Vascular access outpatient service intelligent management system based on artificial intelligence

Abstract

The invention relates to the technical field of medical management, in particular to a vascular access outpatient service intelligent management system based on artificial intelligence. The system comprises a patient outpatient service information file integration module, a patient vascular access use risk optimization selection module, a patient vascular access quality abnormity early warning module and a vascular access abnormity feedback management module. The method comprises the following steps: acquiring basic information data of a patient and previous vascular access use record data, performing information file integration processing, performing vascular access suggestion design and access use risk optimization selection, and generating a patient outpatient service optimal selection to establish a vascular access; and monitoring the physiological data of the patient and the use condition data of the vascular access of the patient in real time by using intelligent equipment, performing intelligent abnormal early warning of the vascular access and abnormal feedback management of the vascular access, and generating an abnormal feedback management scheme of the vascular access of the patient so as to execute corresponding abnormal feedback management work of the vascular access of the patient. The efficiency and accuracy of vascular access management can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical management technology, and in particular to an artificial intelligence-based vascular access outpatient intelligent management system. Background Art

[0002] In recent years, the development of artificial intelligence (AI) technology has provided new opportunities for vascular access management. Through deep learning and data mining, a large amount of patient data can be analyzed to predict potential problems with vascular access. By building an integrated data management platform, the patient's basic information, vascular access status, outpatient records and other data are aggregated and standardized, and machine learning algorithms are used to conduct in-depth analysis of historical data to establish a patient-specific risk assessment model to predict abnormal problems with vascular access. By introducing real-time monitoring technology, the patient's vascular access status is monitored through sensors and mobile devices, and relevant data is automatically updated. These real-time data are combined with prediction models to provide medical staff with accurate decision support, helping them to adjust vascular access management plans in a timely manner and optimize patient care. However, traditional vascular access management systems often lack intelligence and real-time performance, making it difficult to effectively monitor the risk of vascular access use in patients, resulting in frequent vascular access problems due to individual infection complications and allergy differences among patients, thereby reducing the efficiency and accuracy of vascular access management. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an artificial intelligence-based vascular access outpatient intelligent management system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an artificial intelligence-based vascular access outpatient intelligent management system includes the following modules:

[0005] The patient outpatient information file integration module is used to connect to the medical information system through the API interface to obtain the patient's basic information data and previous vascular access use record data in real time and perform information file integration processing to generate a comprehensive information file of the patient's vascular access outpatient clinic;

[0006] The patient vascular access use risk optimization selection module is used to obtain the patient's vascular access use needs, and make vascular access recommendation designs based on the patient's vascular access use needs, and generate the patient's preset vascular access; based on the patient's vascular access outpatient comprehensive information file, each patient's preset vascular access is selected for access use risk optimization to generate the patient's best outpatient choice to establish a vascular access;

[0007] The patient vascular access quality abnormality warning module is used to use intelligent devices to monitor in real time the patient's physiological data corresponding to the vascular access selected for the best outpatient clinic and the patient's vascular access usage status data, and to perform intelligent abnormality warning of the vascular access based on the patient's physiological data and the patient's vascular access usage status data, so as to generate an intelligent abnormality warning signal for the patient's vascular access;

[0008] The vascular access abnormality feedback management module is used to upload the patient's vascular access intelligent abnormality warning signal to the cloud platform for vascular access abnormality feedback management, generate a patient's vascular access abnormality feedback management plan, and perform the corresponding patient's vascular access abnormality feedback management work.

[0009] Furthermore, the patient outpatient information archive integration module includes the following functions:

[0010] Sending a connection call request instruction to the medical information system through the API interface, and configuring and connecting the medical information system to the API interface based on the connection call request instruction to obtain the medical information system API connection configuration file;

[0011] Based on the medical information system API connection configuration file, the connection configuration processing between the API interface and the medical information system is performed to obtain the information request calling connection channel between the API interface and the medical information system;

[0012] The information request calling connection channel between the API interface and the medical information system is used to extract and process the basic information of the patient from the medical information system to obtain the basic information data of the patient, wherein the basic information data of the patient includes the basic identity information of the patient, the medical history information of the patient, and the allergy history information of the patient;

[0013] Using the information request calling connection channel between the API interface and the medical information system, the medical information system is called to process the vascular access use record, and the past vascular access use record data is obtained, wherein the past vascular access use record data includes the vascular access use frequency, the vascular access use location, and the vascular access use time;

[0014] The patient's basic information data and previous vascular access use record data are integrated into the information archive to generate a comprehensive information archive of the patient's vascular access outpatient clinic.

[0015] Furthermore, the information archive integration processing of the patient's basic information data and previous vascular access use record data includes:

[0016] Perform data structure analysis on the fields and formats of different data sources in the patient's basic information data and previous vascular access use record data to obtain the data structure fields and data formats corresponding to different data sources of the patient's vascular access information;

[0017] Based on the data structure fields and data formats corresponding to different data sources of the patient's vascular access information, the patient's basic information data and previous vascular access use record data are structured converted and mapped to obtain a structured mapping framework for the patient's vascular access outpatient information;

[0018] The basic information data of patients and the previous vascular access use record data were integrated and consistency checked to obtain the fusion data set of patient vascular access outpatient information;

[0019] Based on the structured mapping framework of patient vascular access clinic information, the archive format of the patient vascular access clinic information fusion dataset was standardized to generate a comprehensive information archive of patient vascular access clinic information.

[0020] Furthermore, the patient vascular access use risk optimization selection module includes the following functions:

[0021] Obtain the patient's vascular access use requirements;

[0022] Conduct quantitative and qualitative demand analysis on the patient's vascular access use needs to generate a patient vascular access demand analysis report; conduct demand pattern and potential problem mining analysis on the patient's vascular access demand analysis report to obtain the patient's vascular access demand pattern and potential problem points of the patient's vascular access demand;

[0023] Design vascular access solutions for patients based on their vascular access demand patterns to generate personalized vascular access design solutions for patients;

[0024] Based on the potential problems of the patient's vascular access needs, the patient's personalized vascular access design plan is optimized and recommended, and the patient's preset vascular access is generated;

[0025] Based on the comprehensive information file of the patient's vascular access clinic, the access use risk optimization selection is performed for each patient's preset vascular access to generate the best outpatient option for the patient to establish vascular access.

[0026] Furthermore, the risk optimization selection of access use for each patient's preset vascular access based on the patient's vascular access clinic comprehensive information file includes:

[0027] Based on the comprehensive information file of the patient's vascular access clinic, the vascular access is matched and collected for each patient's preset vascular access, and the patient's medical history information, patient allergic reaction information, and patient's previous use information corresponding to each preset vascular access are obtained;

[0028] Based on the patient medical history information and patient allergic reaction information corresponding to each preset vascular access, the infection complication and allergy risk probability of the corresponding patient preset vascular access is statistically calculated to obtain the infection complication risk probability and the allergy risk probability of the patient corresponding to each preset vascular access;

[0029] Based on the patient's previous use information corresponding to each preset vascular access, a corresponding patient preset vascular access is subjected to access use risk assessment and analysis to obtain an access use risk influencing factor corresponding to each preset vascular access;

[0030] Based on the infection complication risk probability of the patient, the allergy risk probability of the patient and the access use risk influencing factor corresponding to each preset vascular access, the vascular access use risk assessment calculation formula is used to perform risk assessment calculation on the corresponding patient preset vascular access to obtain the risk score value corresponding to each preset vascular access;

[0031] According to the risk score value corresponding to each preset vascular access, the corresponding patient's preset vascular access is selected for access risk optimization to generate the best option for the patient to establish vascular access in the outpatient clinic.

[0032] Furthermore, the statistical calculation of infection complications and allergy risk probabilities of the corresponding preset vascular access of the patient based on the patient medical history information and the patient allergic reaction information corresponding to each preset vascular access includes:

[0033] By acquiring corresponding potential infection complication history information from the patient medical history information corresponding to each preset vascular access, the potential infection complication history information of the patient corresponding to each preset vascular access is obtained;

[0034] Based on the patient's potential infection history information corresponding to each preset vascular access, the infection risk factor identification and analysis of the corresponding patient preset vascular access is performed to obtain the patient infection risk factor corresponding to each preset vascular access;

[0035] According to the patient infection complication risk factor corresponding to each preset vascular access, a statistical calculation of the infection complication risk probability of the corresponding patient preset vascular access is performed to obtain the patient infection complication risk probability corresponding to each preset vascular access;

[0036] By acquiring corresponding patient material allergens from patient allergic reaction information corresponding to each preset vascular access, and performing access material property analysis on each patient preset vascular access, the patient vascular access material property corresponding to each preset vascular access is obtained;

[0037] Based on the patient material allergens corresponding to each preset vascular access, the allergy risk probability statistics are calculated for the corresponding patient vascular access material characteristics to obtain the patient allergy risk probability corresponding to each preset vascular access.

[0038] Furthermore, the risk assessment analysis of access use of the corresponding patient preset vascular access based on the patient's previous use information corresponding to each preset vascular access includes:

[0039] Obtaining the previous use frequency and the previous use duration of the patient's vascular access from the patient's previous use information corresponding to each preset vascular access, thereby obtaining the previous use frequency and the previous use duration corresponding to each preset vascular access;

[0040] Calculating the access usage frequency of the corresponding patient preset vascular access based on the previous usage frequency and the previous usage duration corresponding to each preset vascular access, to obtain the access usage frequency corresponding to each preset vascular access;

[0041] Based on the access usage frequency corresponding to each preset vascular access, an access usage risk assessment analysis is performed on the corresponding patient's preset vascular access to obtain the access usage risk influencing factor corresponding to each preset vascular access.

