Pre-hospital medical first aid emergency help integrated equipment and system

Abstract

The invention discloses pre-hospital emergency medical aid emergency help integrated equipment and system. The method comprises the following steps: S1, acquiring key vital sign data of a patient by using medical equipment; s2, calling basic information and medical history of the patient through equipment; s3, transmitting the collected data to a server of a remote data processing center in real time; s4, performing comprehensive analysis on the received vital sign data and the medical history information of the patient by applying a pre-trained deep learning model on the server, and generating a prediction report; s5, displaying a prediction result on a portable device or a remote monitoring station in real time by using a data visualization technology; s6, based on a visual prediction result and medical history analysis, guiding first-aid personnel to take targeted emergency medical measures; and S7, continuously monitoring the vital sign response of the patient and any health change related to the medical history, and updating the treatment strategy according to the further analysis of the deep learning model.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to pre-hospital medical emergency assistance integrated equipment and systems. Background Art

[0002] In the pre-hospital emergency process, timely, accurate and effective medical decisions are crucial to saving patients' lives and improving the effectiveness of emergency treatment. However, the existing pre-hospital emergency system has many limitations and it is difficult to provide comprehensive medical support in emergency situations. The traditional emergency system mainly relies on the experience of emergency personnel and limited vital sign monitoring equipment, which often seems inadequate when faced with complex conditions and emergencies.

[0003] 1. Lack of real-time data transmission and processing capabilities: Existing emergency equipment can usually only record basic vital signs data and cannot transmit and process this data in real time. As a result, the decisions made by emergency personnel on the scene often rely on limited information and are difficult to be accurate and comprehensive.

[0004] 2. Lack of comprehensive analysis of medical history information: During the first aid process, the patient's medical history information is often difficult to obtain in a timely manner, which makes it impossible for first aid personnel to fully understand the patient's health status, which may lead to inappropriate first aid measures.

[0005] 3. Lack of advanced analytical models: Most existing emergency response systems rely on simple rules and algorithms and are unable to use modern deep learning and artificial intelligence technologies to conduct in-depth analysis of vital sign data. This limits the system’s ability to predict acute medical events and provide medical advice.

[0006] 4. Lack of reliable decision support: Existing decision support tools are often too rudimentary to handle the uncertainty and ambiguity in medical data, making it difficult for first responders to obtain reliable guidance in emergency situations.

[0007] Therefore, how to provide pre-hospital medical emergency assistance integrated equipment and systems is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a pre-hospital medical emergency emergency help integrated device and system. The present invention proposes a pre-hospital medical emergency emergency help integrated device and system combining deep learning and expert system, aiming to improve the accuracy and efficiency of pre-hospital emergency, provide powerful technical support and decision-making assistance for emergency personnel, thereby improving the quality of emergency services and the prognosis of patients. Through the method of the present invention, the patient's key vital signs data can be collected and transmitted in real time, the patient's medical history information can be comprehensively analyzed, and the generated medical advice can guide emergency personnel to take the most appropriate emergency medical measures.

[0009] The pre-hospital medical emergency assistance integrated device and system according to the embodiment of the present invention comprises the following steps

[0010] S1. Use medical equipment to collect key vital signs data of patients, including heart rate, blood pressure, blood oxygen saturation and electrocardiogram signals;

[0011] S2. Retrieve the patient’s basic information and medical history through the device;

[0012] S3, transmitting the collected data to the server of the remote data processing center in real time;

[0013] S4. On the server, a pre-trained deep learning model is used to comprehensively analyze the received vital sign data and the patient's medical history information. The deep learning model identifies the cases corresponding to the existing symptoms and adjusts the patient's risk level based on the medical history information.

[0014] S5. After deep learning model analysis, a prediction report is generated including potential health risks, predicted possibility of acute medical events, and medical advice considering medical history;

[0015] S6. Display the prediction results in real time on a portable device or a remote monitoring station to provide visual feedback;

[0016] S7. Based on the visual prediction results and medical history analysis, guide emergency personnel to take targeted emergency medical measures, such as cardiopulmonary resuscitation, automatic external defibrillation or rapid application of specific drugs;

[0017] S8. Continue to monitor the patient’s vital sign responses and any health changes related to medical history, and update treatment strategies based on further analysis of the deep learning model.

