Intelligent pre-hospital emergency treatment ecological management system and method based on 5G creation

Abstract

The invention relates to an intelligent pre-hospital first aid ecological management system and method based on 5G, pre-hospital first aid data is reported to an intelligent pre-hospital first aid ecological management platform through a 5G gateway, the platform intelligently analyzes and predicts the pre-hospital first aid data and outputs Q (x) and Q (y) of a first-aid patient, Q (x) is a pre-hospital first aid strategy, Q (y) is a pre-hospital first aid strategy, and Q (y) is a pre-hospital first aid strategy. Q (y) is a post-hospital emergency treatment and nursing strategy; and respectively issuing the Q (x) and the Q (y) to a 5G emergency terminal and a medical care terminal through a 5G gateway. The pre-hospital first-aid strategy and the post-hospital first-aid and nursing strategy can be generated for the pre-hospital first-aid patient by utilizing an intelligent analysis technology, so that an intelligent pre-hospital first-aid ecological management system is created, closed-loop management measures of pre-hospital first-aid and post-hospital nursing are provided for the patient, and a closed-loop ecological chain of hospital admission management of the first-aid patient is formed. And the intelligent technology is adopted, so that the defects of time-consuming and energy-consuming pre-hospital measure management caused by manual work can be overcome, and the first-aid efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of smart medical technology, and in particular to a smart pre-hospital emergency ecological management system, a smart pre-hospital emergency ecological management method and electronic equipment built based on 5G. Background Art

[0002] The work arrangements of the emergency center quality control meeting clarified the work tasks and goals for the next stage: First, comprehensively improve the pre-hospital medical emergency service capabilities, continuously optimize the allocation of emergency resources, and achieve accurate and effective dispatch; second, continue to do a good job in pre-hospital emergency knowledge training and publicity, and enhance the self-rescue and mutual rescue capabilities of the general public; third, continue to promote emergency quality control and management, strengthen emergency training and drills for medical personnel, improve the success rate of treatment, especially the success rate of cardiopulmonary resuscitation, and continuously improve patient satisfaction; fourth, strengthen the assessment of pre-hospital emergency sites, try assessment innovation, and achieve learning complementarity and improvement through cross-checking and other methods.

[0003] With the rise of smart Internet of Things technology, various hospitals or units have gradually proposed corresponding pre-hospital emergency management systems. For example, the invention patent with publication number CN116825301A provides a 5G-based pre-hospital medical emergency information system (referred to as "comparative document"), including: a vehicle-mounted terminal system, including a vehicle-mounted terminal installed in a 5G ambulance, a driving recorder, a 5G individual device, a camera, a gateway, and medical equipment to realize the information collection of video images, audio data, geographic location, and medical data. The collected information is sent to the command center by the 5G gateway; the command center is used to perform resource supervision, monitoring and early warning, analysis and decision-making, command and dispatch, remote consultation, and emergency assessment. In the pre-hospital medical emergency of this scheme, it is necessary to use the analysis and decision-making module to preliminarily analyze the patient's condition and give at least one emergency recommendation plan based on the monitoring data of the monitoring and early warning module. Specifically: in the analysis and decision-making module, it includes: inputting the correct injury classification, inspection list and medical material scheduling into the artificial neural network as training materials respectively, and training the artificial neural network to learn injury classification, inspection list and medical material scheduling with multiple feature vectors and a label; the dialogue robot captures the patient's self-statement and converts it into multiple word strings, and then converts the multiple word strings into multiple word vectors; obtains multiple physiological information of the patient based on the monitoring data of the monitoring and early warning module; and inputs the multiple word vectors and multiple physiological information into the trained artificial neural network to generate injury classification, inspection list and medical material scheduling.

[0004] In the above patent, although the reference document can preliminarily analyze the patient's condition through the analysis and decision-making module and give at least one emergency suggestion plan, as an ecological management chain of hospital emergency, emergency patients should be given a closed-loop emergency management plan, that is, from the pre-hospital access, pre-hospital treatment, and post-hospital treatment and nursing of emergency patients, a corresponding closed-loop emergency management plan should also be given. However, the reference document only implements pre-hospital emergency resources based on pre-hospital 5G communication collection to provide pre-hospital emergency patients with emergency care, but does not involve post-hospital emergency measures and post-hospital nursing management plans, and cannot form a closed-loop ecological chain for emergency patient admission management, lacking an integrated management plan.

[0005] In addition, although the existing pre-hospital emergency management can achieve the connection and data connectivity between pre-hospital emergency, in-hospital emergency, and inter-hospital collaboration, it still mainly relies on the cooperation between medical staff, which requires a huge amount of working time and energy to deal with the closed-loop management links, so the emergency response efficiency is low. Summary of the invention

[0006] In order to solve the above problems, this application proposes a smart pre-hospital emergency ecological management system and method based on 5G.