[0042] Furthermore, the vascular access use risk assessment calculation formula is specifically:

[0043]

[0044] In the formula, R i is the risk score corresponding to the i-th preset vascular access, t 0 is the initial time of risk assessment, t 1 is the end time of risk assessment, t is the time variable parameter, I i (t) is the risk probability of infection complications of the patient corresponding to the i-th preset vascular access at time t, is the basic probability of infection complications in patients corresponding to the i-th preset vascular access, is the risk influencing factor of infection complications in patients corresponding to the i-th preset vascular access, is the time attenuation coefficient of infection complication risk corresponding to the i-th preset vascular access, A i (t) is the patient allergy risk probability corresponding to the i-th preset vascular access at time t, is the basic probability of patient allergy corresponding to the i-th preset vascular access, is the risk influencing factor of patient allergy corresponding to the i-th preset vascular access, is the allergy risk time attenuation coefficient corresponding to the i-th preset vascular access, Ci (t) is the access use risk influencing factor corresponding to the i-th preset vascular access at time t, U i is the usage frequency corresponding to the i-th preset vascular access, W i is the usage position corresponding to the i-th preset vascular access, T i is the usage time corresponding to the i-th preset vascular access, n is the vascular access use risk attenuation index, and η is the correction coefficient of the risk score value.

[0045] Furthermore, the patient vascular access quality abnormality warning module includes the following functions:

[0046] Use smart devices to monitor in real time the patient's physiological data corresponding to the best choice of vascular access for outpatients and the patient's vascular access usage data;

[0047] Perform feature engineering processing on the patient's physiological data and the patient's vascular access usage status data to obtain the patient's physiological change characteristics and the patient's vascular access usage status characteristics;

[0048] A neural network algorithm is used to construct a patient vascular access abnormality prediction model, and the patient's physiological change characteristics and the patient's vascular access usage status characteristics are input into the patient vascular access abnormality prediction model to perform intelligent vascular access abnormality prediction, and obtain the patient vascular access usage abnormality prediction result;

[0049] Automatic response warning processing is performed on the abnormal prediction results of the patient's vascular access use to generate an intelligent abnormal warning signal for the patient's vascular access.

[0050] Furthermore, the vascular access abnormality feedback management module includes the following functions:

[0051] Upload the patient's vascular access intelligent abnormal warning signal to the cloud platform, and use the cloud platform to identify and analyze the abnormality of the patient's vascular access intelligent abnormal warning signal to obtain the analysis result of the patient's vascular access abnormality;

[0052] Vascular access abnormality feedback management is performed based on the analysis results of the patient's vascular access abnormality problems, and a patient vascular access abnormality feedback management plan is generated to execute corresponding patient vascular access abnormality feedback management work.

[0053] Beneficial effects of the present invention:

[0054] The artificial intelligence-based vascular access outpatient intelligent management system proposed in the present invention is composed of a patient outpatient information archive integration module, a patient vascular access use risk optimization selection module, a patient vascular access quality abnormality warning module and a vascular access abnormality feedback management module. Compared with the prior art, the beneficial effect of the present application is to send a connection call request instruction to the medical information system by using the API interface, aiming to establish a communication connection with the medical information system. The importance of this step lies in that it lays the foundation for subsequent data interaction. Through the request of the API interface, developers can ensure that they have the correct permissions and authentication information, thereby improving data security. A successful connection call request can verify the availability and stability of the system and provide confidence for subsequent configuration work. This process usually involves detailed technical documentation. Developers must carefully read the API documentation to ensure the correctness of the request format and parameter settings. Once the request is successful, the medical information system will generate a corresponding API connection configuration file to record all necessary information required for the connection, such as endpoint URL, request method and data format, etc., to ensure efficient transmission of information requests. Through the established connection channel, the medical information system is used to extract and process the basic information of the patient, so that the latest basic information of the patient, including basic identity information, medical history information and allergy history information, can be obtained in real time through the API interface, thereby ensuring that the doctor can rely on the most accurate data when conducting management analysis, providing basic data guarantee for the subsequent vascular access outpatient management process. The medical information system is also used to call and process the vascular access use record. This process involves extracting detailed records of the patient's previous vascular access use, which includes key information such as the frequency, location and time of vascular access use. These data provide important references for subsequent processing. At the same time, through the information archive integration processing of the patient's basic information data and the previous vascular access use record data, this integration process can form a comprehensive and systematic patient information archive, providing a comprehensive medical information view. This archive not only contains the patient's basic identity information and medical history information, but also integrates the use records related to the vascular access to form a multi-dimensional medical data set. Such an information archive provides comprehensive background information for the subsequent vascular access management process, helping to formulate a more accurate intelligent management plan, thereby greatly improving the quality and efficiency of subsequent vascular access management. Secondly, by obtaining the patient's demand for vascular access, we can understand the patient's basic situation, medical background and specific needs. This process not only involves collecting the patient's physiological data, such as age, gender, medical history and existing health problems, but also includes the patient's expectations and concerns about the medical process, ensuring that medical staff can understand the patient's real needs for vascular access from the patient's perspective.By designing vascular access recommendations based on the patient's vascular access needs, at this stage, the medical team will combine the previous needs analysis to design a vascular access plan that meets the patient's specific needs. The plan design will take into account many factors, such as the patient's physiological condition, disease progression, and expected effects. Personalized design can not only improve patient satisfaction, but also effectively reduce the risk of complications caused by inappropriate access design. The plan design stage will also take into account the patient's quality of life, and strive to make the use of the access as convenient as possible, thereby improving the patient's compliance and participation in vascular access. In addition, based on the patient's vascular access clinic comprehensive information file, the access risk optimization selection is performed for each patient's preset vascular access, aiming to provide patients with the best vascular access options. At this stage, the medical team analyzes the patient's comprehensive information and combines the existing vascular access use data to evaluate the risk of each vascular access use plan. The risk optimization selection not only focuses on the technical parameters of the access, but also comprehensively considers the individual infection complications and allergy differences, clinical manifestations, and lifestyle factors of the patient. This process ensures that potential risks can be minimized in actual use and provides a safe and reliable vascular access use plan. Then, by using smart devices to monitor the patient's physiological data and vascular access usage data in real time, the patient's best choice for outpatient vascular access can be established. By continuously collecting data, immediate feedback on the patient's physiological changes can be obtained, such as heart rate, blood pressure, blood oxygen saturation, blood flow in the access, blockage conditions and infection indicators, etc., which can identify potential problems in a timely manner. Such real-time monitoring can take rapid measures when abnormalities occur. In addition, by performing intelligent abnormal warning of vascular access based on the patient's physiological data and the patient's vascular access usage data, the response speed of vascular access management services can be significantly improved. By setting thresholds and early warning mechanisms, an alarm can be immediately issued when potential abnormalities are predicted to notify relevant medical personnel. This real-time early warning mechanism enables medical personnel to respond quickly and take appropriate measures, thereby improving the efficiency and accuracy of vascular access management. Finally, uploading the patient's vascular access intelligent abnormal warning signal to the cloud platform for vascular access abnormal feedback management is an important part of ensuring that patients receive timely and effective care. Through in-depth analysis of abnormal problems, the medical team can develop targeted feedback management plans and clarify various work responsibilities and operating procedures. The plan includes a detailed description of the abnormal situation, possible consequences and response measures, thereby helping to respond quickly and take appropriate intervention measures. This systematic feedback management not only improves the efficiency of vascular access management services, but also reduces the occurrence of errors in vascular access management. By implementing this management plan, patients' abnormal conditions can be identified and handled in a timely manner, promoting continuous improvement and optimization of the quality of vascular access management. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0056] Figure 1 It is a module schematic diagram of the vascular access outpatient intelligent management system based on artificial intelligence of the present invention;

[0057] Figure 2 for Figure 1 Schematic diagram of the functional flow of the patient outpatient information archive integration module;

[0058] Figure 3 for Figure 1 Schematic diagram of the functional flow of the risk-optimized selection module for vascular access use in patients. DETAILED DESCRIPTION

[0059] The technical system of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0060] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor systems and / or microcontroller systems.

[0061] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0062] To achieve this, please refer to Figures 1 to 3 The present invention provides an artificial intelligence-based vascular access outpatient intelligent management system, the system comprising the following modules:

[0063] The patient outpatient information file integration module is used to connect to the medical information system through the API interface to obtain the patient's basic information data and previous vascular access use record data in real time and perform information file integration processing to generate a comprehensive information file of the patient's vascular access outpatient clinic;

[0064] The patient vascular access use risk optimization selection module is used to obtain the patient's vascular access use needs, and make vascular access recommendation designs based on the patient's vascular access use needs, and generate the patient's preset vascular access; based on the patient's vascular access outpatient comprehensive information file, each patient's preset vascular access is selected for access use risk optimization to generate the patient's best outpatient choice to establish a vascular access;

[0065] The patient vascular access quality abnormality warning module is used to use intelligent devices to monitor in real time the patient's physiological data corresponding to the vascular access selected for the best outpatient clinic and the patient's vascular access usage status data, and to perform intelligent abnormality warning of the vascular access based on the patient's physiological data and the patient's vascular access usage status data, so as to generate an intelligent abnormality warning signal for the patient's vascular access;

[0066] The vascular access abnormality feedback management module is used to upload the patient's vascular access intelligent abnormality warning signal to the cloud platform for vascular access abnormality feedback management, generate a patient's vascular access abnormality feedback management plan, and perform the corresponding patient's vascular access abnormality feedback management work.

