ICU emergency response system

Abstract

The invention relates to the technical field of medical emergency monitoring, in particular to an ICU emergency response system which comprises a preliminary risk assessment module, an emergency recognition module, a treatment scheme optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module and a medical decision support module. According to the method, feature engineering and logistic regression analysis are combined with medical history and real-time vital signs of a patient, emergency medical events are recognized by applying pattern recognition and natural language processing, a treatment scheme is optimized by using a support vector machine and a decision tree, personalized treatment suggestions are provided, and the time sequence and a biometric method are used for finely monitoring the vital signs; health risks are early warned, resources are reasonably allocated in emergency response through a resource scheduling algorithm and a priority queue, illness state prediction is assisted through machine learning and probability statistics, resource utilization is improved through linear programming and a resource optimization strategy, and waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of medical emergency monitoring, and particularly to an ICU emergency response system. Background Art

[0002] The technical field of medical emergency monitoring focuses on the development and use of advanced systems and devices for real-time monitoring of the health status of patients in critical medical environments such as intensive care units (ICUs). The system is designed to improve the response speed and efficiency to sudden emergency medical conditions of patients, including various vital sign monitoring devices (such as heart rate, blood pressure, and oxygen saturation monitors), as well as software for analyzing data and issuing alarms when key parameters are abnormal. The aim is to reduce patient mortality and complications and improve the overall quality of medical care through continuous monitoring and rapid response mechanisms.

[0003] Among them, the ICU emergency response system is used to monitor the vital signs and health status of patients in the intensive care unit. The main purpose of the system is to provide real-time and continuous patient monitoring so that in the event of any potentially life-threatening medical emergency, medical professionals can be notified promptly. The goal is to minimize the response time to events that pose a threat to the patient's life, thereby increasing the patient's survival rate and recovery chances. These systems usually include various vital sign monitoring devices, alarm systems, and communication tools to ensure a quick and effective response at critical moments.

[0004] Traditional systems have deficiencies in actual operation. The risk assessment method lacks the comprehensive analysis ability of big data, resulting in inaccurate assessment. The identification and response to emergencies rely on manual judgment, leading to extended response time and low efficiency. The optimization of treatment plans fails to fully consider the individual differences of patients, and treatment suggestions lack personalization. The intelligent level of the vital sign monitoring and early warning system is insufficient, making it difficult to predict and respond to potential health risks in a timely manner. In terms of resource allocation and management, traditional methods fail to achieve the optimal allocation of resources, resulting in resource waste or shortage. Medical decision support lacks effective data support and scientific decision-making models, affecting the accuracy and effectiveness of decisions. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an ICU emergency response system.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: The ICU emergency response system includes a preliminary risk assessment module, an emergency identification module, a treatment plan optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation and management module, and a medical decision support module;

[0007] The preliminary risk assessment module integrates data, extracts key indicators, and analyzes the patient's risk rating through a risk calculation model based on the patient's medical history and real-time vital signs, using feature engineering methods and logistic regression analysis;

[0008] The emergency situation identification module automatically identifies emergency medical events, analyzes text data, and labels the events through a classification algorithm based on the patient risk rating, using pattern recognition algorithms and natural language processing models to generate emergency medical event identifiers;

[0009] The treatment plan optimization module analyzes the patient's condition, evaluates the treatment plan, and adjusts the treatment plan through an optimization algorithm based on the emergency medical event identifier, using support vector machines and decision tree algorithms to generate personalized treatment recommendations;

[0010] The vital sign monitoring module monitors the patient's vital signs in real time, analyzes the data, and evaluates through a health trend prediction model based on the personalized treatment recommendation, using time series analysis and biometric methods to generate vital sign monitoring results;

[0011] The emergency response coordination module plans the response to emergencies, dynamically allocates medical resources and personnel, and adjusts the response strategy through an efficiency optimization model based on the vital sign monitoring results, using resource scheduling algorithms and priority queue techniques to generate an emergency response plan;

[0012] The disease evolution prediction module analyzes the data of the patient's disease development trend, assesses the risk, and predicts the disease change through a trend prediction algorithm based on the emergency response plan, using machine learning prediction models and probability and statistics methods to generate a disease evolution prediction;

[0013] The resource allocation and management module allocates medical resources, plans management strategies, and optimizes resource allocation through a resource adjustment model based on the disease evolution prediction, using linear programming algorithms and resource optimization strategies to generate a resource allocation plan;

[0014] The medical decision-making support module conducts comprehensive medical decision-making analysis and strategy evaluation, and provides medical advice through a decision-making model based on the resource allocation plan, using expert systems and clinical pathway analysis to generate clinical decision-making support information.

[0015] As a further solution of the present invention, the patient risk rating includes a cardiovascular risk index, an infection risk score, and a postoperative complication risk; the emergency medical event identifier includes an acute cardiac event, acute respiratory failure, and severe trauma classification; the personalized treatment recommendation includes drug dose adjustment, surgical intervention timing, and physical therapy plan; the vital sign monitoring result includes heart rate variability analysis, continuous blood pressure monitoring result, and respiratory pattern chart; the emergency response plan includes first aid team dispatch, emergency supply of equipment and drugs, and ward transfer priority; the disease evolution prediction includes disease deterioration risk assessment, expected recovery time, and estimated complication risk; the resource allocation plan includes ICU bed allocation, key medical equipment dispatch, and medical staff shift arrangement; the clinical decision support information includes individualized treatment recommendation, risk control measures, and long-term care planning.

[0016] As a further solution of the present invention, the preliminary risk assessment module includes a feature analysis sub-module, a risk calculation sub-module, and a health data analysis sub-module;

[0017] Based on the patient's medical history, the feature analysis sub-module uses the principal component analysis algorithm to calculate the covariance matrix of the original data set, extract the principal components, perform data dimensionality reduction, and then uses the decision tree algorithm to generate key health indicators according to the information gain and Gini coefficient.

[0018] Based on the key health indicators, the risk calculation sub-module uses a logistic regression model to model the relationship between features and patient risks, performs risk scoring using probability distributions, and combines a random forest model to improve the accuracy and stability of risk prediction through the ensemble learning of multiple decision trees, generating a risk assessment score.

[0019] Based on the risk assessment score and real-time vital signs, the health data analysis sub-module uses time series analysis methods to analyze the temporal characteristics of vital sign data, reveals the dynamic change trend of the health status, and applies a support vector machine model to classify the patient's health status by constructing an optimal separation hyperplane, generating a patient risk rating.

[0020] As a further solution of the present invention, the emergency situation identification module includes a pattern recognition sub-module, a language processing sub-module, and a clinical manifestation analysis sub-module;

[0021] The pattern recognition sub-module, based on the patient risk rating, uses the support vector machine algorithm to map the patient's vital signs and clinical data in a multi-dimensional feature space, separates the data of different emergency situations using a hyperplane, identifies potential emergency medical conditions, and uses the random forest algorithm to train the data set by constructing multiple decision trees and summarize the prediction results of each tree to improve the accuracy and robustness of pattern recognition and generate an emergency situation feature analysis;

[0022] The language processing sub-module, based on the emergency situation feature analysis, uses the BERT model to pre-train context-related word embeddings from a batch of unlabeled text through deep bidirectional representation, analyzes the patient's electronic health records and the doctor's notes, and uses the LSTM model to capture the long-term dependencies in the text data through a time-recursive network, extracts key clinical information, and generates a clinical information extraction result;

[0023] The clinical manifestation analysis sub-module, based on the clinical information extraction result, uses the K-nearest neighbor algorithm to classify new instances according to the classified instances, determines the nearest neighbor by measuring the distance between different instances, and uses the decision tree classification algorithm to classify and label the instances according to the data attributes in a top-down recursive manner to determine the category and priority of the emergency medical event and generate an emergency medical event identifier.

[0024] As a further solution of the present invention, the treatment plan optimization module includes a decision algorithm sub-module, a path optimization sub-module, and a personalized treatment sub-module;

[0025] The decision algorithm sub-module, based on the emergency medical event identifier, uses the support vector machine algorithm to analyze the patient's vital signs and clinical data, divides different treatment requirement categories by capturing the interval boundaries between the data, and then uses the decision tree algorithm to construct a tree structure according to the patient's age and disease history and recommend a suitable treatment strategy for each patient in a step-by-step refinement manner to generate a preliminary treatment decision plan;

[0026] The path optimization sub-module, based on the preliminary treatment decision plan, uses the genetic algorithm to optimize the patient's treatment path by simulating the selection, crossover, and mutation operations in the biological evolution process, captures the treatment process with the maximum cost-benefit, and at the same time applies the simulated annealing algorithm to randomly search in the solution space to avoid local optimal solutions, and generates a treatment path optimization plan with reference to treatment efficiency and resource utilization;

[0027] The personalized treatment sub-module, based on the treatment path optimization plan, applies the Bayesian network to calculate the success probabilities of multiple treatment plans according to the patient's personal characteristics and disease history, combines multi-criteria decision analysis, comprehensively evaluates the benefits and risks of each treatment plan, and generates personalized treatment recommendations.

[0028] As a further solution of the present invention, the vital sign monitoring module includes a real-time monitoring sub-module, a parameter analysis sub-module, and a treatment effect evaluation sub-module;

[0029] Based on the personalized treatment suggestions, the real-time monitoring sub-module uses a long short-term memory network to perform layer-by-layer neural network analysis on time series data. By extracting time correlation features layer by layer and retaining the long-term memory of the features in the network, it captures the subtle changes and long-term trends in the vital sign data, monitors the patient's vital signs in real time, and generates real-time vital sign data;

[0030] Based on the real-time vital sign data, the parameter analysis sub-module uses multivariate regression analysis, including establishing a statistical model to analyze the interaction between multiple variables, and at the same time referring to the influence of multiple factors on the patient's health status, to identify the key parameters affecting the health status and generate an analysis of key health parameters;

[0031] Based on the analysis of key health parameters, the treatment effect evaluation sub-module uses a random forest model to construct multiple decision trees, and combines the prediction results of the decision trees to analyze the treatment effect, avoid the deviation of a single model, and generate vital sign monitoring results.

