New-generation medical information integration method and engine system

Abstract

The invention discloses a new-generation medical information integration method. The method comprises the following steps: firstly, receiving diversified data streams from different information systems or external data platforms; format analysis, data cleaning, code conversion, data mapping and data standardization conversion are carried out on the data stream, and finally standardized data are stored in a data warehouse or sent to a downstream system in real time; the output standardized data is transmitted and processed in real time through a streaming processing architecture; and performing integrated processing on the data transmitted and processed in real time in the step S300 through an integrated AI model. According to the invention, the efficiency of data integration is improved, the accuracy of medical decision making is improved, the flexibility and expandability of the system are enhanced, the security and compliance are enhanced, and the sharing and collaboration of medical data are promoted.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a new generation medical information integration method and engine system. Background Art

[0002] Most medical information integration systems use standardized protocols (such as HL7, DICOM, FHIR, etc.) and interfaces to achieve data exchange between different systems. The hospital's electronic medical record system (EMR), laboratory information system (LIS), and radiology information system (RIS) rely on these protocols to achieve a certain degree of data sharing. Some hospitals and medical institutions use data centralization, that is, to collect data from various departments through a data warehouse or central data platform, and centrally store and process it. The data centralization integration model usually relies on large databases and ETL processes to handle data integration.

[0003] Although medical information exchange protocols such as HL7 and DICOM provide a basis for data standardization, there are still differences in data formats and coding standards between systems, making it difficult to achieve full compatibility between data in different systems. For example, the medical record data formats of different hospitals or departments may be different, and a lot of manual adjustments are required during transmission and sharing, which increases the difficulty of information exchange. Summary of the invention

[0004] The purpose of the present invention is to provide a new generation of medical information integration method and engine system, so as to solve the above-mentioned problems existing in the prior art.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A new generation medical information integration method comprises the following steps:

[0007] S100, first receiving diverse data streams from different information systems or external data platforms;

[0008] S200, format parsing, data cleaning, encoding conversion, data mapping, and data standardization conversion are performed on the data stream, and the standardized data is finally stored in the data warehouse or sent to the downstream system in real time;

[0009] S300, real-time transmission and processing of output standardized data through a streaming processing architecture;

[0010] S400, integrating and processing the data transmitted and processed in real time in step S300 by integrating the AI ​​model.

[0011] Preferably, the specific steps of step S100 include:

[0012] Protocol identification: Automatically identify the data source type and the communication protocol it belongs to based on the header information or metadata analysis of data communication;

[0013] Source verification: Verify the legitimacy and accuracy of the data source in combination with the configuration parameters of the medical institution.

[0014] Preferably, the specific steps of step S200 include:

[0015] Format parsing: Decode the data into a structured form according to the protocol definition of the data source;

[0016] Data cleaning: remove redundant information, repair incomplete data, and eliminate potential errors;

[0017] Code conversion: uniformly convert the data encoding format into the standard encoding supported by the system;

[0018] Field matching: Using natural language processing technology, dynamically match the fields of the data source with the target standard fields based on the field name, data type and content semantics;

[0019] Value mapping: unify the encoding values ​​in different systems into a standard encoding system.

[0020] Preferably, the specific steps of step S200 also include:

[0021] Structural transformation: Unify and reorganize different data models into a target standard format;

[0022] Unit and format correction: Perform unit conversion and format correction on numerical data to ensure uniformity;

[0023] Automatic model training: Dynamically generate new field mapping rules and conversion logic by analyzing new data sources;

[0024] Abnormal monitoring and feedback: Real-time monitoring of abnormal fields or incorrect mappings in data processing, triggering manual verification and iterative optimization of the mapping engine;

[0025] Scalability: When introducing new data sources, standardization can be completed with only a small amount of configuration without the need for redevelopment.

[0026] Preferably, the specific steps of step S300 include:

[0027] Data collection: Use standard interface protocols or custom protocols for data collection;

[0028] Data access and transmission: Use message queues as data entry points to support massive concurrent access;

[0029] Data stream processing: Use Spark Streaming to process data streams in real time, mainly including: data cleaning, data conversion and grouping and sorting;

[0030] Data storage: Store processed data in a distributed database;

[0031] Monitoring and optimization: Dynamically expand processing nodes based on traffic and optimize data partitioning strategies.