[0067] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of an outpatient vascular access intelligent management system based on artificial intelligence according to the present invention. In this example, the outpatient vascular access intelligent management system based on artificial intelligence includes the following modules:

[0068] S1: Patient outpatient information file integration module, used to connect to the medical information system through the API interface to obtain the patient's basic information data and previous vascular access use record data in real time and perform information file integration processing to generate a comprehensive information file of the patient's vascular access outpatient clinic;

[0069] In the embodiment of the present invention, a connection call request instruction is sent to the medical information system by using a RESTful API interface. The request uses HTTP POST method, and the required authentication information and connection parameters are included in the request body. This request is automatically generated by the calling script. The script is written using the Python programming language and request libraries (such as Requests). After the request is successful, the medical information system will return the API connection configuration file, which includes the necessary access token, connection URL, required request headers and other configuration information, and implements the connection configuration processing between the corresponding API interface and the medical information system. This is achieved by using the parameters in the configuration file to write a connection configuration script. This process involves establishing an HTTP connection to ensure that the API interface can communicate stably with the medical information system. By sending a simple test request (such as obtaining a status code), the successful establishment of the information request call connection channel is confirmed. At the same time, a request for extracting basic patient information is initiated to the medical information system using the established connection channel. The request is sent using the GET method and contains the patient's unique identifier as a query parameter. After receiving the request, the system returns the relevant basic information data based on the patient ID. The data format uses JSON or XML. The returned data includes the patient's identity information (such as name, gender, date of birth), medical history information (such as previous illness, surgical history) and allergy history information (such as drug allergy), thereby obtaining the patient's basic information data. Similarly, by using the established connection channel, the corresponding medical information system is called to process the previous vascular access use records. This process sends a request through the POST method, which includes the patient's identity information and the requested data type. The medical information system returns the record data including the frequency of vascular access use, the location of use, and the time of use according to the request. The returned data is also in JSON or XML format. After data verification and parsing, the accuracy and completeness of the data are ensured, thereby obtaining the previous vascular access use record data. Then, the patient's basic information data obtained by the previous call is integrated with the previous vascular access use record data for information file processing, so as to merge and clean the two data sets by using data integration tools such as Apache NiFi or Talend. During the integration process, ensure that the data fields match and eliminate duplicates, and generate a comprehensive file containing comprehensive patient information. The integrated data is stored in the database, and finally a comprehensive information file of the patient's vascular access outpatient clinic is generated.

[0070] S2: Patient vascular access use risk optimization selection module, used to obtain the patient's vascular access use needs, and make vascular access recommendation design based on the patient's vascular access use needs, and generate the patient's preset vascular access; based on the patient's vascular access outpatient comprehensive information file, each patient's preset vascular access is selected for access use risk optimization to generate the patient's best outpatient choice to establish a vascular access;

[0071] In an embodiment of the present invention, by collecting the patient's basic information and medical history, including existing symptoms, treatment plans, and previous vascular access usage, and using questionnaires or interviews, the specific needs for vascular access are obtained from the patient and his or her family, covering the type of access, frequency of use, and expected duration of use, thereby obtaining the patient's vascular access usage needs. By conducting a quantitative analysis of the patient vascular access needs obtained from the previous analysis, the needs are quantified into specific indicators, such as the frequency of intubation and the expected treatment cycle, and a corresponding qualitative needs analysis is conducted. Through an in-depth understanding of the patient's subjective feelings, the patient's expectations for access comfort, safety, and ease of use are identified, and the collected data are organized into a detailed patient vascular access needs analysis report. The commonality and individuality of needs are identified through data mining technology, and a patient vascular access demand pattern is constructed. For example, based on patients with different symptoms, their preferences for access types and trends in frequency of use are identified. The failure mode and effects analysis (FMEA) method is also applied to systematically evaluate potential problem points, consider the risks and obstacles that may arise in the use of vascular access, and combine statistical analysis tools, such as SPSS or Python's data analysis library, to quantify potential problems and identify high-risk factors, thereby forming a comprehensive analysis report on demand patterns and potential problems. At the same time, by combining the demand patterns obtained from the previous analysis with clinical experience and best practices, the corresponding patient vascular access needs are analyzed. Access design, in order to design a personalized vascular access plan, first by selecting the appropriate access type (such as central venous catheter, peripheral venous catheter, etc.), according to the specific needs of the patient to set parameters, such as catheter length, caliber, material, etc., use computer-aided design (CAD) tools to model the specific structure of the vascular access in order to simulate its performance in the patient's body, compare the design plan with medical standards and guidelines to ensure the scientificity and practicality of the plan, form a detailed patient-specific vascular access design plan, and implement optimization suggestions for the personalized vascular access design plan, so as to re-evaluate the safety and feasibility of the design plan by using risk assessment models for high-risk factors, and test the impact of changes in various parameters in the design on the overall plan by using sensitivity analysis, and then propose corresponding optimization measures. For example, if a certain material is found to be prone to complications under certain circumstances, consider replacing it with a safer material. Through repeated simulation and verification, the design plan is optimized to reduce potential risks, forming a patient-preset vascular access plan containing optimization suggestions, thereby generating a patient-preset vascular access plan.Then, risk optimization selection is performed for each preset vascular access by combining the patient's comprehensive outpatient information file. The patient's comprehensive health data is analyzed using a machine learning algorithm to identify health conditions and physiological characteristics that affect access use. A risk scoring model is established for each vascular access. The best access selection is determined by evaluating the patient's overall health status, potential complications, and vascular status. At the same time, the patient's feedback and historical usage records are taken into consideration to ensure that the selected plan can effectively reduce risks in actual use, thereby outputting a report on the patient's best outpatient choice for establishing vascular access, ensuring that all options are scientifically verified and meet the patient's personalized needs, and finally generating the patient's best outpatient choice for establishing vascular access.

[0072] S3: Patient vascular access quality abnormality warning module, used to use intelligent devices to monitor in real time the patient's physiological data corresponding to the patient's best choice of outpatient vascular access and the patient's vascular access usage status data, and to perform intelligent abnormality warning of the vascular access based on the patient's physiological data and the patient's vascular access usage status data, so as to generate an intelligent abnormality warning signal of the patient's vascular access;

[0073] In an embodiment of the present invention, the best vascular access previously selected by the patient in the outpatient clinic is monitored in real time by using an integrated intelligent device such as a portable physiological monitoring instrument. The device collects physiological data through a variety of sensors, including parameters such as heart rate, blood oxygen saturation and blood pressure. The device also records the patient's vascular access usage status, including access type, insertion time and recent frequency of use, thereby obtaining corresponding patient physiological data and patient vascular access usage status data. The collected patient physiological data and vascular access usage data are cleaned and preprocessed to remove invalid values ​​and abnormal points, and key features are extracted using feature engineering methods. The features extracted from the physiological data include average heart rate changes, blood oxygen fluctuation amplitude, etc.; the vascular access usage status features include access usage time, the interval of the last use, etc. These features are standardized and normalized to eliminate dimensional differences and ensure the effectiveness of subsequent model training. The processed feature data are organized into a data set, and the corresponding patient vascular access abnormality prediction model is constructed by using the neural network algorithm in deep learning to select a suitable neural network architecture, such as the long short-term memory network (LSTM), in order to capture the pattern in the time series data, and the physiological change features and vascular access usage status features extracted in the previous step are used as input for model training. During the training process, cross-validation and hyperparameter tuning methods are used to improve the model performance. After the training is completed, the model is verified using the test set to ensure that the model has good generalization ability. The prediction results generated by the model show the abnormal use risk of the patient's vascular access, thereby predicting abnormal prediction results. Then, by automatically responding to and warning the abnormal prediction results obtained from the previous abnormal prediction, through the set threshold mechanism, when the abnormal prediction probability output by the model exceeds the preset threshold, an intelligent abnormal warning signal is automatically generated. The signal contains the abnormal type, prediction probability and recommended treatment measures. After encryption, it is transmitted to the mobile devices of relevant medical staff and the hospital monitoring system through the wireless network. The warning information is automatically recorded in the patient's electronic medical record for subsequent tracking and management. The whole process ensures timely response to potential risks, improves the patient's safety management efficiency, and finally responds to generate an intelligent abnormal warning signal for the patient's vascular access.

[0074] S4: Vascular access abnormality feedback management module, used to upload the patient's vascular access intelligent abnormality warning signal to the cloud platform for vascular access abnormality feedback management, generate a patient's vascular access abnormality feedback management plan, and execute the corresponding patient's vascular access abnormality feedback management work.