[0032] As a further solution of the present invention, the emergency response coordination module includes a scheduling algorithm sub-module, a team coordination sub-module, and a resource optimization sub-module;

[0033] Based on the vital sign monitoring results, the scheduling algorithm sub-module uses an integer linear programming algorithm for medical resource scheduling. By defining the objective function and constraints of resource allocation, including resource quantity limits and emergency response time, it uses optimization techniques to determine the resource allocation plan. At the same time, it applies the priority queue technique to sort according to the urgency and treatment needs of the patients, and gives priority to allocating resources to critical patients, generating a preliminary resource scheduling plan;

[0034] Based on the preliminary resource scheduling plan, the team coordination sub-module uses a network flow algorithm to optimize the collaboration process of the medical team, dynamically adjusts the division of labor and positions of team members according to the urgency and complexity of medical tasks, and at the same time analyzes the professional skills and work experience of team members, and assigns each emergency medical event to a matching team for handling, generating a team coordination optimization plan;

[0035] Based on the team coordination optimization plan, the resource optimization sub-module uses a simulated annealing algorithm to globally optimize the medical resources. By simulating the annealing cooling strategy in the metallurgical process, it searches for the optimal resource allocation plan, avoids local optimal solutions, and at the same time combines an efficiency optimization model to dynamically adjust the resource allocation, generating an emergency response plan.

[0036] As a further solution of the present invention, the disease evolution prediction module includes a trend analysis sub-module, a machine learning sub-module, and a data prediction sub-module;

[0037] Based on the emergency response plan, the trend analysis sub-module uses the autoregressive integrated moving average model for time series analysis, detects the stationarity of the data set, estimates the autoregressive terms and moving average terms in the model, and selects the optimal model to predict the future trend of the disease by gradually fitting the model with differential parameters, generating the disease trend analysis result;

[0038] Based on the disease trend analysis result, the machine learning sub-module uses the decision tree algorithm, classifies the disease data by constructing a tree model, where each decision node represents a test of a feature and each leaf node represents a category, and recursively divides the data set to iteratively refine the key factors determining the disease evolution, generating the disease risk assessment;

[0039] Based on the disease risk assessment, the data prediction sub-module uses the gradient boosting tree model, optimizes and refines the prediction ability of the model by gradually adding decision trees and modeling the residuals at each step, predicts the evolution path of the future disease, and generates the disease evolution prediction.

[0040] As a further solution of the present invention, the resource allocation management module includes a resource management sub-module, an optimization allocation sub-module, and an execution support sub-module;

[0041] Based on the disease evolution prediction, the resource management sub-module uses the linear programming algorithm for the systematic management of medical resources, including the allocation and scheduling of medical equipment, drug inventory, and human resources. By setting the goals of maximizing resource utilization and minimizing costs, it calculates the optimal resource allocation using mathematical optimization methods and generates the resource management strategy;

[0042] Based on the resource management strategy, the optimization allocation sub-module applies the multi-objective optimization method to comprehensively analyze and optimize the allocation of medical resources, determines the resource allocation ratio for multiple departments with reference to the resource requirements of multiple departments and the dynamic adjustment requirements in case of emergencies, and generates the optimized resource ratio;

[0043] Based on the optimized resource ratio, the execution support sub-module uses the dynamic programming algorithm to dynamically adjust and reallocate resources according to the real-time needs of medical services and changes in the disease condition, including the reallocation of medical staff, the adjustment of equipment usage plans, and the optimization of drug supply, to match the changing medical needs and generate the resource allocation plan.

[0044] As a further solution of the present invention, the medical decision support module includes a decision system sub-module, a clinical guideline analysis sub-module, and a comprehensive evaluation sub-module;

[0045] The decision-making system sub-module, based on the resource allocation plan, adopts a rule-based reasoning algorithm. By defining and applying logical rules in the medical profession, it analyzes the matching degree between various resources and patient needs, selects a resource allocation strategy, and generates a resource allocation decision analysis;

[0046] The clinical guideline analysis sub-module, based on the resource allocation decision analysis, adopts an association rule mining method to analyze batch clinical data. By identifying frequently occurring patterns and rules, it analyzes the key links and potential improvement points in the clinical pathway, optimizes the clinical treatment process, and generates a clinical pathway optimization plan;

[0047] The comprehensive evaluation sub-module, based on the clinical pathway optimization plan, adopts a Bayesian network model. Combining existing medical resources, patient characteristics, and clinical pathway data, through probability inference and causal relationship analysis, it comprehensively evaluates the predicted results and impacts of various medical decisions and generates clinical decision support information.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In the present invention, by using the feature engineering method and logistic regression analysis, the medical history and real-time vital sign data of patients can be more accurately integrated and analyzed, improving the accuracy of risk assessment. The application of pattern recognition algorithms and natural language processing models automatically identifies emergency medical events, enhancing the response speed and efficiency. Support vector machines and decision tree algorithms optimize the treatment plan, providing personalized treatment suggestions for patients. Time series analysis and biostatistical methods make vital sign monitoring more refined, capable of early warning of potential health risks. Resource scheduling algorithms and priority queue technologies ensure reasonable resource allocation in emergency response coordination, optimizing the utilization of medical resources. Machine learning prediction models and probability statistical methods provide data support for clinical decision-making in the prediction of disease evolution, reducing uncertainty. Linear programming algorithms and resource optimization strategies improve resource utilization efficiency and reduce waste in resource allocation management. Expert systems and clinical pathway analysis enhance the scientificity and feasibility of decision-making in medical decision support. Brief Description of the Drawings

[0050] Figure 1 is the system flow chart of the present invention;

[0051] Figure 2 is the schematic diagram of the system framework of the present invention;

[0052] Figure 3 is the flow chart of the preliminary risk assessment module of the present invention;

[0053] Figure 4 is the flow chart of the emergency situation identification module of the present invention;

[0054] Figure 5Flow chart of the treatment plan optimization module of the present invention;

[0055] Figure 6 Flow chart of the vital sign monitoring module of the present invention;

[0056] Figure 7 Flow chart of the emergency response coordination module of the present invention;

[0057] Figure 8 Flow chart of the disease evolution prediction module of the present invention;

[0058] Figure 9 Flow chart of the resource allocation management module of the present invention;

[0059] Figure 10 Flow chart of the medical decision-making support module of the present invention. Detailed implementation manners

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0062] Embodiment 1

[0063] Please refer to Figures 1 to 2 , the ICU emergency response system includes a preliminary risk assessment module, an emergency situation identification module, a treatment plan optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module, and a medical decision-making support module;

[0064] The preliminary risk assessment module, based on the patient's medical history and real-time vital signs, uses feature engineering methods and logistic regression analysis to perform data integration, key index extraction, and through the analysis of a risk calculation model, generates a patient risk rating;

[0065] Based on the patient risk rating, the emergency situation recognition module uses pattern recognition algorithms and natural language processing models to automatically identify emergency medical events, analyze text data, and label the events through classification algorithms to generate emergency medical event identifiers;

[0066] Based on the emergency medical event identifiers, the treatment plan optimization module uses support vector machines and decision tree algorithms to analyze the patient's condition, evaluate treatment plans, and adjust the treatment plan through optimization algorithms to generate personalized treatment recommendations;

[0067] Based on the personalized treatment recommendations, the vital sign monitoring module uses time series analysis and biostatistical methods to monitor the patient's vital signs in real time, analyze data, and evaluate through a health trend prediction model to generate vital sign monitoring results;

[0068] Based on the vital sign monitoring results, the emergency response coordination module uses resource scheduling algorithms and priority queue techniques to plan the response to emergencies, dynamically allocate medical resources and personnel, and adjust the response strategy through an efficiency optimization model to generate an emergency response plan;

[0069] Based on the emergency response plan, the disease evolution prediction module uses machine learning prediction models and probability statistics methods to analyze the data of the patient's disease development trend, assess risks, and predict the disease changes through trend prediction algorithms to generate disease evolution predictions;

[0070] Based on the disease evolution prediction, the resource allocation management module uses linear programming algorithms and resource optimization strategies to allocate medical resources, plan management strategies, and optimize resource allocation through a resource adjustment model to generate a resource allocation plan;

[0071] Based on the resource allocation plan, the medical decision support module uses expert systems and clinical pathway analysis to conduct comprehensive medical decision analysis and strategy evaluation, and provides medical advice through a decision model to generate clinical decision support information.

[0072] Patient risk ratings include cardiovascular risk index, infection risk score, and postoperative complication risk. Emergency medical event identifiers include acute cardiac events, acute respiratory failure, and severe trauma classification. Personalized treatment recommendations include drug dose adjustment, surgical intervention timing, and physical therapy plans. Vital sign monitoring results include heart rate variability analysis, continuous blood pressure monitoring results, and respiratory pattern charts. Emergency response plans include emergency rescue team dispatch, emergency supply of equipment and drugs, and ward transfer priorities. Disease evolution predictions include disease deterioration risk assessment, expected recovery time, and estimated complication risks. Resource allocation plans include ICU bed allocation, key medical equipment dispatch, and medical staff shift arrangements. Clinical decision support information includes individualized treatment recommendations, risk control measures, and long-term care planning.

[0073] In the preliminary risk assessment module, risk assessment is carried out by using feature engineering methods and logistic regression analysis based on the patient's medical history and real-time vital sign data. First, the feature engineering method extracts key information such as age, medical history, and medication records from the medical records, and cleans and formats this data so that the logistic regression model can process it. Then, the logistic regression model estimates the patient's risk level based on these features, such as the cardiovascular disease risk index, infection risk score, etc. This model takes into account the interactions of multiple factors to accurately calculate the risk rating. Finally, the module outputs a detailed risk rating report, which is crucial for doctors to formulate treatment plans and preventive measures.

[0074] In the emergency situation identification module, based on the patient's risk rating, pattern recognition algorithms and natural language processing models are used to automatically identify emergency medical events. The pattern recognition algorithm analyzes the patient's vital signs and medical data to identify abnormal patterns, such as the signs of acute cardiac events. At the same time, the natural language processing model analyzes the records of doctors and nurses to extract key information about the patient's condition. The combination of these two methods enables the module to accurately label emergency medical events, such as acute respiratory failure or severe trauma, and generate an emergency event identifier, providing important information for the medical team to respond quickly.

