Cardiovascular disease early warning and intervention system based on multi-mode large model

Abstract

A cardiovascular disease early warning and intervention system based on a multi-modal large model comprises a user interface layer, a multi-modal information module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module and a safety and privacy protection module. Through fusion of multi-modal data and application of a deep learning algorithm, the system can provide personalized medical suggestions and health management schemes, significantly improve the quality and efficiency of medical services, help users to better manage their health, help doctors to diagnose and treat, and improve the working efficiency and diagnosis accuracy of the doctors; in a word, the cardiovascular disease early warning and intervention system based on the multi-modal large model realizes remarkable improvement in the aspects of understanding patient demands, personalized suggestions, data analysis, knowledge base integration and the like, can effectively improve the efficiency and quality of medical follow-up visit, and provides powerful support for early discovery and intervention of cardiovascular diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and in particular to a cardiovascular disease early warning and intervention system based on a multimodal large model. Background Art

[0002] Cardiovascular disease (CVD) is one of the leading causes of death and disability worldwide. According to statistics from the World Health Organization (WHO), cardiovascular disease causes about 17.7 million deaths each year, accounting for 31% of the total global death toll. Among them, ischemic heart disease and stroke are the main causes of death.

[0003] In China, the incidence and mortality of cardiovascular diseases are also increasing year by year. The number of deaths from cardiovascular diseases in my country increased from 3.09 million in 2005 to 4.58 million in 2020, and 2 out of every 5 deaths were due to cardiovascular diseases. Acute myocardial infarction (AMI) is one of the most acute and dangerous types of cardiovascular disease, with rapid onset, severe condition and high mortality. Although the treatment technology of cardiovascular diseases has been significantly improved in recent years, early warning and intervention are particularly important because once myocardial infarction occurs, the damage progresses rapidly and is irreversible.

[0004] Traditional cardiovascular disease early warning and management systems mainly rely on doctors’ experience and limited biomarkers. These systems have the following shortcomings:

[0005] 1. Limited early warning accuracy: Traditional early warning systems are mainly based on static biomarkers, which make it difficult to capture dynamic changes before the disease occurs, resulting in insufficient accuracy and timeliness of early warnings.

[0006] 2. Weak data integration capabilities: The occurrence and development of cardiovascular diseases involve multiple factors, including genetics, environment, lifestyle, etc. Traditional systems find it difficult to integrate multimodal data (such as clinical data, imaging data, genetic data, etc.) for comprehensive analysis.

[0007] 3. Low level of personalization: Traditional systems find it difficult to provide personalized health management and intervention recommendations based on individual differences, and cannot fully meet the needs of different patients.

[0008] 4. Lack of real-time monitoring and feedback: Traditional systems find it difficult to achieve real-time monitoring and feedback of patients’ physiological parameters, and cannot promptly detect and deal with potential health risks.

[0009] The prior art has the following defects and difficulties in solving:

[0010] 1. Interoperability issues: Existing medical follow-up systems may face integration issues with other medical information systems. Different systems use different standards and technologies, resulting in the inability to effectively share data.

[0011] 2. Data privacy and security issues: Medical information involves sensitive patient health data, and existing systems face challenges in protecting patient privacy and ensuring data security.

[0012] 3. Data quality issues: The data entered into the system may be inaccurate, incomplete or inconsistent, affecting the doctor's accurate assessment of the patient's health status.

[0013] 4. User experience issues: Some systems have problems in user interface design, usability and user experience, which affects the user experience of doctors and patients.

[0014] 5. Technology equipment dependency: Existing systems may require patients and doctors to use specific technology equipment, limiting access for certain people.

[0015] 6. Patient engagement issues: Some patients may be unwilling or have difficulty using these systems due to their technical literacy or other reasons, affecting the actual effectiveness of the system.

[0016] 7. Cost issue: Implementing and maintaining a medical follow-up system requires considerable investment, which imposes a burden on some medical institutions and patients.

[0017] With the development of artificial intelligence (AI) and big data technology, cardiovascular disease early warning and intervention systems based on multimodal big models provide new ideas and methods for solving the above problems. Multimodal big models can integrate and analyze information from different data sources (such as electronic health records, imaging data, genetic data, etc.), use deep learning algorithms to mine potential patterns in the data, and achieve early warning and personalized intervention for cardiovascular diseases.

[0018] In recent years, artificial intelligence technology applied to cardiovascular disease early warning and management has made significant progress. For example, image recognition technology based on deep learning can automatically analyze cardiac imaging data and detect early lesions; natural language processing technology can extract useful information from electronic health records to assist doctors in decision-making. However, most existing AI systems only focus on the analysis of single-modality data and cannot fully utilize the advantages of multi-modal data fusion.