[0075] In an embodiment of the present invention, by uploading the patient's vascular access intelligent abnormal warning signal generated by the previous response to the cloud platform, and using the cloud platform to identify and statistically analyze the abnormality of the access problem of the patient's vascular access intelligent abnormal warning signal, the big data processing technology is used to receive warning signals from multiple patients in real time, and the signal is analyzed by the built-in artificial intelligence algorithm to identify potential vascular access abnormal problems. For example, if a significant decrease in blood flow rate is detected, it will be judged as possible thrombosis or access blockage through pattern recognition technology, and by combining the analysis results of the patient's vascular access abnormality problem obtained by the previous analysis, the cloud platform will automatically generate a vascular access abnormality feedback management plan, which includes specific response measures and suggestions, including adjustments to the patient's vascular access use plan, intervention measures for medical staff and guidance for further examinations, and through a user-friendly interface, the analysis results and corresponding feedback management plans can be intuitively viewed to ensure that relevant medical staff receive information in a timely manner. A tracking mechanism can also be set in the plan to conduct subsequent evaluation of the patient's vascular access management effect. The process also includes generating automated reports to facilitate the hospital's overall management and improvement, thereby achieving comprehensive monitoring and management of vascular access, and finally generating a patient vascular access abnormality feedback management plan to perform corresponding patient vascular access abnormality feedback management work.

[0076] Furthermore, the patient outpatient information archive integration module includes the following functions:

[0077] Sending a connection call request instruction to the medical information system through the API interface, and configuring and connecting the medical information system to the API interface based on the connection call request instruction to obtain the medical information system API connection configuration file;

[0078] Based on the medical information system API connection configuration file, the connection configuration processing between the API interface and the medical information system is performed to obtain the information request calling connection channel between the API interface and the medical information system;

[0079] The information request calling connection channel between the API interface and the medical information system is used to extract and process the basic information of the patient from the medical information system to obtain the basic information data of the patient, wherein the basic information data of the patient includes the basic identity information of the patient, the medical history information of the patient, and the allergy history information of the patient;

[0080] Using the information request calling connection channel between the API interface and the medical information system, the medical information system is called to process the vascular access use record, and the past vascular access use record data is obtained, wherein the past vascular access use record data includes the vascular access use frequency, the vascular access use location, and the vascular access use time;

[0081] The patient's basic information data and previous vascular access use record data are integrated into the information archive to generate a comprehensive information archive of the patient's vascular access outpatient clinic.

[0082] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 The functional flow diagram of the patient outpatient information file integration module in this embodiment includes the following functions:

[0083] S11: Sending a connection call request instruction to the medical information system through the API interface, and configuring and connecting the medical information system to the API interface based on the connection call request instruction to obtain an API connection configuration file of the medical information system;

[0084] In an embodiment of the present invention, a connection call request instruction is sent to a medical information system by using a RESTful API interface. The request uses an HTTP POST method and includes the required authentication information and connection parameters in the request body. The request is automatically generated by a calling script, and the script is written using the Python programming language and a request library (such as Requests). After a successful request, the medical information system returns an API connection configuration file, which includes the necessary access token, connection URL, required request headers, and other configuration information. This process ensures the correct configuration of the API interface and establishes a preliminary connection with the medical information system, and ultimately obtains the medical information system API connection configuration file.

[0085] S12: performing connection configuration processing between the API interface and the medical information system based on the medical information system API connection configuration file to obtain an information request calling connection channel between the API interface and the medical information system;

[0086] In an embodiment of the present invention, the connection configuration processing between the corresponding API interface and the medical information system is performed by combining the medical information system API connection configuration file obtained by the previous configuration, so as to write a connection configuration script by using the parameters in the configuration file. This process involves establishing an HTTP connection and configuring SSL / TLS encryption to ensure the security of data transmission. After the connection processing is completed, the validity of the connection is verified to ensure that the API interface can communicate stably with the medical information system. By sending a simple test request (such as obtaining a status code), the successful establishment of the information request call connection channel is confirmed, and finally the information request call connection channel between the API interface and the medical information system is obtained.

[0087] S13: extracting and processing the basic information of the patient from the medical information system by using the information request calling connection channel between the API interface and the medical information system to obtain the basic information data of the patient, wherein the basic information data of the patient includes the basic identity information of the patient, the medical history information of the patient, and the allergy history information of the patient;

[0088] In an embodiment of the present invention, a request for extracting basic patient information is initiated to a medical information system by using an established information request calling connection channel. The request is sent through a GET method and includes a unique identifier of the patient as a query parameter. After receiving the request, the system returns relevant basic information data based on the patient ID. The data format uses JSON or XML. The returned data includes the patient's identity information (such as name, gender, date of birth), medical history information (such as previous illnesses, surgical history), and allergy history information (such as drug allergies). The extracted data is verified and parsed to ensure the accuracy and completeness of the information, and ultimately the patient's basic information data is obtained.

[0089] S14: using the information request calling connection channel between the API interface and the medical information system to call the medical information system for vascular access use records, and obtaining past vascular access use record data, wherein the past vascular access use record data includes the vascular access use frequency, the vascular access use location, and the vascular access use time;

[0090] In the embodiment of the present invention, similarly, by using the established information request calling connection channel, the corresponding medical information system is called to process the previous vascular access usage record. In this process, the request is sent through the POST method, and the content includes the patient's identity information and the requested data type. The medical information system returns the record data including the frequency of vascular access use, the location of use, and the time of use according to the request. The returned data is also in JSON or XML format. After data verification and parsing, the accuracy and completeness of the data are ensured, and finally the previous vascular access usage record data is obtained.

[0091] S15: Integrate the patient's basic information data and previous vascular access use record data into an information file to generate a comprehensive information file of the patient's vascular access outpatient clinic.

[0092] In an embodiment of the present invention, the patient's basic information data obtained previously by calling and the previous vascular access use record data are integrated into an information archive, so that the two data sets are merged and cleaned by using a data integration tool, such as Apache NiFi or Talend. During the integration process, it is ensured that the data fields match and duplicates are eliminated, and a comprehensive archive containing comprehensive patient information is generated. The integrated data is stored in a database, and finally a comprehensive information archive of the patient's vascular access outpatient clinic is generated.

[0093] Furthermore, the information archive integration processing of the patient's basic information data and previous vascular access use record data includes:

[0094] Perform data structure analysis on the fields and formats of different data sources in the patient's basic information data and previous vascular access use record data to obtain the data structure fields and data formats corresponding to different data sources of the patient's vascular access information;

[0095] In an embodiment of the present invention, a detailed field and format analysis is performed on the patient's basic information data and previous vascular access use record data, and a data parsing tool such as Python's Pandas library is used to import CSV or database files from different data sources, and the field name, data type and format of each data source, such as string, integer, date, etc., are extracted. At the same time, a field mapping table is created to record the description information, example value and data type of each data source field. To ensure the accuracy of the analysis, regular expressions and data verification rules are used to check the integrity and consistency of the fields to ensure that the field format meets the preset standards, and finally the data structure fields and data formats corresponding to different data sources of the patient's vascular access information are obtained.

[0096] Preferably, based on the data structure fields and data formats corresponding to different data sources of the patient's vascular access information, the patient's basic information data and the previous vascular access use record data are structured-converted and mapped to obtain a structured mapping framework of the patient's vascular access outpatient information;

[0097] In an embodiment of the present invention, the corresponding data is structurally converted according to the data structure fields and data formats corresponding to different data sources of the patient vascular access information obtained in the previous step, so as to set data conversion rules by using ETL (extraction, transformation, loading) tools, such as Apache Nifi or Talend, to map the basic information of patients and previous vascular access records in different data sources into a unified structured data framework. The specific operation includes mapping the fields of different data sources according to predefined standard fields, for example, uniformly converting fields such as "patient name" and "identity card number" into "patient_name" and "id_number". In addition, setting data type conversion rules, converting string type date fields into standard date formats to ensure the standardization of data, thereby outputting a structured mapping framework, and finally obtaining a structured mapping framework for patient vascular access outpatient information.

[0098] Preferably, the basic information data of the patient and the previous vascular access use record data are fused, integrated and consistency checked to obtain a fusion data set of the patient's vascular access outpatient information;

[0099] In an embodiment of the present invention, by using data fusion technology and selecting a suitable data merging algorithm, such as a rule-based merging algorithm or a machine learning clustering algorithm, the patient's basic information data and the previous vascular access use record data are merged, and then a consistency check is performed to ensure that the values ​​of all fields are consistent between different data sources. A data quality check tool is used to set consistency check rules, such as checking the repetition and uniqueness of the patient's name and ID number, and eliminating redundant or inconsistent data, so as to obtain complete vascular access information containing each patient, ensure the accuracy and completeness of the data, and finally obtain a fusion data set of the patient's vascular access outpatient information.

[0100] Preferably, the file format of the patient vascular access clinic information fusion data set is standardized based on the patient vascular access clinic information structured mapping framework to generate a patient vascular access clinic comprehensive information file.

[0101] In an embodiment of the present invention, the corresponding patient vascular access clinic information fusion data set is standardized in file format by combining the structured mapping framework of the patient vascular access clinic information previously mapped and output, so as to convert the fusion data set into a standardized file format (such as PDF or Word document) by adopting a document processing tool, such as Apache POI or Microsoft Word API, and set the template format of the file, including structured contents such as title, basic patient information, and vascular access use record, and fill the information in the data set into the template by using data filling technology to ensure beautiful layout and complete information, and perform final data verification to ensure that the data in each field in the file is accurate, and finally generate a comprehensive information file of the patient vascular access clinic.

