吸数据、血氧饱和度数据、有创血压数据、颅内压数据、呼气末二氧化碳数据、体温数据、胎心率数据的综合分析计算、波形分类标记、波形图形等;It should be noted that the user can read the data value corresponding to the vital sign database according to the dynamic data analysis report template content, including: the whole process of dynamic electrocardiogram data, dynamic blood pressure data, respiratory data, blood oxygen saturation data, and invasive blood pressure data. , intracranial pressure data, end-tidal carbon dioxide data, body temperature data, fetal heart rate data comprehensive analysis and calculation, waveform classification marks, waveform patterns, etc.;
还可以包括以下步骤:对数据进行统计并生成数据统计图表,包括:趋势图、直方图、散点图、变异性分析图;It may also include the following steps: counting data and generating statistical charts of data, including: trend graph, histogram, scatter plot, variability analysis graph;
还可以包括以下步骤:根据患者业务流水号,将动态数据分析报告提供给用户进行阅读浏览、下载打印。The method may further include the steps of: providing the dynamic data analysis report to the user for reading and downloading, downloading and printing according to the patient business serial number.
通过动态数据分析报告,解决了生命体征监护设备在应用过程中缺乏电子化数据总结分析记录的问题,同时作为临床医疗的数据依据,医护人员可以对患者疾病状态进行分析诊断,评估临床治疗效果,制定或调整医疗方案决策,能够有效提高用户医疗质量和工作效率,减轻了医护人员的工作负担。Through the dynamic data analysis report, the problem of lacking electronic data summary analysis and record in the application process of vital sign monitoring equipment is solved. At the same time, as the data basis of clinical medical treatment, the medical staff can analyze and diagnose the disease state of the patient and evaluate the clinical treatment effect. Making or adjusting medical plan decisions can effectively improve the quality of medical care and work efficiency of users, and reduce the workload of medical staff.
本申请的第二实施例,公开了一种基于云平台的生命体征数据处理系统,如图2 所示,包括:云平台数据通信子系统、云平台数据支持子系统;云平台数据通信子系统包括数据通信模块、数据预处理模块;云平台数据支持子系统包括消息总线模块、数据存储模块、实时分析处理模块。A second embodiment of the present application discloses a cloud platform-based vital sign data processing system, as shown in FIG. 2, including: a cloud platform data communication subsystem, a cloud platform data support subsystem, and a cloud platform data communication subsystem. The utility model comprises a data communication module and a data preprocessing module; the cloud platform data support subsystem comprises a message bus module, a data storage module and a real-time analysis processing module.
具体来说,数据通信模块用于实时接收多个用户数据,以及数据交互,并将接收的数据传递给数据预处理模块,其中,用户数据包括生命体征监护终端设备ID编码、患者信息和生命体征数据;Specifically, the data communication module is configured to receive a plurality of user data and data interaction in real time, and deliver the received data to a data preprocessing module, wherein the user data includes a vital sign monitoring terminal device ID code, patient information, and vital signs. data;
上述数据通信模块支持多种通讯协议,示例性地,包括:TCP/IP协议、即时通讯协议、HL7协议、DICOM协议、多媒体通信协议、设备制造商通讯协议,自动识别用户身份和设备ID编码,建立网络连接,接收数据,送入数据预处理模块。通过支持多种通讯协议,有效扩大了业务服务面,满足用户各种不同的生命体征监护设备、外部系统的数据交互,所述外部系统包括医院信息管理系统(HIS)、重症监护临床信息系统(CIS),以及体检机构、健康管理机构、保险机构的平台的应用程序接口(Application Programming Interface,API)。The above data communication module supports a plurality of communication protocols, including: TCP/IP protocol, instant communication protocol, HL7 protocol, DICOM protocol, multimedia communication protocol, device manufacturer communication protocol, automatic identification of user identity and device ID coding, Establish a network connection, receive data, and send it to the data preprocessing module. By supporting a variety of communication protocols, the business service surface is effectively expanded to meet the data interaction of various vital vitality monitoring devices and external systems of the user, including the hospital information management system (HIS) and the intensive care clinical information system ( CIS), and the application programming interface (API) of the platform of the medical examination organization, health management organization, and insurance organization.
数据预处理模块用于对获取的每一用户数据进行预处理并进行统一封装后存入生命体征数据库;具体地:The data preprocessing module is configured to preprocess each acquired user data and perform unified encapsulation and then store the data in the vital sign database; specifically:
基于系统编码表规则,将获取的生命体征监护终端设备ID编码和患者信息绑定,生成业务流水号,并与所述设备ID编码双向映射转换;Binding the obtained vital sign monitoring terminal device ID code and the patient information according to the system coding table rule, generating a service serial number, and bidirectionally mapping and converting with the device ID code;
对接收的生命体征数据进行解析、分类、数据格式标准化处理,保留设备原始报警事件标志;Parsing, classifying, and normalizing the data format of the received vital signs data, and retaining the original alarm event flag of the device;
将患者业务流水号和经过预处理的生命体征数据进行统一封装,送入生命体征数据库存储,用于读取、调用、计算分析、检索统计。The patient business serial number and the preprocessed vital sign data are uniformly encapsulated and sent to the vital sign database for reading, invoking, calculating analysis, and retrieving statistics.