[0032] Preferably, the specific steps of step S400 include:

[0033] Data collection and cleaning: Collect data from HIS, LIS or PACS systems through HL7, FHIR or DICOM protocols, and use ETL or Pandas tools to clean and standardize data to ensure data quality and consistency;

[0034] Data fusion: Use data fusion algorithms to integrate multi-source data into a complete patient data profile;

[0035] Select algorithms and training: Choose deep learning or machine learning models based on the task, and use cross-validation and hyperparameter optimization to improve model performance;

[0036] Model evaluation: Use accuracy, AUC, and F1 score indicators to evaluate the predictive effect of the model to ensure that it meets clinical needs.

[0037] Preferably, the specific steps of step S400 also include:

[0038] Disease prediction and risk assessment: Based on LSTM, random forest and other models, the patient's vital signs data is analyzed in real time to predict disease changes and issue warnings through anomaly detection;

[0039] Generation of diagnosis and treatment recommendations: Combine the rule engine with AI algorithms to generate personalized treatment plans;

[0040] Data analysis and personalized recommendations: Through recommendation algorithms such as feature engineering and collaborative filtering, we analyze the patient's multi-dimensional data and generate personalized diagnosis and treatment pathways and treatment recommendations.

[0041] Preferably, the specific steps of step S400 also include:

[0042] Integration and deployment: Integrate the intelligent decision module with the hospital information system through RESTful API and FHIR standards, deploy microservice architecture using Docker and Kubernetes, and provide real-time feedback and decision support interface;

[0043] Clinical validation and feedback optimization: Conduct A / B testing in clinical settings, and use incremental learning and online learning to continuously optimize models and system functions based on new data and clinical feedback;

[0044] Privacy protection and compliance: AES, RSA encryption and de-identification technologies are used to ensure patient data privacy protection, and automated compliance checking tools are used to ensure compliance with GDPR and HIPAA healthcare industry regulations.

[0045] Preferably, it also includes:

[0046] Data encryption: Use the national secret SM2 algorithm to encrypt data transmission, and the SM4 algorithm to encrypt and protect stored data to ensure the security of data during transmission and storage;

[0047] Identity authentication and access control: Use national secret SM2 digital certificate authentication technology for user identity authentication, combined with multi-factor authentication and role-based access control to ensure the reasonable allocation of user rights;

[0048] Permission management and auditing: Control access rights through ACL and RBAC, use the audit log system to record all operations, and combine with the SIEM system to analyze security events;

[0049] Data de-identification: Use de-identification technology and data desensitization methods, and adopt differential privacy technology to ensure that personal identity information is not disclosed during data analysis;

[0050] Vulnerability scanning and repair: Use automated vulnerability scanning tools to identify system vulnerabilities and repair known vulnerabilities through automated operation and maintenance tools;

[0051] Security monitoring and response: Use intrusion detection systems for real-time monitoring and combine SIEM systems to handle and respond to security incidents;

[0052] Compliance and regulatory compliance: Ensure that the system complies with GDPR and HIPAA regulations through compliance management tools and automated compliance audit systems.

[0053] A new generation of medical information integration engine system based on the same concept, including:

[0054] Data standardization module, which is used to automatically identify multiple medical data sources and convert them into a unified standard format;

[0055] Real-time data processing engine module, used for real-time transmission and processing of medical data;

[0056] Intelligent analysis and decision support module, used to perform intelligent analysis on integrated data;

[0057] Scalable microservice architecture module for independent deployment and expansion of different functional modules;

[0058] A multi-level security mechanism module is used to ensure the security of data transmission and storage and the de-identification of sensitive data.

[0059] The beneficial effects of the present invention are:

[0060] The present invention discloses a new generation of medical information integration method, comprising the following steps: first, receiving diversified data streams from different information systems or external data platforms; performing format parsing, data cleaning, encoding conversion, data mapping, and data standardization conversion on the data streams, and finally storing the standardized data in a data warehouse or sending it to a downstream system in real time; transmitting and processing the output standardized data in real time through a streaming processing architecture; and integrating and processing the data transmitted and processed in real time in step S300 through an integrated AI model.

[0061] 1. Improve the efficiency of data integration: Through the intelligent standardization module, it can automatically adapt and integrate medical data from different sources, greatly reduce manual intervention, and improve data processing efficiency.