[0102] Furthermore, the patient vascular access use risk optimization selection module includes the following functions:

[0103] Obtain the patient's vascular access use requirements;

[0104] Conduct quantitative and qualitative demand analysis on the patient's vascular access use needs to generate a patient vascular access demand analysis report; conduct demand pattern and potential problem mining analysis on the patient's vascular access demand analysis report to obtain the patient's vascular access demand pattern and potential problem points of the patient's vascular access demand;

[0105] Design vascular access solutions for patients based on their vascular access demand patterns to generate personalized vascular access design solutions for patients;

[0106] Based on the potential problems of the patient's vascular access needs, the patient's personalized vascular access design plan is optimized and recommended, and the patient's preset vascular access is generated;

[0107] Based on the comprehensive information file of the patient's vascular access clinic, the access use risk optimization selection is performed for each patient's preset vascular access to generate the best outpatient option for the patient to establish vascular access.

[0108] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 FIG. 1 is a functional flow chart of a patient vascular access risk optimization selection module in FIG. 1 . In this embodiment, the patient vascular access risk optimization selection module includes the following functions:

[0109] S21: Obtain the patient's vascular access use requirements;

[0110] In an embodiment of the present invention, by collecting the patient's basic information and medical history, including existing symptoms, treatment plans, and previous vascular access usage, and using questionnaires or interviews, the specific needs of vascular access are obtained from the patient and his or her family, covering the access type, frequency of use, and expected duration of use, and ultimately the patient's vascular access usage needs are obtained.

[0111] S22: Conducting quantitative and qualitative demand analysis on the patient's vascular access use needs to generate a patient's vascular access demand analysis report; conducting demand pattern and potential problem mining analysis on the patient's vascular access demand analysis report to obtain the patient's vascular access demand pattern and potential problem points of the patient's vascular access demand;

[0112] In an embodiment of the present invention, a quantitative analysis is performed on the patient vascular access usage needs obtained through previous analysis, and the needs are quantified into specific indicators, such as the frequency of intubation and the expected treatment cycle. A corresponding qualitative needs analysis is performed, and then the patient's expectations for access comfort, safety, and ease of use are identified through an in-depth understanding of the patient's subjective feelings. The collected data is organized into a detailed patient vascular access needs analysis report, which clearly records the number and type of needs and highlights key demand points, thereby generating a patient vascular access needs analysis report. At the same time, through in-depth analysis and mining of the patient vascular access demand analysis report generated by the previous analysis, the commonalities and individualities of the needs are identified through data mining technology, and the patient vascular access demand pattern is constructed. For example, according to patients with different symptoms, their preferences for access types and trends in frequency of use are identified. By applying the failure mode and effects analysis (FMEA) method, potential problem points are systematically evaluated, and the risks and obstacles that arise in the use of vascular access are considered. Combined with statistical analysis tools, such as SPSS or Python's data analysis library, potential problems are quantified and high-risk factors are identified, thereby forming a comprehensive analysis report on demand patterns and potential problems, and finally obtaining the patient vascular access demand pattern and potential problem points of the patient's vascular access demand.

[0113] S23: Designing a vascular access solution for the corresponding patient vascular access usage needs based on the patient's vascular access demand pattern to generate a personalized vascular access design solution for the patient's needs;

[0114] In an embodiment of the present invention, a vascular access is designed for the corresponding patient vascular access usage needs by combining the patient vascular access demand pattern obtained by previous analysis and combining clinical experience and best practices to design a personalized vascular access plan. First, a suitable access type (such as a central venous catheter, a peripheral venous catheter, etc.) is selected, and then parameters are set according to the specific needs of the patient, such as catheter length, caliber, material, etc., and a computer-aided design (CAD) tool is used to model the specific structure of the vascular access to simulate its performance in the patient's body. The design plan is compared with medical standards and guidelines to ensure the scientificity and practicality of the plan, and a detailed patient-specific vascular access design plan is formed, including every detail of the design and its expected effect, and finally a patient-specific vascular access design plan is generated.

[0115] S24: Based on the potential problem points of the patient's vascular access needs, the patient's personalized vascular access design plan is optimized and recommended, and the patient's preset vascular access is generated;

[0116] In an embodiment of the present invention, optimization suggestions are made for the personalized vascular access design scheme by combining the previously identified potential problem points of the patient's vascular access needs, so as to re-evaluate the safety and feasibility of the design scheme by targeting high-risk factors and using a risk assessment model, and test the impact of changes in various parameters in the design on the overall scheme by using sensitivity analysis, and then propose corresponding optimization measures. For example, if a certain material is found to be prone to complications under certain circumstances, consider replacing it with a safer material. Through repeated simulation and verification, the design scheme is optimized to reduce potential risks, thereby forming a patient preset vascular access that includes optimization suggestions, and finally generating a patient preset vascular access.

[0117] S25: Based on the patient's comprehensive vascular access clinic information file, optimize the access use risk for each patient's preset vascular access to generate the patient's best outpatient choice for establishing vascular access.

[0118] In an embodiment of the present invention, risk optimization selection is performed for each preset vascular access by combining the patient's comprehensive outpatient information file, so as to analyze the patient's comprehensive health data by using a machine learning algorithm, identify the health conditions and physiological characteristics that affect the use of the access, and establish a risk scoring model for each vascular access. By evaluating the patient's overall health condition, potential complications and vascular status, the best access selection is determined. At the same time, the patient's feedback and historical usage records are considered to ensure that the selected solution can effectively reduce risks in actual use, thereby outputting a report on the patient's best outpatient choice for establishing a vascular access, ensuring that all options are scientifically verified and meet the patient's personalized needs, and finally generating the patient's best outpatient choice for establishing a vascular access.

[0119] Furthermore, the risk optimization selection of access use for each patient's preset vascular access based on the patient's vascular access clinic comprehensive information file includes:

[0120] Based on the comprehensive information file of the patient's vascular access clinic, the vascular access is matched and collected for each patient's preset vascular access, and the patient's medical history information, patient allergic reaction information, and patient's previous use information corresponding to each preset vascular access are obtained;

[0121] In an embodiment of the present invention, the vascular access data of each patient's preset vascular access is matched and sorted by using the previously integrated patient vascular access outpatient comprehensive information file. This process includes data extraction and matching. A data management system is used to extract the patient's medical history information, allergic reaction information, and previous use records through the electronic medical record (EMR) and the hospital information system (HIS), and various types of information are associated according to the patient's unique identification code. Each preset vascular access is matched with its corresponding patient information through an algorithm. After a successful match, a comprehensive data table is formed, which includes the patient's medical history, allergy history, and previous use of the vascular access, and finally the patient's medical history information, patient allergic reaction information, and patient's previous use information corresponding to each preset vascular access are obtained.

[0122] Preferably, based on the patient medical history information and patient allergic reaction information corresponding to each preset vascular access, the infection complication and allergy risk probability of the corresponding patient preset vascular access is statistically calculated to obtain the infection complication risk probability and the allergy risk probability of the patient corresponding to each preset vascular access;

[0123] In an embodiment of the present invention, the patient medical history information and allergic reaction information of each preset vascular access are analyzed by using a statistical analysis tool, and statistical software (such as SPSS or R) is selected for data mining and probability calculation. According to the known infection complications and allergic reaction data, a corresponding model is constructed to perform probability statistical analysis, and a cross-analysis method is used. At the same time, the patient's medical history and allergic reaction information are combined to quantitatively calculate the infection complication risk probability and allergy risk probability corresponding to each preset vascular access. This process ensures accurate identification of the potential risks of each patient when using a specific vascular access, and ultimately obtains the patient's infection complication risk probability and patient allergy risk probability corresponding to each preset vascular access.

[0124] Preferably, based on the patient's previous use information corresponding to each preset vascular access, an access use risk assessment analysis is performed on the corresponding patient's preset vascular access to obtain an access use risk influencing factor corresponding to each preset vascular access;

[0125] In an embodiment of the present invention, a machine learning algorithm is used to evaluate and analyze the access use risk of each preset vascular access corresponding to the patient's previous use information previously matched and sorted. This step uses a classification algorithm (such as a decision tree or a random forest) to analyze historical data to identify key factors that affect access use. The specific operation includes inputting the patient's previous use records and their results, establishing a model through training data, analyzing the specific impact of different factors (such as frequency of use, post-use reactions, etc.) on the access use risk, and finally obtaining the access use risk influencing factor corresponding to each preset vascular access.

[0126] Preferably, based on the infection complication risk probability of the patient, the allergy risk probability of the patient and the access use risk influencing factor corresponding to each preset vascular access, a vascular access use risk assessment calculation formula is used to perform a risk assessment calculation on the corresponding patient preset vascular access to obtain a risk score value corresponding to each preset vascular access;

[0127] In an embodiment of the present invention, a suitable vascular access use risk assessment calculation formula is formed by combining the time variable parameters of risk assessment, the patient's infection complication risk probability, the patient's infection complication basic probability, the patient's infection complication risk influencing factors, the infection complication risk time attenuation coefficient, the patient's allergy risk probability, the patient's allergy basic probability, the patient's allergy risk influencing factors, the allergy risk time attenuation coefficient, the access use risk influencing factor, the frequency of use, the location of use, the time of use, the vascular access use risk attenuation index and related parameters to perform risk assessment calculations on the corresponding patient's preset vascular access, quantify each risk factor according to its importance, and form an overall risk score. This score provides an intuitive risk level for each preset vascular access, and finally obtains the risk score value corresponding to each preset vascular access.