需要说明的是:业务流水号包括时间戳、患者信息、用户信息、设备信息、数量计数器;解析是从接收的数据包中抽取数据(包括时间戳、数值),分类是根据数据协议对解析的生命体征数据进行数据类型分类;数据格式标准化处理是采用数位变化、码率转换、再编码方式转换为本实施例的生命体征数据标准格式。It should be noted that the service serial number includes time stamp, patient information, user information, device information, and quantity counter; the parsing is to extract data (including timestamp and numerical value) from the received data packet, and the classification is parsed according to the data protocol. The vital sign data is classified into a data type; the data format normalization process is converted into a vital sign data standard format of the embodiment by using a digital bit change, a rate conversion, and a re-encoding method.
需要强调的是,通过数据格式标准化处理,解决了各厂家设备数据格式不兼容的问题,采用统一的数据格式,提高了云平台的运行效率;业务流水号与生命体征监护设备ID保持双向映射转换,建立了可靠、高效的数据查询、数据交互的内部外部逻辑关系,满足系统内部数据查询,以及与外部数据交互的需求,同时解决了识别同一 台设备(同一病床)不同患者的问题。It should be emphasized that the data format standardization process solves the problem that the data format of each manufacturer is incompatible. The unified data format is adopted to improve the operating efficiency of the cloud platform; the service serial number and the vital sign monitoring device ID maintain bidirectional mapping conversion. It establishes reliable and efficient internal and external logical relationships of data query and data interaction, satisfies the requirements of internal data query and interaction with external data, and solves the problem of identifying different patients of the same device (same bed).
消息总线模块用于连接控制数据通讯模块、数据预处理模块、数据实时分析处理模块、数据存储模块之间的数据传输;The message bus module is used for connecting data transmission between the control data communication module, the data preprocessing module, the data real-time analysis processing module, and the data storage module;
数据存储模块包括生命体征数据库、文件数据库、业务信息数据库、缓存数据库,用于数据存储、调用;该数据存储模块整合了各类数据库、数据存储服务系统的优势,解决了该系统对海量生命体征数据的存储与访问的问题,以及对大规模数据集合、多样数据结构、多重数据种类管理的问题。The data storage module includes a vital sign database, a file database, a business information database, and a cache database for data storage and invoking; the data storage module integrates the advantages of various databases and data storage service systems, and solves the massive vital signs of the system. The problem of storage and access of data, as well as the management of large-scale data collections, diverse data structures, and multiple data types.
具体地,生命体征数据库属于结构化数据库,用于存储经过统一封装后的生命体征数据;提供了海量存储容量和实时查询能力,具有高并发、低延迟、支持灵活的特点;Specifically, the vital sign database belongs to a structured database for storing vital sign data after unified encapsulation; providing massive storage capacity and real-time query capability, featuring high concurrency, low latency, and flexible support;
文件数据库属于对象存储数据库,用于存储系统生成的生命体征数据分析报告文件、临床信息文件、患者信息文件、视频影像文件、医学工具资料文件;The file database belongs to the object storage database, and is used for storing the vital sign data analysis report file, the clinical information file, the patient information file, the video image file, and the medical tool data file generated by the system;
业务信息数据库属于关系型数据库,用于存储结构化业务数据、业务逻辑关系数据;建立在关系模型基础上,具有保持数据一致性的优点;The business information database belongs to a relational database for storing structured business data and business logic relationship data; based on the relationship model, it has the advantages of maintaining data consistency;
缓存数据库属于非关系型数据库,作为各模块之间的数据交换和状态保持的缓存,也用于缓存数据存储模块的查询结果,减少了数据库访问次数,提高系统的响应速度。The cache database belongs to a non-relational database. It acts as a cache for data exchange and state preservation between modules. It is also used to cache the query results of the data storage module, which reduces the number of database accesses and improves the response speed of the system.
实时分析处理模块包括分布式并行计算的深度学习框架,用于实时读取生命体征数据库中的生命体征数据进行处理,生成数据分析报告,并将分析报告发送给用户,同时存入文件数据库;如图4所示,具体地:The real-time analysis processing module includes a deep learning framework for distributed parallel computing, which is used for real-time reading of vital sign data in the vital sign database for processing, generating a data analysis report, and transmitting the analysis report to the user, and simultaneously storing it in a file database; As shown in Figure 4, specifically:
实时读取生命体征数据库的生命体征数据(包含设备原始报警数据),并利用基于Spark引擎的深度学习框架的该类生命体征数据中央模型进行筛查处理,得到分析筛查处理结果,以及异常数据属性,并将经过分析筛查处理的数据存入生命体征数据库。Real-time reading of vital sign data of the vital sign database (including the original alarm data of the device), and using the central model of the vital sign data of the Spark engine-based deep learning framework for screening processing, obtaining analysis screening processing results, and abnormal data Attributes, and the data processed by the analysis and screening is stored in the vital signs database.