[0075] In the treatment plan optimization module, based on the identification of emergency medical events, support vector machines and decision tree algorithms are used to evaluate the patient's condition and treatment plan. The support vector machine predicts the effects of different treatment plans by analyzing the patient's clinical data, such as biochemical test results. The decision tree algorithm recommends the most suitable treatment method according to the patient's specific situation, such as age and complications. These algorithms are combined to provide a comprehensive evaluation of the treatment plan, including suggestions for drug dose adjustment and the timing of surgical intervention. Finally, the module generates a personalized treatment recommendation report to help doctors formulate more effective treatment plans.

[0076] In the vital sign monitoring module, based on the personalized treatment recommendations, time series analysis and biostatistical methods are used to monitor the patient's vital signs in real time. Time series analysis processes continuous measurement data such as heart rate and blood pressure to identify potential health problems. Biostatistical methods evaluate the statistical significance of this data to ensure the accuracy and reliability of the monitoring results. In addition, the module also uses a health trend prediction model to evaluate the patient's health development direction. By analyzing data such as heart rate variability, continuous blood pressure monitoring results, and respiratory pattern charts, the module can predict the patient's health trend and timely alert potential risks. These analysis results are summarized into a vital sign monitoring report, providing doctors with real-time and accurate health status information to help them make timely medical decisions and reduce the patient's risks.

[0077] In the emergency response coordination module, based on the vital sign monitoring results, resource scheduling algorithms and priority queue techniques are applied to conduct emergency response planning. The resource scheduling algorithms analyze the availability of medical resources such as ICU beds, critical medical equipment, and medical staff, and combine the urgency of patients to perform priority ranking. The priority queue technique is used to dynamically allocate resources to ensure that the most urgent situations can receive the fastest response. Through these algorithms, the module generates an efficient emergency response plan, including the dispatch of first aid teams, the emergency supply of equipment and medications, and the determination of ward transfer priorities. This plan not only improves the speed and efficiency of medical response but also ensures the rational allocation of resources, enabling each patient to receive timely and appropriate medical care.

[0078] In the disease evolution prediction module, based on the emergency response plan, machine learning prediction models and probability and statistics methods are used for data analysis and risk assessment. The machine learning models analyze the patient's historical data and current status to predict the estimated development trend of the disease. The probability and statistics methods evaluate the risk of disease deterioration, predict the recovery time, and the probability of complications. These analyses are combined to provide doctors with a comprehensive disease evolution prediction report, including the risk assessment of disease deterioration, the estimated recovery time, and the predicted complication risk. This report is very important for doctors to formulate long-term treatment plans and prevention strategies, helping them better understand the patient's health status and make more reasonable medical decisions.

[0079] In the resource allocation and management module, based on the disease evolution prediction, linear programming algorithms and resource optimization strategies are used for the allocation and management of medical resources. The linear programming algorithms optimize the allocation of limited medical resources such as ICU beds and critical medical equipment to ensure that resources can meet the most urgent and needed situations. The resource optimization strategies consider the needs of different patients and the availability of resources to reasonably arrange the shifts of medical staff. This module also dynamically optimizes the resource configuration through a resource adjustment model to adapt to changes in medical needs. Finally, the module generates a comprehensive resource allocation plan, including ICU bed allocation, critical medical equipment dispatch, and medical staff shift arrangements. This plan effectively improves the utilization efficiency of medical resources, ensures that patients can obtain necessary treatment in a timely manner, and also optimizes the operation efficiency of the hospital.

[0080] In the medical decision support module, based on the resource allocation plan, an integrated medical decision analysis is carried out by applying an expert system and clinical pathway analysis. The expert system aggregates the knowledge and experience of numerous medical experts and can provide personalized treatment suggestions according to the specific conditions of patients. Clinical pathway analysis analyzes the treatment process of patients and evaluates the cost-effectiveness and effects of different treatment plans. Combining these methods can provide doctors with comprehensive decision support information, including individualized treatment suggestions, risk control measures, and long-term care plans. Through the analysis of this module, doctors can formulate more effective and economical treatment plans while ensuring that patients receive the best medical care.

[0081] Please refer to Figure 3 , the preliminary risk assessment module includes a feature analysis sub-module, a risk calculation sub-module, and a health data analysis sub-module;

[0082] The feature analysis sub-module, based on the medical history of patients, uses the principal component analysis algorithm. By calculating the covariance matrix of the original data set, it extracts the principal components, performs data dimensionality reduction, and then uses the decision tree algorithm to generate key health indicators according to information gain and Gini coefficient.

[0083] The risk calculation sub-module, based on the key health indicators, uses a logistic regression model to model the relationship between features and patient risks, performs risk scoring using probability distributions, and combines with a random forest model. Through the ensemble learning of multiple decision trees, it improves the accuracy and stability of risk prediction and generates a risk assessment score.

[0084] The health data analysis sub-module, based on the risk assessment score and real-time vital signs, uses time series analysis methods to analyze the temporal characteristics of vital sign data, reveals the dynamic change trend of the health status, and applies a support vector machine model. By constructing an optimal separating hyperplane, it classifies the health status of patients and generates a patient risk rating.

[0085] In the feature analysis submodule, the first thing received is the patient's medical history data, which usually exists in a structured format, such as tabular data in electronic medical records, containing various medical indicators such as blood pressure, heart rate, body temperature, etc. These data sets are preprocessed, such as missing value processing and standardization, to ensure the effective operation of subsequent algorithms. The principal component analysis (PCA) algorithm plays a core role in this submodule. The algorithm first calculates the covariance matrix of the data set, which is a matrix that describes the closeness of the linear relationship between variables. Through eigenvalue decomposition, PCA identifies the principal components that best represent the variance of the data set. The key to this process is to select the appropriate number of principal components, which usually depends on the cumulative explained variance ratio. The selected principal components are used as new feature sets for data dimensionality reduction, which not only reduces the complexity of the data, but also reduces the computational burden of subsequent algorithms. Next, the decision tree algorithm is applied to the reduced data. The decision tree selects the splitting attributes by calculating the information gain or Gini coefficient and constructs a tree structure. Each node represents an attribute, and the data is divided into different branches according to the different values ​​of the attribute. During this process, the algorithm gradually learns how to segment the data most effectively based on the training data to distinguish different health states. In this way, the algorithm is able to identify the key indicators that have the greatest impact on the patient's health status from a large number of health indicators. The final output of the feature analysis submodule is a streamlined and informative set of key health indicators that provide a solid foundation for risk calculation, making subsequent risk assessments more accurate and efficient. In addition, this submodule can also generate a detailed report showing which indicators have the most significant impact on health status, providing a valuable reference for medical professionals.

[0086] In the risk calculation sub-module, the health risks of patients are evaluated using a logistic regression model and a random forest model. This process starts from the key health indicators received from the feature analysis sub-module. The logistic regression model is used here to analyze the relationship between features and patient risks. Logistic regression is a statistical method widely used in binary classification problems, estimating the relationship between the outcome variable and a series of independent variables (i.e., key health indicators) through the sigmoid logistic function. The training process of the model includes using the maximum likelihood estimation method to estimate the coefficients in the logistic regression equation. Each coefficient reflects the change in the probability of a patient experiencing an adverse health event (such as a heart attack) when the corresponding health indicator changes by one unit. During this process, the model also adopts techniques such as regularization to prevent overfitting and ensure the generalization ability of the model. Then, the output of the logistic regression model is used to generate a risk score for the patient. This score is based on the probability distribution and represents the likelihood of the patient experiencing an adverse health event in a future period. To improve the accuracy and stability of the prediction, the sub-module also combines the random forest model. Random forest is an ensemble learning method that constructs multiple decision trees and combines the prediction results to improve the performance of the overall model. In the sub-module, the random forest builds multiple decision trees by using different subsets of key health indicators and different random subsets of data samples, and then aggregates the prediction results of these trees to obtain the final risk assessment score. This method reduces the bias and variance of the model by introducing randomness, thus improving the accuracy and stability of risk prediction. The final product of the risk calculation sub-module is a report containing the patient's risk assessment score. This report not only provides decision support for doctors to help them identify high-risk patients, but can also be used for personalized patient management and the formulation of intervention plans, thereby improving the overall quality and efficiency of healthcare. In addition, this report can also be integrated into broader medical data analysis to optimize the allocation of medical resources and the formulation of health policies.

[0087] In the health data analysis sub-module, the risk assessment scores and real-time vital sign data are analyzed to reveal the dynamic change trends of the patient's health status and classify the patient's health condition. The time series analysis method and the support vector machine (SVM) model are utilized in this sub-module. The application of time series analysis in this sub-module is to analyze the temporal characteristics of the real-time vital sign data. Vital sign data such as heart rate, blood pressure, and respiratory rate are collected in the form of time series. These data are used to analyze the change trends of the patient's health condition over time. The key technologies involved in time series analysis include stationarity test, trend analysis, and seasonal analysis. Stationarity tests such as the ADF test are used to determine whether the time series data is stable, while trend analysis reveals the long-term change trends of the data over time. Seasonal analysis helps identify the periodic change patterns of the data at fixed time intervals. These analyses assist medical professionals in identifying potential health problems and risk factors. Subsequently, the support vector machine (SVM) model is used to classify the patient's health condition. SVM is a powerful supervised learning model suitable for classification problems of high-dimensional data. In this sub-module, SVM is used to find the optimal separating hyperplane, that is, the decision boundary that can best distinguish different health status categories. SVM achieves this by maximizing the margin between different categories. During the model training process, the data is mapped to a higher-dimensional space through the kernel trick, enabling linearly inseparable data to be linearly separated. This technique is particularly applicable to complex medical data where the patient's health condition is determined by multiple interrelated factors. Finally, the health data analysis sub-module generates a comprehensive patient risk rating. This rating is based on the combined results of time series analysis and the SVM model, providing a comprehensive view of the patient's current and future health conditions. This is crucial for guiding clinical decisions, formulating personalized treatment plans, and developing prevention and intervention measures.

[0088] Suppose in an ICU emergency response system, there are the following simulated data items:

[0089] In the medical history data of Patient A, it includes age, gender, past medical history (such as diabetes, hypertension), hospital hospitalization records in the past year, and important biochemical indicators (such as blood glucose, cholesterol levels).

[0090] In the real-time vital sign data, it includes heart rate (number of heartbeats per minute), blood pressure (systolic blood pressure / diastolic blood pressure), and respiratory rate (number of breaths per minute).