[0128] Preferably, the corresponding preset vascular access for the patient is selected based on the risk score corresponding to each preset vascular access, so as to generate the best option for the patient to establish the vascular access in the outpatient clinic.

[0129] In an embodiment of the present invention, the risk optimization selection of access use is performed according to the risk score value of each preset vascular access, and the risk score of each preset vascular access is analyzed by using an optimization algorithm (such as linear programming or genetic algorithm) to find the preset vascular access option with the minimized risk. This process will comprehensively consider the patient's specific situation, risk score and access availability, output the patient's best vascular access option, generate the patient's outpatient best option report, and store the recommendation results in the information system to guide clinical decision-making, ensure that the patient obtains safe and effective vascular access management services, and finally select the patient's outpatient best option to establish a vascular access.

[0130] Furthermore, the statistical calculation of infection complications and allergy risk probabilities of the corresponding preset vascular access of the patient based on the patient medical history information and the patient allergic reaction information corresponding to each preset vascular access includes:

[0131] By acquiring corresponding potential infection complication history information from the patient medical history information corresponding to each preset vascular access, the potential infection complication history information of the patient corresponding to each preset vascular access is obtained;

[0132] In an embodiment of the present invention, potential infection complication history information is extracted from the patient medical history information corresponding to each preset vascular access, and the structured and unstructured data in the electronic medical record (EMR) are analyzed by using data mining technology and natural language processing (NLP) algorithm. Through keyword matching and text classification algorithms, medical history records related to infection, such as previous infection history, surgical history and chronic diseases, are identified. All identified potential infection information will be aggregated to form a new data set, which will be used for subsequent risk factor analysis to ensure that the extracted information is accurate and comprehensive, and finally the patient potential infection complication history information corresponding to each preset vascular access is obtained.

[0133] Preferably, based on the patient's potential infection complication history information corresponding to each preset vascular access, the infection complication risk factor identification analysis is performed on the corresponding patient's preset vascular access to obtain the patient's infection complication risk factor corresponding to each preset vascular access;

[0134] In an embodiment of the present invention, infection complication risk factors of the corresponding patient preset vascular access are identified and analyzed by combining the previously obtained patient potential infection complication history information corresponding to each preset vascular access, so as to construct a risk factor model by using a machine learning algorithm (such as logistic regression or random forest). By taking the potential infection history information as an input variable and combining it with historical data, the contribution of each variable to the infection risk is analyzed. The model will identify specific risk factors, such as age, past medical history, and management method, and generate a corresponding infection complication risk factor list for each preset vascular access, and finally obtain the patient infection complication risk factor corresponding to each preset vascular access.

[0135] Preferably, according to the patient infection complication risk factor corresponding to each preset vascular access, a statistical calculation of the infection complication risk probability of the corresponding patient preset vascular access is performed to obtain the patient infection complication risk probability corresponding to each preset vascular access;

[0136] In an embodiment of the present invention, a statistical calculation of the infection complication risk probability is performed based on the previously identified infection complication risk factors, and the risk factors of each preset vascular access are summarized using statistical analysis software (such as the Pandas library in R or Python). The infection complication risk probability corresponding to each preset vascular access is generated by calculating the ratio of the number of infected people to the total number of people and then multiplying it by the infection complication risk factor. This process not only considers the risk factors of the current patient, but also refers to the infection events in the historical data to ensure the scientificity and accuracy of the probability calculation, and finally obtains the patient infection complication risk probability corresponding to each preset vascular access.

[0137] Preferably, the corresponding patient material allergen is obtained from the patient allergic reaction information corresponding to each preset vascular access, and the access material property of each patient preset vascular access is analyzed to obtain the patient vascular access material property corresponding to each preset vascular access;

[0138] In an embodiment of the present invention, the material allergens of the patient are extracted from the patient's allergic reaction information corresponding to each preset vascular access, and the relevant allergen information is screened from the patient's allergy record using database query and data integration technology. Subsequently, the material property analysis of each preset vascular access is performed, and the analysis focuses on evaluating the composition of the vascular access material and its interaction with known allergens. The access material is analyzed using material science analysis tools to ensure that the information obtained accurately reflects the characteristics of the material, and finally the patient's vascular access material characteristics corresponding to each preset vascular access are obtained.

[0139] Preferably, based on the patient substance allergens corresponding to each preset vascular access, the corresponding patient vascular access material characteristics are statistically calculated to obtain the patient allergy risk probability corresponding to each preset vascular access.

[0140] In an embodiment of the present invention, the probability of allergic risk is statistically calculated by combining the patient substance allergens corresponding to each preset vascular access obtained by previous analysis with the corresponding vascular access material characteristics, and the relationship between each allergen and the access material is calculated using statistical analysis software, and the incidence of allergic reactions is quantitatively calculated therefrom. Based on historical data, an allergic risk model is established to calculate the probability of allergic reactions caused by each preset vascular access material. This process ensures that various factors are comprehensively considered, and ultimately the patient allergy risk probability corresponding to each preset vascular access is obtained.

[0141] Furthermore, the risk assessment analysis of access use of the corresponding patient preset vascular access based on the patient's previous use information corresponding to each preset vascular access includes:

[0142] Obtaining the previous use frequency and the previous use duration of the patient's vascular access from the patient's previous use information corresponding to each preset vascular access, thereby obtaining the previous use frequency and the previous use duration corresponding to each preset vascular access;

[0143] In an embodiment of the present invention, the corresponding past usage frequency and past usage time are obtained from the patient's past usage information corresponding to each preset vascular access previously divided, and the collected historical data are sorted and analyzed by using data processing tools, such as Python's Pandas library, to count the number of times each vascular access is used within a specified time period, and record the start time and end time of each use, calculate the usage duration, and summarize these data through an aggregation function to generate a detailed list of the usage frequency and duration of each preset vascular access, and finally obtain the past usage frequency and past usage duration corresponding to each preset vascular access.

[0144] Preferably, based on the previous usage frequency and the previous usage duration corresponding to each preset vascular access, the access usage frequency of the corresponding patient preset vascular access is calculated to obtain the access usage frequency corresponding to each preset vascular access;

[0145] In an embodiment of the present invention, a statistical calculation of access usage frequency is performed on the corresponding patient's preset vascular access by combining the previously acquired previous usage frequency and duration. The access usage frequency is calculated using a weighted average method, taking into account the usage patterns and time periods of different patients, and by analyzing the usage data of each vascular access. The calculation formula is that the usage frequency is equal to the previous usage frequency divided by the corresponding observation time period. Using a data analysis tool (such as R or Excel), the results are organized into a new data set, and finally the access usage frequency corresponding to each preset vascular access is obtained.

[0146] Preferably, based on the access usage frequency corresponding to each preset vascular access, an access usage risk assessment analysis is performed on the corresponding patient preset vascular access to obtain an access usage risk influencing factor corresponding to each preset vascular access.

[0147] In an embodiment of the present invention, a risk assessment analysis is performed on the corresponding patient preset vascular access by combining the access usage frequency corresponding to each preset vascular access obtained by previous analysis, so as to adopt a risk assessment model, such as a decision tree or a logistic regression model, to analyze various factors affecting the access usage risk, including usage frequency, historical complication rate, patient basic medical history, etc., and by constructing a model and training it, the risk influencing factor of each vascular access is evaluated. During the analysis process, cross-validation is performed using existing data sets to ensure the effectiveness and accuracy of the model. The generated risk influencing factor provides a specific risk assessment result for each preset vascular access, and finally the access usage risk influencing factor corresponding to each preset vascular access is obtained.

[0148] Furthermore, the vascular access use risk assessment calculation formula is specifically:

[0149]

[0150]

[0151] In the formula, R i is the risk score corresponding to the i-th preset vascular access, t 0 is the initial time of risk assessment, t 1 is the end time of risk assessment, t is the time variable parameter, I i (t) is the risk probability of infection complications of the patient corresponding to the i-th preset vascular access at time t, is the basic probability of infection complications in patients corresponding to the i-th preset vascular access, is the risk influencing factor of infection complications in patients corresponding to the i-th preset vascular access, is the time attenuation coefficient of infection complication risk corresponding to the i-th preset vascular access, A i (t) is the patient allergy risk probability corresponding to the i-th preset vascular access at time t, is the basic probability of patient allergy corresponding to the i-th preset vascular access, is the risk influencing factor of patient allergy corresponding to the i-th preset vascular access, is the allergy risk time attenuation coefficient corresponding to the i-th preset vascular access, C i (t) is the access use risk influencing factor corresponding to the i-th preset vascular access at time t, U i is the usage frequency corresponding to the i-th preset vascular access, W i is the usage position corresponding to the i-th preset vascular access, T i is the usage time corresponding to the i-th preset vascular access, n is the vascular access use risk attenuation index, and η is the correction coefficient of the risk score value.

