A cardiovascular disease diagnosis and treatment scheme optimization system based on a Transformer architecture

Abstract

The application relates to the technical field of cardiovascular disease diagnosis and treatment and artificial intelligence, and discloses a cardiovascular disease diagnosis and treatment scheme optimization system based on architecture, which comprises an original data preprocessing module, which is used for collecting and standardizing time series monitoring data and static data of medical history texts, adopts an improved and normalized algorithm, utilizes model structured text data, and outputs standardized patient feature data; and an improved logic analysis module, which is used for receiving the standardized patient feature data, optimizing the architecture by introducing a sparse attention mechanism, and performing time series correlation analysis, pathological feature mapping and individual difference modeling. The improved logic analysis module is used for introducing the sparse attention mechanism, deeply mining time series correlation and implicit pathological logic coupling relationships in complex time series monitoring data, generating a high-dimensional patient state feature vector, and solving the problem that traditional methods are shallow in analysis and cannot accurately identify individualized pathological states.

Description

Technical Field

[0001] This invention relates to the intersection of cardiovascular disease diagnosis and treatment technology and artificial intelligence, specifically a method based on... A system for optimizing cardiovascular disease diagnosis and treatment protocols. Background Technology

[0002] Cardiovascular disease, as one of the leading causes of death worldwide, demands highly personalized and precise treatment plans due to its complexity and diversity. Thanks to the advanced development of medical informatics, modern medical systems can collect vast amounts of high-frequency time-series monitoring data, including dynamic electrocardiograms, blood pressure, and blood oxygen saturation, as well as rich static text data such as medical history and lifestyle habits. This multimodal data provides an unprecedented foundation for implementing precision medicine, but also poses significant challenges to data processing and decision analysis.

[0003] In the development of cardiovascular disease treatment plans, the processing of raw physiological monitoring data from patients remains at the superficial feature extraction stage. This fails to delve deeper into and capture the key temporal correlations within these complex time-series data, as well as the implicit pathological logical couplings between different physiological indicators. This superficial analysis prevents models from accurately identifying the individualized pathological state and potential risks of patients, ultimately resulting in a low degree of matching between the generated treatment plan and the patient's actual condition, making it difficult to achieve truly personalized and precise treatment.

[0004] Existing treatment protocols, once initiated and implemented, rely heavily on regular manual intervention by medical staff for efficacy evaluation and subsequent adjustments, lacking a self-learning and optimization mechanism driven by real-time patient feedback data. Because manual adjustments are time-consuming, they cannot capture subtle changes in the patient's condition and their response to current treatment in a timely and dynamic manner. This results in a significant lag in protocol adjustments, which may lead to patients receiving inappropriate treatment for a period of time, causing overtreatment or undertreatment, thus affecting efficacy and patient safety.

[0005] In existing technologies, pre-established treatment plan databases use simple keyword matching or tag matching for retrieval and matching. This method can only perform literal matching and fails to conduct in-depth semantic understanding and correlation analysis between the patient's current real-time physiological state and the treatment plan content stored in the database. It is not easy to intelligently judge and retrieve the historical or standard treatment plan that is most similar and most consistent with the current condition based on the patient's complex condition and multi-dimensional data. This results in low accuracy and efficiency of plan retrieval, affecting the doctor's decision-making speed and the smoothness of the treatment process.

[0006] To address the above problems, this invention proposes a method based on A cardiovascular disease diagnosis and treatment plan optimization system is presented. This system utilizes a sparse attention mechanism to deeply analyze patient states, achieving semantic-level benchmarking retrieval. Furthermore, it employs deep reinforcement learning for efficacy-driven dynamic optimization, enabling precise matching and real-time adjustment of treatment plans. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method based on... A cardiovascular disease diagnosis and treatment optimization system based on the architecture is proposed to address the problems mentioned in the background section.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method based on The architecture-based cardiovascular disease diagnosis and treatment solution optimization system includes:

[0009] The raw data preprocessing module is used to collect and standardize static data from time-series monitoring data and medical history texts, employing improved methods. and Normalization algorithm, utilizing The model structures text data and outputs standardized patient feature data.