[0091] In the feature analysis sub-module, through principal component analysis (PCA) and decision tree algorithms, key health indicators are extracted from the complex historical and biochemical data. PCA helps identify the indicators that can best represent the patient's overall health condition, and the decision tree further screens out the factors that are most critical for predicting the patient's health risks.

[0092] Next, in the risk calculation sub-module, logistic regression models and random forest models are used to analyze the relationships between these key metrics and the patient's health risks. The logistic regression model generates a probability score of the patient's health risk, while the random forest model enhances the accuracy and stability of the prediction. Together, these models produce a detailed risk assessment report, providing valuable decision-making support for the medical team.

[0093] Finally, in the health data analysis sub-module, time series analysis is applied to real-time vital sign data, revealing the dynamic change trends of the patient's health status. The Support Vector Machine (SVM) model then classifies the patient's health status based on these analysis results, generating the patient's risk rating.

[0094] Please refer to Figure 4 , the emergency situation recognition module includes a pattern recognition sub-module, a language processing sub-module, and a clinical manifestation analysis sub-module;

[0095] Based on the patient's risk rating, the pattern recognition sub-module uses the support vector machine algorithm to map the patient's vital signs and clinical data in a multi-dimensional feature space, separates the data of different emergency situations using a hyperplane, and identifies potential emergency medical conditions. It also uses the random forest algorithm to train the data set by constructing multiple decision trees and aggregates the prediction results of each tree to improve the accuracy and robustness of pattern recognition, generating an analysis of emergency situation features;

[0096] Based on the analysis of emergency situation features, the language processing sub-module uses the BERT model to pre-train context-related word embeddings from a batch of unlabeled text through deep bidirectional representations, analyzes the patient's electronic health records and the doctor's notes, and uses the LSTM model to capture the long-term dependencies in the text data through a time-recursive network, extracting key clinical information and generating the results of clinical information extraction;

[0097] Based on the results of clinical information extraction, the clinical manifestation analysis sub-module uses the K-nearest neighbor algorithm to classify new instances based on the classified instances, determines the nearest neighbor by measuring the distance between different instances, and uses the decision tree classification algorithm to classify and label the instances according to the data attributes in a top-down recursive manner, determining the category and priority of the emergency medical event and generating an emergency medical event identifier.

[0098] In the pattern recognition sub-module, the system processes the patient's vital signs and clinical data through the Support Vector Machine (SVM) algorithm. First, the data is converted into a format suitable for SVM, which usually means transforming the vital signs and clinical records into numerical forms, with each feature mapped to a coordinate point in a multi-dimensional feature space. The SVM algorithm uses these data points to construct a hyperplane, which is designed to maximize the margin between two classes of data points (normal and emergency situations). In this process, the algorithm involves kernel techniques to handle more complex data distributions, such as using a Gaussian kernel to capture non-linear relationships. Then, the module adopts the Random Forest algorithm to improve the accuracy and robustness of recognition. The Random Forest trains the dataset by constructing multiple decision trees, and each tree independently analyzes the data and makes predictions. The prediction results are aggregated to obtain the final judgment. This method reduces the risk of overfitting through ensemble learning and improves the generalization ability of the model to unseen data. During operation, the Random Forest algorithm randomly selects different features and data subsets to construct each tree, ensuring the differences between the trees. Finally, the sub-module generates a feature analysis report indicating the emergency medical conditions of the patient.

[0099] In the language processing sub-module, based on the results of the emergency situation feature analysis, the BERT model is used to process the patient's electronic health records and the doctor's notes. The BERT model first learns context information from a large amount of unlabeled text through deep bidirectional representations. In this process, BERT is pre-trained through self-supervised learning to identify and understand the context associations in medical texts. Then, the system uses the LSTM model to process time series data, such as the progress records or laboratory results in the patient's medical records. The LSTM model captures long-term dependencies through its recurrent network structure, and is able to remember and utilize past information to influence current and future outputs. This structure is particularly suitable for processing clinical text data because the patient's medical records often contain information over a long time span. The LSTM network extracts key clinical information, such as changes in symptoms and treatment responses, by gradually analyzing the data at each time point. In this process, the text data is transformed into numerical vectors, and the LSTM network learns and predicts the key patterns of clinical information through these vectors. Finally, this sub-module generates the results of clinical information extraction, which provide doctors with a deeper understanding of the patient's condition and help them make more accurate diagnosis and treatment decisions.

[0100] In the clinical manifestation analysis sub-module, the K-Nearest Neighbor (K-NN) algorithm and decision tree classification algorithm are used to analyze the data obtained from the clinical information extraction sub-module. The K-NN algorithm determines the classification of a new instance by measuring the distance between the new instance and the classified instances. In this process, the algorithm calculates the distance between the new instance and each classified instance, selects the K nearest instances, and determines the classification of the new instance based on the classifications of these neighboring instances. This method is particularly suitable for processing clinical data as it can flexibly adapt to the specific distribution of the data without a pre-set model structure. Subsequently, the decision tree classification algorithm is used to further refine the data classification. The decision tree classifies and labels instances in a top-down recursive manner, where each node represents an attribute of the data, each branch of the tree represents an attribute value, and finally reaches the leaf node, which is the classification result. When constructing the decision tree, the algorithm selects the best splitting attribute based on the information gain or Gini impurity of the data attributes to ensure that each split maximizes the clarity of the classification. This method allows the system to refine the categories and priorities of emergency medical events based on the specific clinical manifestations of the patient. Another advantage of the decision tree algorithm is that its results are easy to interpret. The generated tree structure provides a clear visual representation showing how to classify and determine emergency events based on different clinical indicators. This is crucial for medical decision-making as it allows doctors to quickly understand the reasoning process of the model and make information-driven decisions accordingly. Finally, this sub-module generates an emergency medical event identification, which is a detailed report listing various emergency situations and their corresponding priorities. This report is extremely valuable to clinicians as it helps quickly identify which patients require immediate attention and what emergency measures to take.

[0101] Suppose a patient is admitted to the ICU with a series of vital sign data and electronic health records. This data includes heart rate, blood pressure, respiratory rate, body temperature, and past medical history such as heart disease, diabetes, etc. The system first analyzes this data through the pattern recognition sub-module, where the SVM algorithm identifies abnormal combinations of vital signs, such as an abnormally high heart rate and low blood pressure, indicating that the patient is in shock. The random forest algorithm confirms this judgment by aggregating the predictions of multiple decision trees. In the language processing sub-module, BERT and LSTM models analyze the patient's electronic health records and extract key information, such as the patient having a recent history of heart surgery and experiencing chest pain symptoms in the recent few days. This information is used to further confirm the nature of the emergency medical condition. Finally, in the clinical manifestation analysis sub-module, the K-NN and decision tree algorithms combine this information to generate an emergency medical event identification. This report shows that the patient is highly likely experiencing heart failure and requires immediate high-priority intervention.

[0102] Please refer to Figure 5, the treatment plan optimization module includes a decision algorithm sub-module, a path optimization sub-module, and a personalized treatment sub-module;

[0103] Based on the emergency medical event identifier, the decision algorithm sub-module uses the support vector machine algorithm to analyze the patient's vital signs and clinical data. By capturing the interval boundaries between the data, it classifies different treatment need categories. Subsequently, using the decision tree algorithm, a tree structure is constructed based on the patient's age and disease history, and a suitable treatment strategy is recommended for each patient in a step-by-step refinement manner to generate a preliminary treatment decision plan;

[0104] Based on the preliminary treatment decision plan, the path optimization sub-module uses the genetic algorithm to optimize the patient's treatment path by simulating the selection, crossover, and mutation operations in the biological evolution process, capturing the treatment process with the maximum cost-benefit. At the same time, the simulated annealing algorithm is applied to randomly search in the solution space to avoid local optimal solutions, and a treatment path optimization plan is generated with reference to treatment efficiency and resource utilization;

[0105] Based on the treatment path optimization plan, the personalized treatment sub-module applies the Bayesian network to calculate the success probabilities of multiple treatment plans according to the patient's personal characteristics and disease history. Combining multi-criteria decision analysis, it comprehensively evaluates the benefits and risks of each treatment plan to generate personalized treatment recommendations.

[0106] In the decision algorithm sub-module, the Support Vector Machine (SVM) algorithm is used to analyze the patient's vital signs and clinical data. SVM is a supervised learning algorithm commonly used for classification problems. The core idea is to find an optimal hyperplane in the dataset to separate data of different classes. In this sub-module, SVM first receives the patient's vital signs and clinical data, which include heart rate, blood pressure, blood oxygen saturation, etc., as well as medical history and laboratory test results. The data format is a structured data table, where each row represents a patient and each column represents a feature. The SVM algorithm works by constructing one or more hyperplanes in a high-dimensional space. First, the algorithm tries to maximize the margin between different classes of data. Here, the class prediction is for emergency and non-emergency medical events. By choosing a kernel function (such as the radial basis function), SVM can handle non-linearly separable data. The determination of the hyperplane is based on support vectors, i.e., those data points closest to the boundary, and the steps can divide different treatment need categories. Next, the decision tree algorithm is used to construct a tree structure based on the patient's age and disease history. Decision tree is a popular classification algorithm that simulates the step-by-step refinement of the decision-making process. When constructing a decision tree, each node represents a feature (such as age group, specific disease type), and each branch represents a decision rule, and the final leaf node represents a treatment strategy. The algorithm starts from the root node and selects the optimal feature for splitting, and this process usually selects features based on information gain or Gini impurity. In this way, the decision tree can recommend a suitable treatment strategy for each patient and generate a preliminary treatment decision plan. The final output is a decision tree model that will directly guide the emergency medical team in patient classification and treatment strategy selection. The role of the decision tree here is to make the treatment decision-making process more transparent and easy to understand. Medical staff can quickly determine the treatment plan suitable for the patient based on the tree structure, improving the efficiency and accuracy of handling emergency medical events. The generated decision plan is of great significance for optimizing medical resource allocation, improving patient survival rate and treatment effect.