[0007] On the one hand, this application proposes a smart pre-hospital emergency ecological management system based on 5G, including: 5G emergency terminal, used to collect pre-hospital emergency data of emergency patients and display Q (x) of emergency patients; The 5G gateway is used to realize data communication between the 5G emergency terminal and the intelligent pre-hospital emergency ecological management platform, including: reporting the pre-hospital emergency data to the intelligent pre-hospital emergency ecological management platform; The intelligent pre-hospital emergency ecological management platform is used to intelligently analyze the pre-hospital emergency data, predict and output Q(x) and Q(y) of emergency patients, where Q(x) is the pre-hospital emergency strategy and Q(y) is the post-hospital emergency and nursing strategy; and send Q(x) and Q(y) to the 5G emergency terminal and medical terminal through the 5G gateway respectively; Medical end, used to display Q(y) of emergency patients; The 5G emergency terminal and the medical terminal are respectively connected to the intelligent pre-hospital emergency ecological management platform through the 5G gateway.

[0008] As an optional implementation scheme of the present application, optionally, the intelligent pre-hospital emergency ecological management platform includes: A data collection unit, used to receive the pre-hospital emergency data reported by the 5G gateway and forward it to a data analysis unit; A data analysis unit, used to analyze the pre-hospital emergency data, obtain the emergency symptom data and identity information of the emergency patient, and write the emergency symptom data of the emergency patient into a preset HIS system according to the identity information; A pre-hospital emergency knowledge base, which is used to read the emergency symptom data of emergency patients, match the emergency disease types that match the emergency symptom data from the library, and import the emergency disease types into the pre-deployed pre-hospital ecological management AI model; A pre-hospital ecological management AI model is used to identify the type of emergency disease and match the Q(x) and Q(y) corresponding to the type of emergency disease; A policy management module, used to record the Q(x) and the Q(y) under the identity information of the emergency patient and write them into the HIS system; and to send the Q(x) to the corresponding 5G emergency terminal in a timely manner through the message middleware; Backend database, used for data storage.

[0009] As an optional embodiment of the present application, optionally, the medical end is also used for: When the emergency patient completes the execution of Q(x), a request message for obtaining the emergency patient's Q(y) is sent to the intelligent pre-hospital emergency ecological management platform.

[0010] As an optional implementation scheme of the present application, optionally, the intelligent pre-hospital emergency ecological management platform is also used for: The request information is forwarded to the policy management module, which retrieves the Q(y) of the emergency patient and sends the Q(y) to the corresponding medical end in a timely manner through the message middleware to enter the post-hospital emergency and nursing stage.

[0011] As an optional implementation scheme of the present application, optionally, the method for generating the pre-hospital ecological management AI model includes: Collect several sets of pre-hospital emergency big data and post-hospital emergency and nursing big data corresponding to different types of emergency diseases; Perform feature engineering on the pre-hospital emergency big data to obtain the corresponding feature set, including: Extracting a first strategy feature corresponding to the emergency disease type from each pre-hospital emergency strategy of the pre-hospital emergency big data, and marking the first strategy feature with a corresponding pre-hospital emergency value level, wherein the pre-hospital emergency value level is defined by the emergency department and is used to indicate the pre-hospital emergency priority corresponding to the first strategy feature; Extracting a second strategy feature corresponding to the emergency disease type from each post-hospital emergency and nursing strategy of the post-hospital emergency and nursing big data; constructing the first strategy feature and the second strategy feature into the feature set; Dividing the feature set into a training set and a validation set according to a preset ratio; Importing the training set into a preset machine learning model to perform feature training and learning to generate the pre-hospital ecological management AI model; The validation set was used to perform performance verification on the pre-hospital ecological management AI model: If the verification is passed, the pre-hospital ecological management AI model is deployed on the intelligent pre-hospital emergency ecological management platform; On the contrary, readjust the big data and retrain.

[0012] As an optional implementation scheme of the present application, optionally, the policy management module is further used to: If the pre-hospital ecological management AI model outputs 1 Q(x) for the emergency patient, the Q(x) is promptly sent to the corresponding 5G emergency terminal through the message middleware; If the pre-hospital ecological management AI model outputs at least 2 Q(x)s for emergency patients, the pre-hospital emergency value level marked on each Q(x) is identified and determined, and each Q(x) is sorted in order from large to small according to the size of the pre-hospital emergency value level, and the corresponding Q(x) is promptly sent to the corresponding 5G emergency terminal through the message middleware according to the sorting.

[0013] On the other hand, the present application proposes a smart pre-hospital emergency ecological management method, which is implemented based on the smart pre-hospital emergency ecological management system built based on 5G, and includes the following steps: Collect pre-hospital emergency data of emergency patients through 5G emergency terminals and report them to the smart pre-hospital emergency ecological management platform through 5G gateways; The intelligent pre-hospital emergency ecological management platform intelligently analyzes the pre-hospital emergency data, predicts and outputs Q(x) and Q(y) of emergency patients, where Q(x) is the pre-hospital emergency strategy and Q(y) is the post-hospital emergency and nursing strategy; and sends Q(x) and Q(y) to the 5G emergency terminal and medical terminal through the 5G gateway respectively; The 5G emergency terminal displays the Q(x) of the emergency patient, and the emergency patient enters the pre-hospital emergency phase; When the emergency patient completes the Q(x), a request message for obtaining the Q(y) of the emergency patient is sent to the smart pre-hospital emergency ecological management platform through the medical terminal; The intelligent pre-hospital emergency ecological management platform forwards the request information to the policy management module, which retrieves the Q(y) of the emergency patient and sends the Q(y) to the corresponding medical terminal in a timely manner through the message middleware; The medical end receives and displays Q(y) of the emergency patient, and the emergency patient enters the post-hospital emergency and nursing stage.

