Intelligent monitoring system for critical patients in urinary surgery

Abstract

The invention relates to the technical field of physiological status monitoring, in particular to a urinary surgery critical patient intelligent monitoring system which comprises a time sequence analysis module, an illness state prediction module, a complication alarm module, a dynamic monitoring adjustment module, a data optimization extraction module, a health data comprehensive analysis module, a monitoring parameter calibration module and a curative effect evaluation module. According to the method, the autoregressive integral moving average and seasonal decomposition algorithm and the long-short-term memory network are combined, accurate time sequence analysis is performed on the medical data, the convolutional neural network and the recurrent neural network are combined, and the autoencoder and the attention mechanism are matched, so that the disease prediction accuracy is improved; through the application of the random forest, the gradient elevator algorithm and the isolated forest, the early recognition reliability of complications is improved, timely prevention is facilitated, the dynamic monitoring and adjusting module adjusts parameters according to real-time data, the data optimization and extraction module deeply analyzes health data, and the effectiveness and pertinence of a treatment scheme are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of physiological condition monitoring, and particularly to an intelligent monitoring system for critically ill patients in urology. Background Art

[0002] The technical field of physiological condition monitoring focuses on using technical means to monitor and evaluate an individual's health status in real time, especially in a medical environment. Such technologies usually involve continuous monitoring of vital signs, such as heart rate, blood pressure, respiratory rate, and body temperature. The purpose of physiological condition monitoring technology is to provide timely and accurate health information so that medical staff can quickly respond and provide appropriate medical interventions.

[0003] An intelligent monitoring system for critically ill patients in urology is a monitoring system designed specifically for critically ill patients in urology, aiming to provide comprehensive and real-time health monitoring. The core purpose of this system is to ensure continuous monitoring of critically ill patients in urology so as to timely identify any potential health risks or complications and respond quickly. By continuously monitoring and analyzing key vital signs, this system aims to improve patient safety, optimize treatment plans, and reduce the risk of medical errors. In addition, it can also help reduce the workload of medical staff, enabling them to allocate resources and attention more effectively.

[0004] Traditional intelligent monitoring systems for critically ill patients in urology have deficiencies in many aspects. In traditional systems, there are no effective machine learning tools and algorithms, the functions of disease prediction and complication alarm are inaccurate, and potential health risks cannot be identified in time. The adjustment of monitoring parameters is not flexible enough and cannot reflect the latest health status of patients in real time, affecting the monitoring efficiency. There are insufficient tools and methods for optimizing data extraction and comprehensive analysis of health data, and it is impossible to comprehensively and deeply understand patient data. The evaluation of treatment efficacy is too single and subjective, lacking comprehensive systematic analysis and objective evaluation, which limits the effectiveness and adaptability of treatment plans. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose an intelligent monitoring system for critically ill patients in urology.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: An intelligent monitoring system for critically ill patients in urology includes a time series analysis module, a disease prediction module, a complication alarm module, a dynamic monitoring adjustment module, a data optimization extraction module, a comprehensive health data analysis module, a monitoring parameter calibration module, and a treatment efficacy evaluation module;

[0007] The time series analysis module processes data based on the patient's historical and real-time medical data, uses the autoregressive integrated moving average algorithm and the seasonal decomposition time series prediction algorithm, combines with the long short-term memory network, and generates time series analysis results;

[0008] The disease prediction module is based on the time series analysis results, uses a convolutional neural network and a recurrent neural network, combines an autoencoder and an attention mechanism to predict the disease progression, and generates a disease prediction report;

[0009] The complication alert module is based on the disease prediction report, uses random forest and gradient boosting machine algorithms, performs anomaly detection through an isolation forest, evaluates potential complications, and generates a complication alert signal;

[0010] The dynamic monitoring adjustment module is based on the complication alert signal, uses a long short-term memory network and a Kalman filter, adjusts the monitoring strategy through a dynamic time warping algorithm, and generates optimized monitoring parameters;

[0011] The data optimization and extraction module is based on the optimized monitoring parameters, uses principal component analysis and Lasso regression, combines support vector machines and random forests to extract key medical data, and generates a key data feature set;

[0012] The comprehensive health data analysis module is based on the key data feature set, applies particle swarm optimization and ant colony optimization algorithms, combines genetic algorithms to comprehensively analyze the health status, and generates a comprehensive health status analysis report;

[0013] The monitoring parameter calibration module is based on the comprehensive health status analysis report, uses Bayesian optimization and fuzzy logic to calibrate the monitoring parameters, and generates calibrated monitoring parameters;

[0014] The treatment efficacy evaluation module is based on the calibrated monitoring parameters and the comprehensive health status analysis report, uses statistical analysis and logistic regression methods to evaluate the effectiveness of the treatment plan, and generates a treatment efficacy evaluation report.

[0015] As a further solution of the present invention, the time series analysis module includes a historical data processing sub-module, a real-time data analysis sub-module, and a deep learning sub-module;

[0016] The historical data processing sub-module is based on the patient's historical medical data, uses an autoregressive integrated moving average algorithm for historical trend analysis and pattern recognition, and generates historical data analysis results;

[0017] The real-time data analysis sub-module is based on the patient's real-time medical data, uses a seasonal decomposition time series prediction algorithm for real-time status monitoring and trend prediction, and generates real-time data analysis results;

[0018] The deep learning sub-module is based on the historical data analysis results and the real-time data analysis results, uses a long short-term memory network to comprehensively process the time series data, and generates time series analysis results;

[0019] The autoregressive integrated moving average algorithm includes data stationarization processing, model parameter estimation, and time series prediction. The seasonal decomposition time series prediction algorithm includes seasonal component extraction, trend component analysis, and residual effect processing. The long short-term memory network includes a gating mechanism, state update, and long-term dependency learning.