[0107] In the path optimization sub-module, the genetic algorithm and the simulated annealing algorithm act jointly to optimize the treatment path. The genetic algorithm is a heuristic search algorithm that simulates selection, crossover, and mutation in the process of biological evolution. In the sub-module, the algorithm first creates an initial population, where each individual represents a treatment path. Each individual in the population consists of a series of genes that represent different decision points in the treatment path, such as treatment methods, treatment order, etc. The genetic algorithm makes selections by evaluating the fitness of each individual in the population (i.e., the cost-benefit of the treatment path). Individuals with higher fitness are more likely to be selected to generate the next generation. The crossover (or hybridization) operation allows two individuals to combine to produce new individuals, which simulates chromosome exchange in biological inheritance. The mutation operation randomly changes some genes in an individual to introduce new treatment paths. The simulated annealing algorithm is used to avoid local optimal solutions and ensure finding the globally optimal treatment path. This algorithm simulates the metal annealing process and finds the optimal solution by randomly searching the solution space and gradually reducing the search range. Combined with the genetic algorithm, the simulated annealing algorithm plays a key role in the optimization of the treatment path, helping to identify the optimal treatment process that maximizes cost-benefit while considering treatment efficiency and resource utilization. Ultimately, the treatment path optimization plan generated by this sub-module aims to improve the overall efficiency of medical services, reduce unnecessary treatment steps and waiting times, thereby enhancing patient satisfaction and treatment outcomes.

[0108] The personalized treatment sub-module applies Bayesian networks and multi-criteria decision analysis to generate personalized treatment recommendations. A Bayesian network is a graphical model used to represent probabilistic relationships between variables. In this sub-module, it is used to calculate the success probabilities of multiple treatment options based on a patient's personal characteristics (such as age, gender, disease history) and clinical data. This network can capture the dependencies between variables and update the success probabilities of treatment options when new information becomes available. In a Bayesian network, each node represents a variable, such as a specific disease indicator or treatment method. The edges represent the probabilistic dependencies between variables. By inputting patient-specific data, the network can calculate the posterior probabilities of different treatment options. For example, for a specific disease and patient condition, the network indicates that one treatment option is more likely to be successful than another. After calculating the success probabilities of various treatment options, multi-criteria decision analysis is used to comprehensively evaluate the benefits and risks of each option. This analysis takes into account various factors, such as treatment cost, potential side effects, expected treatment effects, etc. By weighing these factors, the algorithm can propose a treatment recommendation with the highest comprehensive score for each patient. The generation of personalized treatment recommendations makes the treatment plan not only based on general medical knowledge but also deeply considers individual differences, thus significantly improving the pertinence and effectiveness of treatment. Such an approach is particularly suitable for dealing with complex and variable medical situations, such as intensive care or chronic disease management. The output of this sub-module is a set of personalized treatment recommendations, which will directly guide doctors to develop the most suitable treatment plan for each patient. The generation of personalized treatment recommendations not only increases the success rate of treatment but also enhances the patient's treatment experience because everyone receives a treatment plan customized specifically for them.

[0109] Consider an embodiment of an ICU emergency response system. Suppose there is a patient, male, 45 years old, with a history of heart disease, who is urgently admitted to the ICU. His vital sign data (such as heart rate, blood pressure, blood oxygen saturation, etc.) and clinical data (such as electrocardiogram, blood test results) are input into the decision algorithm sub-module.

[0110] In the decision algorithm sub-module, the SVM algorithm analyzes this data and classifies the patient into a specific treatment need category. The decision tree algorithm further recommends treatment strategies based on the patient's age and disease history. For example, the preferred treatment is drug therapy combined with monitoring of heart function.

[0111] The path optimization sub-module uses genetic algorithms and simulated annealing algorithms to optimize the treatment path. Considering resource allocation and treatment efficiency, the most cost-effective treatment process is to first perform drug therapy and then arrange a heart function test.

[0112] Finally, in the personalized treatment sub-module, the Bayesian network and multi-criteria decision analysis comprehensively consider the patient's personal characteristics and disease history, and propose personalized treatment suggestions that are most suitable for the patient. For example, based on the patient's history of heart disease and current clinical data, the network calculates that a treatment plan combining a specific drug treatment with regular electrocardiogram monitoring has the highest success rate. The multi-criteria decision analysis further considers factors such as treatment cost and potential side effects, and finally determines that this is the most suitable treatment plan for the patient.

[0113] Please refer to Figure 6 , the vital sign monitoring module includes a real-time monitoring sub-module, a parameter analysis sub-module, and a treatment effect evaluation sub-module;

[0114] Based on the personalized treatment suggestions, the real-time monitoring sub-module uses a long short-term memory network to perform layer-by-layer neural network analysis on time series data. By extracting time-correlated features layer by layer and retaining the long-term memory of the features in the network, it captures the subtle changes and long-term trends in the vital sign data, monitors the patient's vital signs in real time, and generates real-time vital sign data;

[0115] Based on the real-time vital sign data, the parameter analysis sub-module uses multivariate regression analysis, including establishing a statistical model to analyze the interactions between multiple variables, and at the same time referring to the impacts of multiple factors on the patient's health status to identify the key parameters affecting the health status and generate an analysis of key health parameters;

[0116] Based on the analysis of key health parameters, the treatment effect evaluation sub-module uses a random forest model to construct multiple decision trees and combines the prediction results of the decision trees to analyze the treatment effect, avoid the bias of a single model, and generate vital sign monitoring results.

[0117] In the real-time monitoring sub-module, a long short-term memory network (LSTM) is used to deeply analyze the time series data of vital signs. First, the input data is in the format of time series, and these data exist in a standardized form, including vital sign parameters such as heart rate, blood pressure, body temperature, etc. The LSTM model is designed to capture short-term and long-term dependencies in these time series data. Specifically in the algorithm execution process, the LSTM network first processes the data through input gates, forget gates, and output gates. The input gate determines the importance of new information, the forget gate determines the degree of discarding old information, and the output gate controls the output of the current cell state. The structures of these gates are adjusted through weight and bias parameters, and these parameters are optimized through the backpropagation algorithm during the training process. As the network deeply processes the data, it can extract key time-correlated features from it and retain the long-term memory of these features in the network. Through this method, LSTM can monitor and identify subtle changes and long-term trends in vital sign data. The advantage of this deep learning method is that it can automatically learn and identify complex time dependencies without the need to manually design specific time windows or features. Finally, what this sub-module generates is a trained model that can perform real-time analysis on new time series data and provide support for the next step of decision-making.

[0118] In the parameter analysis sub-module, multivariate regression analysis is used to deeply process the real-time vital sign data. The input data of this module is the vital sign data obtained from the real-time monitoring sub-module, and these data are used as input variables for the regression model. The purpose of multivariate regression analysis is to establish a statistical model to analyze the interactions between different variables and their comprehensive impact on the patient's health status. During the algorithm execution process, first, a correlation analysis is performed on the input multiple vital sign parameters to determine which variables have significant mutual relationships. Then, a regression model is constructed to estimate the mutual dependencies between these variables. The regression model includes linear or non-linear equations, and its parameters are estimated by minimizing the error (such as the least squares method). This process involves complex mathematical calculations, including matrix operations and gradient descent, to find the optimal parameter values to minimize the difference between the model prediction and the actual data. After the model is established, it will be used to identify the key parameters that affect the health status, and these key parameters are determined by analyzing the comprehensive impact of each variable on the patient's health status. For example, the model finds that a specific combination of blood pressure and heart rate is important for predicting a certain health risk. Through this analysis, more personalized health monitoring and intervention measures can be provided for patients. The generated analysis results of key health parameters are a set of data points and correlation reports, and these data points and reports indicate the associations between different vital sign parameters and their impact on the health status. These analysis results are crucial for understanding the patient's health status and timely adjusting the treatment plan.

[0119] In the treatment effect evaluation sub-module, a random forest model is used to further process the analysis results based on key health parameters. Random forest is an ensemble learning method that improves the prediction accuracy and robustness by constructing multiple decision trees and synthesizing the prediction results. The input of the sub-module is the key health parameters obtained from the parameter analysis sub-module. During execution, the random forest first creates multiple decision trees. Each tree is trained on a random subset of the dataset and considers a random subset of variables during construction. This "randomness" helps improve the generalization ability of the model and reduce the risk of overfitting. Each tree makes predictions on the same input data, and the final prediction result is the average or majority vote of all tree prediction results. In addition, random forest can also provide an estimate of variable importance, which is very helpful for understanding which health parameters are most critical for treatment effect evaluation. Through this method, the model can not only evaluate the treatment effect but also reveal potential influencing factors, providing guidance for future treatment plans. Finally, the sub-module generates a comprehensive evaluation report that includes a specific analysis of the treatment effect of each patient and an evaluation of the importance of health parameters related to treatment. This information is valuable to the medical team as it can be used to adjust and optimize treatment strategies to ensure the best treatment effect for patients.

[0120] Suppose there is a patient whose key vital sign data includes heart rate, blood pressure, body temperature, etc. These data are captured by the real-time monitoring sub-module and analyzed by a long short-term memory network to identify any abnormal trends or sudden changes. For example, the heart rate gradually increases from an average of 70 beats per minute to 90 beats per minute, and the blood pressure rises from 120 / 80 mmHg to 140 / 90 mmHg. Next, the parameter analysis sub-module uses multivariate regression analysis to identify that the increase in heart rate and blood pressure has the greatest impact on the patient's health status. Subsequently, the treatment effect evaluation sub-module analyzes the relationship between these changes and the patient's overall health status through a random forest model and generates a comprehensive evaluation report. This report indicates that the increase in heart rate and blood pressure is related to the patient's stress response and recommends further diagnostic and treatment interventions.

[0121] Please refer to Figure 7 , the emergency response coordination module includes a scheduling algorithm sub-module, a team coordination sub-module, and a resource optimization sub-module;

[0122] Based on the vital sign monitoring results, the scheduling algorithm sub-module uses an integer linear programming algorithm for medical resource scheduling. By defining the objective function and constraints of resource allocation, including resource quantity limits and emergency response time, it uses optimization techniques to determine the resource allocation plan. At the same time, it applies the priority queue technique to sort according to the urgency and treatment needs of patients, and gives priority to allocating resources to critical patients to generate a preliminary resource scheduling plan;

[0123] Based on the preliminary resource scheduling plan, the Team Coordination Sub-module uses the network flow algorithm to optimize the collaboration process of the medical team, dynamically adjusts the division of labor and positions of team members according to the urgency and complexity of medical tasks, analyzes the professional skills and work experience of team members at the same time, assigns each emergency medical event to the matching team for handling, and generates a team coordination optimization plan;

[0124] Based on the team coordination optimization plan, the Resource Optimization Sub-module globally optimizes medical resources using the simulated annealing algorithm. By simulating the annealing cooling strategy in the metallurgical process, it searches for the optimal resource allocation plan to avoid local optimal solutions. At the same time, combined with the efficiency optimization model, it dynamically adjusts the resource allocation and generates an emergency response plan.