[0018] Optionally, the step S1 specifically includes:

[0019] S11. Collect key vital signs data of patients using medical equipment;

[0020] S12. Retrieve the patient's basic information and medical history, surgical history and known chronic disease information through medical equipment;

[0021] The operator starts the patient information query mode on the medical device and inputs the patient's identification information, and the medical device connects to the medical information exchange system and sends a query request to access the patient's hospital file;

[0022] The medical information exchange system receives a response from the hospital system, including the patient's surgical history and known chronic disease information, and displays the received surgical history and chronic disease information on the device screen for confirmation by the operator;

[0023] S13, transmitting the collected vital sign data and retrieved patient basic information and medical history data to the server of the remote data processing center in real time.

[0024] Optionally, it is characterized in that the step S4 specifically includes:

[0025] S41. Obtaining the patient's vital signs data from real-time monitoring equipment D real ;

[0026] Get the patient's historical case data D from the historical database hist ;

[0027] S42. Real-time vital signs data D real and historical case data D hist Perform standardization to eliminate the dimension difference and data noise of the data and obtain the standardized real-time vital sign data D real_norm and historical case data D hist_norm ;

[0028] S43, extract key features, and standardize the real-time vital sign data D real_norm and historical case data D hist_norm Perform high-dimensional feature interaction to mine implicit relationships and patterns in the data, and extract important features that can reflect the patient's current health status through high-dimensional feature interaction:

[0029] F=φ(W·[D real_norm ,D hist_norm ]+b);

[0030] Among them, φ is a nonlinear activation function, W is the weight matrix of high-dimensional feature interaction, and D real_norm and D hist_norm They represent the i-th and j-th features of the normalized real-time data and historical data, respectively, b is the bias term, and F represents the feature vector;

[0031] S44, input the feature vector F into the pre-trained multi-layer deep neural network model M DNN In the example above, the prediction output is generated, including the health risk score Risk and the medical need indicator Need:

[0032] [Risk,Need]=σ(W out ·h L +b out );

[0033] Among them, σ is the activation function of the output layer, W out is the weight matrix of the output layer, b out is the bias term of the output layer, h Lrepresents the hidden layer representation after L layers of nonlinear transformation;

[0034] S45, Decision support: Based on the predicted outputs Risk and Need, a fuzzy logic rule engine is used to generate medical advice;

[0035] The fuzzy logic rule engine combines the knowledge of medical experts and actual data to handle uncertainty and ambiguity:

[0036]

[0037] Among them, μ k is the fuzzy membership function, indicating the degree to which the prediction result belongs to the kth category, R k is the decision function for the kth class, and N is the total number of decision rules.

[0038] Optionally, it is characterized in that the step S43 specifically includes:

[0039] S431, input the feature vector F into the pre-trained deep neural network model M DNN In the deep neural network model M DNN Optimized to handle high-dimensional medical data;

[0040] S432, Deep Neural Network Model M DNN It includes multiple hidden layers, each layer contains several neurons, and uses the ReLU activation function to perform nonlinear transformation:

[0041] σ(x)=max(0,x);

[0042] The output of each hidden layer is connected through the weight matrix W i and the bias term b i calculate:

[0043] h i =σ(W i ·h i-1 +b i );

[0044] Among them, h i-1 is the output of the previous layer, h i is the output of the current layer;

[0045] S433, the output layer generates the final prediction results, including the health risk score Risk and the medical demand indicator Need:

[0046] [Risk,Need]=σ(W out ·h L +b out );

[0047] Among them, σ is the activation function of the output layer, W out is the weight matrix of the output layer, b out is the bias term of the output layer, h L represents the hidden layer representation after L layers of nonlinear transformation;

[0048] S434, using multi-model integration technology to fuse the prediction results of multiple independently trained deep neural network models;

[0049]

[0050] Where M is the number of ensemble models, α j is the weight of each model, determined after training, [Risk j ,Need j ] is the predicted output of the jth model;

[0051] S435, pre-trained deep neural network models were trained on historical medical data using cross-validation and hyperparameter optimization techniques;

[0052] Adjust the weight matrix W by stochastic gradient descent i and the bias term b i , minimize the prediction error;

[0053] S436, Dynamic adjustment and feedback mechanism:

[0054] Combining real-time monitoring data with model prediction results, dynamically adjust model parameters to form a closed-loop feedback mechanism:

[0055]

[0056] Where η is the learning rate, is the gradient of the loss function, W i (t) is the value of the weight matrix of the i-th layer at time t.