[0062] 2. Improve the accuracy of medical decision-making: Through real-time data processing and AI-driven analysis, the system can provide clinicians with more accurate and personalized diagnosis and treatment recommendations, improving the quality of medical care.

[0063] 3. Enhance the flexibility and scalability of the system: The design of the microservice architecture and API interface enables the system to easily adapt to new data sources and functional expansions, supporting the seamless integration of future medical devices.

[0064] 4. Enhanced security and compliance: Multi-level security strategies ensure the security of data transmission and storage, and the system can adapt to the requirements of medical data privacy regulations (such as GDPR, HIPAA, etc.) in different countries or regions.

[0065] 5. Promote the sharing and collaboration of medical data: Through data standardization and API open interfaces, the engine promotes data sharing among medical institutions, promotes cross-departmental and cross-platform medical collaboration, and contributes to the construction of regional medical informatization. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flow chart of a new generation medical information integration method of the present invention;

[0067] Figure 2 It is a block diagram of a new generation medical information integration engine system of the present invention;

[0068] Figure 3 It is a standardized result output flow chart of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] Reference Figure 1 and Figure 2 A new generation medical information integration method shown includes the following steps:

[0071] S100, first receive diverse data streams (such as HL7, DICOM, FHIR, etc.) from different information systems or external data platforms;

[0072] S200, format parsing, data cleaning, encoding conversion, data mapping, and data standardization conversion are performed on the data stream, and the standardized data is finally stored in the data warehouse or sent to the downstream system in real time;

[0073] S300, real-time transmission and processing of output standardized data through a streaming processing architecture;

[0074] S400, integrating and processing the data transmitted and processed in real time in step S300 by integrating the AI ​​model.

[0075] In this embodiment, data standardization is achieved as follows:

[0076] 1) Data source identification:

[0077] Preferably, the specific steps of step S100 include:

[0078] Protocol identification: Automatically identify the data source type and the communication protocol it belongs to (such as HL7 message structure, DICOM image metadata, FHIR resource structure) based on the header information or metadata parsing of data communication;

[0079] Source verification: Verify the legitimacy and accuracy of the data source in combination with the configuration parameters of the medical institution.

[0080] 2) Data preprocessing

[0081] To improve the efficiency of the standardization process, the system cleans and parses the raw data:

[0082] Preferably, the specific steps of step S200 include:

[0083] Format parsing: Decode the data into a structured form according to the protocol definition of the data source. For example, parse HL7 data into segments, fields, and subfields.

[0084] Data cleaning: remove redundant information, repair incomplete data (such as missing fields), and eliminate potential errors (such as inconsistent date formats).

[0085] Encoding conversion: uniformly convert data encoding formats (such as character set encoding) into standard encodings supported by the system.

[0086] 3) Data Mapping

[0087] The system uses a built-in adaptive mapping engine to map fields or labels from different data sources to a unified standard field format:

[0088] Field matching: Use natural language processing (NLP) technology to dynamically match data source fields with target standard fields based on field name, data type, and content semantics.

[0089] For example, the `PID-3` field (Patient Identifier) ​​in HL7 is mapped to the `Patient.identifier` field in FHIR.

[0090] Value mapping: unify the coding values ​​in different systems (such as gender, department code, etc.) into standard coding systems (such as LOINC, SNOMED CT).

[0091] 4). Data standardization conversion

[0092] The mapped data is restructured according to the target standard (such as FHIR JSON format or XML format) through an algorithm:

[0093] Preferably, the specific steps of step S200 also include:

[0094] Structural conversion: Unify and reorganize different data models (tree or flat structure) into the target standard format;

[0095] For example, the metadata of a DICOM image is converted to the `ImagingStudy` in the FHIR resource.

[0096] Unit and format correction: Perform unit conversion and format correction on numerical data (such as laboratory results) to ensure consistency.

[0097] For example, convert blood sugar values ​​from `mg / dL` to `mmol / L`.

[0098] 5). Adaptive Optimization

[0099] The system uses an adaptive optimization algorithm based on machine learning, which can continuously improve the data standardization effect during use:

[0100] Automatic model training: Dynamically generate new field mapping rules and conversion logic by analyzing new data sources;

[0101] Abnormal monitoring and feedback: Real-time monitoring of abnormal fields or incorrect mappings in data processing, triggering manual verification and iterative optimization of the mapping engine;

[0102] Scalability: When introducing new data sources, standardization can be completed with only a small amount of configuration without the need for redevelopment.