[0014] In another aspect, the present application further provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement the intelligent pre-hospital emergency ecological management method when executing the executable instructions.

[0015] Technical effects of the present invention: This application reports the pre-hospital emergency data to the intelligent pre-hospital emergency ecological management platform through the 5G gateway, and the intelligent pre-hospital emergency ecological management platform performs intelligent analysis on the pre-hospital emergency data, predicts and outputs Q (x) and Q (y) of the emergency patients, where Q (x) is the pre-hospital emergency strategy, and Q (y) is the post-hospital emergency and nursing strategy; and Q (x) and Q (y) are sent to the 5G emergency terminal and the medical terminal through the 5G gateway respectively. Intelligent analysis technology can be used to generate pre-hospital emergency strategies and post-hospital emergency and nursing strategies for pre-hospital emergency patients, thereby creating an intelligent pre-hospital emergency ecological management system, providing patients with closed-loop management measures for pre-hospital emergency and post-hospital nursing, and forming a closed-loop ecological chain for the admission management of emergency patients. In addition, the present invention adopts intelligent technology, which can solve the time-consuming and labor-intensive pre-hospital measure management defects caused by manual labor and improve emergency efficiency.

[0016] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0018] Figure 1 A schematic diagram of a 5G-based pre-hospital medical emergency information system of the prior art is shown; Figure 2 Shown is a schematic diagram of an application system of the present invention; Figure 3 It is a schematic diagram showing the composition of the platform service functions of the present invention; Figure 4 It is shown as a schematic diagram of the model training process of the present invention; Figure 5 Shown is a schematic diagram of the application of the electronic device of the present invention. DETAILED DESCRIPTION

[0019] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0020] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0021] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure. Example 1

[0022] like Figure 1 As shown, a 5G-based pre-hospital medical emergency information system proposed by the patent in the background technology. In the present invention, for the 5G emergency terminal, 5G gateway, and medical terminal, reference can be made to their application background and service functions. Therefore, its data collection and transmission can be understood in combination with the prior art. The 5G emergency terminal can be a 5G ambulance or other emergency terminals / equipment built based on 5G, which can pre-collect and report emergency data for emergency patients.

[0023] According to the present invention, in the hospital, the medical staff can communicate with the background server through the gateway, and the corresponding data routing transmission can be realized by the 5G gateway installed in the hospital.

[0024] The medical end can be a doctor's PC, or a nursing end such as an EDA or PDA.

[0025] The network configuration and deployment of routing communication connections through 5G gateways can be completed by hospital technicians.

[0026] like Figure 2 As shown, on the one hand, this application proposes a smart pre-hospital emergency ecological management system based on 5G, including: 5G emergency terminal, used to collect pre-hospital emergency data of emergency patients and display Q (x) of emergency patients; The 5G gateway is used to realize data communication between the 5G emergency terminal and the intelligent pre-hospital emergency ecological management platform, including: reporting the pre-hospital emergency data to the intelligent pre-hospital emergency ecological management platform; The intelligent pre-hospital emergency ecological management platform is used to intelligently analyze the pre-hospital emergency data, predict and output Q(x) and Q(y) of emergency patients, where Q(x) is the pre-hospital emergency strategy and Q(y) is the post-hospital emergency and nursing strategy; and send Q(x) and Q(y) to the 5G emergency terminal and medical terminal through the 5G gateway respectively; Medical end, used to display Q(y) of emergency patients; The 5G emergency terminal and the medical terminal are respectively connected to the intelligent pre-hospital emergency ecological management platform through the 5G gateway.

[0027] The present invention uses an intelligent pre-hospital emergency ecological management platform deployed in the hospital background to intelligently analyze the pre-hospital emergency data of pre-hospital emergency patients and intelligently recommend corresponding closed-loop management strategies, including analyzing and recommending corresponding pre-hospital emergency strategies for emergency patients based on pre-hospital emergency data collected and transmitted back by pre-hospital terminals such as 5G ambulances, as well as post-hospital emergency and nursing strategies that match the symptom type of the emergency patient.

[0028] Through the platform's intelligent model, strategy recommendations can be made to generate pre-hospital and post-hospital emergency plans and post-hospital nursing measures for emergency patients, thus realizing closed-loop medical care for emergency medical management. The strategies intelligently predicted and output by the platform can be sent to the hospital's 5G emergency terminal and medical care terminal through the 5G gateway.