[0020] As a further solution of the present invention, the disease prediction module includes an image analysis sub-module, a time series prediction sub-module, and a feature extraction sub-module;

[0021] Based on the time series analysis results, the image analysis sub-module uses a convolutional neural network to analyze medical image data and generates image feature analysis results;

[0022] Based on the image feature analysis results, the time series prediction sub-module uses a recurrent neural network to perform time series prediction of the disease development and generates time series prediction results;

[0023] Based on the time series prediction results, the feature extraction sub-module uses the long short-term memory network algorithm to perform key feature extraction and generates a disease prediction report;

[0024] The convolutional neural network includes a feature extraction layer, a pooling layer, and a fully connected layer. The recurrent neural network includes a recurrent unit structure, data processing at each time step, and sequence output generation;

[0025] The long short-term memory network algorithm follows the formula:

[0026] ;

[0027] Calculate the predicted value;

[0028] Where, represents the forgetting gate weight, which controls the amount of past information retained; represents the output of the previous LSTM cell, reflecting the information of the previous state; represents the current input, such as the patient's physiological parameters; represents the bias term of the forgetting gate, which adjusts the activation level of the forgetting gate, represents the concatenation of the previous output and the current input, is the sigmoid function, which determines the degree to which the forgetting gate closes.

[0029] As a further solution of the present invention, the complication alert module includes a risk assessment sub-module, an anomaly detection sub-module, and a classification algorithm sub-module;

[0030] Based on the disease prediction report, the risk assessment sub-module uses the random forest algorithm to perform risk assessment of potential complications and generates risk assessment results;

[0031] The abnormal detection sub-module detects abnormal patterns using the Isolation Forest algorithm based on the risk assessment results and generates abnormal detection results;

[0032] The classification algorithm sub-module classifies data using the Gradient Boosting Machine algorithm based on the abnormal detection results, identifies specific complication types, and generates complication alarm signals;

[0033] The Random Forest algorithm includes multi-tree ensemble decision-making, random feature selection, and result aggregation strategies. The Isolation Forest algorithm is specifically a tree structure-based data partitioning and outlier identification mechanism, and the Gradient Boosting Machine algorithm is specifically a gradient descent-based model enhancement and error correction method.

[0034] As a further solution of the present invention, the dynamic monitoring and adjustment module includes a dynamic parameter adjustment sub-module, a filter application sub-module, and a time warping sub-module;

[0035] The dynamic parameter adjustment sub-module dynamically adjusts the monitoring parameters using a Long Short-Term Memory network based on the complication alarm signals and generates dynamic adjustment results;

[0036] The filter application sub-module performs signal processing and data correction using a Kalman filter based on the dynamic adjustment results and generates filter processing results;

[0037] The time warping sub-module calibrates the monitoring strategy in time series using the Dynamic Time Warping algorithm based on the filter processing results and generates optimized monitoring parameters;

[0038] The Long Short-Term Memory network includes gated processing of sequential data, state retention, and time-dependent relationship modeling. The Kalman filter is specifically a dynamic system state estimation and noise data filtering method, and the Dynamic Time Warping algorithm is specifically a time series alignment, shape matching, and non-linear transformation technique.

[0039] As a further solution of the present invention, the data optimization and extraction module includes a feature selection sub-module, a data extraction sub-module, and a machine learning application sub-module;

[0040] The feature selection sub-module extracts key features using principal component analysis and Least Absolute Shrinkage and Selection Operator regression based on the optimized monitoring parameters and generates feature selection results;

[0041] The data extraction sub-module classifies data using an improved Support Vector Machine algorithm based on the feature selection results, optimizes the kernel function to extract key patterns in medical data, and generates data extraction results;

[0042] The machine learning application sub-module performs machine learning analysis using the random forest based on the data extraction results, identifies key medical data, and generates a key data feature set;

[0043] The principal component analysis and least absolute shrinkage and selection operator regression include data dimensionality reduction, eigenvalue decomposition, and regularized feature selection. The random forest includes decision tree ensemble, random feature selection, and voting decision mechanism;

[0044] The improved support vector machine algorithm calculates the classification decision function according to the formula:

[0045] ;

[0046] ;

[0047] Calculate the classification decision function ;

[0048] where, and are input vectors representing the medical index values of the patient, is the Lagrange multiplier obtained by solving the dual problem, reflecting the importance of the corresponding support vector in the definition of the decision boundary, is the class label determined by the medical diagnosis result, is the bandwidth parameter of the kernel function, and the optimal value is determined according to cross-validation to minimize the validation error, is the variance parameter, usually the average of the dimensional differences, is the offset obtained through the optimization problem solution, usually represents the total number of samples participating in the training, i is the sample index, and j is the index of the feature dimension, represents the i-th training sample is the similarity between the i-th training sample and the sample x to be classified, represents the training sample and the training sample

[0049] As a further solution of the present invention, the health data comprehensive analysis module includes a swarm intelligence analysis sub-module, a genetic algorithm sub-module, and a data integration sub-module;

[0050] The swarm intelligence analysis sub-module performs data analysis using the particle swarm optimization algorithm and the ant colony optimization algorithm based on the key data feature set, searches for the optimal solution, and generates a swarm intelligence analysis result;

[0051] The genetic algorithm sub-module performs iterative optimization of the solution using the genetic algorithm based on the swarm intelligence analysis result and generates a genetic algorithm optimization result;

[0052] Based on the optimization result of the genetic algorithm, the data synthesis sub-module performs data synthesis analysis and conducts a final assessment of the health status, generating a comprehensive analysis report on the health status;

[0053] The particle swarm optimization algorithm and the ant colony optimization algorithm include particle position update, pheromone tracking, and optimal path search. The genetic algorithm includes population generation, fitness evaluation, and genetic operations. The data synthesis analysis includes algorithm result fusion, data pattern recognition, and comprehensive health status evaluation.