需要强调的是,实时分析处理模块采用基于分布式并行计算的深度学习框架,可以是通用的Spark、Storm、Flink、Samza框架的其中一种。It should be emphasized that the real-time analysis processing module adopts a deep learning framework based on distributed parallel computing, and can be one of the general Spark, Storm, Flink, and Samza frameworks.
具体来说,通过Spark分布式并行计算的深度学习框架读取生命体征数据中的生命体征数据,按照设置的微批处理间隔时间(大于等于0.01秒),Spark引擎并行创建多个任务,触发Spark流将数据按类型切分为RDD数据集合,同时控制相应类型的中央模型对该类型数据进行分析处理;中央模型内置包括二阶差分计算工具和/或逻 辑分析工具,实时计算分析生命体征数据的形态、节律、速率、数值,对波形进行分类标记、对数值进行统计归纳,实时分析筛查超出基准的异常数据。Specifically, the deep learning framework of the Spark distributed parallel computing reads the vital sign data in the vital sign data, and according to the set micro-batch processing interval (0.01 seconds or more), the Spark engine creates multiple tasks in parallel, triggering Spark. The stream divides the data into RDD data sets by type, and controls the central model of the corresponding type to analyze and process the type data; the central model includes second-order difference calculation tools and/or logic analysis tools to calculate and analyze vital sign data in real time. Morphology, rhythm, rate, and numerical values are used to classify the waveforms, statistically summarize the logarithmic values, and analyze the abnormal data beyond the baseline in real time.
需要说明的是,生命体征数据分为波形类和数值类;具体来说,波形类生命体征数据包括:全程总心博、心电波间期、QRS时限、ST段形态、QT间期,全程呼吸总次数、呼吸波间期,脉搏容积波峰谷值,颅内压波峰谷值,呼气末二氧化碳分压波峰谷值、呼气末二氧化碳分压波间期;数值类生命体征数据包括:全程无创/有创血压中的收缩压和舒张压、脉率、血氧饱和度、体温、胎心率;无创心排量的心搏量、心脏指数、总外周阻力值;呼吸力学的气道压力值、气道流量值、气道容积值。It should be noted that the vital sign data is divided into waveform class and numerical class; specifically, the waveform type vital sign data includes: total heart beat, ECG interval, QRS time limit, ST segment shape, QT interval, full breathing Total number, respiratory wave interval, pulse volume peak-to-valley value, intracranial pressure peak-to-valley value, end-tidal carbon dioxide partial pressure peak-to-peak value, end-tidal carbon dioxide partial pressure wave interval; numerical vital sign data including: non-invasive Systolic and diastolic blood pressure, pulse rate, blood oxygen saturation, body temperature, fetal heart rate in non-invasive blood pressure; stroke volume, cardiac index, total peripheral resistance value of non-invasive cardiac output; airway pressure value of respiratory mechanics , airway flow value, airway volume value.
上述异常数据特征包括:心动过速、心动过缓、扑动颤动、频发早搏、停博、RonT、QT间期延长、ST段抬高/压低,呼吸暂停、呼吸过缓、呼吸过快,血氧饱和度升高/下降、收缩压和舒张压升高/下降、平均动脉压升高/下降,脉搏容积波峰值升高/下降,颅内压波峰值升高/下降,呼气末二氧化碳分压波升高/下降,胎心率升高/下降,无创心排量下降,呼吸力学值升高/下降。The above abnormal data features include: tachycardia, bradycardia, fluttering, frequent premature beats, stop, RonT, QT interval prolongation, ST segment elevation/depression, apnea, hypopnea, and rapid breathing. Increase/decrease in blood oxygen saturation, increase/decrease in systolic and diastolic blood pressure, increase/decrease in mean arterial pressure, increase/decrease in peak volume of pulse volume, increase/decrease in peak intracranial pressure wave, end-tidal carbon dioxide The partial pressure wave rises/falls, the fetal heart rate increases/decreases, the non-invasive cardiac output decreases, and the respiratory mechanics value increases/decreases.
需要说明的是:中央模型分为两类,一类对波形类数据的形态、节律、速率进行计算分析,另一类对数值型数据幅值进行计算分析;当中央模型发现超出设定基准的异常数据时,分析异常数据特征,计算异常事件持续时间,标记异常数据属性;实时分析处理模块将异常数据生成实时数据分析报告,存入文件数据库,同时发送给用户。It should be noted that the central model is divided into two categories, one is to calculate and analyze the shape, rhythm and rate of waveform data, and the other is to calculate and analyze the amplitude of numerical data; when the central model finds that it exceeds the set reference When abnormal data is used, the abnormal data characteristics are analyzed, the abnormal event duration is calculated, and the abnormal data attribute is marked; the real-time analysis processing module generates the real-time data analysis report of the abnormal data, stores it in the file database, and simultaneously sends it to the user.