[0010] improve The logic analysis module receives standardized patient characteristic data and optimizes it by introducing a sparse attention mechanism. The architecture performs temporal correlation analysis, pathological feature mapping, and individual difference modeling, outputting a high-dimensional patient state feature vector.

[0011] The treatment plan database module includes a plan database for storing standard plans and clinically optimized plans classified by disease type and risk level. The plans in the plan database are all pre-encoded as plan feature vectors. This module receives patient status feature vectors, performs semantic-level benchmarking retrieval based on the cosine similarity between the patient status feature vectors and the plan feature vectors, and outputs personalized treatment plans.

[0012] The protocol execution and efficacy monitoring module is used to implement personalized treatment protocols, collect efficacy monitoring data in real time after the personalized treatment protocols are implemented, and output efficacy monitoring data.

[0013] The reinforcement learning optimization module receives efficacy monitoring data, uses a deep reinforcement learning algorithm with the patient prognosis improvement rate as the core reward function, analyzes the reasons for the deviation between the efficacy monitoring data and the target threshold, dynamically generates optimized protocol parameters, and feeds the optimized protocol parameters back to the protocol execution and efficacy monitoring module.

[0014] Preferably, the raw data preprocessing module further includes:

[0015] The data acquisition and preliminary separation unit is used to acquire dynamic data. Time-series monitoring data such as heart rate, blood pressure, and blood oxygen saturation, as well as static text data such as medical history and lifestyle habits, are stored separately;

[0016] An outlier correction and numerical standardization unit is used to receive the time-series monitoring data and adopt improved... The algorithm performs outlier correction, specifically for data points. Correction value The judgment condition is: if Make corrections.

[0017] in, for The mean of the data within the sliding window per second. for The standard deviation of the data within the sliding window per second. The threshold for outlier detection;

[0018] The corrected dataset uses Normalization algorithm, where the standardized numerical values The calculation formula is: ,in, For the original data points, and These are the minimum and maximum values ​​of all time-series monitoring data;

[0019] The text data structuring unit is used to receive the static text data and employ... The model performs semantic encoding and structured processing on the static text data, converting it into numerical text feature vectors;

[0020] The core feature filtering unit receives the standardized time-series monitoring data and text feature vectors, and uses the Pearson correlation analysis algorithm to calculate the linear correlation coefficient between each feature. :

[0021] ,

[0022] in, and For the feature data points to be analyzed, and The mean;

[0023] The importance of each feature is evaluated using a random forest algorithm, and core physiological features and structured text features are extracted and output. The core physiological features include... of Segment offset and blood pressure fluctuation slope together constitute the output to the improvement Standardized patient characteristic data from the logical analysis module.

[0024] Preferably, the improvement The logic analysis module further includes:

[0025] A sparse attention computation unit is used to receive standardized patient feature data output, and to apply a sparse attention mechanism to the standardized patient feature data for temporal feature extraction. The sparse attention mechanism is designed for standardized... The self-attention mechanism is optimized, where the attention weight matrix... The calculation formula is:

[0026] ,

[0027] in, For query vector, For key vectors, Let be the dimension of the key vector. It is a sparse mask matrix;

[0028] The sparse mask matrix The computational positions corresponding to the temporal features in the standardized patient feature data are assigned non-zero values, and the temporal features include... Abnormal waveform segments;

[0029] The pathological logic mapping and determination unit is used to receive the temporal features extracted by the sparse attention mechanism, and to perform pathological feature mapping on the temporal features. The condition for determining myocardial ischemia is as follows: of Low segment pressure value ≥ ;in, for The vertical offset of the segment relative to the equipotential line;

[0030] Furthermore, a preliminary patient pathological model is constructed based on the mapping results and individual differences modeling. The individual differences modeling is based on the patient's age, metabolic capacity, and postoperative status to make personalized corrections to the preliminary patient pathological model.

[0031] The patient state vector encoding unit is used to encode the modified patient pathology model to generate a high-dimensional patient state feature vector, which includes cardiovascular disease risk level, core pathological features and individual difference parameters.

[0032] Preferably, the treatment plan database module further includes:

[0033] The protocol database construction and management unit is used to store structured treatment protocols classified by disease type, risk level and treatment stage. The protocols include standard protocols from the "Guidelines for the Prevention and Treatment of Cardiovascular Diseases in China" and optimized protocols from a large number of clinical cases.