[0029] In the pre-hospital emergency phase, emergency personnel can receive and display pre-hospital emergency strategies and execute them through the 5G emergency terminal outside the hospital. After the patient is admitted to the hospital and enters the post-hospital care or treatment stage, the corresponding medical staff can request the platform for the patient's post-hospital emergency and care measures through the medical terminal after the corresponding post-hospital medical care time point is triggered.

[0030] Therefore, through the intelligent analysis of the platform, emergency medical and nursing strategies are intelligently generated for emergency patients before and after hospitalization, saving time and improving efficiency for follow-up medical treatment, while reducing the medical burden on medical staff for emergency patients.

[0031] As attached Figure 3 As shown, as an optional implementation scheme of the present application, optionally, the intelligent pre-hospital emergency ecological management platform includes: A data collection unit, used to receive the pre-hospital emergency data reported by the 5G gateway and forward it to a data analysis unit; A data analysis unit, used to analyze the pre-hospital emergency data, obtain the emergency symptom data and identity information of the emergency patient, and write the emergency symptom data of the emergency patient into a preset HIS system according to the identity information; A pre-hospital emergency knowledge base, which is used to read the emergency symptom data of emergency patients, match the emergency disease types that match the emergency symptom data from the library, and import the emergency disease types into the pre-deployed pre-hospital ecological management AI model; A pre-hospital ecological management AI model is used to identify the type of emergency disease and match the Q(x) and Q(y) corresponding to the type of emergency disease; A policy management module, used to record the Q(x) and the Q(y) under the identity information of the emergency patient and write them into the HIS system; and to send the Q(x) to the corresponding 5G emergency terminal in a timely manner through the message middleware; Backend database, used for data storage.

[0032] The platform can be deployed on the in-hospital backend server, and can realize corresponding data collection and message sending through the message receiving and forwarding modules on the backend server.

[0033] Regarding the connection configuration for the platform to communicate with various medical and nursing terminals and pre-hospital terminals through the 5g gateway, the hospital's technical staff can perform the corresponding communication configuration and settings, which is not limited in this embodiment.

[0034] This embodiment does not limit the communication protocol and communication type between the platform and the gateway, etc.

[0035] After the 5G emergency terminal reports the data, the platform's data collection unit can receive the pre-hospital emergency data of the emergency patients reported by the 5G gateway, including various symptoms of the emergency patients and temporary out-of-hospital test data, etc. The data analysis unit will parse the reported data to obtain the identity information of the emergency patients to be received and the emergency symptom data, etc.

[0036] The platform has an electronic medical record system such as HIS system, which can record the patient's medical records. Therefore, after the data analysis unit obtains the corresponding information and data, it can write the identity information of the emergency patient into the HIS system, create a corresponding file for it, and record its emergency symptom data at the same time.

[0037] In this solution, a pre-hospital emergency knowledge base is set up on the platform. The pre-hospital emergency knowledge base can match the corresponding emergency symptom type according to the emergency symptom data of emergency patients, such as vital signs such as heart rate and blood pressure, or medical history and description of symptoms. In other words, the emergency symptoms of emergency patients are intelligently analyzed through the pre-hospital emergency knowledge base, and a preliminary evaluation is output of which emergency diseases the emergency patient may have. For example, through the patient's abnormal heart rate and breathing difficulties and other abnormal conditions, it can be preliminarily determined that the emergency patient has coronary heart disease or hypotension, etc.

[0038] The method for constructing a pre-hospital emergency knowledge base of the present invention may include the following steps: 1. Data Collection: Collect medical literature, clinical guidelines, case reports and other information to obtain the symptoms, signs, diagnostic criteria and treatment methods of various diseases.

[0039] Work with healthcare experts to gain their experience and insights.

[0040] 2. Knowledge organization: The collected information is classified and organized to establish the relationship between diseases and symptoms.

[0041] Identify typical, common, rare, and emergency symptoms for each disease.

[0042] 3. Knowledge base design: Design a structured knowledge base framework, including disease entities, symptom entities, association rules, etc.

[0043] Use ontology or semantic network to represent the relationship between diseases and symptoms.

[0044] 4. Knowledge encoding: Diseases and symptoms were coded using standardized medical coding systems (e.g., ICD-10, SNOMED CT).

[0045] Ensure that the coding system covers all relevant diseases and symptoms and is kept up to date.

[0046] 5. Knowledge base development: Develop a database system to store and manage knowledge base content.

[0047] Implement a user interface to facilitate medical personnel to query and update the knowledge base.

[0048] 6. Knowledge verification: The accuracy and practicality of the knowledge base were verified through expert review and clinical cases.

[0049] The knowledge base is regularly updated to reflect the latest medical research and clinical practice.

[0050] 7. Knowledge application: Develop intelligent diagnostic aids, such as expert systems or machine learning models, that leverage knowledge bases for symptom analysis and disease prediction.

[0051] Testing and optimizing these tools in the pre-hospital emergency setting ensures they can effectively aid medical decision making.

[0052] 8. User training and feedback: Provide medical staff with training on the use of the knowledge base to ensure that they can use it correctly.

[0053] Establish a feedback mechanism to collect users’ experience with the knowledge base and suggestions for improvements.