为了进一步提高分析筛查处理的效率和准确度,减少误报警事件,实时分析处理模块使用生命体征数据库中分析筛查处理后的定量定性的生命体征数据,实时对每类中央模型进行训练优化,得到该类型数据新的中央模型。In order to further improve the efficiency and accuracy of the analysis screening process and reduce false alarm events, the real-time analysis and processing module uses the quantitative and qualitative vital sign data after analysis and screening in the vital sign database to train and optimize each type of central model in real time. Get a new central model of this type of data.
本实施例的实时分析处理模块还可以将经过分析筛查处理的每一用户全程生命体征数据进行整合,生成动态数据分析报告,并存入文件数据库,根据患者业务流水号将动态数据分析报告发送给用户。The real-time analysis processing module of the embodiment may further integrate the whole life vital sign data of each user subjected to the analysis screening process, generate a dynamic data analysis report, and store the data in the file database, and send the dynamic data analysis report according to the patient service serial number. To the user.
需要说明的是,实时分析处理模块生成的动态数据分析报告内容包括:全程的动态心电数据、动态血压数据、呼吸数据、血氧饱和度数据、有创血压数据、颅内压数据、呼气末二氧化碳分压数据、体温数据、胎心率数据、无创心排量数据、呼吸力学数据的综合分析计算、波形分类标记、波形图形,以及它们的趋势图、直方图、散点图、变异性分析图。It should be noted that the dynamic data analysis report generated by the real-time analysis processing module includes: full-range dynamic electrocardiogram data, dynamic blood pressure data, respiratory data, blood oxygen saturation data, invasive blood pressure data, intracranial pressure data, and exhalation. Carbon dioxide partial pressure data, body temperature data, fetal heart rate data, non-invasive cardiac output data, comprehensive analysis and calculation of respiratory mechanics data, waveform classification markers, waveform patterns, and their trend graphs, histograms, scatter plots, variability diagram.
实时数据分析报告内容包括:异常心电数据、异常血压数据、异常呼吸数据、异 常血氧饱和度数据、异常颅内压数据、异常呼气末二氧化碳分压数据、异常体温、异常胎心率数据、异常无创心排量数据、异常呼吸力学数据的实时计算分析、波形分类标记、异常波形图形,以及趋势图。The real-time data analysis report includes: abnormal ECG data, abnormal blood pressure data, abnormal respiratory data, abnormal blood oxygen saturation data, abnormal intracranial pressure data, abnormal end-tidal carbon dioxide partial pressure data, abnormal body temperature, abnormal fetal heart rate data Abnormal non-invasive cardiac output data, real-time computational analysis of abnormal respiratory mechanics data, waveform classification markers, abnormal waveform graphics, and trend graphs.
本实施例的动态数据分析报告,解决了生命体征监护设备在应用过程中缺少电子化数据总结分析记录的问题,同时作为临床医疗的数据依据,可以对临床治疗效果和患者状态进行评估,制定或调整医疗方案决策;实时数据分析报告解决了在异常事件发生时缺少电子化异常数据分析报记录的问题,同时作为异常事件数据依据,支持医护人员快速干预;上述电子化数据分析报告有效提高了医疗质量和工作效率,减轻了医护人员的劳动强度和工作负担。用户还可以向云平台发出请求指令,进行检索查询,统计分析,回顾总结临床经验。The dynamic data analysis report of the embodiment solves the problem that the vital sign monitoring device lacks the summary and analysis record of the electronic data in the application process, and at the same time, as the data basis of the clinical medical treatment, the clinical treatment effect and the patient state can be evaluated, and Adjust medical plan decision-making; real-time data analysis report solves the problem of lack of electronic abnormal data analysis report record when abnormal event occurs, and serves as the basis of abnormal event data to support rapid intervention of medical staff; the above-mentioned electronic data analysis report effectively improves medical treatment Quality and work efficiency reduce the labor intensity and workload of medical staff. Users can also send request instructions to the cloud platform, perform search queries, statistical analysis, and review and summarize clinical experience.
需要说明的是,本申请基于云平台的生命体征数据处理方法及系统可以在公有云或私有云上部署实施和运行,可以采用云端的服务器、数据库、应用服务系统来实现,采用集群的形式实现系统中所涉及模块的功能。It should be noted that the method and system for processing vital signs based on the cloud platform can be implemented and executed on a public cloud or a private cloud, and can be implemented by using a cloud server, a database, and an application service system, and implemented in the form of a cluster. The functionality of the modules involved in the system.
上述方法实施例和系统实施例基于相同或相似的原理,其相似之处可相互借鉴,且能达到相同的效果。The above method embodiments and system embodiments are based on the same or similar principles, and the similarities can be learned from each other and can achieve the same effect.