[0034] The scheme feature vector precoding unit is used to receive all treatment schemes in the scheme database, perform structured encoding processing on the treatment schemes, and convert each treatment scheme into a scheme feature vector with semantic information. The feature vector of the scheme With patient state feature vector Consistent dimensions;

[0035] Among them, the patient state feature vector Improved Output of the logic analysis module;

[0036] The semantic-level mapping retrieval unit is used to receive the output patient state feature vector. And for the patient state feature vector With all scheme feature vectors in the scheme database Cosine similarity is calculated for matching. The calculation formula is:

[0037] ,

[0038] in, The dot product of the patient state feature vector and the protocol feature vector. and The semantic-level benchmarking retrieval unit outputs the personalized treatment plan with the highest similarity to the plan execution and efficacy monitoring module, which is the Euclidean norm of the patient state feature vector and the plan feature vector.

[0039] Preferably, the protocol execution and efficacy monitoring module further includes:

[0040] The scheme implementation and instruction receiving unit is used to receive the output personalized diagnosis and treatment plan, set the initial plan parameters according to the personalized diagnosis and treatment plan, and initiate the plan implementation process; at the same time, it receives the optimized plan parameters fed back by the reinforcement learning optimization module to make real-time dynamic adjustments to the plan parameters.

[0041] A multi-dimensional efficacy data acquisition unit is used to collect real-time efficacy monitoring data of efficacy indicators during the implementation of the treatment plan. The collection frequency of these efficacy indicators is determined based on the stage of diagnosis and treatment.

[0042] If it is determined that the current situation is in the acute phase, the sampling frequency is once per hour;

[0043] If the current period is determined to be stable, the data collection frequency is once a day.

[0044] The efficacy assessment and feedback data generation unit is used to receive the real-time efficacy monitoring data and calculate the target achievement rate of efficacy indicators. Among them, the blood pressure target achievement rate The calculation formula is: ,

[0045] in, For blood pressure to be within the preset target range The length of time within, This refers to the total monitoring time.

[0046] The efficacy assessment and feedback data generation unit generates efficacy monitoring data based on the target achievement rate and outputs the efficacy monitoring data to the reinforcement learning optimization module.

[0047] Preferably, the reinforcement learning optimization module further includes:

[0048] The state feature extraction and reward generation unit is used to receive the output efficacy monitoring data and encode the efficacy monitoring data and the current treatment plan parameters into the current state of the Markov decision process. Calculate core rewards and auxiliary rewards The core reward Based on patient prognosis improvement rate Compared with the preset target value The difference is calculated, and the auxiliary reward The current state is calculated based on auxiliary indicators such as solution effectiveness and personalization adaptation rate. and rewards ,

[0049] depth Network learning units, used to employ deep learning The network algorithm performs policy learning to determine the optimal action, the optimal action The choice is based on Maximizing the value, the The objective function of the value The update follows the Bellman equation, which can be simplified to:

[0050] ,

[0051] in, For the goal value, For the next reward value, As a discount factor, For the goal Network status for the next step Optimal action Value prediction, For the goal Network parameters;

[0052] This unit outputs the action feature vector corresponding to the optimal action;

[0053] A dynamic protocol parameter generation and feedback unit is used to receive the action feature vector and decode the action feature vector into specific optimized protocol parameters. The optimized protocol parameters include the adjustment amount of medication dosage and the rate of change of physical therapy intensity. This unit dynamically adjusts the optimization frequency according to the treatment stage, wherein the condition determination includes:

[0054] If it is determined that the current phase is acute, the optimization frequency is once a day to optimize the parameters of the proposed solution.

[0055] If the current period is determined to be stable, the optimization frequency is once a week to optimize the parameters of the proposed scheme.

[0056] Preferably, the scheme feature vector precoding unit uses a bidirectional long short-term memory network to extract features from the text description of the treatment plan to generate the scheme feature vector. The semantic-level benchmarking retrieval unit, after calculating cosine similarity, further performs a secondary screening based on risk level, retaining those that match the patient's state feature vector. Treatment protocols consistent with medium-risk levels will be provided.