[0054] As a further solution of the present invention, the monitoring parameter calibration module includes a parameter optimization sub-module, an algorithm application sub-module, and a data correction sub-module;

[0055] Based on the comprehensive analysis report on the health status, the parameter optimization sub-module uses Bayesian optimization to perform preliminary optimization of the monitoring parameters, generating a preliminary optimization result;

[0056] Based on the preliminary optimization result, the algorithm application sub-module uses fuzzy logic to iteratively adjust the monitoring parameters, generating a fuzzy logic adjustment result;

[0057] Based on the fuzzy logic adjustment result, the data correction sub-module performs final data correction, verifies the accuracy of the monitoring parameters, and generates calibrated monitoring parameters;

[0058] The Bayesian optimization includes using a probability model to estimate the objective function and selecting an optimization strategy. The fuzzy logic includes establishing fuzzy sets, defining rules, and implementing an inference mechanism. The data correction includes data filtering, noise reduction, and error analysis.

[0059] As a further solution of the present invention, the treatment efficacy evaluation module includes a statistical analysis sub-module, a treatment effect evaluation sub-module, and a machine learning analysis sub-module;

[0060] Based on the calibrated monitoring parameters, the statistical analysis sub-module uses statistical analysis methods to evaluate the effectiveness of the treatment plan, generating a statistical analysis result;

[0061] Based on the statistical analysis result, the treatment effect evaluation sub-module uses logistic regression to conduct in-depth treatment effect evaluation, generating a treatment effect evaluation result;

[0062] Based on the treatment effect evaluation result and the comprehensive analysis report on the health status, the machine learning analysis sub-module uses machine learning algorithms to conduct a comprehensive treatment efficacy evaluation, generating a treatment efficacy evaluation report;

[0063] The statistical analysis methods include descriptive statistics, inferential statistics, and model fitting. The logistic regression method includes probability model construction, variable weight assignment, and result probability prediction. The machine learning algorithms include decision trees, neural networks, and support vector machines.

[0064] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0065] In the present invention, through the autoregressive integrated moving average algorithm and the seasonal decomposition time series prediction algorithm, combined with the long short-term memory network, historical and real-time medical data are analyzed to provide accurate time series analysis results. The combination of the convolutional neural network and the recurrent neural network, supplemented by the autoencoder and the attention mechanism, enhances the accuracy of the prediction of the disease progression. In addition, the application of the random forest, gradient boosting machine algorithm, and isolation forest improves the reliability of the early identification of complications, which helps to prevent them in a timely manner. The dynamic monitoring and adjustment module enables the monitoring parameters to be flexibly adjusted according to the real-time data, improving the responsiveness and efficiency. The data optimization and extraction module ensures the extraction of key information from complex medical data, making the comprehensive analysis of health data more comprehensive and in-depth. The precise calibration of the monitoring parameters and the comprehensive evaluation of the curative effect ensure the effectiveness and pertinence of the treatment plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is the system flow chart of the present invention;

[0067] Figure 2 is the schematic diagram of the system framework of the present invention;

[0068] Figure 3 is the flow chart of the time series analysis module of the present invention;

[0069] Figure 4 is the flow chart of the disease prediction module of the present invention;

[0070] Figure 5 is the flow chart of the complication alarm module of the present invention;

[0071] Figure 6 is the flow chart of the dynamic monitoring and adjustment module of the present invention;

[0072] Figure 7 is the flow chart of the data optimization and extraction module of the present invention;

[0073] Figure 8 is the flow chart of the comprehensive health data analysis module of the present invention;

[0074] Figure 9 is the flow chart of the monitoring parameter calibration module of the present invention;

[0075] Figure 10 is the flow chart of the curative effect evaluation module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0076] 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.

[0077] 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 to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0078] Example 1: Please refer to Figures 1 to 2 , an intelligent monitoring system for urological critical patients includes a time series analysis module, a disease progression prediction module, a complication alarm module, a dynamic monitoring adjustment module, a data optimization and extraction module, a comprehensive health data analysis module, a monitoring parameter calibration module, and a treatment efficacy evaluation module;

[0079] The time series analysis module processes data based on the patient's historical and real-time medical data, using the autoregressive integrated moving average algorithm and the seasonal decomposition time series prediction algorithm, combined with the long short-term memory network, and generates the time series analysis result;

[0080] The disease progression prediction module predicts the disease progression based on the time series analysis result, using the convolutional neural network and the recurrent neural network, combined with the autoencoder and the attention mechanism, and generates the disease progression prediction report;

[0081] The complication alarm module evaluates potential complications and generates a complication alarm signal based on the disease progression prediction report, using the random forest and gradient boosting machine algorithms, and performing anomaly detection through the isolation forest;

[0082] The dynamic monitoring adjustment module adjusts the monitoring strategy through the dynamic time warping algorithm based on the complication alarm signal, using the long short-term memory network and the Kalman filter, and generates the optimized monitoring parameters;

[0083] The data optimization and extraction module extracts key medical data based on the optimized monitoring parameters, using the principal component analysis and Lasso regression, combined with the support vector machine and the random forest, and generates the key data feature set;

[0084] The comprehensive health data analysis module conducts a comprehensive analysis of the health status based on the key data feature set, applying particle swarm optimization and ant colony optimization algorithms and combining with genetic algorithms, and generates a comprehensive health status analysis report;

[0085] The monitoring parameter calibration module calibrates the monitoring parameters based on the comprehensive health status analysis report, using Bayesian optimization and fuzzy logic, and generates the calibrated monitoring parameters;

[0086] The treatment efficacy evaluation module evaluates the effectiveness of the treatment plan based on the calibrated monitoring parameters and the comprehensive health status analysis report, using statistical analysis and logistic regression, and generates a treatment efficacy evaluation report.