[0152] The present invention obtains a vascular access use risk assessment calculation formula by using a specific mathematical model and after verification, which is used to perform risk assessment calculations on the corresponding patient preset vascular access. The vascular access use risk assessment calculation formula can quantify the potential infection complication risk, allergic risk and access use risk, so that doctors and nursing staff can intuitively understand the risk level of each preset vascular access. The time variable in the formula allows dynamic monitoring of the change of risk over time, providing a more accurate risk assessment. By integrating the patient's medical history, allergic reaction and previous use information, the risk assessment can be customized for individual patients, thereby improving safety and effectiveness. Based on the risk scoring results, medical staff can choose the vascular access that best suits the patient and reduce unnecessary risks. By considering multiple influencing factors such as infection probability, allergic probability, access use frequency and location, the risk of each vascular access can be comprehensively assessed to provide more comprehensive decision support. In addition, the risk scoring formula contains a correction coefficient, which can be adjusted in real time according to the data continuously obtained in clinical practice to improve the accuracy of the assessment. By reducing the frequency of use of high-risk accesses, reducing the incidence of complications, and improving the overall safety and satisfaction of patients. Therefore, the formula fully considers the risk score value R corresponding to the i-th preset vascular access i , initial time of risk assessment t 0 , risk assessment end time t 1 , time variable parameter t, the patient infection complication risk probability I corresponding to the i-th preset vascular access at time t i (t), the basic probability of infection complications in patients corresponding to the i-th preset vascular access Factors influencing the risk of infection complications in patients corresponding to the i-th preset vascular access The time attenuation coefficient of infection complication risk corresponding to the i-th preset vascular access The patient allergy risk probability A corresponding to the i-th preset vascular access at time t i (t), the basic probability of patient allergy corresponding to the i-th preset vascular access Factors influencing the risk of allergy in patients corresponding to the i-th preset vascular access Allergy risk time attenuation coefficient corresponding to the i-th preset vascular access The access use risk influencing factor C corresponding to the i-th preset vascular access at time t i (t), the usage frequency U corresponding to the i-th preset vascular access i , the usage position W corresponding to the i-th preset vascular access i , the usage time T corresponding to the i-th preset vascular access i, the vascular access use risk attenuation index n, the risk score correction coefficient η, where, by combining the time variable parameter t, the basic probability of infection complications in patients corresponding to the i-th preset vascular access Factors influencing the risk of infection complications in patients corresponding to the i-th preset vascular access And the time attenuation coefficient of infection complication risk corresponding to the i-th preset vascular access It constitutes a patient infection complication risk probability I corresponding to the i-th preset vascular access at time t i (t) By combining the time variable parameter t, the basic probability of patient allergy corresponding to the i-th preset vascular access Factors influencing the risk of allergy in patients corresponding to the i-th preset vascular access And the allergy risk time attenuation coefficient corresponding to the i-th preset vascular access It constitutes a patient allergy risk probability A corresponding to the i-th preset vascular access at time t i (t) By combining the time variable parameter t, the usage frequency U corresponding to the i-th preset vascular access i , the usage position W corresponding to the i-th preset vascular access i , the usage time T corresponding to the i-th preset vascular access i and the vascular access use risk attenuation index n form a functional relationship According to the risk score R corresponding to the i-th preset vascular access i The correlation between the above parameters constitutes a functional relationship This formula can realize the risk assessment calculation process of the corresponding patient's preset vascular access. At the same time, by introducing the correction coefficient η of the risk score value, it can be adjusted according to the error situation occurring in the calculation process, thereby improving the accuracy and applicability of the risk assessment calculation formula for vascular access use.

[0153] Furthermore, the patient vascular access quality abnormality warning module includes the following functions:

[0154] Use smart devices to monitor in real time the patient's physiological data corresponding to the best choice of vascular access for outpatients and the patient's vascular access usage data;

[0155] In an embodiment of the present invention, an integrated intelligent device such as a portable physiological monitoring instrument is used to monitor the optimal vascular access previously selected by the patient in the outpatient clinic in real time. The device collects physiological data through a variety of sensors, including parameters such as heart rate, blood oxygen saturation and blood pressure. The device also records the patient's vascular access usage status, including access type, insertion time and recent frequency of use. The monitored data is transmitted to the central data processing system via a secure wireless network (such as Wi-Fi or Bluetooth) to ensure the real-time and accuracy of the data, and finally obtain the corresponding patient physiological data and patient vascular access usage status data.

[0156] Preferably, feature engineering is performed on the patient's physiological data and the patient's vascular access usage status data to obtain the patient's physiological change characteristics and the patient's vascular access usage status characteristics;

[0157] In an embodiment of the present invention, the collected patient physiological data and vascular access usage status data are cleaned and preprocessed to remove invalid values ​​and abnormal points, and key features are extracted using feature engineering methods. The features extracted from the physiological data include average heart rate changes, blood oxygen fluctuation amplitude, etc.; the vascular access usage status features include access usage duration, interval between the last use, etc. These features are standardized and normalized to eliminate dimensional differences and ensure the effectiveness of subsequent model training. The processed feature data are organized into a data set, and finally the patient's physiological change characteristics and the patient's vascular access usage status characteristics are obtained.

[0158] Preferably, a neural network algorithm is used to construct a patient vascular access abnormality prediction model, and the patient's physiological change characteristics and the patient's vascular access usage status characteristics are input into the patient vascular access abnormality prediction model to perform intelligent vascular access abnormality prediction, thereby obtaining a patient vascular access usage abnormality prediction result;

[0159] In an embodiment of the present invention, a corresponding patient vascular access abnormality prediction model is constructed by using a neural network algorithm in deep learning, so as to capture patterns in time series data by selecting a suitable neural network architecture, such as a long short-term memory network (LSTM), and taking the patient's physiological change characteristics and vascular access usage status characteristics extracted in the previous step as input to perform model training. During the training process, cross-validation and hyperparameter tuning methods are used to improve the model performance. After the training is completed, the model is verified using a test set to ensure that the model has good generalization ability. The prediction results generated by the model show the abnormal use risk of the patient's vascular access, and finally the abnormal use prediction results of the patient's vascular access are predicted.

[0160] Preferably, the abnormal prediction result of the patient's vascular access is automatically responded to and warned to generate an intelligent abnormal warning signal of the patient's vascular access.

[0161] In an embodiment of the present invention, an automatic response warning process is performed on the abnormal prediction results of the patient's vascular access obtained by the previous abnormal prediction, so that through the set threshold mechanism, when the abnormal prediction probability output by the model exceeds the preset threshold, an intelligent abnormal warning signal is automatically generated. The signal includes the abnormal type, prediction probability and recommended treatment measures, and is encrypted and transmitted to the mobile devices of relevant medical staff and the hospital monitoring system through a wireless network. The warning information is automatically recorded in the patient's electronic medical record for subsequent tracking and management. The whole process ensures timely response to potential risks, improves the patient's safety management efficiency, and finally responds to generate an intelligent abnormal warning signal for the patient's vascular access.

[0162] Furthermore, the vascular access abnormality feedback management module includes the following functions:

[0163] Upload the patient's vascular access intelligent abnormal warning signal to the cloud platform, and use the cloud platform to identify and analyze the abnormality of the patient's vascular access intelligent abnormal warning signal to obtain the analysis result of the patient's vascular access abnormality;

[0164] In an embodiment of the present invention, the patient's vascular access intelligent abnormal warning signal generated by the previous response is uploaded to the cloud platform, and the cloud platform is used to perform statistical analysis on the patient's vascular access intelligent abnormal warning signal to identify abnormal access problem conditions, so as to adopt big data processing technology to receive warning signals from multiple patients in real time, and analyze the signals through the built-in artificial intelligence algorithm to identify potential vascular access abnormal problems. For example, if a significant decrease in blood flow rate is detected, it will be judged as possible thrombosis or access blockage through pattern recognition technology, and finally the analysis results of the patient's vascular access abnormality problem are obtained.

[0165] Preferably, vascular access abnormality feedback management is performed based on the analysis results of the patient's vascular access abnormality problem, and a patient vascular access abnormality feedback management plan is generated to execute corresponding patient vascular access abnormality feedback management work.

[0166] In an embodiment of the present invention, by combining the analysis results of the patient's vascular access abnormality problem obtained by previous analysis, the cloud platform will automatically generate a vascular access abnormality feedback management plan, which includes specific response measures and suggestions, including adjustments to the patient's vascular access use plan, medical staff's intervention measures and guidance for further examinations, and through a user-friendly interface, the analysis results and the corresponding feedback management plan can be intuitively viewed, and the feedback plan is integrated with other management systems of the hospital through the electronic medical record system (EMR) to ensure that relevant medical staff receive information in a timely manner. A tracking mechanism can also be set in the plan to conduct subsequent evaluation of the patient's vascular access management effect. The process also includes generating automated reports to facilitate the hospital's overall management and improvement, thereby achieving comprehensive monitoring and management of vascular access, and finally generating a patient vascular access abnormality feedback management plan to perform corresponding patient vascular access abnormality feedback management work.