[0057] Preferably, in the efficacy evaluation and feedback data generation unit of the protocol execution and efficacy monitoring module, the target achievement rate of the efficacy indicators is... Further including heart rate variability pass rate ,in, The standard deviation of the interval between adjacent heartbeats, the pass rate The determination criteria are:

[0058] If the current Value greater than If the treatment is deemed to have met the criteria, the efficacy monitoring data will also include the aforementioned data. pass rate .

[0059] Preferably, the encoding of the feature vector of the scheme adopts... The model is trained to ensure that the feature vectors of the proposed solutions are comparable to the feature vectors of the patient states in the semantic space. The feature vectors of the standard and clinically optimized solutions in the solution database are encoded and stored in a vector database, which employs... The algorithm performs index optimization to accelerate the retrieval speed of the semantic-level benchmark retrieval unit.

[0060] Preferably, the deep reinforcement learning algorithm is trained using an experience replay mechanism and a target network, wherein the experience replay mechanism stores the current state... Optimal action Next step reward and the next step status The experience quadruples are stored in the experience pool and are randomly sampled during training for batch updates to break the correlation between data.

[0061] The target network The parameters of each Updated once per step, among which, This is the preset update frequency.

[0062] This invention provides a method based on A cardiovascular disease diagnosis and treatment solution optimization system based on architecture. It has the following beneficial effects:

[0063] 1. This invention employs an improved method. The logic analysis module, by introducing a sparse attention mechanism, delves into the temporal correlations and implicit pathological logical coupling relationships in complex time-series monitoring data, generating high-dimensional patient state feature vectors. This addresses the shortcomings of traditional methods' shallow analysis, which cannot accurately identify individualized pathological states.

[0064] 2. This invention employs a reinforcement learning optimization module, which uses a deep reinforcement learning algorithm with the patient prognosis improvement rate as the core reward. Based on real-time efficacy monitoring data, it drives autonomous learning and dynamically generates optimized protocol parameters, thus solving the shortcomings of existing systems that lack a real-time feedback-driven autonomous learning and optimization mechanism and have a lag in protocol adjustments.

[0065] 3. This invention employs a treatment plan database module, which encodes treatment plans into plan feature vectors and performs semantic-level benchmarking retrieval based on cosine similarity calculation. This enables a deep semantic understanding and association analysis of the patient's real-time status and plan content, thus overcoming the shortcomings of existing systems in terms of simple retrieval methods, low accuracy, and low efficiency. Attached Figure Description

[0066] Figure 1 This invention is based on Framework diagram of the cardiovascular disease diagnosis and treatment solution optimization system. Detailed Implementation

[0067] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0068] The present invention will now be described in detail with reference to the accompanying drawings:

[0069] Example:

[0070] Please see the appendix Figure 1 This invention provides a method based on The architecture of the cardiovascular disease diagnosis and treatment plan optimization system is described in detail through a case study of the optimization of diagnosis and treatment plans for hypertension patients.

[0071] The patient is The initial monitoring data and individual information of a male patient with hypertension aged 30 are as follows:

[0072] Systolic blood pressure / diastolic blood pressure is Heart rate Second-rate ,dynamic No obvious abnormalities were found, but the patient's medical history included a high-salt diet.

[0073] Raw data preprocessing module: Collects the aforementioned time-series monitoring data and static text data through the data acquisition and preliminary separation unit;

[0074] Outlier Correction and Numerical Standardization Unit: Receives time-series monitoring data and performs outlier correction on missing blood oxygen saturation data, i.e., the raw data points. Application Improvement Algorithm. Through calculation, Mean within a sliding window and standard deviation Substitute the judgment condition:

[0075] like If an error is detected or a missing element is found, it is corrected.

[0076] After correction, the original blood pressure Heart rate Second-rate Corrected blood oxygen Data sets adopt Normalization algorithm converts to standardized numerical values The calculation formula is:

[0077] ,in, and For the minimum and maximum values ​​of all patient data, ensure that the values ​​fall within... Interval.

[0078] Text data structuring unit: Receives static text data such as high-salt diet, and uses... The model performs semantic encoding and structured processing, converting it into numerical text feature vectors.