[0087] The time series analysis results include historical data trends, real-time status analysis, and prediction model outputs. The disease prediction report includes future health trends, complication risk assessments, and key indicator warnings. The complication alarm signals include potential risk levels, abnormal physiological indicators, and warning trigger factors. The optimized monitoring parameters include updated monitoring frequencies, adjusted monitoring indicators, and optimized data collection scopes. The key data feature set includes key physiological indicator features, health parameter trends, and data pattern recognition results. The comprehensive health status analysis report includes comprehensive health scores, disease progression analyses, and treatment response predictions. The calibrated monitoring parameters include adjusted monitoring indicators, optimized data collection strategies, and corrected monitoring thresholds. The treatment efficacy evaluation report includes statistical analyses of treatment effects, prognostic change assessments, and treatment optimization plans.

[0088] Through the time series analysis module, the medical team can accurately predict changes in the patient's health status, timely adjust treatment strategies, and effectively prevent the deterioration of the condition. The precise analysis of the disease prediction module provides a basis for early intervention and reduces the risk of severe complications. The early warning mechanism of the complication alarm module is the key to preventing the deterioration of the condition. Especially when dealing with critically ill patients, timely intervention can save lives. The dynamic monitoring adjustment module and the data optimization extraction module ensure that the monitoring strategy and data collection always remain in an optimal state, improving the monitoring efficiency and data quality, and providing a solid foundation for accurate medical decision-making. The comprehensive health data analysis and monitoring parameter calibration module improve the accuracy and sensitivity of the overall monitoring system through comprehensive analysis and parameter adjustment, making the treatment plan more personalized and precise. The treatment efficacy evaluation module provides important feedback on the effectiveness of the treatment plan for the medical team, promoting the continuous improvement of treatment methods.

[0089] Please refer to Figure 3 and the time series analysis module includes a historical data processing sub-module, a real-time data analysis sub-module, and a deep learning sub-module;

[0090] The historical data processing sub-module conducts historical trend analysis and pattern recognition on the patient's historical medical data using the autoregressive integrated moving average algorithm, and generates historical data analysis results;

[0091] The real-time data analysis sub-module conducts real-time status monitoring and trend prediction on the patient's real-time medical data using the seasonal decomposition time series forecasting algorithm, and generates real-time data analysis results;

[0092] The deep learning sub-module comprehensively processes time series data using a long short-term memory network based on the historical data analysis results and real-time data analysis results, and generates time series analysis results.

[0093] In the historical data processing sub-module, the patient's historical medical data is processed using the autoregressive integrated moving average algorithm. This includes data stationarity processing to eliminate non-stationary trends in the time series and ensure the accuracy of the analysis. Model parameter estimation is performed to accurately capture the trends and patterns in the data. Time series prediction is based on these parameters to generate historical data analysis results, which reveal the historical change trends and patterns of the patient's health status.

[0094] In the real-time data analysis sub-module, the seasonal decomposition time series forecasting algorithm is used to process the patient's real-time medical data. This involves extracting the seasonal component to identify and analyze the seasonal patterns in the data. Subsequently, trend component analysis is performed to identify the long-term change trends in the data. Residual effects are processed to ensure the accuracy of the prediction. The output of this module is the real-time data analysis result, providing an immediate view of the patient's current health status and a prediction of future trends.

[0095] In the deep learning sub-module, combining the historical data analysis results and real-time data analysis results, a long short-term memory network (LSTM) is used for more in-depth data processing. The gating mechanism of LSTM allows the network to effectively process and store long-term dependencies, which is crucial for understanding the patient's health status and predicting its future changes. Through state updates, LSTM can continuously adapt to new data inputs and maintain the relevance and accuracy of its predictions. The final output of this module is the time series analysis result, which is a comprehensive view combining historical trends, current status, and future predictions, providing a comprehensive tool for medical teams to monitor and manage the condition.

[0096] Please refer to Figure 4 , the disease prediction module includes an image analysis sub-module, a time series prediction sub-module, and a feature extraction sub-module;

[0097] The image analysis sub-module analyzes medical image data using a convolutional neural network based on the time series analysis result, and generates image feature analysis results;

[0098] The time series prediction sub-module, based on the results of image feature analysis, uses a recurrent neural network to perform time series prediction of the disease progression and generates time series prediction results;

[0099] The feature extraction sub-module, based on the time series prediction results, uses the long short-term memory network algorithm to perform key feature extraction and generates a disease prediction report;

[0100] The convolutional neural network includes a feature extraction layer, a pooling layer, and a fully connected layer. The recurrent neural network includes a recurrent unit structure, data processing at each time step, and sequence output generation;

[0101] The long short-term memory network algorithm follows the formula:

[0102] ;

[0103] Calculate the predicted value;

[0104] Among them, represents the forgetting gate weight, which controls the amount of past information retained; represents the output of the LSTM cell at the previous moment, reflecting the information of the previous state; represents the input at the current moment, such as the patient's physiological parameters; represents the bias term of the forgetting gate, which adjusts the activation level of the forgetting gate, represents the connection of the previous output and the current input, is the sigmoid function, which determines the degree of closure of the forgetting gate.

[0105] In the image analysis sub-module, medical image data is processed by a convolutional neural network (CNN). This process covers identifying key features in the image from the feature extraction layer, reducing the data dimension through the pooling layer, to integrating these features in the fully connected layer to generate the image feature analysis results, providing a basis for subsequent disease prediction.

[0106] In the time series prediction sub-module, data based on the results of image feature analysis is processed by a recurrent neural network (RNN). The recurrent unit structure of the RNN can effectively process time series data, gradually analyze the information at each time point, generate time series prediction results, and reveal the potential development path of the disease.

[0107] In the feature extraction sub-module, the time series prediction results are processed by combining an autoencoder and an attention mechanism. The autoencoder is responsible for unsupervised feature encoding to extract important features, while the attention mechanism highlights the most critical information among these features. These processes together generate a disease prediction report, providing a detailed prediction of the patient's future health status and providing important support for medical decision-making.

[0108] Formula:

[0109] ;

[0110] Detailed Explanation of the Formula and Derivation Process of Formula Calculation:

[0111] Detailed Explanation: This formula is a calculation step for the Long Short-Term Memory Network (LSTM), where represents the sigmoid activation function, which is used to compress the input value between 0 and 1, representing the probability of information retention. is the weight matrix of the forget gate, which determines how much information of the output of the previous time step and how much information of the current input to retain. is the bias term of the forget gate, which is used to fine-tune the activation level of the forget gate.