本领域技术人员可以理解,实现上述实施例方法的全部或部分流程,可以通过计算机程序指令相关的硬件来完成,所述的程序可存储于计算机可读存储介质中。其中,所述计算机可读存储介质为磁盘、光盘、只读存储记忆体或随机存储记忆体等。It will be understood by those skilled in the art that all or part of the process of implementing the above embodiments may be performed by a computer program instructing related hardware, and the program may be stored in a computer readable storage medium. The computer readable storage medium is a magnetic disk, an optical disk, a read-only storage memory, or a random storage memory.
以上所述,仅为本申请较佳的具体实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到的变化或替换,都应涵盖在本申请的保护范围之内。The above description is only a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto, and any person skilled in the art can easily think of changes or within the technical scope disclosed in the present application. Replacement should be covered by the scope of this application.
Claims (16)
- 一种基于云平台的生命体征数据处理方法,其特征在于,包括以下步骤:A cloud platform-based vital sign data processing method, comprising the following steps:步骤S1,实时获取多个用户数据;所述用户数据包括生命体征监护终端设备ID编码、患者信息和生命体征数据;Step S1: acquiring a plurality of user data in real time; the user data includes vital sign monitoring terminal device ID code, patient information, and vital sign data;步骤S2,对获取的每一用户数据进行预处理并进行统一封装后存入生命体征数据库;Step S2: pre-processing each acquired user data and performing unified encapsulation and storing the data into the vital sign database;步骤S3,实时读取上述生命体征数据库中的生命体征数据,并利用分布式并行计算的深度学习框架进行分析筛查处理,得到分析筛查结果,生成数据分析报告。In step S3, the vital sign data in the vital sign database is read in real time, and the deep learning framework of the distributed parallel computing is used for analysis and screening processing, and the analysis screening result is obtained, and a data analysis report is generated.
- 根据权利要求1所述的方法,其特征在于,在步骤S2中,The method of claim 1 wherein in step S2,基于系统编码表规则,将生命体征监护终端设备ID编码和患者信息绑定,生成患者业务流水号,并与所述设备ID编码保持双向映射转换;Binding the vital sign monitoring terminal device ID code and the patient information according to the system coding table rule, generating a patient service serial number, and maintaining bidirectional mapping conversion with the device ID code;对接收的生命体征数据进行解析、分类、数据格式标准化处理,保留设备原始报警事件数据标志;Parsing, classifying, and normalizing the data format of the received vital signs data, and retaining the original alarm event data flag of the device;将患者业务流水号和经过预处理的生命体征数据进行统一封装,存入生命体征数据库。The patient business serial number and the preprocessed vital sign data are uniformly encapsulated and stored in the vital sign database.
- 根据权利要求1或2所述的方法,其特征在于,实时读取上述生命体征数据库中的生命体征数据,并利用分布式并行计算的深度学习框架进行分析筛查处理,是采用在线实时数据分析处理方式及基于Spark引擎的深度学习框架实现:The method according to claim 1 or 2, wherein the vital sign data in the vital sign database is read in real time, and the deep learning framework of the distributed parallel computing is used for analysis and screening processing, which is an online real-time data analysis. Processing method and deep learning framework based on Spark engine:通过Spark分布式并行计算的深度学习框架读取生命体征数据中的生命体征数据,按照设置的微批处理间隔时间,Spark引擎并行创建多个任务,触发Spark流将数据按类型切分为RDD数据集合,同时控制相应类型的中央模型对该类型数据进行计算处理。The Spark distributed parallel computing deep learning framework reads the vital sign data in the vital sign data. According to the set micro-batch processing interval, the Spark engine creates multiple tasks in parallel, triggering the Spark stream to divide the data into RDD data by type. The collection, while controlling the central model of the corresponding type, performs calculation processing on the type data.
- 根据权利要求3所述的方法,其特征在于,所述中央模型分为两类,一类对波形类数据的形态、节律、速率进行分析计算,另一类对数值型数据幅值进行分析计算;中央模型内置包括二阶差分计算工具和/或逻辑分析工具,实时计算分析生命体征数据的形态、节律、速率、数值,对波形进行分类标记、对数值进行统计归纳,实时分析筛查超出基准的异常数据。The method according to claim 3, wherein the central model is divided into two categories, one class analyzes and calculates the morphology, rhythm and rate of the waveform data, and the other analyzes and calculates the amplitude of the numerical data. The central model includes a second-order differential calculation tool and/or logic analysis tool to calculate and analyze the shape, rhythm, rate, and value of vital sign data in real time, classify and mark the waveform, and statistically summarize the logarithm. Real-time analysis and screening exceeds the benchmark. Exception data.
- 根据权利要求4所述的方法,其特征在于,当所述中央模型计算处理发现超出设定基准的异常数据时,分析异常数据特征,计算持续时间,标记异常数据属性。The method according to claim 4, wherein when the central model calculation process finds abnormal data exceeding a set reference, analyzing the abnormal data feature, calculating the duration, and marking the abnormal data attribute.