[0054] 9. Continuous Improvement: The knowledge base is regularly updated and optimized based on user feedback and medical research progress.

[0055] Consider integrating new technologies, such as artificial intelligence and big data analytics, to increase the intelligence of your knowledge base.

[0056] The construction method of the above-mentioned knowledge base, data collection and knowledge base structure building can refer to the construction methods of various existing knowledge bases or knowledge graphs.

[0057] After the pre-hospital emergency knowledge base analyzes the type of emergency disease that the emergency patient has, the pre-hospital ecological management AI model deployed on the platform can perform intelligent analysis of the emergency disease type and intelligently recommend corresponding pre-hospital emergency strategies as well as post-hospital emergency and nursing strategies for the patient's emergency disease.

[0058] The AI ​​model is generated through training of pre-hospital emergency big data and post-hospital emergency and nursing big data, and is based on neural network training to recommend emergency strategies and nursing measures for corresponding diseases. Therefore, the AI ​​model on the platform can generate pre-hospital and post-hospital strategies for patients. The strategy management module can record the pre-hospital emergency measures, as well as post-hospital emergency and nursing measures generated for the emergency patient in the medical record file, and at the same time send the corresponding pre-hospital emergency strategy to the gateway through the message middleware on the background server (sent by the kafka message queue mechanism), and then sent by the gateway to the corresponding 5g emergency terminal.

[0059] As for pre-hospital emergency strategies, they need to be issued in a timely manner; as for post-hospital emergency and nursing strategies, when the strategy management module receives the request reported by subsequent medical staff, the post-hospital emergency and nursing strategies generated in advance for the patient can be issued to the corresponding medical end through the message middleware, so that medical staff can perform corresponding stage medical or nursing for the patient according to different strategies.

[0060] As an optional embodiment of the present application, optionally, the medical end is also used for: When the emergency patient completes the execution of Q(x), a request message for obtaining the emergency patient's Q(y) is sent to the intelligent pre-hospital emergency ecological management platform.

[0061] As an optional implementation scheme of the present application, optionally, the intelligent pre-hospital emergency ecological management platform is also used for: The request information is forwarded to the policy management module, which retrieves the Q(y) of the emergency patient and sends the Q(y) to the corresponding medical end in a timely manner through the message middleware to enter the post-hospital emergency and nursing stage.

[0062] While emergency patients are receiving emergency treatment in the hospital, medical staff can determine whether the emergency patients have completed the pre-hospital emergency measures or strategies. If they have completed the execution, they will enter post-hospital care or post-hospital emergency treatment. When the emergency patients reach the corresponding emergency nodes, medical staff can send corresponding post-hospital requests to the platform through the medical terminal. The platform can retrieve and send the post-hospital emergency and nursing strategies that were intelligently generated for the emergency patients in the early stage to the medical terminal through the policy management module. In this way, the medical terminal can display the relevant post-hospital emergency and nursing measures that the current emergency patients should perform at this stage, which can realize the closed-loop medical care management of patients before and after the hospital.

[0063] like Figure 4 As shown, as an optional implementation scheme of the present application, optionally, the method for generating the pre-hospital ecological management AI model includes: Collect several sets of pre-hospital emergency big data and post-hospital emergency and nursing big data corresponding to different types of emergency diseases; Perform feature engineering on the pre-hospital emergency big data to obtain the corresponding feature set, including: Extracting a first strategy feature corresponding to the emergency disease type from each pre-hospital emergency strategy of the pre-hospital emergency big data, and marking the first strategy feature with a corresponding pre-hospital emergency value level, wherein the pre-hospital emergency value level is defined by the emergency department and is used to indicate the pre-hospital emergency priority corresponding to the first strategy feature; Extracting a second strategy feature corresponding to the emergency disease type from each post-hospital emergency and nursing strategy of the post-hospital emergency and nursing big data; constructing the first strategy feature and the second strategy feature into the feature set; Dividing the feature set into a training set and a validation set according to a preset ratio; Importing the training set into a preset machine learning model to perform feature training and learning to generate the pre-hospital ecological management AI model; The validation set was used to perform performance verification on the pre-hospital ecological management AI model: If the verification is passed, the pre-hospital ecological management AI model is deployed on the intelligent pre-hospital emergency ecological management platform; On the contrary, readjust the big data and retrain.

[0064] To train an AI model that can recognize and output appropriate first aid measures, you need to follow these steps: 1. Data collection: Collect information on pre-hospital and post-hospital emergency strategies related to various diseases, including but not limited to common emergencies such as heart disease, stroke, trauma, suffocation, burns, etc. This information can come from medical textbooks, first aid guides, professional medical websites, clinical practice guidelines, etc.

[0065] The data collection here can be done by the administrator to collect corresponding big data from the background database, including pre-hospital emergency data and post-hospital emergency and nursing data of several emergency patients.

[0066] Pre-hospital emergency data includes the disease type and symptom data of the emergency patients and the corresponding pre-hospital emergency strategies.

[0067] Post-hospital emergency and nursing data include the disease type of the emergency patient, rehabilitation data after pre-hospital treatment, symptom data after pre-hospital treatment, and post-hospital emergency strategies or post-hospital nursing strategies issued by the corresponding medical staff.