[0112] Quantization Processing: 1. The value of is obtained through training with historical data. Assuming the update result of backpropagation based on the most recent batch of data, the current is the output of the LSTM cell at the previous time step, for example, obtained by processing the disease condition indicators of the previous step . 3. is the input at the current moment, for example, the current heart rate value of the patient, which is directly obtained through a heart rate monitoring instrument and converted into the LSTM input format. Assuming . 4. is the bias value adjusted through the training set, currently set to .

[0113] Derivation Process of Formula Calculation: 1. First, calculate the concatenation operation , that is, juxtapose the values of and to obtain . 2. Then perform the weight multiplication operation , that is, . 3. Next, add the bias term to obtain . 4. Finally, process through the sigmoid function and calculate . The obtained sigmoid value is used to simulate the value of , .

[0114] This result indicates that given the current parameters and inputs, the predicted output represents the predicted value of the patient's disease trend. This result is used for further decision-making and predictive analysis in the neural network, which helps in the optimization and adjustment of disease management and treatment plans.

[0115] Please refer to Figure 5, the complication alert module includes a risk assessment sub-module, an anomaly detection sub-module, and a classification algorithm sub-module;

[0116] Based on the condition prediction report, the risk assessment sub-module uses the random forest algorithm to conduct a risk assessment of potential complications and generates a risk assessment result;

[0117] Based on the risk assessment result, the anomaly detection sub-module uses the isolation forest algorithm to detect abnormal patterns and generates an anomaly detection result;

[0118] Based on the anomaly detection result, the classification algorithm sub-module uses the gradient boosting machine algorithm to classify data, identify specific complication types, and generate a complication alert signal;

[0119] The random forest algorithm includes multi-tree ensemble decision-making, random feature selection, and result aggregation strategies. The isolation forest algorithm is specifically a tree-structure-based data partitioning and outlier identification mechanism. The gradient boosting machine algorithm is specifically a gradient descent-based model enhancement and error correction method.

[0120] In the risk assessment sub-module, the random forest algorithm is used. Random forest is an ensemble learning method for classification and regression. Here, it is applied to risk assessment.

[0121] Please refer to Figure 6 , the dynamic monitoring and adjustment module includes a dynamic parameter adjustment sub-module, a filter application sub-module, and a time warping sub-module;

[0122] Based on the complication alert signal, the dynamic parameter adjustment sub-module uses the long short-term memory network to dynamically adjust the monitoring parameters and generates a dynamic adjustment result;

[0123] Based on the dynamic adjustment result, the filter application sub-module uses the Kalman filter to process signals and correct data, and generates a filter processing result;

[0124] Based on the filter processing result, the time warping sub-module uses the dynamic time warping algorithm to calibrate the time series of the monitoring strategy and generates optimized monitoring parameters;

[0125] The long short-term memory network includes gated processing of sequential data, state retention, and modeling of time-dependent relationships. The Kalman filter is specifically a method for estimating the state of a dynamic system and filtering out noise data. The dynamic time warping algorithm is specifically a technique for time series alignment, shape matching, and non-linear transformation.

[0126] In the dynamic parameter adjustment sub-module, the complication alert signal is processed by a Long Short-Term Memory network (LSTM). The gating mechanism of the LSTM allows for effective processing of sequential data, maintaining the state and modeling temporal dependencies. Based on these analyses, the monitoring parameters are dynamically adjusted to respond to the patient's current health status and potential risks, generating dynamic adjustment results.

[0127] In the filter application sub-module, the dynamic adjustment results are processed by a Kalman filter. As a tool for estimating the state of a dynamic system, the Kalman filter can effectively filter out noise data, improving the accuracy and reliability of the signal. This process not only corrects the data but also enhances the quality of the monitoring data, generating filter processing results.

[0128] In the time warping sub-module, the filter processing results are processed by the dynamic time warping algorithm. This algorithm includes techniques for aligning time series, shape matching, and non-linear transformation, ensuring that the monitoring strategy closely corresponds to the patient's actual condition. Through calibration, optimized monitoring parameters are generated to more accurately reflect the patient's health status and treatment response.

[0129] Please refer to Figure 7 , the data optimization and extraction module includes a feature selection sub-module, a data extraction sub-module, and a machine learning application sub-module;

[0130] The feature selection sub-module extracts key features based on the optimized monitoring parameters using principal component analysis and least absolute shrinkage and selection operator regression, generating feature selection results;

[0131] The data extraction sub-module classifies data based on the feature selection results using an improved support vector machine algorithm, optimizing the kernel function to extract key patterns in the medical data and generating data extraction results;

[0132] The machine learning application sub-module applies random forests for machine learning analysis based on the data extraction results to identify key medical data and generate a key data feature set;

[0133] Principal component analysis and least absolute shrinkage and selection operator regression include data dimensionality reduction, eigenvalue decomposition, and regularized feature selection, while random forests include decision tree ensembles, random feature selection, and voting decision mechanisms;

[0134] In the feature selection sub-module, key features are extracted from the optimized monitoring parameters using principal component analysis and least absolute shrinkage and selection operator regression. Principal component analysis is responsible for data dimensionality reduction and eigenvalue decomposition, screening out the most informative features, while least absolute shrinkage and selection operator regression further optimizes the feature set through regularized feature selection, generating feature selection results that ensure only the most important features for disease prediction and analysis are included.

[0135] In the data extraction sub-module, based on the feature selection results, data classification is performed in combination with the Support Vector Machine (SVM). The SVM distinguishes different patterns in medical data through kernel methods and boundary maximization techniques, creating a classification decision function. This process enables the key patterns in medical data to be clearly extracted, generating data extraction results and providing a clear data structure for subsequent analysis.