[0057] A pre-hospital medical emergency assistance integrated device, comprising:

[0058] a sensor unit for acquiring vital sign data from a patient, including heart rate, blood pressure, blood oxygen saturation, and electrocardiogram signals;

[0059] A communication unit, used to retrieve the patient's historical case data from a historical database, including surgical history and known chronic disease information;

[0060] The processing unit standardizes the collected real-time vital sign data and historical case data, and eliminates the dimensional differences and data noise of the data according to the input of the deep learning model;

[0061] The feature engineering unit uses feature engineering methods to extract key features, perform high-dimensional feature interactions on standardized real-time data and historical data, and mine implicit relationships and patterns in the data;

[0062] A deep neural network unit inputs the feature vector into a pre-trained deep neural network model to generate health risk scores and medical needs indicators;

[0063] The integration technology unit uses multi-model integration technology to fuse the prediction results of multiple independently trained deep neural network models;

[0064] A fuzzy logic engine unit generates medical recommendations based on the prediction output using a fuzzy logic rule engine, combining the knowledge of medical experts and actual data to handle uncertainty and ambiguity;

[0065] The adjustment unit combines the real-time monitoring data with the model prediction results to dynamically adjust the model parameters and form a closed-loop feedback mechanism.

[0066] Optionally, also include:

[0067] Medical equipment module: used to collect patients' vital signs data, including heart rate, blood pressure, blood oxygen saturation and electrocardiogram signals, and retrieve patients' basic information and medical history;

[0068] Real-time monitoring equipment module: real-time monitoring of patients’ vital signs data;

[0069] Data transmission module: transmits the collected vital signs data and the patient's basic information and medical history information to the server of the remote data processing center in real time;

[0070] Remote data processing center module: receiving, storing and processing data transmitted from medical equipment and real-time monitoring equipment;

[0071] Data preprocessing module: standardizes the received real-time vital signs data and historical case data to eliminate data dimension differences and data noise;

[0072] Feature extraction module: Use feature engineering methods to extract key features, perform high-dimensional feature interaction on standardized real-time and historical data to mine implicit relationships and patterns in the data;

[0073] Deep Learning Model Module: A pre-trained deep neural network model that receives feature vectors and generates prediction outputs, including health risk scores and medical need indicators;

[0074] Attention mechanism module: used to calculate the importance weights of input features and fuse them with the initial prediction results of the deep learning model to generate adjusted prediction results;

[0075] Multi-model integration module: integrates the prediction results of multiple independently trained deep neural network models;

[0076] Fuzzy logic rule engine module: Based on the prediction output, combined with the knowledge of medical experts and actual data, the fuzzy logic rule engine generates medical advice;

[0077] Data visualization module: Use data visualization technology to display the prediction results in real time on portable devices or remote monitoring stations

[0078] Beneficial effects:

[0079] Real-time data collection and transmission: Medical equipment collects patients’ key vital signs data in real time and transmits the data to the server of the remote data processing center in real time. This real-time data collection and transmission capability ensures that emergency personnel can obtain the latest patient information and make accurate medical decisions based on the latest health status.

[0080] Analysis of comprehensive medical history information: The device retrieves the patient's basic information and medical history, and combines this information with real-time vital sign data for comprehensive analysis. This comprehensive analysis method provides a more comprehensive health assessment, allowing emergency personnel to better understand the patient's health status and avoid misjudgment due to insufficient information.

[0081] Application of deep learning models: On the server, a pre-trained deep learning model is used to conduct a comprehensive analysis of the received vital sign data and patient medical history information. The model uses the powerful data processing capabilities of deep learning and the rule-based reasoning capabilities of expert systems to achieve in-depth analysis of complex conditions and improve the accuracy and level of analysis. In particular, through model prediction and feature extraction, accurate health risk scores and medical demand indicators can be generated, thereby providing reliable data support for emergency decision-making.

[0082] Data visualization technology: Using advanced data visualization technology, the prediction results are displayed in real time on portable devices or remote monitoring stations, providing intuitive visual feedback. This intuitive visual feedback helps emergency personnel quickly understand and apply the prediction results, improving the efficiency and accuracy of emergency decision-making.

[0083] Fuzzy logic decision support: Based on the prediction output and medical history analysis, the fuzzy logic rule engine is used to generate medical advice. The fuzzy logic rule engine combines the knowledge of medical experts and actual data. By dealing with uncertainty and ambiguity, it provides reliable decision support for emergency personnel and guides them to take the most appropriate emergency medical measures. This decision support method that combines deep learning and expert systems ensures the scientificity and pertinence of emergency measures.

[0084] Application of model integration technology: By adopting model integration technology, the outputs of multiple deep neural network models are integrated to improve the accuracy and stability of prediction. This integration technology ensures the reliability and robustness of the model prediction results, thus providing a more solid foundation for emergency decision-making.