[0068] The specific strategy depends on the settings of medical staff.

[0069] 2. Data preprocessing: Organize the collected data to ensure the accuracy and consistency of the information. Remove duplicate or irrelevant information and classify the data to distinguish between pre-hospital emergency and post-hospital emergency measures.

[0070] 3. Feature extraction: Extract key information from the preprocessed data as input features for the model. These features may include symptom descriptions, first aid steps, required equipment and drugs, precautions, etc.

[0071] Perform feature engineering to obtain the corresponding feature set. You can use the text recognition algorithm to extract the corresponding strategy features (strategy keywords or text, word segmentation) from each emergency strategy. Specifically: First, the first aid plan text needs to be preprocessed, including word segmentation, stop word removal, part-of-speech tagging, etc. Then, extract key information from the text, such as symptom description, first aid measures, required materials, etc. Next, natural language processing techniques, such as TF-IDF algorithm, word embedding model or topic model, can be used to identify and extract features in the text. Finally, the extracted features are integrated to form the feature vector of the first aid plan for subsequent analysis and application.

[0072] By adopting the above method, a first strategy feature corresponding to the type of emergency disease can be extracted from each pre-hospital emergency strategy of the pre-hospital emergency big data; a second strategy feature corresponding to the type of emergency disease can be extracted from each post-hospital emergency and nursing strategy of the post-hospital emergency and nursing big data; and the first strategy feature and the second strategy feature can be combined to construct the feature set.

[0073] In this embodiment, in order to achieve different priorities of pre-hospital emergency care for diseases with different emergency degrees, the first policy feature of the corresponding degree is marked with the corresponding pre-hospital emergency value level (level digital label or score, etc., the larger the value, the higher the priority). The pre-hospital emergency value level is defined by the emergency department and is used to indicate the pre-hospital emergency priority corresponding to the first policy feature.

[0074] The priority of pre-hospital emergency care for different emergency diseases can be determined based on the rescue value, urgency and significance of the disease in pre-hospital emergency care. For example, diseases such as heart disease take precedence over surgical trauma.

[0075] The pre-hospital emergency care value levels corresponding to these diseases can be referred to as follows: In an emergency situation, the ranking of the importance of rescuing different diseases may vary based on a variety of factors, including but not limited to the severity of the disease, the degree of threat to the patient's life, the urgency of the rescue time window, and potential long-term effects. The following is a general ranking based on these factors, but please note that the specific situation should be judged by professional medical personnel based on the on-site situation: 1). Cardiac arrest (cardiopulmonary resuscitation); 2). Respiratory arrest or difficulty breathing (such as choking or asthma attack); 3). Severe allergic reaction (anaphylactic shock); 4). Heavy bleeding (bleeding caused by trauma); 5). Stroke (cerebral infarction); 6). Acute coronary syndrome (such as angina pectoris or myocardial infarction); 7). Diabetic ketoacidosis or hypoglycemia coma; 8). Status epilepticus; 9). Severe burns; 10). Poisoning (drugs or chemicals).

[0076] Through labeling, the model can identify the degree of pre-hospital emergency care for different diseases.

[0077] 4. Model selection: Choose a suitable machine learning or deep learning model. For text data, you can use natural language processing (NLP) techniques such as convolutional neural network models CNN, recurrent neural networks (RNN), long short-term memory networks (LSTM), or Transformer models.

[0078] The present invention preferably uses a convolutional neural network model CNN, and specifically the model structure and training process of the convolutional neural network model CNN can be implemented in combination with the structure and principle of the convolutional neural network model CNN.

[0079] 5. Training model: Use the extracted features and corresponding first aid measures as training data to train the model. During the training process, the model parameters need to be continuously adjusted to improve the recognition accuracy.

[0080] 6. Model evaluation: Evaluate the performance of the model through the test set to ensure that the model can accurately identify the first aid measures in different disease situations. Use indicators such as accuracy, recall, and F1 score to measure model performance.

[0081] The validation set and performance verification results of the model evaluation are completed by the administrator to perform the corresponding performance tests.

[0082] 7. Model optimization: Optimize the model based on the evaluation results, which may include adjusting the model structure, increasing training data, improving feature engineering, etc.

[0083] 8. Application deployment: Deploy the trained model to actual applications (the platform of the present invention), such as first aid knowledge question-and-answer system, medical auxiliary decision support system, etc. Ensure that the model can respond to user queries in real time and provide accurate first aid measures.

[0084] 9. Continuous updating: As medical knowledge is updated and clinical practice improves, the model’s data and algorithms are updated regularly to maintain the model’s accuracy and timeliness.

[0085] Through the above steps, an AI model can be trained to identify and output corresponding first aid measures, providing strong technical support for medical first aid.