[0136] The improved Support Vector Machine algorithm, according to the formula:

[0137] ;

[0138] Calculates the classification decision function ;

[0139] Where:

[0140] ;

[0141] Detailed explanation of the formula and the derivation process of the formula calculation: and are input vectors, coming from the actual medical data set, representing the medical index values of different patients, such as blood pressure, blood sugar, etc. is the Lagrange multiplier, obtained by solving the dual problem, and its value reflects the importance of the corresponding support vector in the definition of the decision boundary. is the class label, determined by the medical diagnosis result. For example, the positive class (1) represents the presence of the disease, and the negative class (-1) represents the absence of the disease. is the bandwidth parameter of the kernel function, determined by cross-validation to find the optimal value to minimize the validation error. is the variance parameter, calculated according to the statistical characteristics of the input data, usually the average of the differences in each dimension. is the offset, obtained in the optimization problem solution to ensure that the model has the best generalization ability. represents the i-th training sample The similarity between the training sample and the sample x to be classified. represents the training sample and the training sample The degree of similarity between them.

[0142] Numerical example and calculation derivation:

[0143] Set specific values for example calculation: Suppose there are three data points, namely , and the specific data is as follows: , , , , , , , , , , determined according to the historical data processing experience. , based on the input data 's statistical variance. , obtained from the optimized results.

[0144] Kernel function calculation:

[0145] For , calculate :

[0146] ;

[0147] ;

[0148] ;

[0149] Decision function calculation:

[0150] Calculate :

[0151] ;

[0152] This indicates that the classification result is the positive class, that is, according to the model prediction, the disease condition exists.

[0153] In the machine learning application sub-module, based on the data extraction results, random forest is applied for machine learning analysis. Random forest combines the prediction results of multiple models through decision tree integration, random feature selection, and voting decision mechanism, thereby improving the accuracy and reliability of prediction. This process identifies the key information in the medical data, generates the key data feature set, and provides accurate data reference for the medical team.

[0154] Please refer to Figure 8 , the health data comprehensive analysis module includes a swarm intelligence analysis sub-module, a genetic algorithm sub-module, and a data synthesis sub-module;

[0155] The swarm intelligence analysis sub-module is based on the key data feature set, and uses the particle swarm optimization algorithm and the ant colony optimization algorithm for data analysis, searches for the optimal solution, and generates the swarm intelligence analysis result;

[0156] The genetic algorithm sub-module is based on the swarm intelligence analysis result, and uses the genetic algorithm for iterative optimization of the solution, and generates the genetic algorithm optimization result;

[0157] The data synthesis sub-module is based on the genetic algorithm optimization result, performs data comprehensive analysis, and conducts the final evaluation of the health status, and generates the health status comprehensive analysis report;

[0158] The particle swarm optimization algorithm and the ant colony optimization algorithm include particle position update, pheromone tracking, and optimal path search. The genetic algorithm includes population generation, fitness evaluation, and genetic operations. The comprehensive data analysis includes algorithm result fusion, data pattern recognition, and comprehensive health status assessment.

[0159] In the swarm intelligence analysis sub-module, the key data feature set is analyzed through the particle swarm optimization algorithm and the ant colony optimization algorithm. The particle swarm optimization algorithm updates the particle positions, while the ant colony optimization algorithm uses pheromone tracking and optimal path search to jointly find the optimal solution for data analysis and generate the swarm intelligence analysis results. These results reflect the key insights extracted from medical data and the best treatment strategies.

[0160] In the genetic algorithm sub-module, based on the swarm intelligence analysis results, the genetic algorithm is used to iteratively optimize the solution. This process involves population generation, fitness evaluation, and genetic operations, continuously improving and refining the solution to generate the genetic algorithm optimization results. Through this method, treatment plans more suitable for the specific conditions of the patient can be effectively discovered.

[0161] In the comprehensive data sub-module, based on the genetic algorithm optimization results, the comprehensive analysis of the data is performed. This process includes the fusion of algorithm results, data pattern recognition, and comprehensive health status assessment. Through this comprehensive analysis, a comprehensive health status analysis report is generated, providing a detailed overview of the patient's overall health status, including disease progression, treatment response, and prognosis prediction, providing comprehensive data support for medical decision-making.

[0162] Please refer to Figure 9 , the monitoring parameter calibration module includes a parameter optimization sub-module, an algorithm application sub-module, and a data correction sub-module;

[0163] The parameter optimization sub-module preliminarily optimizes the monitoring parameters using Bayesian optimization based on the comprehensive health status analysis report to generate the preliminary optimization results;

[0164] The algorithm application sub-module iteratively adjusts the monitoring parameters using fuzzy logic based on the preliminary optimization results to generate the fuzzy logic adjustment results;

[0165] The data correction sub-module performs final data correction based on the fuzzy logic adjustment results to verify the accuracy of the monitoring parameters and generate the calibrated monitoring parameters;

[0166] Bayesian optimization includes using a probability model to estimate the objective function and selecting an optimization strategy. Fuzzy logic includes establishing fuzzy sets, rule definition, and implementation of an inference mechanism. Data correction includes data filtering, noise reduction, and error analysis.

[0167] In the parameter optimization sub-module, the monitoring parameters are initially optimized through Bayesian optimization. Bayesian optimization uses a probability model to estimate the objective function and selects the best optimization strategy. Based on the comprehensive health analysis report, this process adjusts the monitoring parameters to more accurately reflect the actual health status of the patient, generating the initial optimization results.

[0168] In the algorithm application sub-module, based on the initial optimization results, fuzzy logic is used to further iteratively adjust the monitoring parameters. Fuzzy logic conducts a more meticulous adjustment of the monitoring parameters by establishing fuzzy sets, defining rules, and implementing an inference mechanism. This process generates the fuzzy logic adjustment results, enhancing the adaptability and sensitivity of the monitoring parameters.