[0125] In the Scheduling Algorithm Sub-module, the system uses the integer linear programming algorithm for medical resource scheduling based on the vital sign monitoring results. The specific operation process involves the standardization of data formats, the definition of the objective function and constraint conditions, the determination of the resource allocation plan, and the application of the priority queue technology. First, the input data includes vital sign monitoring results such as heart rate and blood pressure, which are stored in a time series format to ensure the continuity and real-time nature of the data. The core of the integer linear programming algorithm is to construct an objective function aimed at maximizing the resource utilization efficiency and the success rate of patient treatment. This objective function combines the available quantity of various resources, the allocation cost, and the urgency of the patients. The constraint conditions include the limitation of resource quantity and the requirement for emergency response time to ensure the feasibility and effectiveness of the plan. In the process of determining the resource allocation plan, the algorithm iteratively optimizes and adjusts the allocation of each resource until the optimal solution that meets all constraint conditions is found. In addition, the system also applies the priority queue technology to sort according to the urgency of the patients and their treatment needs to ensure that critically ill patients can obtain resources first. The implementation result of this process is the generation of a preliminary resource scheduling plan, which details the allocation details of various resources and plays a significant role in improving the treatment efficiency and resource utilization rate.

[0126] In the team coordination sub-module, based on the preliminary resource scheduling plan, the network flow algorithm is used to optimize the collaboration process of the medical team. In this sub-module, first, the urgency and complexity of medical tasks are used as input data, usually in the format of a task list and its attribute tags. The key to the network flow algorithm lies in simulating the flow of medical resources (such as personnel and equipment) between different tasks to find the optimal resource allocation path. The algorithm takes into account the division of labor and locations of team members and dynamically adjusts to meet the requirements of different tasks. At the same time, data on the professional skills and work experience of team members are also considered, which are stored in the form of skill matrices and experience records to ensure accurate assessment of individual capabilities. During the execution of the algorithm, a network model is constructed, where nodes represent tasks or team members, and edges represent task assignments or resource flows. The algorithm iteratively adjusts the flow path to optimize the working efficiency of the entire system. This process is not only an optimization of resource allocation but also a reshaping of the medical team's collaboration mode to make it more flexible and efficient. The generated team coordination optimization plan details the roles and locations of each team member in different tasks, ensuring that each emergency medical event can be handled by the most suitable team, thus greatly improving the quality and efficiency of medical services.

[0127] In the resource optimization sub-module, based on the team coordination optimization plan, the simulated annealing algorithm is used to globally optimize medical resources. The core of this sub-module is to search for the optimal resource allocation plan by simulating the annealing cooling strategy in the metallurgical process. The input data includes the current resource allocation status, resource demand prediction, and historical allocation data, usually in the form of a multi-dimensional data table. The characteristic of the simulated annealing algorithm is that it can effectively avoid the trap of local optimal solutions during the process of finding the global optimal solution. The algorithm starts from an initial solution, randomly selects a neighboring solution as the new solution, and decides whether to accept the new solution according to specific criteria (such as the quality of the solution and the temperature parameter). This "temperature" parameter gradually decreases as the algorithm progresses, reducing the probability of the system accepting a worse solution, thus making the search process tend to be stable. In addition, combined with the efficiency optimization model, the algorithm can also dynamically adjust resource allocation to respond to real-time changing medical needs. This global optimization strategy not only considers the current resource allocation efficiency but also anticipates future demand changes, thus achieving the maximum benefit of resource allocation. The finally generated emergency response plan details the optimal allocation methods of all medical resources, including the allocation of equipment, personnel, drugs, etc. This plan is not only the best solution for the current situation but also a dynamic adjustment strategy that can quickly adapt to future changes. Through this global optimization, medical institutions can more effectively respond to emergencies, improve the response speed to critical patients, and the quality of treatment.

[0128] Suppose there is a series of emergency medical events, each with detailed data items and simulated values. For example, the data items of an event include the patient's heart rate (110 bpm), blood pressure (150 / 90 mmHg), state of consciousness (coma), etc. These data are fed into the scheduling algorithm sub-module, and the integer linear programming algorithm, based on this data, preferentially allocates resources to this critically ill patient. For example, allocate a doctor with expertise in heart disease and necessary monitoring equipment. Then, the team coordination sub-module readjusts the team configuration according to the current task load and the professional skills of team members to ensure that each medical event can receive an appropriate response. Finally, the resource optimization sub-module globally optimizes the overall resources through the simulated annealing algorithm to cope with future emergencies.

[0129] Please refer to Figure 8 , the disease evolution prediction module includes a trend analysis sub-module, a machine learning sub-module, and a data prediction sub-module;

[0130] The trend analysis sub-module, based on the emergency response plan, uses the autoregressive integrated moving average model for time series analysis, conducts stationarity detection on the data set, estimates the autoregressive terms and moving average terms in the model, and selects the optimal model to predict the future trend of the disease by gradually fitting the model with differential parameters, generating the disease trend analysis result;

[0131] The machine learning sub-module, based on the disease trend analysis result, uses the decision tree algorithm. By constructing a tree-like model, it classifies the disease data. Each decision node represents a test of a feature, and each leaf node represents a category. By recursively partitioning the data set and repeatedly refining the key factors determining the disease evolution, it generates the disease risk assessment;

[0132] The data prediction sub-module, based on the disease risk assessment, uses the gradient boosting tree model. By gradually adding decision trees and modeling the residuals at each step, it optimizes and refines the prediction ability of the model, predicts the evolution path of the future disease, and generates the disease evolution prediction.

[0133] In the trend analysis sub-module, the system uses the Autoregressive Integrated Moving Average (ARIMA) model to conduct in-depth analysis on time series data for the emergency response plan. The dataset format mainly appears in the form of time series, recording the evolution history of the disease condition. The ARIMA model first performs stationarity detection, converting the non-stationary time series into a stationary series through differencing operations. The key step lies in determining the differencing order to ensure the stationarity of the series. Subsequently, the model estimates the autoregressive (AR) terms and moving average (MA) terms, and selects the most appropriate model parameters through the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). During the step-by-step fitting process, the ARIMA model refines various parameters, such as the autoregressive order, differencing order, and moving average order, to adapt to the characteristics of the dataset. Finally, the model predicts the future trend of the disease condition and generates a detailed disease condition trend analysis report. This report not only reveals the estimated development path of the disease condition but also provides data support for medical intervention.

[0134] In the machine learning sub-module, based on the trend analysis results of the previous module, the system uses the decision tree algorithm to classify and assess the risk of the disease condition data. The dataset format remains time series data, but at this time, it focuses on the characteristic manifestations of the disease condition. The decision tree is constructed through a tree-like model, and each decision node tests a specific disease condition characteristic, such as symptom severity, disease course progression speed, etc. During this process, the algorithm continuously performs recursive partitioning of the dataset, selecting the best splitting point through information gain or Gini impurity, thereby gradually refining the key factors affecting the evolution of the disease condition. The finally generated disease condition risk assessment report details the risk levels of different disease condition categories, providing decision support for doctors. The sub-module not only effectively classifies the data but also reveals the key driving factors for the development of the disease condition, providing a targeted basis for subsequent treatment.

[0135] In the data prediction sub-module, based on the disease condition risk assessment results, the system uses the Gradient Boosting Tree (GBT) model to predict the evolution path of the disease condition. The GBT model is particularly suitable for handling complex non-linear relationships, which is especially important when dealing with variable medical data. This model iteratively adds decision trees, and at each step, a new tree is built for the prediction residuals of all previous trees, thereby gradually optimizing the prediction ability of the model. In the specific implementation process, the model refines key parameters such as the learning rate, number of trees, and depth of the trees to adapt to the characteristics of the disease condition data. By modeling the residuals at each step, the GBT model is continuously adjusted, improving the prediction accuracy. The finally generated disease condition evolution prediction report not only predicts the future trend of the disease condition but also identifies the estimated high-risk nodes, providing a real-time and dynamic disease condition monitoring tool for the medical team.

[0136] Suppose the ICU emergency response system processes a set of simulated data: including patients' vital signs, medical records, treatment responses, etc. For example, the patient's body temperature data sequence is [36.5, 37.0, 37.5, 38.0, 38.5], and the heart rate data is [75, 80, 85, 90, 95]. In the trend analysis sub-module, the ARIMA model analyzes these time series data, detects the growth trends of body temperature and heart rate data, and predicts future numerical changes. For example, it is predicted that the future body temperature will reach 39 degrees, and the heart rate may rise to 100 beats per minute. In the machine learning sub-module, the decision tree algorithm classifies the patient's condition based on these data and other clinical indicators, such as blood pressure, blood oxygen saturation, etc., determines whether it belongs to moderate risk or high risk, and identifies which factors are most critical to the progression of the condition. For example, a continuous increase in body temperature is a sign of infection. Finally, in the data prediction sub-module, the gradient boosting tree model predicts the evolution of the condition in the next few days based on these risk assessments and clinical data. For example, it is predicted that the probability of the patient developing into a critical condition within the next three days is 60%.

[0137] Please refer to Figure 9 , the resource allocation management module includes a resource management sub-module, an optimization allocation sub-module, and an execution support sub-module;

[0138] Based on the prediction of the evolution of the condition, the resource management sub-module uses the linear programming algorithm to systematically manage medical resources, including the allocation and scheduling of medical equipment, drug inventory, and human resources. By setting the goals of maximizing resource utilization and minimizing costs, it uses mathematical optimization methods to calculate the optimal resource allocation and generate a resource management strategy;

[0139] Based on the resource management strategy, the optimization allocation sub-module applies the multi-objective optimization method to comprehensively analyze and optimize the allocation of medical resources. Referring to the resource requirements of multiple departments and the dynamic adjustment requirements in case of emergencies, it determines the resource allocation ratio of multiple departments and generates an optimized resource ratio;

[0140] Based on the optimized resource ratio, the execution support sub-module uses the dynamic programming algorithm to dynamically adjust and reallocate resources according to the real-time needs of medical services and changes in the condition, including the reallocation of medical staff, the adjustment of equipment usage plans, and the optimization of drug supply, to match the changing medical needs and generate a resource allocation plan.