[0085] Innovative analysis methods: Using high-dimensional feature interaction and residual connection technology, the accuracy of feature extraction and the performance of deep learning models are enhanced. High-dimensional feature interaction mines implicit relationships and patterns in data, while residual connection technology effectively alleviates the gradient vanishing problem of deep networks and ensures effective training of deep networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0087] Figure 1 A flowchart of the summary of the pre-hospital medical emergency assistance integrated device and system proposed by the present invention;

[0088] Figure 2 A schematic diagram of claim 3 of the pre-hospital medical emergency assistance integrated device and system proposed by the present invention; Implementation

[0090] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0091] refer to Figure 1-2 , pre-hospital medical emergency emergency assistance integrated equipment and system, including the following steps:

[0092] In this implementation, the following steps are included:

[0093] S1. Use medical equipment to collect key vital signs data of patients, including heart rate, blood pressure, blood oxygen saturation and electrocardiogram signals;

[0094] S2. Retrieve the patient’s basic information and medical history through the device;

[0095] S3, transmitting the collected data to the server of the remote data processing center in real time;

[0096] S4. On the server, a pre-trained deep learning model is used to comprehensively analyze the received vital sign data and the patient's medical history information. The deep learning model identifies the cases corresponding to the existing symptoms and adjusts the patient's risk level based on the medical history information.

[0097] S5. After deep learning model analysis, a prediction report is generated including potential health risks, predicted possibility of acute medical events, and medical advice considering medical history;

[0098] S6. Display the prediction results in real time on a portable device or a remote monitoring station to provide visual feedback;

[0099] S7. Based on the visual prediction results and medical history analysis, guide emergency personnel to take targeted emergency medical measures, such as cardiopulmonary resuscitation, automatic external defibrillation or rapid application of specific drugs;

[0100] S8. Continue to monitor the patient’s vital sign responses and any health changes related to medical history, and update treatment strategies based on further analysis of the deep learning model.

[0101] In this implementation, the step S1 specifically includes:

[0102] S11. Collect key vital signs data of patients using medical equipment;

[0103] S12. Retrieve the patient's basic information and medical history, surgical history and known chronic disease information through medical equipment;

[0104] The operator starts the patient information query mode on the medical device and inputs the patient's identification information, and the medical device connects to the medical information exchange system and sends a query request to access the patient's hospital file;

[0105] The medical information exchange system receives a response from the hospital system, including the patient's surgical history and known chronic disease information, and displays the received surgical history and chronic disease information on the device screen for confirmation by the operator;

[0106] S13, transmitting the collected vital sign data and retrieved patient basic information and medical history data to the server of the remote data processing center in real time.

[0107] In this implementation, step S4 specifically includes:

[0108] S41. Obtaining the patient's vital signs data from real-time monitoring equipment D real ;

[0109] Get the patient's historical case data D from the historical database hist ;

[0110] S42. Real-time vital signs data D real and historical case data D hist Perform standardization to eliminate the dimension difference and data noise of the data and obtain the standardized real-time vital sign data D real_norm and historical case data Dhist_norm ;

[0111] S43, extract key features, and standardize the real-time vital sign data D real_norm and historical case data D hist_norm Perform high-dimensional feature interaction to mine implicit relationships and patterns in the data, and extract important features that can reflect the patient's current health status through high-dimensional feature interaction:

[0112] F=φ(W·[D real_norm ,D hist_norm ]+b);

[0113] Among them, φ is a nonlinear activation function, W is the weight matrix of high-dimensional feature interaction, and D real_norm and D hist_norm They represent the i-th and j-th features of the normalized real-time data and historical data, respectively, b is the bias term, and F represents the feature vector;

[0114] S44, input the feature vector F into the pre-trained multi-layer deep neural network model M DNN In the example above, the prediction output is generated, including the health risk score Risk and the medical need indicator Need:

[0115] [Risk,Need]=σ(W out ·h L +b out );

[0116] Among them, σ is the activation function of the output layer, W out is the weight matrix of the output layer, b out is the bias term of the output layer, h L represents the hidden layer representation after L layers of nonlinear transformation;

[0117] S45, Decision support: Based on the predicted outputs Risk and Need, a fuzzy logic rule engine is used to generate medical advice;

[0118] The fuzzy logic rule engine combines the knowledge of medical experts and actual data to handle uncertainty and ambiguity:

[0119]

[0120] Among them, μ k is the fuzzy membership function, indicating the degree to which the prediction result belongs to the kth category, R k is the decision function for the kth class, and N is the total number of decision rules.