[0019] Based on the above background, the present invention proposes a cardiovascular disease early warning and intervention system based on a multimodal large model, which aims to achieve accurate early warning and personalized intervention for cardiovascular diseases by integrating and analyzing information from multiple data sources. The system combines multiple advanced technologies such as natural language processing, image recognition, and machine learning to provide an effective solution for the early detection and intervention of cardiovascular diseases. Summary of the invention

[0020] The present invention proposes a cardiovascular disease early warning and intervention system based on a multimodal large model, including a user interface layer, a multimodal information module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module, and a security and privacy protection module. The system integrates and analyzes information from multiple data sources to achieve accurate early warning and personalized intervention for cardiovascular diseases, solving many difficulties and problems in the prior art.

[0021] A cardiovascular disease early warning and intervention system based on a multimodal large model, including a user interface layer, a multimodal information module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module and a security and privacy protection module.

[0022] The user interface layer includes a mobile app and a web page, through which users can interact with the system, including filling out follow-up questionnaires, obtaining medical advice, etc.

[0023] The multimodal information module is used to convert the user's voice input into text form so that the system can process and analyze it.

[0024] The natural language processing module is based on a large language model and is used to understand and process text information input by the user, including answering questions raised by the user, analyzing the user's description of the condition, etc.

[0025] The medical knowledge base includes medical literature, clinical guidelines, physician experience, etc., which are used to systematically provide medical knowledge and advice.

[0026] The data management module is used to manage the user's personal health data, including medical records, examination reports, medication records, etc.

[0027] The intelligent recommendation module provides users with personalized medical advice and health management solutions based on their personal health data and medical knowledge base.

[0028] The doctor assistance module is used to present the user's questions and condition descriptions to the doctor, and to assist the doctor in making diagnosis and treatment recommendations.

[0029] The data analysis module is used to analyze the user's health data, identify potential health risks and provide preventive measures.

[0030] The security and privacy protection module ensures that the user's personal health data is fully protected and complies with relevant privacy laws and regulations.

[0031] 1. Core Algorithms and Frameworks

[0032] 1. Transformer algorithm: Use the Transformer algorithm to process the patient's multimodal information input, including text parsing, semantic understanding, entity recognition, audio, image, video and other technologies, so that the system can fully understand the patient's needs and problems.

[0033] The system uses the Transformer algorithm to process multimodal information input. The following is the public notice:

[0034]

[0035] Among them, (Q), (K), (V) represent query, key and value matrices respectively, and (d_k) represents the dimension of the key. Through the Attention mechanism, the system can effectively process multimodal information such as text parsing, semantic understanding, entity recognition, audio, image and video, and fully understand the needs and problems of patients.

[0036] 2. Language model: The system needs to build a powerful large language model so that it can perform tasks such as language generation, text completion, and question answering based on the patient's input.

[0037]

[0038] Among them, (P(wi|w-{i-1},..,w-1)) represents the probability of generating the next word given the context, (e) represents the word embedding vector, (h-{i-1}) is the hidden state of the previous step, and (V) is the vocabulary.

[0039] 3. Data mining and analysis algorithms: The system needs to be able to analyze the patient's health data in real time, including physical signs, biochemical indicators, etc., so that abnormal conditions can be detected and warnings can be issued in time. This can be done by using data mining and machine learning algorithms for data analysis and pattern recognition.

[0040] (1) Linear regression

[0041] y=β0+β1x1+β2x2++β n x n +∈

[0042] (2) Support Vector Machine (SVM)

[0043]

[0044] subject to you i (w T x i +b)≥1―ξ i ,ξ i ≥0

[0045] (3) Random Forest

[0046]

[0047] 4. Personalized recommendation algorithm: The system needs to be able to provide personalized health warning suggestions and treatment plans based on the patient’s health status and historical records. This can be done with the help of the recommendation system algorithm to recommend suitable health management plans based on the patient’s specific situation and needs.

[0048] (1) Collaborative filtering

[0049]

[0050] Among them, (r_{ui}) represents the rating of user (u) on item (i), (\mu) is the global average rating, (b_u) and (b_i) are the deviations of user and item respectively, (q_i) and Pu are the latent vectors of item and user.

[0051] (2) Content recommendation

[0052]

[0053] 5. Knowledge base and expert system: The system needs to integrate rich medical knowledge and clinical experience to provide reference materials for doctors to help them better serve patients. This can be achieved with the help of technologies such as knowledge graphs and expert systems.