[0141] In the resource management sub-module, the system conducts systematic management of medical resources based on the prediction of disease evolution, using the linear programming algorithm. This process involves the precise allocation and scheduling of medical equipment, drug inventory, and human resources. First, the module receives disease data, which is formatted as a time series and includes key parameters such as the number of patients, disease severity indicators, and bed occupancy rates. The linear programming algorithm inputs this data by setting the goals of maximizing resource utilization and minimizing costs as constraints. During the implementation of the algorithm, resource utilization rates and cost indicators are set as objective functions, while considering constraints such as equipment usage frequency and drug expiration dates. After the algorithm runs, it outputs the optimal resource allocation plan, which details the allocation amounts of various resources and the scheduling time table. This process not only improves the utilization efficiency of resources but also reduces operating costs, ensuring the continuity and efficiency of medical services.

[0142] In the optimization allocation sub-module, the system applies the multi-objective optimization method to comprehensively analyze and optimize the allocation of medical resources based on the above-generated resource management strategy. In this process, the module first collects resource demand data from multiple departments and dynamic adjustment requirements in emergency situations. These data are formatted as multi-dimensional indicators, such as the patient flow in each department, the demand for special diseases, and the frequency of emergency situations. The multi-objective optimization algorithm runs on this basis. Its core is to find the optimal resource allocation ratio while meeting the basic needs of all departments. The algorithm sets multiple optimization goals, such as improving service quality and reducing resource waste, and seeks a balance between these goals. The algorithm outputs the optimized resource allocation plan, which includes the detailed resource allocation ratio between departments and the resource allocation plan in emergency situations. This process improves the accuracy and adaptability of resource allocation, ensuring the fair and efficient allocation of medical resources among different departments.

[0143] In the execution support sub-module, the system uses the dynamic programming algorithm to perform real-time dynamic adjustment and reallocation of resources according to the optimized resource allocation plan. This sub-module receives real-time medical service demand data and disease change data, which are formatted as instantaneously updated service demand indicators and disease indicators, such as real-time bed occupancy rates, the number of emergency patients, and the usage frequency of specific drugs. The dynamic programming algorithm runs on this basis. The key lies in making the optimal reallocation of resources based on the current medical resource status and future demand prediction. The algorithm first sets the objective function with the goal of maximizing resource utilization efficiency and response speed. Then, considering the dynamic changes of medical resources, such as the working hours of human resources, the maintenance cycle of equipment, and the expiration date of drugs, the algorithm adjusts the allocation and scheduling of these resources in real time. The output resource allocation plan includes the reallocation plan of medical staff, the equipment usage schedule, and the drug supply adjustment plan. This process ensures that resource allocation can respond in a timely manner to changes in actual needs, improving the flexibility and response efficiency of medical services.

[0144] Suppose there is a series of detailed data items and corresponding simulation values. For example, there are currently 20 beds in the ICU, 15 of which are in use. The average severity index of the patients is 7.5 (out of 10), the average daily drug usage is 500 units, and the demand for special equipment (such as ventilators) in different departments.

[0145] In the resource management sub-module, the system first analyzes this data and uses a linear programming algorithm to calculate the optimal resource allocation based on the current disease evolution. Suppose the algorithm result shows that in order to maximize resource utilization and cost-effectiveness, 5 of the idle beds need to be reserved for expected emergency cases, and at the same time, the drug inventory is adjusted to ensure that the drug needs of critically ill patients are met.

[0146] In the optimization allocation sub-module, a multi-objective optimization method is applied to further refine the resource allocation. Based on the resource requirements of multiple departments, such as the special needs of the cardiology department, neurology department, etc., the algorithm indicates that most of the ventilators need to be allocated to the cardiology department because the patients there are in a more urgent condition.

[0147] In the execution support sub-module, according to these optimized plans, a dynamic programming algorithm is used to reallocate resources according to real-time needs. For example, if the cardiology department suddenly receives multiple critically ill patients, the system will immediately reallocate ventilators and relevant medical staff to ensure that resources can quickly respond to the most urgent needs.

[0148] Please refer to Figure 10 , the medical decision support module includes a decision system sub-module, a clinical guideline analysis sub-module, and a comprehensive evaluation sub-module;

[0149] The decision system sub-module, based on the resource allocation plan, adopts a rule-based reasoning algorithm. By defining and applying the logical rules of the medical profession, it analyzes the matching degree between various resources and patient needs, selects a resource allocation strategy, and generates a resource allocation decision analysis;

[0150] The clinical guideline analysis sub-module, based on the resource allocation decision analysis, adopts an association rule mining method to analyze batch clinical data. By identifying frequently occurring patterns and rules, it analyzes the key links and potential improvement points in the clinical pathway, optimizes the clinical treatment process, and generates a clinical pathway optimization plan;

[0151] The comprehensive evaluation sub-module, based on the clinical pathway optimization plan, adopts a Bayesian network model. Combining the existing medical resources, patient characteristics, and clinical pathway data, through probability inference and causal relationship analysis, it comprehensively evaluates the estimated results and impacts of various medical decisions, and generates clinical decision support information.

[0152] In the decision-making system sub-module, the system carefully processes the medical resource allocation problem through a rule-based reasoning algorithm. The data format covers the types and quantities of medical resources as well as the demand characteristics of patients, such as the number of hospital beds, the allocation of medical staff, drug inventory, the severity level of patients' conditions, and treatment requirements. The algorithm first defines a set of logical rules in the medical profession, such as "If the utilization rate of ICU beds exceeds 80%, then give priority to allocating them to critically ill patients", "If the inventory of a certain drug is below the safety level, then reallocate it to the ward with high emergency demand". Through these rules, the system analyzes the matching degree between various resources and patients' needs to determine the most appropriate resource allocation strategy. In this process, the algorithm takes into account the current state of resources, historical usage data, and expected demand to ensure the accuracy and practicality of the decision. The generated resource allocation decision analysis report details the allocation strategy for each resource and its expected impact, effectively guiding the rational allocation of hospital resources, improving resource utilization efficiency, and ensuring that key resources can be supplied to the patients who need them most in a timely manner.

[0153] In the clinical guideline analysis sub-module, the system uses the association rule mining method to optimize the clinical treatment process. The data format is mainly a batch of clinical data, including patients' medical histories, treatment plans, treatment effects, and related medical indicators. This algorithm explores the frequent patterns and association rules in the data, such as "When a patient has a certain chronic disease, a certain drug has a better treatment effect", "There is a significant association between a specific disease and a specific treatment plan". The algorithm first calculates the association degree between various data items, and then screens out statistically significant rules according to support and confidence criteria. In this process, the association rule mining method refines parameters such as the minimum support threshold and the minimum confidence threshold to ensure that the mined rules are both universal enough and strongly relevant. The generated clinical pathway optimization plan details the key links and potential improvement points in the clinical pathway, providing data-driven insights for clinical decision-making, helping hospitals optimize treatment plans and processes, and improving the quality of medical services.

[0154] In the comprehensive evaluation sub-module, the system comprehensively evaluates the estimated results and impacts of various medical decisions using a Bayesian network model. The data processed by this sub-module includes existing medical resource information, patient characteristic data, and clinical pathway data. The Bayesian network model processes this data through probabilistic inference and causal relationship analysis. The model first establishes a probability distribution network to clarify the causal relationships between various variables, such as the impact of "disease severity" on "treatment method selection" and the potential impact of "resource allocation" on "treatment effect". In the specific implementation process, the model refines the prior probabilities of each node and updates these probabilities with data to form posterior probabilities. For example, the success rate of a certain treatment method under specific conditions is calculated based on historical data, and then this probability is adjusted according to the latest clinical data. This probability-based reasoning method enables the model to make judgments in the presence of uncertainties. The generated clinical decision support information not only provides the possible results of various medical decisions but also evaluates the risks and benefits of different decision-making options, providing comprehensive decision-making references for doctors and helping to achieve individualized and precise medical services.

[0155] Suppose the system processes a set of simulated data, including the bed utilization rate in different wards, the status of medical equipment, drug inventory, basic patient information, and disease severity levels. For example, the bed utilization rate in the cardiac ward is 85%, the bed utilization rate in the respiratory department is 60%, the inventory of an important antibiotic is only 20% left, a patient's heart function score is 3 (severe), and the lung function score is 2 (moderate). In the decision-making system sub-module, the system analyzes and decides to preferentially allocate new beds and antibiotics to the cardiac ward according to the defined rules, such as "preferentially allocate resources to high-occupancy wards". In the clinical guideline analysis sub-module, the system analyzes historical treatment data through the association rule mining method and finds that for patients with a heart function of level 3, using a specific antibiotic in combination with heart support treatment has a better effect. Finally, in the comprehensive evaluation sub-module, the Bayesian network model comprehensively considers the resource allocation decision and the results of clinical guideline analysis and evaluates the impact of different treatment options on the patient's condition.

[0156] The above is only the preferred embodiment of the present invention and does not limit the present invention in other forms. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. ICU emergency response system, characterized by: The system includes a preliminary risk assessment module, an emergency identification module, a treatment plan optimization module, a vital signs monitoring module, an emergency response coordination module, a disease progression prediction module, a resource allocation management module, and a medical decision support module; The preliminary risk assessment module uses feature engineering methods and logistic regression analysis to integrate data and extract key indicators based on the patient's medical history and real-time vital signs, and generates a patient risk rating through risk calculation model analysis; The emergency identification module uses pattern recognition algorithms and natural language processing models based on patient risk ratings to automatically identify emergency medical events, analyze text data, and annotate events through classification algorithms to generate emergency medical event identifiers; The treatment plan optimization module uses support vector machines and decision tree algorithms based on emergency medical event identification to analyze patient conditions and evaluate treatment plans, and adjusts treatment plans through optimization algorithms to generate personalized treatment recommendations; The vital signs monitoring module uses time series analysis and biostatistics methods to conduct real-time monitoring and data analysis of patients' vital signs based on personalized treatment recommendations, and evaluates them through health trend prediction models to generate vital signs monitoring results; The emergency response coordination module uses resource scheduling algorithms and priority queue technology based on vital sign monitoring results to carry out emergency response planning, dynamic configuration of medical resources and personnel, and adjusts the response strategy through an efficiency optimization model to generate an emergency response plan; The disease evolution prediction module is based on the emergency response plan, uses machine learning prediction models and probability statistics methods to perform data analysis and risk assessment on the patient's disease development trend, and predicts disease changes through trend prediction algorithms to generate disease evolution predictions; The resource allocation management module uses a linear programming algorithm and a resource optimization strategy based on the prediction of the disease evolution to carry out medical resource allocation and management strategy planning, and optimizes resource configuration through a resource adjustment model to generate a resource allocation plan; The medical decision support module is based on the resource allocation plan, uses expert system and clinical pathway analysis, conducts comprehensive medical decision analysis and strategy evaluation, provides medical advice through decision models, and generates clinical decision support information.