[0079] Core Feature Screening Unit: Receives standardized time-series monitoring data and text feature vectors, and uses Pearson correlation analysis and random forest algorithm to screen and extract core physiological features and structured text features, namely, elevated blood pressure and high-salt diet, which together constitute standardized patient feature data output to the next module.

[0080] improve Logical analysis module: Receives standardized patient characteristic data and performs in-depth logical analysis.

[0081] Sparse attention computation unit: extracts temporal features from standardized patient feature data; attention weight matrix. The calculation follows the formula ,in, As a sparse mask matrix, we focus solely on temporal features such as blood pressure fluctuations and heart rate changes. The overall analysis process is as follows: Completed within seconds.

[0082] Pathological logic mapping and judgment unit: Based on the extracted temporal features, the conditions for determining myocardial ischemia are as follows: Low segment pressure value Less than This means that no myocardial ischemia was determined, and individual differences were modeled based on parameters such as age of 55.

[0083] Patient State Vector Encoding Unit: Encodes the modified patient pathology model to generate a high-dimensional patient state feature vector. The vector contains in-depth warnings, abnormal blood pressure, and high-salt diet, i.e., high risk, core pathological features, and individual difference parameters.

[0084] Treatment plan database module: Receives patient status feature vectors Then, perform solution matching.

[0085] Protocol Feature Vector Precoding Unit: Pre-encodes all standard protocols and clinically optimized protocols in the database into protocol feature vectors. And stored in using Algorithm-optimized vector databases for indexing.

[0086] Semantic-level benchmarking retrieval unit: Calculates patient state feature vector With all scheme feature vectors cosine similarity : The search results showed the most similar personalized treatment plans, with a matching accuracy rate of [percentage missing]. .

[0087] Treatment plan execution and efficacy monitoring module: Executes personalized treatment plans and collects efficacy data in real time.

[0088] Solution Implementation and Command Receiving Unit: Sets initial solution parameters and begins solution implementation.

[0089] Multi-dimensional efficacy data collection unit: Condition determination indicates the patient is currently in the acute phase, i.e., hypertension is under control; the data collection frequency is set to once per hour for real-time blood pressure monitoring; initial treatment plan implementation. Blood pressure was measured again after minutes. .

[0090] Treatment efficacy assessment and feedback data generation unit: Calculates the target achievement rate of treatment efficacy indicators. For example, during the first week of monitoring, the average blood pressure reached [value missing]. Blood pressure target rate promote The calculation formula is: ,in, For blood pressure to be within the target range The unit outputs treatment efficacy monitoring data. (Time duration within the specified time frame.)

[0091] Reinforcement learning optimization module: Receives efficacy monitoring data and optimizes protocol parameters.

[0092] State Feature Extraction and Reward Generation Unit: Encodes efficacy monitoring data and current protocol parameters into the current state. And calculate the core reward and auxiliary rewards ,in, Based on the improvement of blood pressure target rate Prognostic improvement rate Perform the calculation.

[0093] depth Online learning units: using The algorithm performs policy learning, stores experience quadruples through an experience replay mechanism, and updates the target using the Bellman equation. value :

[0094] ,

[0095] in, As a discount factor, For the goal Network parameters; determine the optimal action and output the action feature vector.

[0096] Dynamic protocol parameter generation and feedback unit: decodes the optimal action into optimized protocol parameters; after determining that the patient's condition has entered a stable period after one week, the optimization frequency is adjusted to once a week. This optimization changes the physiotherapy frequency from twice a day to once a day. The optimized protocol parameters are fed back to the protocol execution and efficacy monitoring module.

[0097] Through optimization and iteration, the patient's blood pressure control rate stabilized at [a certain level]. The goal is to reduce the frequency of treatment interventions without compromising efficacy, and to verify the optimization effect of the cardiovascular disease diagnosis and treatment plan optimization system, the accuracy of plan adaptation, and the significant improvement in efficacy after iteration, so as to achieve the optimal diagnosis and treatment goal.