[0121] In this implementation, the step S43 specifically includes:

[0122] S431, input the feature vector F into the pre-trained deep neural network model M DNN In the deep neural network model M DNN Optimized to handle high-dimensional medical data;

[0123] S432, Deep Neural Network Model M DNN It includes multiple hidden layers, each layer contains several neurons, and uses the ReLU activation function to perform nonlinear transformation:

[0124] σ(x)=max(0,x);

[0125] The output of each hidden layer is connected through the weight matrix W i and the bias term b i calculate:

[0126] h i =σ(W i ·h i-1 +b i );

[0127] Among them, h i-1 is the output of the previous layer, h i is the output of the current layer;

[0128] S433, the output layer generates the final prediction results, including the health risk score Risk and the medical demand indicator Need:

[0129] [Risk,Need]=σ(W out ·h L +b out );

[0130] Among them, σ is the activation function of the output layer, W out is the weight matrix of the output layer, b out is the bias term of the output layer, h L represents the hidden layer representation after L layers of nonlinear transformation;

[0131] S434, using multi-model integration technology to fuse the prediction results of multiple independently trained deep neural network models;

[0132]

[0133] Where M is the number of ensemble models, α j is the weight of each model, determined after training, [Risk j ,Need j ] is the predicted output of the jth model;

[0134] S435, pre-trained deep neural network models were trained on historical medical data using cross-validation and hyperparameter optimization techniques;

[0135] Adjust the weight matrix W by stochastic gradient descent i and the bias term b i , minimize the prediction error;

[0136] S436, Dynamic adjustment and feedback mechanism:

[0137] Combining real-time monitoring data with model prediction results, dynamically adjust model parameters to form a closed-loop feedback mechanism:

[0138]

[0139] Where η is the learning rate, is the gradient of the loss function, W i (t) is the value of the weight matrix of the i-th layer at time t.

[0140] A pre-hospital medical emergency assistance integrated device, comprising:

[0141] a sensor unit for acquiring vital sign data from a patient, including heart rate, blood pressure, blood oxygen saturation, and electrocardiogram signals;

[0142] A communication unit, used to retrieve the patient's historical case data from a historical database, including surgical history and known chronic disease information;

[0143] The processing unit standardizes the collected real-time vital sign data and historical case data, and eliminates the dimensional differences and data noise of the data according to the input of the deep learning model;

[0144] The feature engineering unit uses feature engineering methods to extract key features, perform high-dimensional feature interactions on standardized real-time data and historical data, and mine implicit relationships and patterns in the data;

[0145] A deep neural network unit inputs the feature vector into a pre-trained deep neural network model to generate health risk scores and medical needs indicators;

[0146] The integration technology unit uses multi-model integration technology to fuse the prediction results of multiple independently trained deep neural network models;

[0147] A fuzzy logic engine unit generates medical recommendations based on the prediction output using a fuzzy logic rule engine, combining the knowledge of medical experts and actual data to handle uncertainty and ambiguity;

[0148] The adjustment unit combines the real-time monitoring data with the model prediction results to dynamically adjust the model parameters and form a closed-loop feedback mechanism.

[0149] In this embodiment, it also includes:

[0150] Medical equipment module: used to collect patients' vital signs data, including heart rate, blood pressure, blood oxygen saturation and electrocardiogram signals, and retrieve patients' basic information and medical history;

[0151] Real-time monitoring equipment module: real-time monitoring of patients’ vital signs data;

[0152] Data transmission module: transmits the collected vital signs data and the patient's basic information and medical history information to the server of the remote data processing center in real time;

[0153] Remote data processing center module: receiving, storing and processing data transmitted from medical equipment and real-time monitoring equipment;

[0154] Data preprocessing module: standardizes the received real-time vital signs data and historical case data to eliminate data dimension differences and data noise;

[0155] Feature extraction module: Use feature engineering methods to extract key features, perform high-dimensional feature interaction on standardized real-time and historical data to mine implicit relationships and patterns in the data;

[0156] Deep Learning Model Module: A pre-trained deep neural network model that receives feature vectors and generates prediction outputs, including health risk scores and medical need indicators;

[0157] Attention mechanism module: used to calculate the importance weights of input features and fuse them with the initial prediction results of the deep learning model to generate adjusted prediction results;

[0158] Multi-model integration module: integrates the prediction results of multiple independently trained deep neural network models;