- 根据权利要求5所述的方法,其特征在于,在发现异常数据时,将异常数据生成实时数据分析报告,向用户发出异常事件预警,并将实时数据分析报告发送给用户。The method according to claim 5, wherein when abnormal data is found, the abnormal data is generated into a real-time data analysis report, an abnormal event warning is issued to the user, and the real-time data analysis report is sent to the user.
- 根据权利要求6所述的方法,其特征在于,使用所述生命体征数据库中经过分析筛查处理后的生命体征数据,实时对每类中央模型进行训练优化,得到该类型数据新的中央模型。The method according to claim 6, wherein the vital sign data after the analysis and screening process in the vital sign database is used to perform training optimization on each type of central model in real time to obtain a new central model of the type data.
- 根据权利要求7所述的方法,其特征在于,将经过分析筛查处理的每一用户全程生命体征数据进行整合,自动生成动态数据分析报告,根据患者业务流水号,将动态数据分析报告发送给用户。The method according to claim 7, wherein the entire life vital sign data of each user subjected to the analysis screening process is integrated, a dynamic data analysis report is automatically generated, and the dynamic data analysis report is sent according to the patient service serial number. user.
- 一种基于云平台的生命体征数据处理系统,其特征在于,包括:云平台数据通信子系统、云平台数据支持子系统;所述云平台数据通信子系统包括数据通信模块、数据预处理模块;所述云平台数据支持子系统包括消息总线模块、数据存储模块、实时分析处理模块;A cloud platform-based vital sign data processing system, comprising: a cloud platform data communication subsystem and a cloud platform data support subsystem; the cloud platform data communication subsystem comprises a data communication module and a data preprocessing module; The cloud platform data support subsystem includes a message bus module, a data storage module, and a real-time analysis processing module;所述消息总线模块用于连接控制数据通信模块、数据预处理模块、实时分析处理模块、数据存储模块之间的数据传输;The message bus module is configured to connect data transmission between the control data communication module, the data preprocessing module, the real-time analysis processing module, and the data storage module;所述数据通信模块用于实时接收多个用户数据,还用于和用户之间的数据交互,并将接收的数据传递给所述数据预处理模块,所述用户数据包括生命体征监护终端设备ID编码、患者信息和生命体征数据;The data communication module is configured to receive a plurality of user data in real time, and is also used for data interaction with the user, and the received data is transmitted to the data preprocessing module, where the user data includes a vital sign monitoring terminal device ID. Coding, patient information and vital signs data;所述数据预处理模块包括系统编码表,用于对获取的每一用户数据进行预处理并进行统一封装后存入生命体征数据库;The data pre-processing module includes a system coding table, configured to perform pre-processing on each acquired user data, and perform unified encapsulation and then store the data in the vital sign database;所述数据存储模块包括生命体征数据库、文件数据库、业务信息数据库、缓存数据库,用于数据存储、调用;The data storage module includes a vital sign database, a file database, a business information database, and a cache database for data storage and calling;所述实时分析处理模块包括分布式并行计算的深度学习框架,用于实时读取生命体征数据库中的数据进行分析筛查处理,生成数据分析报告,并将分析报告发送给用户,同时存入文件数据库。The real-time analysis processing module includes a deep learning framework for distributed parallel computing, which is used for real-time reading data in the vital sign database for analysis and screening processing, generating a data analysis report, and transmitting the analysis report to the user, and simultaneously depositing the file database.
- 根据权利要求9所述的系统,其特征在于,所述数据预处理模块用于:The system of claim 9 wherein said data preprocessing module is operative to:基于系统编码表规则将生命体征监护终端设备ID编码和患者信息绑定,生成业务流水号,并与所述设备ID编码保持双向映射转换;Binding the vital sign monitoring terminal device ID code and the patient information according to the system coding table rule, generating a service serial number, and maintaining bidirectional mapping conversion with the device ID code;对接收的生命体征数据进行解析、分类、数据格式标准化处理,保留设备原始报 警事件标志;Parsing, classifying, and normalizing the data format of the received vital signs data, and retaining the original alarm event flag of the device;将患者业务流水号和经过预处理的生命体征数据进行统一封装,存入生命体征数据库。The patient business serial number and the preprocessed vital sign data are uniformly encapsulated and stored in the vital sign database.
- 根据权利要求9或10所述的系统,其特征在于,所述实时分析处理模块实时读取所述生命体征数据库中的生命体征数据,采用在线实时数据分析处理方式及基于Spark引擎的深度学习框架进行分析筛查处理:The system according to claim 9 or 10, wherein the real-time analysis processing module reads the vital sign data in the vital sign database in real time, adopts an online real-time data analysis and processing method, and a deep learning framework based on the Spark engine. Perform analytical screening processing:通过Spark分布式并行计算的深度学习框架读取生命体征数据中的生命体征数据,按照设置的微批处理间隔时间,Spark引擎并行创建多个任务,触发Spark流将数据按类型切分为RDD数据集合,同时控制相应类型的中央模型对该类型数据进行计算处理。The Spark distributed parallel computing deep learning framework reads the vital sign data in the vital sign data. According to the set micro-batch processing interval, the Spark engine creates multiple tasks in parallel, triggering the Spark stream to divide the data into RDD data by type. The collection, while controlling the central model of the corresponding type, performs calculation processing on the type data.