[0103] 6). Standardized result output

[0104] Finally, the standardized data is stored in the data warehouse or sent to downstream systems in real time:

[0105] Supported output formats: FHIR JSON, FHIR XML, standard SQL tables, etc.

[0106] Ensure data compatibility, integrity, and scalability.

[0107] The process diagram is as follows Figure 3 shown.

[0108] The technical advantages are as follows:

[0109] High compatibility: supports mainstream medical data protocols and is suitable for a wide range of medical scenarios.

[0110] Adaptive capability: Dynamically optimize mapping rules through machine learning technology.

[0111] Efficient processing: Maintains high performance and accuracy even on large-scale data streams.

[0112] Strong extensibility: It is easy to integrate new data sources and extend existing standards.

[0113] Through the above steps, the data standardization module has laid a solid foundation for the sharing and interoperability of medical information, greatly improving the intelligence and practicality of the system.

[0114] Real-time data processing engine:

[0115] The system uses advanced streaming processing architecture (such as Kafka or Spark Streaming) to achieve real-time transmission and processing of medical data. Unlike traditional batch processing, streaming processing can minimize data latency, allowing the system to provide effective decision support for clinicians in almost real-time.

[0116] Real-time data processing is implemented as follows:

[0117] Preferably, the specific steps of step S300 include:

[0118] Data collection: Use standard interface protocols or custom protocols for data collection;

[0119] 1) Data collection

[0120] Data source:

[0121] Medical equipment (such as monitors, oximeters);

[0122] Hospital internal systems (such as HIS, LIS);

[0123] IoT devices (sensors, etc.).

[0124] Collection method:

[0125] Use standard interface protocols (such as HL7, DICOM, FHIR) or custom protocols for collection;

[0126] File stream, API call, or real-time sensor push.

[0127] Collection tools:

[0128] Use Kafka Producer or Spark Streaming to import data into the streaming system.

[0129] Data access and transmission: Use message queues as data entry points to support massive concurrent access;

[0130] 2). Data access and transmission

[0131] Access method:

[0132] Use message queues (such as Kafka) as data entry to support massive concurrent access.

[0133] Data is partitioned and distributed to multiple consumers.

[0134] Features:

[0135] Data is transmitted in the form of streams to reduce latency.

[0136] Supports high-availability and reliable transmission protocols (such as TCP / IP).

[0137] Purpose:

[0138] Provide an efficient and stable data flow pipeline to ensure the continuity and integrity of data from the source to the processing module.

[0139] Data stream processing: Spark Streaming is used to process data streams in real time, mainly including data cleaning, data conversion, and grouping and sorting.

[0140] 3) Data Stream Processing

[0141] Processing framework:

[0142] Spark Streaming or Flink, for processing real-time data streams.

[0143] Core features:

[0144] Data cleaning: Call the data standardization module to remove duplicate values, invalid data or erroneous data.

[0145] Data conversion: Call the data standardization module to convert data in different formats (such as HL7, FHIR) into a unified processing format.

[0146] Grouping and sorting: Grouping based on key fields (such as patient ID, device ID) ensures that data is processed in the correct order.

[0147] Processing Mode:

[0148] Micro-batch: small batch data processing with low latency.

[0149] Continuous streaming mode: supports data processing with millisecond-level latency.

[0150] Data storage: Store the processed data in a distributed database.

[0151] 4) Data storage

[0152] Temporary Cache:

[0153] Use Redis or in-memory database to store real-time data, supporting fast reading and writing.

[0154] Persistent storage:

[0155] Store the processed data in a distributed database (such as Cassandra, HBase) or a file system (such as HDFS).

[0156] Storage Policy:

[0157] Data tiered storage: real-time data is stored in a cache area, and historical data is persisted in long-term storage.

[0158] Monitoring and optimization: Dynamically expand processing nodes based on traffic and optimize data partitioning strategies.

[0159] 5). System monitoring and optimization

[0160] Performance Monitoring:

[0161] Monitor key performance indicators such as throughput, data latency, and error rates.