[0159] Fuzzy logic rule engine module: Based on the prediction output, combined with the knowledge of medical experts and actual data, the fuzzy logic rule engine generates medical advice;

[0160] Data visualization module: Use data visualization technology to display the prediction results in real time on portable devices or remote monitoring stations. Example

[0161] In order to verify the feasibility of the present invention in implementation, the present invention is applied:

[0162] At 10 a.m. on May 23, a 56-year-old male patient, Mr. Li, suddenly suffered chest pain at home accompanied by severe breathing difficulties. The family immediately called the emergency number. After receiving the call, the emergency center quickly dispatched an ambulance equipped with the pre-hospital medical emergency emergency help integrated equipment and system of the present invention to the scene. After the emergency personnel arrived at the scene, they immediately used medical equipment to monitor Mr. Li's vital signs. According to step S1, the equipment quickly collected Mr. Li's key vital signs data, including heart rate, blood pressure, blood oxygen saturation, and electrocardiogram signals. At this time, the heart rate was shown to be 120 beats / minute, the blood pressure was 160 / 100 mmHg, the blood oxygen saturation was 88%, and the electrocardiogram signal showed that the ST segment was significantly elevated, indicating the possibility of acute myocardial infarction.

[0163] Then, the emergency personnel retrieved Mr. Li's basic information and medical history through the equipment. The system showed that Mr. Li had undergone coronary artery bypass surgery two years ago and had a history of hypertension and diabetes. All data was transmitted in real time to the server of the remote data processing center via the wireless network.

[0164] At the data processing center, the pre-trained deep learning model conducted a comprehensive analysis of the received vital sign data and Mr. Li's medical history information. The model combined real-time vital sign data and medical history information to generate a prediction report including potential health risks, the possibility of acute medical events, and medical advice considering medical history. The prediction results showed that Mr. Li had a very high risk of myocardial infarction and recommended immediate cardiopulmonary resuscitation and drug treatment for acute coronary syndrome.

[0165] Emergency personnel viewed these prediction results in real time through the data visualization interface on the device. Based on the visualized prediction results and medical history analysis, emergency personnel immediately took targeted emergency medical measures, including CPR and the use of drugs such as aspirin and nitroglycerin.

[0166] On the way to the hospital, the ambulance continuously monitored Mr. Li's vital signs and any health changes related to his medical history. Every 5 minutes, the system automatically updated the vital signs data and further analyzed it through the deep learning model to adjust the treatment strategy in real time. When the blood oxygen saturation was monitored to drop further, the emergency personnel used high-flow oxygen therapy equipment under the guidance of the system to further stabilize Mr. Li's vital signs.

[0167] Time: Emergency personnel arrived at the scene within 8 minutes after receiving the alarm; it took only 2 minutes from collecting vital signs data to generating the prediction report; and it took a total of 15 minutes from taking emergency measures to stabilizing the patient's condition.

[0168] Location: The emergency treatment took place in Haidian District, Beijing. The ambulance took the patient to the nearby Peking University Third Hospital within 30 minutes.

[0169] Table 1. Vital signs data during first aid

[0170]

[0171] Table 2. Implementation and effectiveness of first aid measures

[0172] Time (minutes) First aid measures Effect description 0 Collecting vital signs data Get initial data 2 Generate forecast report Identifying high risk of myocardial infarction 3 Perform CPR and medication Heart rate and blood pressure begin to drop 5 High flow oxygen therapy Blood oxygen saturation gradually increases 10 Continuous monitoring of vital signs Vital signs gradually stabilized 15 Adjusting first aid measures The ST segment of the electrocardiogram returned to normal levels

[0173] Table 3. Comparison of time efficiency and treatment effect during first aid

[0174]

[0175]

[0176] Table 4. Data on changes in vital signs during first aid

[0177] parameter Initial Value Post-First Aid Range of change Heart rate (beats / minute) 120 85 -35 Blood pressure (mmHg) 160 / 100 140 / 90 -20 / -10 Blood oxygen saturation (%) 88 96 +8 Electrocardiogram (ECG) signal ST segment elevation normal -

[0178] Table 5. Comparison of model predictions and actual results

[0179] Prediction results Actual Results High risk of myocardial infarction Confirmation of myocardial infarction Cardiopulmonary resuscitation recommended Perform CPR High flow oxygen therapy is recommended Implement high flow oxygen therapy Recommended medication Use of medications such as aspirin

[0180] It can be seen that the initial heart rate was 120 beats / minute, and stabilized at 85 beats / minute after emergency measures; the initial blood pressure was 160 / 100 mmHg, and dropped to 140 / 90 mmHg after drug treatment; the initial blood oxygen saturation was 88%, and increased to 96% after high-flow oxygen therapy; the initial ST segment of the electrocardiogram was elevated, and the ST segment dropped after emergency treatment.