[0098] Through integration improvements Algorithms and Normalization algorithms can normalize the collected dynamic data. High-frequency time-series monitoring data such as blood pressure undergo efficient and accurate outlier correction and numerical standardization to ensure the quality and consistency of input data. Simultaneously, [the system] employs [various methods / technologies]. The model performs deep semantic encoding and structuring on unstructured static text data such as medical history and lifestyle habits. Combining Pearson correlation analysis and random forest algorithm, it accurately selects features from a massive dataset. of Core physiological features such as segment offset and blood pressure fluctuation slope are effectively integrated and dimensionality reduced for multimodal data, laying a high-quality feature foundation for in-depth analysis.

[0099] Introducing sparse attention mechanisms for standards The self-attention mechanism is optimized to efficiently focus on the long-term characteristics of cardiovascular diseases. By identifying abnormal waveform segments and other temporal features, unnecessary computational overhead is significantly reduced, improving the efficiency of temporal correlation analysis. Furthermore, integrating pathological logic mapping and individual difference modeling based on parameters such as patient age and metabolic capacity enables accurate determination of pathological states such as myocardial ischemia. It also allows for personalized correction of preliminary pathological models, generating high-dimensional patient state feature vectors that accurately reflect an individual's pathological state and potential risks, achieving a precise profile of the patient's condition.

[0100] A structured database has been established, incorporating standard protocols from the "Chinese Guidelines for the Prevention and Treatment of Cardiovascular Diseases" and optimized protocols from a vast number of clinical cases, ensuring the authority and timeliness of the treatment protocols. The most crucial advantage lies in the adoption of... The model pre-encodes all treatment plans, generating feature vectors with rich semantic information. An algorithm-optimized vector database is used for efficient indexing and storage. During retrieval, based on the cosine similarity calculation between the patient's state feature vector and the treatment plan feature vector, a deep semantic-level matching retrieval is achieved. This overcomes the limitations of traditional keyword matching and ensures that the most suitable personalized treatment plan for the patient's current complex condition can be accurately matched at the semantic level, significantly improving the accuracy and efficiency of treatment plan retrieval.

[0101] It provides a rigorous yet flexible mechanism for program implementation and efficacy evaluation. It can receive and implement personalized treatment plans, set initial plan parameters, and dynamically adjust the plan based on feedback instructions from the reinforcement learning unit, ensuring real-time execution. The multi-dimensional efficacy data collection unit determines conditions based on the treatment stage and dynamically adjusts the collection frequency of efficacy indicators, avoiding resource waste and improving the targeting of monitoring.

[0102] Using depth Deep reinforcement learning algorithms, such as network algorithms, transform efficacy monitoring data into the current state of a Markov decision process through state feature extraction and reward generation units. The core reward is designed around the patient prognosis improvement rate, directly linking the learning objective to clinical efficacy. The introduction of experience playback mechanisms and target networks effectively stabilizes... The training process of the network model improves learning efficiency and convergence. The dynamic protocol parameter generation and feedback unit can dynamically adjust the optimization frequency according to the treatment stage, decoding the optimal action into specific optimized protocol parameters such as the optimized medication dosage adjustment amount and the rate of change of physical therapy intensity. This enables real-time, dynamic, and efficacy-driven adjustment and optimization of the treatment protocol, solving the problem of lag in manual adjustments.

[0103] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method based on The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, include: The raw data preprocessing module is used to collect and standardize static data from time-series monitoring data and medical history texts, employing improved methods. and Normalization algorithm, utilizing The model structures text data and outputs standardized patient feature data. improve The logic analysis module receives standardized patient characteristic data and optimizes it by introducing a sparse attention mechanism. The architecture performs temporal correlation analysis, pathological feature mapping, and individual difference modeling, outputting a high-dimensional patient state feature vector. The treatment plan database module includes a plan database for storing standard plans and clinically optimized plans classified by disease type and risk level. The plans in the plan database are all pre-encoded as plan feature vectors. This module receives patient status feature vectors, performs semantic-level benchmarking retrieval based on the cosine similarity between the patient status feature vectors and the plan feature vectors, and outputs personalized treatment plans. The protocol execution and efficacy monitoring module is used to implement personalized treatment protocols, collect efficacy monitoring data in real time after the personalized treatment protocols are implemented, and output efficacy monitoring data. The reinforcement learning optimization module receives efficacy monitoring data, uses a deep reinforcement learning algorithm with the patient prognosis improvement rate as the core reward function, analyzes the reasons for the deviation between the efficacy monitoring data and the target threshold, dynamically generates optimized protocol parameters, and feeds the optimized protocol parameters back to the protocol execution and efficacy monitoring module.

2. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The raw data preprocessing module further includes: The data acquisition and preliminary separation unit is used to acquire dynamic data. Time-series monitoring data such as heart rate, blood pressure, and blood oxygen saturation, as well as static text data such as medical history and lifestyle habits, are stored separately; An outlier correction and numerical standardization unit is used to receive the time-series monitoring data and adopt improved... The algorithm performs outlier correction, specifically for data points. Correction value The judgment condition is: if Make corrections. in, for The mean of the data within the sliding window per second. for The standard deviation of the data within the sliding window per second. The threshold for outlier detection; The corrected dataset uses Normalization algorithm, where the standardized numerical values The calculation formula is: ,in, For the original data points, and These are the minimum and maximum values ​​of all time-series monitoring data; The text data structuring unit is used to receive the static text data and employ... The model performs semantic encoding and structured processing on the static text data, converting it into numerical text feature vectors; The core feature filtering unit receives the standardized time-series monitoring data and text feature vectors, and uses the Pearson correlation analysis algorithm to calculate the linear correlation coefficient between each feature. : ， in, and For the feature data points to be analyzed, and The mean; The importance of each feature is evaluated using a random forest algorithm, and core physiological features and structured text features are extracted and output. The core physiological features include... of Segment offset and blood pressure fluctuation slope together constitute the output to the improvement Standardized patient characteristic data from the logical analysis module.

3. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The improvements The logic analysis module further includes: A sparse attention computation unit is used to receive standardized patient feature data output, and to apply a sparse attention mechanism to the standardized patient feature data for temporal feature extraction. The sparse attention mechanism is designed for standardized... The self-attention mechanism is optimized, where the attention weight matrix... The calculation formula is: ， in, For query vector, For key vectors, Let be the dimension of the key vector. It is a sparse mask matrix; The sparse mask matrix The computational positions corresponding to the temporal features in the standardized patient feature data are assigned non-zero values, and the temporal features include... Abnormal waveform segments; The pathological logic mapping and determination unit is used to receive the temporal features extracted by the sparse attention mechanism, and to perform pathological feature mapping on the temporal features. The condition for determining myocardial ischemia is as follows: of Low segment pressure value ≥ ;in, for The vertical offset of the segment relative to the equipotential line; Furthermore, a preliminary patient pathological model is constructed based on the mapping results and individual differences modeling. The individual differences modeling is based on the patient's age, metabolic capacity, and postoperative status to make personalized corrections to the preliminary patient pathological model. The patient state vector encoding unit is used to encode the modified patient pathology model to generate a high-dimensional patient state feature vector, which includes cardiovascular disease risk level, core pathological features and individual difference parameters.

4. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The treatment protocol database module further includes: The protocol database construction and management unit is used to store structured treatment protocols classified by disease type, risk level and treatment stage. The protocols include standard protocols from the "Guidelines for the Prevention and Treatment of Cardiovascular Diseases in China" and optimized protocols from a large number of clinical cases. The scheme feature vector precoding unit is used to receive all treatment schemes in the scheme database, perform structured encoding processing on the treatment schemes, and convert each treatment scheme into a scheme feature vector with semantic information. The feature vector of the scheme With patient state feature vector Consistent dimensions; Among them, the patient state feature vector Improved Output of the logic analysis module; The semantic-level mapping retrieval unit is used to receive the output patient state feature vector. And for the patient state feature vector With all scheme feature vectors in the scheme database Cosine similarity is calculated for matching. The calculation formula is: ， in, The dot product of the patient state feature vector and the protocol feature vector. and The semantic-level benchmarking retrieval unit outputs the personalized treatment plan with the highest similarity to the plan execution and efficacy monitoring module, which is the Euclidean norm of the patient state feature vector and the plan feature vector.

5. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The protocol execution and efficacy monitoring module further includes: The scheme implementation and instruction receiving unit is used to receive the output personalized diagnosis and treatment plan, set the initial plan parameters according to the personalized diagnosis and treatment plan, and initiate the plan implementation process; at the same time, it receives the optimized plan parameters fed back by the reinforcement learning optimization module to make real-time dynamic adjustments to the plan parameters. A multi-dimensional efficacy data acquisition unit is used to collect real-time efficacy monitoring data of efficacy indicators during the implementation of the treatment plan. The collection frequency of these efficacy indicators is determined based on the stage of diagnosis and treatment. If it is determined that the current situation is in the acute phase, the sampling frequency is once per hour; If the current period is determined to be stable, the data collection frequency is once a day. The efficacy assessment and feedback data generation unit is used to receive the real-time efficacy monitoring data and calculate the target achievement rate of efficacy indicators. Among them, the blood pressure target achievement rate The calculation formula is: , in, For blood pressure to be within the preset target range The length of time within, This refers to the total monitoring time. The efficacy assessment and feedback data generation unit generates efficacy monitoring data based on the target achievement rate and outputs the efficacy monitoring data to the reinforcement learning optimization module.

6. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The reinforcement learning optimization module further includes: The state feature extraction and reward generation unit is used to receive the output efficacy monitoring data and encode the efficacy monitoring data and the current treatment plan parameters into the current state of the Markov decision process. Calculate core rewards and auxiliary rewards The core reward Based on patient prognosis improvement rate Compared with the preset target value The difference is calculated, and the auxiliary reward The current state is calculated based on auxiliary indicators such as solution effectiveness and personalization adaptation rate. and rewards , depth Network learning units, used to employ deep learning The network algorithm performs policy learning to determine the optimal action, the optimal action The choice is based on Maximizing the value, the The objective function of the value The update follows the Bellman equation, which can be simplified to: ， in, For the goal value, For the next reward value, As a discount factor, For the goal Network status for the next step Optimal action Value prediction, For the goal Network parameters; This unit outputs the action feature vector corresponding to the optimal action; A dynamic protocol parameter generation and feedback unit is used to receive the action feature vector and decode the action feature vector into specific optimized protocol parameters. The optimized protocol parameters include the adjustment amount of medication dosage and the rate of change of physical therapy intensity. This unit dynamically adjusts the optimization frequency according to the treatment stage, wherein the condition determination includes: If it is determined that the current phase is acute, the optimization frequency is once a day to optimize the parameters of the proposed solution. If the current period is determined to be stable, the optimization frequency is once a week to optimize the parameters of the proposed scheme.

7. A method based on claim 4 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The scheme feature vector precoding unit uses a bidirectional long short-term memory network to extract features from the text description of the treatment plan in order to generate the scheme feature vector. The semantic-level benchmarking retrieval unit, after calculating cosine similarity, further performs a secondary screening based on risk level, retaining those that match the patient's state feature vector. Treatment protocols consistent with medium-risk levels will be provided.

8. A method based on claim 5 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The efficacy assessment and feedback data generation unit in the protocol execution and efficacy monitoring module, the target achievement rate of the efficacy indicators Further including heart rate variability pass rate ,in, The standard deviation of the interval between adjacent heartbeats, the pass rate The determination criteria are: If the current Value greater than If the treatment is deemed to have met the criteria, the efficacy monitoring data will also include the aforementioned data. pass rate .

9. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The encoding of the feature vector of the scheme adopts The model is trained to ensure that the feature vectors of the proposed solutions are comparable to the feature vectors of the patient states in the semantic space. The feature vectors of the standard and clinically optimized solutions in the solution database are encoded and stored in a vector database, which employs... The algorithm performs index optimization to accelerate the retrieval speed of the semantic-level benchmark retrieval unit.

10. A method based on claim 1 The cardiovascular disease diagnosis and treatment solution optimization system based on the architecture is characterized by, The deep reinforcement learning algorithm is trained using an experience replay mechanism and a target network. The experience replay mechanism stores the current state. Optimal action Next step reward and the next step status The experience quadruples are stored in the experience pool and are randomly sampled during training for batch updates to break the correlation between data. The target network The parameters of each Updated once per step, among which, This is the preset update frequency.

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