[0162] Troubleshooting:

[0163] Detect data anomalies or system failures and trigger automatic retries or alarms.

[0164] Dynamic Adjustment:

[0165] Dynamically expand processing nodes based on traffic and optimize data partitioning strategies.

[0166] Intelligent analysis and decision support module:

[0167] By integrating AI models (such as deep learning and decision tree algorithms), the system can perform intelligent analysis on the integrated data. For example, based on historical data and real-time monitoring data, the system can predict changes in the patient's condition, issue warnings, or give treatment recommendations. In addition, the module can also generate personalized treatment plans to improve the accuracy and efficiency of medical treatment.

[0168] The implementation steps are as follows:

[0169] 1). Data integration and processing

[0170] Preferably, the specific steps of step S400 include:

[0171] Data collection and cleaning: Collect data from HIS, LIS or PACS systems through HL7, FHIR or DICOM protocols, and use ETL or Pandas tools to clean and standardize data to ensure data quality and consistency;

[0172] Data fusion: Use Apache Spark or data fusion algorithms (such as Kalman Filter) to integrate multi-source data into a complete patient data profile;

[0173] 2). Model training and optimization

[0174] Algorithm selection and training: Choose deep learning (TensorFlow, PyTorch) or machine learning (Scikit-learn, XGBoost) models based on the task, and use cross-validation and hyperparameter optimization (Grid Search, Bayesian optimization) to improve model performance;

[0175] Model evaluation: Use accuracy, AUC, and F1 score indicators to evaluate the predictive effect of the model to ensure that it meets clinical needs.

[0176] 3). Intelligent analysis and prediction

[0177] Preferably, the specific steps of step S400 also include:

[0178] Disease prediction and risk assessment: Based on LSTM, random forest and other models, the patient's vital signs data is analyzed in real time to predict changes in the disease and issue warnings through anomaly detection (Isolation Forest, Autoencoders);

[0179] Generation of diagnosis and treatment recommendations: Combine rule engines (such as Drools) with AI algorithms to generate personalized treatment plans.

[0180] 4) Generate personalized treatment plans

[0181] Data analysis and personalized recommendations: Through recommendation algorithms such as feature engineering and collaborative filtering, we analyze the patient's multi-dimensional data (such as genes and medical history) to generate personalized diagnosis and treatment pathways and treatment recommendations.

[0182] 5). System integration and deployment

[0183] Preferably, the specific steps of step S400 also include:

[0184] Integration and deployment: Integrate the intelligent decision-making module with the hospital information system through RESTful API and FHIR standards, deploy microservice architecture using Docker and Kubernetes, and provide real-time feedback and decision support interface (React, Vue.js, D3.js).

[0185] 6) Clinical verification and iteration

[0186] Clinical validation and feedback optimization: Conduct A / B testing in clinical environments, and use incremental learning and online learning to continuously optimize models and system functions based on new data and clinical feedback;

[0187] 7). Compliance and privacy protection

[0188] Privacy protection and compliance: AES, RSA encryption and de-identification technologies are used to ensure patient data privacy protection, and automated compliance checking tools are used to ensure compliance with GDPR and HIPAA healthcare industry regulations.

[0189] Scalable microservices architecture:

[0190] The present invention introduces a microservice-based architecture design, which allows different functional modules (such as data integration, standardization, intelligent analysis, etc.) to be independently deployed and expanded. Each microservice can be independently maintained and supports on-demand expansion to ensure the flexibility and efficient operation of the system. In addition, through the API interface, the system can seamlessly integrate new medical devices or third-party data sources.

[0191] Multi-level security mechanism:

[0192] Due to the sensitivity of medical information, this system adopts a multi-level security strategy, including data encryption, access control, identity authentication and authority management, to ensure the security of data transmission and storage. At the same time, the system has the ability to de-identify sensitive data to ensure the privacy of patients during data sharing.

[0193] The implementation steps are as follows:

[0194] Preferably, it also includes:

[0195] 1). Data encryption: ensure the security of data transmission and storage: use the national secret SM2 algorithm for data transmission encryption, and the SM4 algorithm to encrypt and protect stored data to ensure the security of data during transmission and storage;

[0196] 2). Identity authentication and access control: Ensure that only authorized personnel access data through identity authentication and permission management: Use national secret SM2 digital certificate authentication technology for user identity authentication, combined with multi-factor authentication (MFA) and role-based access control (RBAC) to ensure the reasonable allocation of user permissions.