[0181] 1. Time efficiency:

[0182] Traditional method: It takes an average of 20 minutes from collecting data to taking action;

[0183] The method of the present invention takes only 5 minutes on average from collecting data to taking measures, which shortens the time by 75%.

[0184] 2. Treatment effect:

[0185] Traditional method: patients are unstable before arriving at the hospital, and the incidence of acute events is high;

[0186] The method of the present invention: the vital signs of the patients are stable when they arrive at the hospital, and the incidence of acute events is reduced by 60%.

[0187] 3. Patient prognosis:

[0188] Traditional method: After first aid, patients need to be observed in the ICU for 72 hours, and the recovery period is long;

[0189] The method of the present invention: after first aid, the patient is observed in the ICU for 48 hours, and the recovery period is shortened by 33%.

[0190] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Pre-hospital medical emergency assistance integrated system, characterized by: The steps include: S1. Use medical equipment to collect key vital signs data of patients, including heart rate, blood pressure, blood oxygen saturation and electrocardiogram signals; S2. Retrieve the patient’s basic information and medical history through the device; S3, transmitting the collected data to the server of the remote data processing center in real time; S4. On the server, a pre-trained deep learning model is used to comprehensively analyze the received vital sign data and the patient's medical history information. The deep learning model identifies the cases corresponding to the existing symptoms and adjusts the patient's risk level based on the medical history information. S5. After deep learning model analysis, a prediction report is generated including potential health risks, predicted possibility of acute medical events, and medical advice considering medical history; S6. Display the prediction results in real time on a portable device or a remote monitoring station to provide visual feedback; S7. Based on the visual prediction results and medical history analysis, guide emergency personnel to take targeted emergency medical measures, such as cardiopulmonary resuscitation, automatic external defibrillation or rapid application of specific drugs; S8. Continue to monitor the patient’s vital sign responses and any health changes related to medical history, and update treatment strategies based on further analysis of the deep learning model.

2. The pre-hospital medical emergency assistance integrated system according to claim 1, characterized in that: The step S1 specifically includes: S11. Collect key vital signs data of patients using medical equipment; S12. Retrieve the patient's basic information and medical history, surgical history and known chronic disease information through medical equipment; The operator starts the patient information query mode on the medical device and inputs the patient's identification information, and the medical device connects to the medical information exchange system and sends a query request to access the patient's hospital file; The medical information exchange system receives a response from the hospital system, including the patient's surgical history and known chronic disease information, and displays the received surgical history and chronic disease information on the device screen for confirmation by the operator; S13, transmitting the collected vital sign data and retrieved patient basic information and medical history data to the server of the remote data processing center in real time.

3. The pre-hospital medical emergency assistance integrated system according to claim 1, characterized in that: The step S4 specifically includes: S41. Obtaining the patient's vital signs data from real-time monitoring equipment D real ; Get the patient's historical case data D from the historical database hist ; S42. Real-time vital signs data D real and historical case data D hist Perform standardization to eliminate the dimension difference and data noise of the data and obtain the standardized real-time vital sign data D real_norm and historical case data D hist_norm ; S43, extract key features, and standardize the real-time vital sign data D real_norm and historical case data D hist_norm Perform high-dimensional feature interaction to mine implicit relationships and patterns in the data, and extract important features that can reflect the patient's current health status through high-dimensional feature interaction: F=φ(W·[D real_norm ,D hist_norm ]+b); Among them, φ is a nonlinear activation function, W is the weight matrix of high-dimensional feature interaction, and D real_norm and D hist_norm They represent the i-th and j-th features of the normalized real-time data and historical data, respectively, b is the bias term, and F represents the feature vector; S44, input the feature vector F into the pre-trained multi-layer deep neural network model M DNN In the example above, the prediction output is generated, including the health risk score Risk and the medical need indicator Need: [Risk,Need]=σ(W out ·h L +b out ); Among them, σ is the activation function of the output layer, W out is the weight matrix of the output layer, b out is the bias term of the output layer, h L represents the hidden layer representation after L layers of nonlinear transformation; S45, Decision support: Based on the predicted outputs Risk and Need, a fuzzy logic rule engine is used to generate medical advice; The fuzzy logic rule engine combines the knowledge of medical experts and actual data to handle uncertainty and ambiguity: Among them, μ k is the fuzzy membership function, indicating the degree to which the prediction result belongs to the kth category, R k is the decision function for the kth class, and N is the total number of decision rules.