[0169] In the data correction sub-module, based on the fuzzy logic adjustment results, the final data correction is performed. This includes data filtering, noise reduction, and error analysis to ensure the accuracy and reliability of the monitoring parameters. The correction process ensures that the monitoring parameters can accurately reflect the patient's health status, generating the calibrated monitoring parameters, providing accurate data support for the medical team to make more effective medical decisions.

[0170] Please refer to Figure 10 , the treatment efficacy evaluation module includes a statistical analysis sub-module, a treatment effect evaluation sub-module, and a machine learning analysis sub-module;

[0171] The statistical analysis sub-module, based on the calibrated monitoring parameters, uses statistical analysis methods to evaluate the effectiveness of the treatment plan, generating the statistical analysis results;

[0172] The treatment effect evaluation sub-module, based on the statistical analysis results, applies logistic regression to conduct in-depth treatment effect evaluation, generating the treatment effect evaluation results;

[0173] The machine learning analysis sub-module, based on the treatment effect evaluation results and the comprehensive health analysis report, uses machine learning algorithms to conduct a comprehensive treatment efficacy evaluation, generating the treatment efficacy evaluation report;

[0174] Statistical analysis methods include descriptive statistics, inferential statistics, and model fitting. Logistic regression includes probability model construction, variable weight assignment, and result probability prediction. Machine learning algorithms include decision trees, neural networks, and support vector machines.

[0175] In the statistical analysis sub-module, the calibrated monitoring parameters are evaluated by using statistical analysis methods. This includes descriptive statistical analysis to summarize the basic characteristics of the data, inferential statistical analysis to draw broader conclusions, and model fitting to understand the relationships between different variables. This process generates the statistical analysis results, providing a preliminary quantitative basis for evaluating the effectiveness of the treatment plan.

[0176] In the treatment effect evaluation sub-module, based on the statistical analysis results, the logistic regression method is applied for in-depth treatment effect evaluation. The logistic regression method generates treatment effect evaluation results by constructing a probability model, assigning variable weights, and predicting the probabilities of success for different treatment options, providing an in-depth analysis of the effectiveness of treatment options.

[0177] In the machine learning analysis sub-module, based on the treatment effect evaluation results and the comprehensive analysis report of the health status, machine learning algorithms are used for comprehensive treatment efficacy evaluation. This includes algorithms such as decision trees, neural networks, and support vector machines to comprehensively analyze and evaluate the effectiveness of treatment options. Through these advanced algorithms, a treatment efficacy evaluation report is generated, providing a comprehensive view of the treatment effect and assisting the medical team in making more effective treatment decisions.

[0178] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the 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 the technical solution content of the present invention is not departed from, any simple modifications, equivalent changes, and modifications 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. An intelligent monitoring system for critically ill patients in urology, characterized by: The system comprises: The time series analysis module uses the autoregressive integrated moving average algorithm and seasonal decomposition time series prediction algorithm to process data based on patient history and real-time medical data and generate time series analysis results; The disease prediction module uses convolutional neural networks and recurrent neural networks based on the results of time series analysis to predict disease progression and generate a disease prediction report; The complication alert module uses random forest and gradient boosting algorithms based on the disease prediction report to evaluate potential complications and generate complication alert signals; The dynamic monitoring adjustment module adjusts the monitoring strategy based on complication alarm signals through a dynamic time warping algorithm and generates optimized monitoring parameters; The data optimization and extraction module extracts key medical data based on the optimized monitoring parameters and generates key data feature sets using principal component analysis and least absolute shrinkage and selection operator regression; The health data comprehensive analysis module uses particle swarm optimization and ant colony optimization algorithms to conduct a comprehensive analysis of health conditions based on key data feature sets and generate a comprehensive analysis report on health conditions; The monitoring parameter calibration module calibrates the monitoring parameters based on the comprehensive analysis report of the health status, adopts Bayesian optimization and fuzzy logic, and generates calibrated monitoring parameters; The efficacy evaluation module evaluates the effectiveness of the treatment plan based on the calibrated monitoring parameters and comprehensive analysis report of the health status, and generates an efficacy evaluation report.

2. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The time series analysis module includes a historical data processing submodule, a real-time data analysis submodule, and a deep learning submodule; The historical data processing submodule uses an autoregressive integral sliding average algorithm to perform historical trend analysis and pattern recognition based on the patient's historical medical data, and generates historical data analysis results; The real-time data analysis submodule uses a seasonal decomposition time series prediction algorithm to perform real-time status monitoring and trend prediction based on the patient's real-time medical data, and generates real-time data analysis results; The deep learning submodule uses a long short-term memory network to comprehensively process time series data based on historical data analysis results and real-time data analysis results, and generates time series analysis results; The autoregressive integrated moving average algorithm includes data stabilization processing, model parameter estimation and time series prediction, the seasonal decomposition time series prediction algorithm includes seasonal component extraction, trend component analysis and residual effect processing, and the long short-term memory network includes a gating mechanism, state updating and long-term dependency relationship learning.

3. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The disease prediction module includes an image analysis submodule, a time series prediction submodule, and a feature extraction submodule; The image analysis submodule uses a convolutional neural network to analyze medical image data based on the time series analysis results and generates image feature analysis results; The time series prediction submodule uses a recurrent neural network to perform time series prediction of disease progression based on the image feature analysis results and generates a time series prediction result; The feature extraction submodule uses a long short-term memory network algorithm based on the time series prediction results to extract key features and generate a disease prediction report; The convolutional neural network includes a feature extraction layer, a pooling layer and a fully connected layer, and the recurrent neural network includes a recurrent unit structure, time step data processing and sequence output generation; The long short-term memory network algorithm is based on the formula: ； Calculate predicted values; in, Represents the weight of the forget gate, which controls the amount of past information retained; Represents the LSTM unit output at the previous moment, reflecting the information of the previous state; Represents the input at the current moment, such as the patient's physiological parameters; Represents the bias term of the forget gate, which adjusts the activation level of the forget gate. Represents the connection between the previous output and the current input, It is the sigmoid function, which determines the degree to which the forget gate is closed.

4. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The complication alarm module includes a risk assessment submodule, an anomaly detection submodule, and a classification algorithm submodule; The risk assessment submodule uses a random forest algorithm to perform a risk assessment of potential complications based on the disease prediction report and generates a risk assessment result; The anomaly detection submodule uses the isolation forest algorithm to detect abnormal patterns based on the risk assessment results and generates anomaly detection results; The classification algorithm submodule uses a gradient boosting algorithm to classify data based on the abnormal detection results, identifies specific complication types, and generates complication alarm signals; The random forest algorithm includes multi-tree ensemble decision, feature random selection and result aggregation strategy, the isolation forest algorithm is specifically a data partitioning and isolated point identification mechanism based on tree structure, and the gradient boosting machine algorithm is specifically a model enhancement and error correction method based on gradient descent.

5. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The dynamic monitoring and adjustment module includes a dynamic parameter adjustment submodule, a filter application submodule, and a time regularization submodule; The dynamic parameter adjustment submodule dynamically adjusts the monitoring parameters based on the complication alarm signal using a long short-term memory network and generates a dynamic adjustment result; The filter application submodule uses a Kalman filter to perform signal processing and data correction based on the dynamic adjustment result, and generates a filter processing result; The time warping submodule uses a dynamic time warping algorithm to calibrate the monitoring strategy in time series based on the filter processing results and generates optimized monitoring parameters; The long short-term memory network includes gated processing, state retention and time dependency modeling of sequence data, the Kalman filter is specifically a dynamic system state estimation and noise data filtering method, and the dynamic time warping algorithm is specifically time series alignment, shape matching and nonlinear transformation technology.

6. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The data optimization and extraction module includes a feature selection submodule, a data extraction submodule, and a machine learning application submodule; The feature selection submodule uses principal component analysis and minimum absolute shrinkage and selection operator regression to extract key features based on the optimized monitoring parameters and generate feature selection results; The data extraction submodule uses an improved support vector machine algorithm based on the feature selection results to classify data by optimizing the kernel function, extract key patterns in the medical data, and generate data extraction results; The machine learning application submodule applies random forest to perform machine learning analysis based on the data extraction results, identifies key medical data, and generates a key data feature set; The principal component analysis and least absolute shrinkage and selection operator regression include data dimension reduction, eigenvalue decomposition and regularized feature selection, and the random forest includes decision tree integration, random feature selection and voting decision mechanism; The improved support vector machine algorithm is based on the formula: ； ； Calculate the classification decision function ; in, and is the input vector, representing the patient's medical indicator value, is the Lagrange multiplier obtained by solving the dual problem, reflecting the importance of the corresponding support vector in the definition of the decision boundary. is the category label, determined by the medical diagnosis results, is the bandwidth parameter of the kernel function, and the optimal value is determined based on cross-validation to minimize the validation error. is the variance parameter, usually the mean of the dimension differences, is the offset, obtained by solving the optimization problem, It usually represents the total number of samples involved in training, i is the sample index, and j is the index of the feature dimension. Represents the i-th training sample The similarity with the sample x to be classified, Represents training samples With training samples The degree of similarity between them.

7. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The health data comprehensive analysis module includes a swarm intelligence analysis submodule, a genetic algorithm submodule, and a data comprehensive submodule; The swarm intelligence analysis submodule uses particle swarm optimization algorithm and ant colony optimization algorithm to perform data analysis based on key data feature sets, find the optimal solution, and generate swarm intelligence analysis results; The genetic algorithm submodule uses the genetic algorithm to iteratively optimize the solution based on the swarm intelligence analysis results to generate the genetic algorithm optimization results; The data integration submodule performs data comprehensive analysis based on the genetic algorithm optimization results, makes a final assessment of the health status, and generates a comprehensive analysis report of the health status; The particle swarm optimization algorithm and the ant colony optimization algorithm include particle position update, pheromone tracking and optimal path search, the genetic algorithm includes population generation, fitness evaluation and genetic operation, and the data comprehensive analysis includes algorithm result fusion, data pattern recognition and comprehensive health status evaluation.

8. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The monitoring parameter calibration module includes a parameter optimization submodule, an algorithm application submodule, and a data correction submodule; The parameter optimization submodule uses Bayesian optimization to perform preliminary optimization of monitoring parameters based on the comprehensive analysis report of the health status and generates preliminary optimization results; The algorithm application submodule uses fuzzy logic to iteratively adjust the monitoring parameters based on the preliminary optimization results to generate fuzzy logic adjustment results; The data correction submodule performs final data correction based on the fuzzy logic adjustment results, verifies the accuracy of the monitoring parameters, and generates calibrated monitoring parameters; The Bayesian optimization includes using probability models to estimate the objective function and select the optimization strategy, the fuzzy logic includes establishing fuzzy sets, rule definitions and implementation of reasoning mechanisms, and the data correction includes data filtering, noise reduction and error analysis.

9. The intelligent monitoring system for critically ill patients in urology department according to claim 1 is characterized by: The efficacy evaluation module includes a statistical analysis submodule, a treatment effect evaluation submodule, and a machine learning analysis submodule; The statistical analysis submodule uses a statistical analysis method to evaluate the effectiveness of the treatment plan based on the calibrated monitoring parameters and generates statistical analysis results; The treatment effect evaluation submodule uses the logistic regression method to perform in-depth treatment effect evaluation based on the statistical analysis results to generate treatment effect evaluation results; The machine learning analysis submodule uses a machine learning algorithm to conduct a comprehensive efficacy evaluation based on the treatment effect evaluation results and the comprehensive health status analysis report to generate an efficacy evaluation report; The statistical analysis method includes descriptive statistics, inferential statistics and model fitting, the logistic regression method includes probability model construction, variable weight allocation and result probability prediction, and the machine learning algorithm includes decision tree, neural network and support vector machine.

Images