2. The ICU emergency response system according to claim 1, characterized in that: The patient risk rating includes cardiovascular risk index, infection risk score, and postoperative complication risk. The emergency medical event identifier includes acute cardiac events, acute respiratory failure, and severe trauma classification. The personalized treatment recommendations include drug dosage adjustment, timing of surgical intervention, and physical therapy plan. The vital signs monitoring results include heart rate variability analysis, continuous blood pressure monitoring results, and breathing pattern charts. The emergency response plan includes emergency team dispatch, emergency supply of equipment and drugs, and ward transfer priority. The disease evolution prediction includes disease deterioration risk assessment, expected recovery time, and estimated complication risk. The resource allocation plan includes ICU bed allocation, key medical equipment dispatch, and medical staff shift arrangements. The clinical decision support information includes personalized treatment recommendations, risk control measures, and long-term care planning.

3. The ICU emergency response system according to claim 1, characterized in that: The preliminary risk assessment module includes a feature analysis submodule, a risk calculation submodule, and a health data analysis submodule; The feature analysis submodule uses the principal component analysis algorithm based on the patient's medical history to calculate the covariance matrix of the original data set, extract the principal components, perform data dimension reduction, and then use the decision tree algorithm to generate key health indicators based on information gain and Gini coefficient; The risk calculation submodule uses a logistic regression model based on key health indicators to model the relationship between features and patient risks, uses probability distribution to score risks, and combines a random forest model to improve the accuracy and stability of risk prediction through integrated learning of multiple decision trees to generate a risk assessment score; The health data analysis submodule uses time series analysis methods based on risk assessment scores and real-time vital signs to analyze the time series characteristics of vital signs data, revealing the dynamic change trend of health status, and applies the support vector machine model to classify the patient's health status by constructing the optimal separating hyperplane to generate a patient risk rating.

4. The ICU emergency response system according to claim 1, characterized in that: The emergency identification module includes a pattern recognition submodule, a language processing submodule, and a clinical manifestation analysis submodule; The pattern recognition submodule uses a support vector machine algorithm based on patient risk rating to map the patient's vital signs and clinical data into a multidimensional feature space, uses a hyperplane to separate the data of differentiated emergency situations, identifies potential emergency medical conditions, and uses a random forest algorithm to train the data set by constructing multiple decision trees, summarizes the prediction results of each tree to improve the accuracy and robustness of pattern recognition, and generates an emergency situation feature analysis; The language processing submodule is based on emergency feature analysis, adopts the BERT model, pre-trains context-related word embedding from batch unlabeled text through deep bidirectional representation, analyzes patients' electronic health records and doctors' notes, and uses the LSTM model to capture long-term dependencies in text data through a time recursive network, extracts key clinical information, and generates clinical information extraction results; The clinical manifestation analysis submodule is based on the clinical information extraction results, adopts the K-nearest neighbor algorithm, classifies new instances according to the classified instances, determines the nearest neighbors by measuring the distances between differentiated instances, and adopts the decision tree classification algorithm to classify and label instances according to data attributes in a top-to-bottom recursive manner, determines the category and priority of emergency medical events, and generates emergency medical event identification.

5. The ICU emergency response system according to claim 1, characterized in that: The treatment plan optimization module includes a decision algorithm submodule, a pathway optimization submodule, and a personalized treatment submodule; The decision algorithm submodule uses a support vector machine algorithm to analyze the patient's vital signs and clinical data based on the emergency medical event identification, and divides the differentiated treatment demand categories by capturing the interval boundaries between the data. Then, a decision tree algorithm is used to build a tree structure based on the patient's age and disease history, and a suitable treatment strategy is recommended for each patient in a layer-by-layer manner to generate a preliminary treatment decision plan; The pathway optimization submodule uses a genetic algorithm based on a preliminary treatment decision plan to optimize the patient's treatment pathway by simulating the selection, crossover, and mutation operations in the biological evolution process, capturing the treatment process that maximizes cost-effectiveness. At the same time, a simulated annealing algorithm is applied to randomly search in the solution space to avoid local optimal solutions, and to generate a treatment pathway optimization plan with reference to treatment efficiency and resource utilization; The personalized treatment submodule is based on the treatment pathway optimization plan, applies the Bayesian network, calculates the success probability of multiple treatment plans according to the patient's personal characteristics and disease history, combines multi-criteria decision analysis, comprehensively evaluates the benefits and risks of each treatment plan, and generates personalized treatment recommendations.

6. The ICU emergency response system according to claim 1, characterized in that: The vital signs monitoring module includes a real-time monitoring submodule, a parameter analysis submodule, and a treatment effect evaluation submodule; The real-time monitoring submodule is based on personalized treatment recommendations and uses a long short-term memory network to perform layer-by-layer neural network analysis on time series data. By extracting time-related features layer by layer and retaining the long-term memory of the features in the network, it captures subtle changes and long-term trends in vital sign data, monitors patients' vital signs in real time, and generates real-time vital sign data. The parameter analysis submodule uses multivariate regression analysis based on real-time vital sign data, including establishing a statistical model to analyze the interaction between multiple variables, and referring to the impact of multiple factors on the patient's health status, identifying key parameters that affect the health status, and generating key health parameter analysis; The treatment effect evaluation submodule is based on key health parameter analysis, adopts a random forest model, constructs multiple decision trees, and integrates the prediction results of the decision trees to analyze the treatment effect, avoid the bias of a single model, and generate vital sign monitoring results.

7. The ICU emergency response system according to claim 1, characterized in that: The emergency response coordination module includes a scheduling algorithm submodule, a team coordination submodule, and a resource optimization submodule; The scheduling algorithm submodule uses an integer linear programming algorithm to schedule medical resources based on vital sign monitoring results, and uses optimization technology to determine the resource allocation plan by defining the objective function and constraints of resource allocation, including resource quantity restrictions and emergency response time. At the same time, priority queue technology is used to sort patients according to their urgency and treatment needs, and resources are allocated to critical patients first, generating a preliminary resource scheduling plan; The team coordination submodule uses the network flow algorithm to optimize the collaboration process of the medical team based on the preliminary resource scheduling plan, dynamically adjusts the division of labor and position of team members according to the urgency and complexity of the medical task, and analyzes the professional skills and work experience of team members. Each emergency medical event is assigned to a matching team to generate a team coordination optimization plan; The resource optimization submodule is based on a team coordination optimization plan and uses a simulated annealing algorithm to globally optimize medical resources. By simulating the annealing cooling strategy in the metallurgical process, it searches for the optimal resource allocation plan and avoids local optimal solutions. At the same time, it combines the efficiency optimization model to dynamically adjust resource allocation and generate an emergency response plan.

8. The ICU emergency response system according to claim 1, characterized in that: The disease evolution prediction module includes a trend analysis submodule, a machine learning submodule, and a data prediction submodule; The trend analysis submodule uses an autoregressive integrated moving average model to perform time series analysis based on the emergency response plan, performs a stationarity test on the data set, estimates the autoregressive term and the moving average term in the model, and selects the optimal model to predict the future trend of the disease by gradually fitting the model of differentiated parameters, thereby generating a disease trend analysis result; The machine learning submodule uses a decision tree algorithm based on the results of disease trend analysis to classify disease data by building a tree model. Each decision node represents a test of a feature, and each node represents a category. By recursively segmenting the data set, the key factors that determine the evolution of the disease are cyclically refined to generate a disease risk assessment. The data prediction submodule is based on disease risk assessment and adopts a gradient boosting tree model. By gradually adding decision trees and modeling the residuals of each step, it optimizes and refines the prediction ability of the model, predicts the future evolution path of the disease, and generates a disease evolution prediction.

9. The ICU emergency response system according to claim 1, characterized in that: The resource allocation management module includes a resource management submodule, an optimization allocation submodule, and an execution support submodule; The resource management submodule uses a linear programming algorithm to systematically manage medical resources based on the prediction of disease evolution, including the allocation and scheduling of medical equipment, drug inventory and human resources. By setting the goals of maximizing resource utilization and minimizing costs, the mathematical optimization method is used to calculate the optimal resource allocation and generate a resource management strategy. The optimization allocation submodule uses a multi-objective optimization method based on resource management strategies to comprehensively analyze and optimize the allocation of medical resources, and determines the resource allocation ratio of multiple departments with reference to the resource requirements of multiple departments and the dynamic adjustment requirements in emergency situations, and generates an optimized resource allocation ratio; The execution support submodule is based on optimizing resource allocation and using a dynamic programming algorithm to dynamically adjust and reallocate resources according to the real-time demand for medical services and changes in the condition of the patient, including the reallocation of medical personnel, adjustment of equipment use plans, and optimization of drug supply, to match changing medical needs and generate a resource allocation plan.

10. The ICU emergency response system according to claim 1, characterized in that: The medical decision support module includes a decision system submodule, a clinical guideline analysis submodule, and a comprehensive evaluation submodule; The decision system submodule is based on the resource allocation scheme and adopts a rule-based reasoning algorithm to analyze the matching degree between various resources and patient needs, select resource allocation strategies, and generate resource allocation decision analysis by defining and applying the logical rules of medical profession; The clinical guideline analysis submodule analyzes batch clinical data based on resource allocation decision analysis and adopts association rule mining methods to analyze key links and potential improvement points in the clinical pathway by identifying frequently occurring patterns and rules, optimize the clinical treatment process, and generate a clinical pathway optimization plan; The comprehensive evaluation submodule is based on the clinical pathway optimization solution, adopts the Bayesian network model, combines the existing medical resources, patient characteristics and clinical pathway data, and comprehensively evaluates the estimated results and impacts of various medical decisions through probabilistic inference and causal analysis to generate clinical decision support information.

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