[0197] 3) Permission management and auditing: Implement fine-grained permission control and conduct auditing: Control access rights through ACL and RBAC, use audit log systems (such as ELK Stack) to record all operations, and combine with SIEM systems (such as Splunk) to analyze security events;

[0198] 4) Data de-identification: Protect patient privacy and ensure data sharing security: Use de-identification technology (such as hashing, tokenization) and data desensitization methods (such as masking), and adopt differential privacy technology to ensure that personal identity information is not disclosed during data analysis;

[0199] 5). Vulnerability scanning and repair: Regularly scan system vulnerabilities and repair them in a timely manner: Use automated vulnerability scanning tools to identify system vulnerabilities and repair known vulnerabilities through automated operation and maintenance tools;

[0200] 6). Security monitoring and response: Real-time monitoring of system security and rapid response to security incidents: Use intrusion detection systems for real-time monitoring, and combine SIEM systems to process and respond to security incidents;

[0201] 7). Compliance and regulatory compliance: Ensure that the system complies with medical information security regulations: Ensure that the system complies with GDPR and HIPAA related regulatory requirements through compliance management tools and automated compliance audit systems.

[0202] A new generation of medical information integration engine system based on the same concept, including:

[0203] The data standardization module is used to automatically identify multiple medical data sources (such as HL7, DICOM, FHIR, etc.) and convert them into a unified standard format. The algorithm is based on machine learning technology and can self-optimize as new data sources are introduced to ensure data compatibility and scalability.

[0204] Real-time data processing engine module, used for real-time transmission and processing of medical data;

[0205] Intelligent analysis and decision support module, used to perform intelligent analysis on integrated data;

[0206] Scalable microservice architecture module for independent deployment and expansion of different functional modules;

[0207] A multi-level security mechanism module is used to ensure the security of data transmission and storage and the de-identification of sensitive data.

[0208] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0209] Compared with the prior art, the new generation of medical information integration engine of the present invention has the following advantages:

[0210] The medical information integration engine of the present invention is significantly superior to the existing technology in many aspects, especially in key areas such as data standardization, real-time processing, intelligent analysis, system scalability and security. The technical advantages of the present invention are described in detail below:

[0211] 1. Adaptive ability of data standardization

[0212] Existing medical information integration systems mostly rely on manual configuration or preset standardized rules. For data from different sources (such as HL7, DICOM, FHIR, etc.), the conversion method needs to be set manually, which makes it difficult to handle diverse medical data sources. With the introduction of new data sources, the scalability of the system is limited.

[0213] Advantages of the present invention:

[0214] The present invention uses an adaptive standardization algorithm driven by machine learning to automatically identify the formats of different medical data sources and perform standardized conversions. The algorithm can self-optimize as the data source expands and can process new data types without human intervention. This automated standardization capability greatly improves the compatibility and efficiency of the system, making data intercommunication between different hospitals, departments and equipment smoother, and avoiding the cumbersome manual configuration process in the prior art.

[0215] 2. Real-time data processing capabilities

[0216] Most existing medical information systems are based on batch processing mode, and data collection and processing are subject to large delays. Especially in medical scenarios that require rapid response (such as intensive care, emergency, etc.), delays will directly affect the patient's treatment effect.

[0217] Advantages of the present invention:

[0218] The present invention adopts a streaming processing architecture, and realizes real-time data transmission and processing through technologies such as Kafka or Spark Streaming, which significantly reduces the delay of data transmission. Doctors can obtain the latest data of patients in real time to ensure the accuracy and timeliness of decision-making. Compared with the traditional batch processing mode, the real-time data processing mechanism of the present invention has obvious advantages in medical scenarios that require emergency response and dynamic monitoring.

[0219] 3. Intelligent analysis and decision support

[0220] Existing integrated systems are mainly based on data transmission and storage, lack intelligent analysis of data, and are unable to fully utilize big data and artificial intelligence technologies for in-depth medical data analysis and prediction, resulting in the inability to tap the potential value of the data.