[0167] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0168] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An artificial intelligence-based vascular access outpatient intelligent management system, characterized in that: Includes the following modules: The patient outpatient information file integration module is used to connect to the medical information system through the API interface to obtain the patient's basic information data and previous vascular access use record data in real time and perform information file integration processing to generate a comprehensive information file of the patient's vascular access outpatient clinic; The patient vascular access use risk optimization selection module is used to obtain the patient's vascular access use needs, and make vascular access recommendation designs based on the patient's vascular access use needs, and generate the patient's preset vascular access; based on the patient's vascular access outpatient comprehensive information file, each patient's preset vascular access is selected for access use risk optimization to generate the patient's best outpatient choice to establish a vascular access; The patient vascular access quality abnormality warning module is used to use intelligent devices to monitor in real time the patient's physiological data corresponding to the vascular access selected for the best outpatient clinic and the patient's vascular access usage status data, and to perform intelligent abnormality warning of the vascular access based on the patient's physiological data and the patient's vascular access usage status data, so as to generate an intelligent abnormality warning signal for the patient's vascular access; The vascular access abnormality feedback management module is used to upload the patient's vascular access intelligent abnormality warning signal to the cloud platform for vascular access abnormality feedback management, generate a patient's vascular access abnormality feedback management plan, and perform the corresponding patient's vascular access abnormality feedback management work.

2. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 1 is characterized in that: The patient outpatient information archive integration module includes the following functions: Sending a connection call request instruction to the medical information system through the API interface, and configuring and connecting the medical information system to the API interface based on the connection call request instruction to obtain the medical information system API connection configuration file; Based on the medical information system API connection configuration file, the connection configuration processing between the API interface and the medical information system is performed to obtain the information request calling connection channel between the API interface and the medical information system; The information request calling connection channel between the API interface and the medical information system is used to extract and process the basic information of the patient from the medical information system to obtain the basic information data of the patient, wherein the basic information data of the patient includes the basic identity information of the patient, the medical history information of the patient, and the allergy history information of the patient; Using the information request calling connection channel between the API interface and the medical information system, the medical information system is called to process the vascular access use record, and the past vascular access use record data is obtained, wherein the past vascular access use record data includes the vascular access use frequency, the vascular access use location, and the vascular access use time; The patient's basic information data and previous vascular access use record data are integrated into the information archive to generate a comprehensive information archive of the patient's vascular access outpatient clinic.

3. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 2 is characterized in that: The information archive integration processing of the patient's basic information data and previous vascular access use record data includes: Perform data structure analysis on the fields and formats of different data sources in the patient's basic information data and previous vascular access use record data to obtain the data structure fields and data formats corresponding to different data sources of the patient's vascular access information; Based on the data structure fields and data formats corresponding to different data sources of the patient's vascular access information, the patient's basic information data and previous vascular access use record data are structured converted and mapped to obtain a structured mapping framework for the patient's vascular access outpatient information; The basic information data of patients and the previous vascular access use record data were integrated and consistency checked to obtain the fusion data set of patient vascular access outpatient information; Based on the structured mapping framework of patient vascular access clinic information, the archive format of the patient vascular access clinic information fusion dataset was standardized to generate a comprehensive information archive of patient vascular access clinic information.

4. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 1 is characterized in that: The patient vascular access use risk optimization selection module includes the following functions: Obtain the patient's vascular access use requirements; Conduct quantitative and qualitative demand analysis on the patient's vascular access use needs to generate a patient vascular access demand analysis report; conduct demand pattern and potential problem mining analysis on the patient's vascular access demand analysis report to obtain the patient's vascular access demand pattern and potential problem points of the patient's vascular access demand; Design vascular access solutions for patients based on their vascular access demand patterns to generate personalized vascular access design solutions for patients; Based on the potential problems of the patient's vascular access needs, the patient's personalized vascular access design plan is optimized and recommended, and the patient's preset vascular access is generated; Based on the comprehensive information file of the patient's vascular access clinic, the access use risk optimization selection is performed for each patient's preset vascular access to generate the best outpatient option for the patient to establish vascular access.

5. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 4 is characterized in that: The risk optimization selection of access use for each patient's preset vascular access based on the patient's vascular access clinic comprehensive information file includes: Based on the comprehensive information file of the patient's vascular access clinic, the vascular access is matched and collected for each patient's preset vascular access, and the patient's medical history information, patient allergic reaction information, and patient's previous use information corresponding to each preset vascular access are obtained; Based on the patient medical history information and patient allergic reaction information corresponding to each preset vascular access, the infection complication and allergy risk probability of the corresponding patient preset vascular access are statistically calculated to obtain the infection complication risk probability and the allergy risk probability of the patient corresponding to each preset vascular access; Based on the patient's previous use information corresponding to each preset vascular access, an access use risk assessment analysis is performed on the corresponding patient's preset vascular access to obtain an access use risk influencing factor corresponding to each preset vascular access; Based on the infection complication risk probability of the patient, the allergy risk probability of the patient and the access use risk influencing factor corresponding to each preset vascular access, the vascular access use risk assessment calculation formula is used to perform risk assessment calculation on the corresponding patient preset vascular access to obtain the risk score value corresponding to each preset vascular access; According to the risk score value corresponding to each preset vascular access, the corresponding patient's preset vascular access is selected for access risk optimization to generate the best option for the patient to establish vascular access in the outpatient clinic.

6. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 5 is characterized in that: The statistical calculation of infection complications and allergy risk probability of the corresponding patient preset vascular access based on the patient medical history information and patient allergic reaction information corresponding to each preset vascular access includes: By acquiring corresponding potential infection complication history information from the patient medical history information corresponding to each preset vascular access, the potential infection complication history information of the patient corresponding to each preset vascular access is obtained; Based on the patient's potential infection history information corresponding to each preset vascular access, the infection risk factor identification and analysis of the corresponding patient preset vascular access is performed to obtain the patient infection risk factor corresponding to each preset vascular access; According to the patient infection complication risk factor corresponding to each preset vascular access, a statistical calculation of the infection complication risk probability of the corresponding patient preset vascular access is performed to obtain the patient infection complication risk probability corresponding to each preset vascular access; By acquiring corresponding patient material allergens from patient allergic reaction information corresponding to each preset vascular access, and performing access material property analysis on each patient preset vascular access, the patient vascular access material property corresponding to each preset vascular access is obtained; Based on the patient material allergens corresponding to each preset vascular access, the allergy risk probability statistics are calculated for the corresponding patient vascular access material characteristics to obtain the patient allergy risk probability corresponding to each preset vascular access.

7. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 5 is characterized in that: The risk assessment analysis of access use of the corresponding patient preset vascular access based on the patient's previous use information corresponding to each preset vascular access includes: Obtaining the previous use frequency and the previous use duration of the patient's vascular access from the patient's previous use information corresponding to each preset vascular access, thereby obtaining the previous use frequency and the previous use duration corresponding to each preset vascular access; Calculating the access usage frequency of the corresponding patient preset vascular access based on the previous usage frequency and the previous usage duration corresponding to each preset vascular access, to obtain the access usage frequency corresponding to each preset vascular access; Based on the access usage frequency corresponding to each preset vascular access, an access usage risk assessment analysis is performed on the corresponding patient's preset vascular access to obtain the access usage risk influencing factor corresponding to each preset vascular access.

8. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 5 is characterized in that: The specific calculation formula for the vascular access use risk assessment is: In the formula, R i is the risk score value corresponding to the i-th preset vascular access, t0 is the initial time of risk assessment, t1 is the end time of risk assessment, t is the time variable parameter, I i (t) is the risk probability of infection complications of the patient corresponding to the i-th preset vascular access at time t, is the basic probability of infection complications in patients corresponding to the i-th preset vascular access, is the risk influencing factor of infection complications in patients corresponding to the i-th preset vascular access, is the time attenuation coefficient of infection complication risk corresponding to the i-th preset vascular access, A i (t) is the patient allergy risk probability corresponding to the i-th preset vascular access at time t, is the basic probability of patient allergy corresponding to the i-th preset vascular access, is the risk influencing factor of patient allergy corresponding to the i-th preset vascular access, is the allergy risk time attenuation coefficient corresponding to the i-th preset vascular access, C i (t) is the access use risk influencing factor corresponding to the i-th preset vascular access at time t, U i is the usage frequency corresponding to the i-th preset vascular access, W i is the usage position corresponding to the i-th preset vascular access, T i is the usage time corresponding to the i-th preset vascular access, n is the vascular access use risk attenuation index, and η is the correction coefficient of the risk score value.

9. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 1 is characterized in that: The patient vascular access quality abnormality early warning module includes the following functions: Use smart devices to monitor in real time the patient's physiological data corresponding to the best choice of vascular access for outpatients and the patient's vascular access usage data; Perform feature engineering processing on the patient's physiological data and the patient's vascular access usage status data to obtain the patient's physiological change characteristics and the patient's vascular access usage status characteristics; A neural network algorithm is used to construct a patient vascular access abnormality prediction model, and the patient's physiological change characteristics and the patient's vascular access usage status characteristics are input into the patient vascular access abnormality prediction model to perform intelligent vascular access abnormality prediction, and obtain the patient vascular access usage abnormality prediction result; Automatic response warning processing is performed on the abnormal prediction results of the patient's vascular access use to generate an intelligent abnormal warning signal for the patient's vascular access.

10. The artificial intelligence-based vascular access outpatient intelligent management system according to claim 1 is characterized in that: The vascular access abnormality feedback management module includes the following functions: Upload the patient's vascular access intelligent abnormal warning signal to the cloud platform, and use the cloud platform to identify and analyze the abnormality of the patient's vascular access intelligent abnormal warning signal to obtain the analysis result of the patient's vascular access abnormality; Vascular access abnormality feedback management is performed based on the analysis results of the patient's vascular access abnormality problems, and a patient vascular access abnormality feedback management plan is generated to execute corresponding patient vascular access abnormality feedback management work.

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