- 根据权利要求11所述的系统,其特征在于,所述中央模型分为两类,一类对波形类数据的形态、节律、速率进行分析计算;另一类对数值型数据幅值进行分析计算,中央模型内置包括二阶差分计算工具和/或逻辑分析工具,实时计算分析生命体征数据的形态、节律、速率、数值,对波形进行分类标记、对数值进行统计归纳,实时分析筛查超出基准的异常数据。The system according to claim 11, wherein the central model is divided into two categories, one class analyzes and calculates the morphology, rhythm and rate of the waveform data; and another class analyzes the amplitude of the numerical data. The central model includes second-order differential calculation tools and/or logic analysis tools to calculate and analyze the shape, rhythm, rate, and value of vital sign data in real time, classify and mark the waveform, and statistically summarize the logarithm. Real-time analysis and screening exceeds the benchmark. Exception data.
- 根据权利要求12所述的系统,其特征在于,所述实时分析处理模块在中央模型计算处理发现超出设定基准的异常数据时,分析异常数据特征,计算持续时间,标记异常数据属性。The system according to claim 12, wherein the real-time analysis processing module analyzes the abnormal data feature, calculates the duration, and marks the abnormal data attribute when the central model calculation process finds abnormal data exceeding the set reference.
- 根据权利要求13所述的系统,其特征在于,所述实时分析处理模块在发现异常数据时,将异常数据生成实时数据分析报告,向用户发出异常事件预警,并将实时数据分析报告发送给用户。The system according to claim 13, wherein the real-time analysis processing module generates a real-time data analysis report of the abnormal data when the abnormal data is found, sends an abnormal event warning to the user, and sends the real-time data analysis report to the user. .
- 根据权利要求14所述的系统,其特征在于,所述实时分析处理模块使用生命体征数据库中经过分析筛查处理后的生命体征数据,实时对每类中央模型进行训练优化,得到该类型数据新的中央模型。The system according to claim 14, wherein the real-time analysis processing module uses the vital sign data after the analysis and screening process in the vital sign database to perform training optimization for each type of central model in real time, and obtains new data of the type. Central model.
- 根据权利要求15所述的系统,其特征在于,所述实时分析处理模块将经过分析筛查处理的每一用户全程生命体征数据进行整合,自动生成动态数据分析报告,并存入文件数据库,根据患者业务流水号将动态数据分析报告发送给用户。The system according to claim 15, wherein the real-time analysis processing module integrates the global vital sign data of each user subjected to the analysis screening process, automatically generates a dynamic data analysis report, and stores the data in a file database, according to The patient business serial number sends a dynamic data analysis report to the user.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810468634.8A CN108847274B (en) | 2018-05-16 | 2018-05-16 | Vital sign data processing method and system based on cloud platform |
CN201810468634.8 | 2018-05-16 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2019219035A1 true WO2019219035A1 (en) | 2019-11-21 |
Family
ID=64213155
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2019/087109 WO2019219035A1 (en) | 2018-05-16 | 2019-05-15 | Vital sign data processing method and system based on cloud platform |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN108847274B (en) |
WO (1) | WO2019219035A1 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112635037A (en) * | 2020-12-21 | 2021-04-09 | 深圳市南方国讯科技有限公司 | 5G intelligent medical collaborative cloud management system |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108847274B (en) * | 2018-05-16 | 2021-07-06 | 上海术木医疗科技有限公司 | Vital sign data processing method and system based on cloud platform |
CN110867248A (en) * | 2019-11-13 | 2020-03-06 | 秒针信息技术有限公司 | Postoperative monitoring method and device |
CN111128331B (en) * | 2020-02-18 | 2022-07-26 | 上海术木医疗科技有限公司 | Method and system for generating data analysis report of multi-parameter monitoring equipment |
CN111445990A (en) * | 2020-04-13 | 2020-07-24 | 上海联影医疗科技有限公司 | Scanning scheme adjusting method and device, electronic equipment and storage medium |
CN113658677A (en) * | 2020-05-12 | 2021-11-16 | 深圳市科瑞康实业有限公司 | System and method for identifying and early warning monitored data |
CN113712561B (en) * | 2020-05-12 | 2024-05-17 | 深圳市科瑞康实业有限公司 | Monitoring data early warning system and method |
CN113658678B (en) * | 2020-05-12 | 2023-11-21 | 深圳市科瑞康实业有限公司 | Monitoring data early warning method |
CN112700835B (en) * | 2021-02-25 | 2024-01-26 | 四川大学华西医院 | Medical care period electronic body temperature bill filling supervision and management method, device and medium |
CN114760440A (en) * | 2022-03-22 | 2022-07-15 | 浙江大学 | Visual monitoring glasses |
CN116431684B (en) * | 2023-04-18 | 2024-03-19 | 中船海神医疗科技有限公司 | Diagnosis and treatment data storage and playback method and system for portable life support system |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104115144A (en) * | 2011-12-12 | 2014-10-22 | 安大略理工大学 | System, method and computer program for multi-dimensional temporal and relative data mining framework, analysis and sub-grouping |