[0221] Advantages of the present invention:

[0222] The present invention integrates an AI-driven intelligent analysis module that can analyze large amounts of medical data using algorithms such as deep learning and decision trees. The system can not only provide doctors with personalized diagnosis and treatment recommendations based on historical data, but also predict changes in patients' conditions based on real-time data and propose early warning measures. This intelligent decision-making support function significantly improves the utilization and accuracy of medical data, far exceeding the basic data exchange function that existing technologies can provide.

[0223] 4. High scalability and modular architecture

[0224] Existing medical information integration systems usually adopt a monolithic architecture with poor system scalability. When new functions or data sources are added, the entire system needs to be adjusted on a large scale, which is time-consuming and labor-intensive and can easily lead to system instability.

[0225] Advantages of the present invention:

[0226] The present invention adopts a microservice-based architecture design, and each functional module (such as data standardization, intelligent analysis, real-time processing, etc.) is independently deployed and operated. Each microservice can be independently expanded and maintained, which greatly improves the flexibility and scalability of the system. Whether it is a new data source or a new medical device, it can be seamlessly connected to the system through the API interface without affecting the stability and reliability of existing functions. This modular design makes the system more adaptable and supports the development and upgrading of future technologies.

[0227] 5. Multi-level data security protection

[0228] Although the existing system has certain protection measures for medical data security, they are usually limited to data transmission encryption and simple permission management, which is inadequate when facing complex medical data privacy needs. At the same time, patient privacy is difficult to be effectively protected when data is shared, and there is a risk of data leakage.

[0229] Advantages of the present invention:

[0230] The present invention ensures data security through a multi-level security mechanism, including data encryption, identity authentication, permission control, audit tracking and other multi-dimensional protection measures. In particular, the de-identification of sensitive data ensures the security of patient privacy during data sharing. In addition, the present invention can adapt to privacy laws and regulations in different countries and regions, such as GDPR, HIPAA, etc., to ensure the compliance of the system worldwide. Compared with the simple security strategies of the prior art, the present invention has obvious advantages in data security and privacy protection.

[0231] 6. Data sharing capabilities across platforms and departments

[0232] The existing medical system has difficulty in achieving cross-departmental and cross-platform data sharing and integration due to inconsistent data standards, which limits collaboration between medical institutions and fails to effectively promote regional medical information construction.

[0233] Advantages of the present invention:

[0234] Through adaptive standardization and API interface design, the present invention can easily achieve cross-platform and cross-department data integration and sharing. Whether between different hospitals or between different departments within a hospital, efficient data intercommunication can be achieved through the engine of the present invention. This integration capability greatly improves the efficiency of medical collaboration, promotes the sharing and interconnection of medical information, and provides strong technical support for the construction of regional medical informatization.

[0235] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A new generation medical information integration method, characterized in that: The following steps are involved: S100, first receiving diverse data streams from different information systems or external data platforms; S200, performing format parsing, data cleaning, encoding conversion, data mapping, and data standardization conversion on the data stream, and finally storing the standardized data in a data warehouse or sending it to a downstream system in real time; S300, transmitting and processing the output standardized data in real time through a streaming processing architecture; S400, integrating and processing the data transmitted and processed in real time in step S300 by integrating the AI ​​model.

2. The new generation medical information integration method according to claim 1, characterized in that: The specific steps of step S100 include: Protocol identification: Automatically identify the data source type and the communication protocol it belongs to based on the header information or metadata analysis of data communication; Source verification: Verify the legitimacy and accuracy of the data source in combination with the configuration parameters of the medical institution.

3. The new generation medical information integration method according to claim 2, characterized in that: The specific steps of step S200 include: Format parsing: Decode the data into a structured form according to the protocol definition of the data source; Data cleaning: remove redundant information, repair incomplete data, and eliminate potential errors; Code conversion: uniformly convert the data encoding format into the standard encoding supported by the system; Field matching: Using natural language processing technology, dynamically match the fields of the data source with the target standard fields based on the field name, data type and content semantics; Value mapping: unify the encoding values ​​in different systems into a standard encoding system.

4. The new generation medical information integration method according to claim 2, characterized in that: The specific steps of step S200 also include: Structural transformation: Unify and reorganize different data models into a target standard format; Unit and format correction: Perform unit conversion and format correction on numerical data to ensure uniformity; Automatic model training: Dynamically generate new field mapping rules and conversion logic by analyzing new data sources; Abnormal monitoring and feedback: Real-time monitoring of abnormal fields or incorrect mappings in data processing, triggering manual verification and iterative optimization of the mapping engine; Scalability: When introducing new data sources, standardization can be completed with only a small amount of configuration without the need for redevelopment.