[0086] As an optional implementation scheme of the present application, optionally, the policy management module is further used to: If the pre-hospital ecological management AI model outputs 1 Q(x) for the emergency patient, the Q(x) is promptly sent to the corresponding 5G emergency terminal through the message middleware; If the pre-hospital ecological management AI model outputs at least 2 Q(x)s for emergency patients, the pre-hospital emergency value level marked on each Q(x) is identified and determined, and each Q(x) is sorted in order from large to small according to the size of the pre-hospital emergency value level, and the corresponding Q(x) is promptly sent to the corresponding 5G emergency terminal through the message middleware according to the sorting.

[0087] Because early emergency patients may have several symptoms, the knowledge base is used to match the corresponding disease type for the emergency patients, and multiple symptom diseases may be matched at this time. Therefore, the model will also generate pre-hospital emergency strategies of different types and degrees for patients.

[0088] In order to take care of the pre-hospital emergency situation of emergency patients in a timely manner and avoid subsequent magnified losses, a priority model output screening method is adopted. The policy management module prioritizes and sorts the pre-hospital emergency strategies output by the AI ​​model: if the policy management module finds that the AI ​​model only outputs one pre-hospital emergency strategy, it will be directly sent to the front end; if it is found that multiple strategies are generated, the policy management module needs to identify and determine whether each pre-hospital emergency strategy has a priority label, or identify the priority size of each strategy, and sort and send the corresponding priority pre-hospital emergency strategies in the sorting order. In this way, the front-end staff can care for emergency patients according to the priority pre-hospital emergency strategy; at the same time, they can view other priority nursing and emergency strategies to prevent possible diseases of patients.

[0089] In this embodiment, the text feature extraction algorithm and other methods can be performed by the administrator using a corresponding feature extraction method, such as using a text recognition model to extract text features.

[0090] Obviously, those skilled in the art should understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory. Example 2

[0091] Based on the implementation principle of Example 1, on the other hand, this application proposes a smart pre-hospital emergency ecological management method, which is implemented based on the smart pre-hospital emergency ecological management system based on 5G described in Example 1, including the following steps: Collect pre-hospital emergency data of emergency patients through 5G emergency terminals and report them to the smart pre-hospital emergency ecological management platform through 5G gateways; The intelligent pre-hospital emergency ecological management platform intelligently analyzes the pre-hospital emergency data, predicts and outputs Q(x) and Q(y) of emergency patients, where Q(x) is the pre-hospital emergency strategy and Q(y) is the post-hospital emergency and nursing strategy; and sends Q(x) and Q(y) to the 5G emergency terminal and medical terminal through the 5G gateway respectively; The 5G emergency terminal displays the Q(x) of the emergency patient, and the emergency patient enters the pre-hospital emergency phase; When the emergency patient completes the Q(x), a request message for obtaining the Q(y) of the emergency patient is sent to the smart pre-hospital emergency ecological management platform through the medical terminal; The intelligent pre-hospital emergency ecological management platform forwards the request information to the policy management module, which retrieves the Q(y) of the emergency patient and sends the Q(y) to the corresponding medical terminal in a timely manner through the message middleware; The medical end receives and displays Q(y) of the emergency patient, and the emergency patient enters the post-hospital emergency and nursing stage.

[0092] The above steps are implemented in combination with the system working principle of Example 1, and will not be described in detail in this example.

[0093] The modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software. Example 3

[0094] like Figure 5 As shown, further, in another aspect, the present application also proposes an electronic device, including: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement the intelligent pre-hospital emergency ecological management method described in Example 2 when executing the executable instructions.

[0095] The electronic device according to the embodiment of the present disclosure includes a processor and a memory for storing processor executable instructions. The processor is configured to implement the smart pre-hospital emergency ecological management method described in Example 2 when executing the executable instructions.

[0096] Here, it should be noted that the number of processors can be one or more. At the same time, the electronic device of the embodiment of the present disclosure may also include an input device and an output device. Among them, the processor, memory, input device and output device may be connected through a bus or in other ways, which are not specifically limited here.

[0097] As a computer-readable storage medium, the memory can be used to store software programs, computer executable programs and various modules, such as the program or module corresponding to the intelligent pre-hospital emergency ecological management method of the embodiment of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0098] The input device can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output device can include a display device such as a display screen.

[0099] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A smart pre-hospital emergency ecological management system based on 5G, characterized in that: include: 5G emergency terminal, used to collect pre-hospital emergency data of emergency patients and display Q (x) of emergency patients; The 5G gateway is used to realize data communication between the 5G emergency terminal and the intelligent pre-hospital emergency ecological management platform, including: reporting the pre-hospital emergency data to the intelligent pre-hospital emergency ecological management platform; The intelligent pre-hospital emergency ecological management platform is used to intelligently analyze the pre-hospital emergency data, predict and output Q(x) and Q(y) of emergency patients, where Q(x) is the pre-hospital emergency strategy and Q(y) is the post-hospital emergency and nursing strategy; and send Q(x) and Q(y) to the 5G emergency terminal and medical terminal through the 5G gateway respectively; Medical end, used to display Q(y) of emergency patients; The 5G emergency terminal and the medical terminal are respectively communicated and connected with the intelligent pre-hospital emergency ecological management platform through the 5G gateway.