CN105962923A (en) * | 2016-04-21 | 2016-09-28 | 南京大学 | Intelligent health full-information acquisition and analysis system |
CN106815337A (en) * | 2016-12-31 | 2017-06-09 | 深圳市易特科信息技术有限公司 | Medical big data associated storage system and method |
CN107242858A (en) * | 2017-07-10 | 2017-10-13 | 郑州航空工业管理学院 | A kind of personalized pregnant baby's monitoring method and system |
CN108847274A (en) * | 2018-05-16 | 2018-11-20 | 上海术木医疗科技有限公司 | A kind of vital sign data processing method and system based on cloud platform |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101926675B (en) * | 2009-10-30 | 2012-08-08 | 华为技术有限公司 | Method, device and system for remotely acquiring physical detection data of user |
-
2018
- 2018-05-16 CN CN201810468634.8A patent/CN108847274B/en active Active
-
2019
- 2019-05-15 WO PCT/CN2019/087109 patent/WO2019219035A1/en active Application Filing
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104115144A (en) * | 2011-12-12 | 2014-10-22 | 安大略理工大学 | System, method and computer program for multi-dimensional temporal and relative data mining framework, analysis and sub-grouping |
CN105962923A (en) * | 2016-04-21 | 2016-09-28 | 南京大学 | Intelligent health full-information acquisition and analysis system |
CN106815337A (en) * | 2016-12-31 | 2017-06-09 | 深圳市易特科信息技术有限公司 | Medical big data associated storage system and method |
CN107242858A (en) * | 2017-07-10 | 2017-10-13 | 郑州航空工业管理学院 | A kind of personalized pregnant baby's monitoring method and system |
CN108847274A (en) * | 2018-05-16 | 2018-11-20 | 上海术木医疗科技有限公司 | A kind of vital sign data processing method and system based on cloud platform |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112635037A (en) * | 2020-12-21 | 2021-04-09 | 深圳市南方国讯科技有限公司 | 5G intelligent medical collaborative cloud management system |
Also Published As
Publication number | Publication date |
---|---|
CN108847274B (en) | 2021-07-06 |
CN108847274A (en) | 2018-11-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2019219035A1 (en) | Vital sign data processing method and system based on cloud platform | |
US11769586B2 (en) | Medical cloud platform data sharing system and method based on third-party business | |
US20230395253A1 (en) | Cloud-edge collaborative processing system and method for icu data based on third-party business | |
CN107242858B (en) | Personalized pregnant infant monitoring method and system | |
US20130162433A1 (en) | Systems and methods for storing, analyzing, retrieving and displaying streaming medical data | |
US20130096936A1 (en) | Systems and methods for storing, analyzing, and retrieving medical data | |
CA2461612A1 (en) | System for supporting clinical decision making through the modeling of acquired patient medical information | |
Sun et al. | INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit | |
US20230368906A1 (en) | Method and system for generating data analysis report of multi-parameter monitoring device | |
Li et al. | Mobicardio: A clinical-grade mobile health system for cardiovascular disease management | |
CN110277158A (en) | A method of being suitable for all kinds of ventilators and carries out automation adaptation and arrange storing data | |
WO2024001010A1 (en) | Mechanical ventilation treatment data management method and system | |
Agbo et al. | A scalable patient monitoring system using Apache Storm | |
McGregor et al. | The e-baby data warehouse: a case study | |
CN116825337A (en) | Patient safety nursing early warning system | |
Zhang et al. | Real-time evaluation of patient monitoring algorithms for critical care at the bedside | |
US20160371457A1 (en) | System and Method for Data Analyzing of Health-Related Data | |
US20220383997A1 (en) | Data Processing Method, Data Processing Apparatus, and Health Management Apparatus | |
AU2020103910A4 (en) | An IoT based Health Care System to find Medical Emergency and Initiate Expert Care | |
CN203244376U (en) | Sip protocol based wireless medical call monitoring system | |
Ardeti et al. | Development of real time ECG monitoring and unsupervised learning classification framework for cardiovascular diagnosis | |
CN113284592A (en) | Medical equipment integrated management system based on hospital equipment department management | |
KR20120116629A (en) | Healthcare systems and healthcare service method | |
Turnip et al. | Backend Design of Web-based ECG Signal Monitoring System | |
Alhussein et al. | Energy-efficient adaptive sensing technique for smart healthcare in connected healthcare systems |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 19804516 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 17.03.2021) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19804516 Country of ref document: EP Kind code of ref document: A1 |