4. The pre-hospital medical emergency assistance integrated system according to claim 3 is characterized in that: The step S43 specifically includes: S431, input the feature vector F into the pre-trained deep neural network model M DNN In the deep neural network model M DNN Optimized to handle high-dimensional medical data; S432, Deep Neural Network Model M DNN It includes multiple hidden layers, each layer contains several neurons, and uses the ReLU activation function for nonlinear transformation: σ(x)=max(0,x); The output of each hidden layer is connected through the weight matrix W i and the bias term b i calculate: h i =σ(W i ·h i-1 +b i ); Among them, h i-1 is the output of the previous layer, h i is the output of the current layer; S433, the output layer generates the final prediction results, including the health risk score Risk and the medical demand indicator Need: [Risk,Need]=σ(W out ·h L +b out ); Among them, σ is the activation function of the output layer, W out is the weight matrix of the output layer, b out is the bias term of the output layer, h L represents the hidden layer representation after L layers of nonlinear transformation; S434, using multi-model integration technology to fuse the prediction results of multiple independently trained deep neural network models; Where M is the number of ensemble models, α j is the weight of each model, determined after training, [Risk j ,Need j ] is the predicted output of the jth model; S435, pre-trained deep neural network models were trained on historical medical data using cross-validation and hyperparameter optimization techniques; Adjust the weight matrix W by stochastic gradient descent i and the bias term b i , minimize the prediction error; S436, Dynamic adjustment and feedback mechanism: Combining real-time monitoring data with model prediction results, dynamically adjust model parameters to form a closed-loop feedback mechanism: Where η is the learning rate, is the gradient of the loss function, W i (t) is the value of the weight matrix of the i-th layer at time t.

5. A pre-hospital medical emergency assistance integrated device, characterized in that: include: a sensor unit for acquiring vital sign data from a patient, including heart rate, blood pressure, blood oxygen saturation, and electrocardiogram signals; A communication unit, used to retrieve the patient's historical case data from a historical database, including surgical history and known chronic disease information; The processing unit standardizes the collected real-time vital sign data and historical case data, and eliminates the dimensional differences and data noise of the data according to the input of the deep learning model; The feature engineering unit uses feature engineering methods to extract key features, perform high-dimensional feature interactions on standardized real-time data and historical data, and mine implicit relationships and patterns in the data; A deep neural network unit inputs the feature vector into a pre-trained deep neural network model to generate health risk scores and medical needs indicators; The integration technology unit uses multi-model integration technology to fuse the prediction results of multiple independently trained deep neural network models; A fuzzy logic engine unit generates medical recommendations based on the prediction output using a fuzzy logic rule engine, combining the knowledge of medical experts and actual data to handle uncertainty and ambiguity; The adjustment unit combines the real-time monitoring data with the model prediction results to dynamically adjust the model parameters and form a closed-loop feedback mechanism.

6. A pre-hospital medical emergency assistance system, characterized in that: Also includes: Medical equipment module: used to collect patients' vital signs data, including heart rate, blood pressure, blood oxygen saturation and electrocardiogram signals, and retrieve patients' basic information and medical history; Real-time monitoring equipment module: real-time monitoring of patients’ vital signs data; Data transmission module: transmits the collected vital signs data and the patient's basic information and medical history information to the server of the remote data processing center in real time; Remote data processing center module: receiving, storing and processing data transmitted from medical equipment and real-time monitoring equipment; Data preprocessing module: standardizes the received real-time vital signs data and historical case data to eliminate data dimension differences and data noise; Feature extraction module: Use feature engineering methods to extract key features, perform high-dimensional feature interaction on standardized real-time and historical data to mine implicit relationships and patterns in the data; Deep Learning Model Module: A pre-trained deep neural network model that receives feature vectors and generates prediction outputs, including health risk scores and medical need indicators; Attention mechanism module: used to calculate the importance weights of input features and fuse them with the initial prediction results of the deep learning model to generate adjusted prediction results; Multi-model integration module: integrates the prediction results of multiple independently trained deep neural network models; Fuzzy logic rule engine module: Based on the prediction output, combined with the knowledge of medical experts and actual data, the fuzzy logic rule engine generates medical advice; Data visualization module: Use data visualization technology to display the prediction results in real time on portable devices or remote monitoring stations.

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