2. According to the 5G-based intelligent pre-hospital emergency ecological management system of claim 1, it is characterized in that: The smart pre-hospital emergency ecological management platform includes: A data collection unit, used to receive the pre-hospital emergency data reported by the 5G gateway and forward it to a data analysis unit; A data analysis unit, used to analyze the pre-hospital emergency data, obtain the emergency symptom data and identity information of the emergency patient, and write the emergency symptom data of the emergency patient into a preset HIS system according to the identity information; A pre-hospital emergency knowledge base, which is used to read the emergency symptom data of emergency patients, match the emergency disease types that match the emergency symptom data from the library, and import the emergency disease types into the pre-deployed pre-hospital ecological management AI model; A pre-hospital ecological management AI model is used to identify the type of emergency disease and match the Q(x) and Q(y) corresponding to the type of emergency disease; A policy management module, used to record the Q(x) and the Q(y) under the identity information of the emergency patient and write them into the HIS system; and to send the Q(x) to the corresponding 5G emergency terminal in a timely manner through the message middleware; Backend database, used for data storage.

3. According to claim 2, the intelligent pre-hospital emergency ecological management system based on 5G is characterized in that: The medical end is also used for: When the emergency patient completes the execution of Q(x), a request message for obtaining the emergency patient's Q(y) is sent to the intelligent pre-hospital emergency ecological management platform.

4. According to claim 3, the intelligent pre-hospital emergency ecological management system based on 5G is characterized in that: The smart pre-hospital emergency ecological management platform is also used for: The request information is forwarded to the policy management module, which retrieves the Q(y) of the emergency patient and sends the Q(y) to the corresponding medical end in a timely manner through the message middleware to enter the post-hospital emergency and nursing stage.

5. According to claim 2, the intelligent pre-hospital emergency ecological management system based on 5G is characterized in that: The method for generating the pre-hospital ecological management AI model includes: Collect several sets of pre-hospital emergency big data and post-hospital emergency and nursing big data corresponding to different types of emergency diseases; Perform feature engineering on the pre-hospital emergency big data to obtain the corresponding feature set, including: Extracting a first strategy feature corresponding to the emergency disease type from each pre-hospital emergency strategy of the pre-hospital emergency big data, and marking the first strategy feature with a corresponding pre-hospital emergency value level, wherein the pre-hospital emergency value level is defined by the emergency department and is used to indicate the pre-hospital emergency priority corresponding to the first strategy feature; Extracting a second strategy feature corresponding to the emergency disease type from each post-hospital emergency and nursing strategy of the post-hospital emergency and nursing big data; constructing the first strategy feature and the second strategy feature into the feature set; Dividing the feature set into a training set and a validation set according to a preset ratio; Importing the training set into a preset machine learning model to perform feature training and learning to generate the pre-hospital ecological management AI model; The validation set was used to perform performance verification on the pre-hospital ecological management AI model: If the verification is passed, the pre-hospital ecological management AI model is deployed on the intelligent pre-hospital emergency ecological management platform; On the contrary, readjust the big data and retrain.

6. The intelligent pre-hospital emergency ecological management system based on 5G according to claim 5 is characterized in that: The policy management module is further used for: If the pre-hospital ecological management AI model outputs 1 Q(x) for the emergency patient, the Q(x) is promptly sent to the corresponding 5G emergency terminal through the message middleware; If the pre-hospital ecological management AI model outputs at least 2 Q(x)s for emergency patients, the pre-hospital emergency value level marked on each Q(x) is identified and determined, and each Q(x) is sorted in order from large to small according to the size of the pre-hospital emergency value level, and the corresponding Q(x) is promptly sent to the corresponding 5G emergency terminal through the message middleware according to the sorting.

7. A smart pre-hospital emergency ecological management method, implemented based on the smart pre-hospital emergency ecological management system based on 5G as described in any one of claims 1-6, characterized in that: The steps include: Collect pre-hospital emergency data of emergency patients through 5G emergency terminals and report them to the smart pre-hospital emergency ecological management platform through 5G gateways; The intelligent pre-hospital emergency ecological management platform intelligently analyzes the pre-hospital emergency data, predicts and outputs Q(x) and Q(y) of emergency patients, where Q(x) is the pre-hospital emergency strategy and Q(y) is the post-hospital emergency and nursing strategy; and sends Q(x) and Q(y) to the 5G emergency terminal and medical terminal through the 5G gateway respectively; The 5G emergency terminal displays the Q(x) of the emergency patient, and the emergency patient enters the pre-hospital emergency phase; When the emergency patient completes the Q(x), a request message for obtaining the Q(y) of the emergency patient is sent to the smart pre-hospital emergency ecological management platform through the medical terminal; The intelligent pre-hospital emergency ecological management platform forwards the request information to the policy management module, which retrieves the Q(y) of the emergency patient and sends the Q(y) to the corresponding medical terminal in a timely manner through the message middleware; The medical end receives and displays Q(y) of the emergency patient, and the emergency patient enters the post-hospital emergency and nursing stage.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the intelligent pre-hospital emergency ecological management method described in claim 7 when executing the executable instructions.

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