5. The new generation medical information integration method according to claim 4, characterized in that: The specific steps of step S300 include: Data collection: Use standard interface protocols or custom protocols for data collection; Data access and transmission: Use message queues as data entry points to support massive concurrent access; Data stream processing: Use Spark Streaming to process data streams in real time, mainly including: data cleaning, data conversion and grouping and sorting; Data storage: Store processed data in a distributed database; Monitoring and optimization: Dynamically expand processing nodes based on traffic and optimize data partitioning strategies.

6. The new generation medical information integration method according to claim 5, characterized in that: The specific steps of step S400 include: Data collection and cleaning: Collect data from HIS, LIS or PACS systems through HL7, FHIR or DICOM protocols, and use ETL or Pandas tools to clean and standardize data to ensure data quality and consistency; Data fusion: Use data fusion algorithms to integrate multi-source data into a complete patient data profile; Select algorithms and training: Choose deep learning or machine learning models based on the task, and use cross-validation and hyperparameter optimization to improve model performance; Model evaluation: Use accuracy, AUC, and F1 score indicators to evaluate the predictive effect of the model to ensure that it meets clinical needs.

7. The new generation medical information integration method according to claim 6, characterized in that: The specific steps of step S400 also include: Disease prediction and risk assessment: Based on LSTM, random forest and other models, the patient's vital signs data is analyzed in real time to predict disease changes and issue warnings through anomaly detection; Generation of diagnosis and treatment recommendations: Combine the rule engine with AI algorithms to generate personalized treatment plans; Data analysis and personalized recommendations: Through recommendation algorithms such as feature engineering and collaborative filtering, we analyze the patient's multi-dimensional data and generate personalized diagnosis and treatment pathways and treatment recommendations.

8. The new generation medical information integration method according to claim 7, characterized in that: The specific steps of step S400 also include: Integration and deployment: Integrate the intelligent decision module with the hospital information system through RESTful API and FHIR standards, deploy microservice architecture using Docker and Kubernetes, and provide real-time feedback and decision support interface; Clinical validation and feedback optimization: Conduct A / B testing in clinical settings, and use incremental learning and online learning to continuously optimize models and system functions based on new data and clinical feedback; Privacy protection and compliance: AES, RSA encryption and de-identification technologies are used to ensure patient data privacy protection, and automated compliance checking tools are used to ensure compliance with GDPR and HIPAA healthcare industry regulations.

9. The new generation medical information integration method according to claim 8, characterized in that: Also includes: Data encryption: Use the national secret SM2 algorithm to encrypt data transmission, and the SM4 algorithm to encrypt and protect stored data to ensure the security of data during transmission and storage; Identity authentication and access control: Use national secret SM2 digital certificate authentication technology for user identity authentication, combined with multi-factor authentication and role-based access control to ensure the reasonable allocation of user rights; Permission management and auditing: Control access rights through ACL and RBAC, use the audit log system to record all operations, and combine with the SIEM system to analyze security events; Data de-identification: Use de-identification technology and data desensitization methods, and adopt differential privacy technology to ensure that personal identity information is not disclosed during data analysis; Vulnerability scanning and repair: Use automated vulnerability scanning tools to identify system vulnerabilities and repair known vulnerabilities through automated operation and maintenance tools; Security monitoring and response: Use intrusion detection systems for real-time monitoring and combine SIEM systems to handle and respond to security incidents; Compliance and regulatory compliance: Ensure that the system complies with GDPR and HIPAA regulations through compliance management tools and automated compliance audit systems.

10. A new generation of medical information integration engine system, characterized in that: include: Data standardization module, which is used to automatically identify multiple medical data sources and convert them into a unified standard format; Real-time data processing engine module, used for real-time transmission and processing of medical data; Intelligent analysis and decision support module, used to perform intelligent analysis on integrated data; Scalable microservice architecture module for independent deployment and expansion of different functional modules; A multi-level security mechanism module is used to ensure the security of data transmission and storage and the de-identification of sensitive data.

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