[0054] The knowledge graph is used to represent entities and their relationships. The formula is as follows:

[0055] G=(V,E)

[0056] Among them, G represents the knowledge graph, V represents the node set, and E represents the edge set. Each node represents an entity, and each edge represents the relationship between entities.

[0057] The probability of predicting an entity in a given context can be expressed as:

[0058]

[0059] Among them, P(entity|context) represents the probability of predicting the entity given the context, and score(e,c) represents the score of the entity and the context.

[0060] Expert System

[0061] Expert systems make inferences and decisions through a series of rules. The typical rule format is:

[0062] IF condition THEN action

[0063] The specific formula can be expressed as:

[0064] R i :IF(A1 AND A2 AND...AND A n )THEN B

[0065] Where, represents the rule number, A, A2, ... A n A represents the precondition, and B represents the action taken when the condition is met. The rule engine matches and infers the input data and the rules in the knowledge base, and finally generates output suggestions. The process can be expressed as:

[0066] Output=RuleEngine(Input,KnowledgeBase)

[0067] 2. Data Processing and Optimization

[0068] 1. Data preprocessing: The system needs to preprocess the patient's input, including text cleaning, word segmentation, entity recognition, etc., so that it can extract effective information and perform semantic understanding. When processing health data, data cleaning, standardization and denoising are also required to ensure the accuracy and completeness of the data.

[0069] (1) Data cleaning:

[0070] Remove noisy data

[0071] Handling missing values ​​(e.g. using mean, median or interpolation)

[0072] (2) Data normalization:

[0073] Linear Normalization:

[0074]

[0075] ·standardization:

[0076]

[0077] (3) Data enhancement:

[0078] Image data enhancement (rotation, scaling, translation, etc.) Audio data enhancement (noise addition, speed change, etc.)

[0079] 2. Data integration and labeling: The system needs to integrate medical data from multiple sources, including patients’ medical records, physical signs, biochemical indicators, medical literature, audio, video, etc., and label and associate these data to enable data mining and analysis.

[0080] (1) Text feature extraction:

[0081] TF-IDF:

[0082] tf―idf(t,d,D)=tf(t,d)×idf(t,D)

[0083]

[0084] Word vectors (Word2Vec, GloVe, BERT, etc.)

[0085] (2) Image feature extraction:

[0086] Convolutional Neural Networks (CNN):

[0087]

[0088] Feature Pyramid Network (FPN)

[0089] (3) Audio feature extraction:

[0090] Mel-Frequency Cepstral Coefficients (MFCC):

[0091]

[0092] 3. Data mining and model training: The system needs to use machine learning and data mining algorithms to analyze and model medical data to discover potential rules and patterns. This includes using supervised learning, unsupervised learning and other techniques to train models to improve the system's understanding of patient needs and health conditions.

[0093] Supervised Learning

[0094] Supervised learning algorithms are trained on labeled data to predict new data. Common supervised learning algorithms include linear regression, decision trees, support vector machines, neural networks, etc.

[0095] (1) Linear regression:

[0096] y=β0+β1x1+β2x2++β n x n +∈

[0097] Among them, (y) is the predicted value, (x_i) is the eigenvalue, (\beta_i) is the regression coefficient, and (\epsilon) is the error term.

[0098] (2) Decision Tree

[0099]

[0100] (3) Support Vector Machine (SVM)

[0101]

[0102] subject to you i (w T x i +b)≥1―ξ i ,ξ i ≥0

[0103] (4) Neural Network

[0104] y=σ(W2·σ(W1·x+b1)+b2)

[0105] Among them, (W_1) and (W_2) are weight matrices, (b_1) and (b_2) are biases, and (\sigma) is the activation function.

[0106] Unsupervised Learning

[0107] Unsupervised learning algorithms are trained on unlabeled data to discover the intrinsic structure of the data. Common unsupervised learning algorithms include clustering and dimensionality reduction.

[0108] (1) K-means clustering:

[0109]

[0110] Among them, (C_i) is the (i)th cluster and (\mu_i) is the centroid of the (i)th cluster.

[0111] (2) Principal Component Analysis (PCA):

[0112]

[0113] Where (S) is the covariance matrix and (\mathbf{w}) is the projection vector.

[0114] 4. Model training

[0115] Model training is the process of using data to train a machine learning model. The training process includes model selection, loss function definition, optimization method, etc.

[0116] (1) Model selection:

[0117] Choose an appropriate model such as linear regression, decision tree, support vector machine, neural network, etc.

[0118] (2) Loss function:

[0119] A loss function is defined to measure the difference between the predicted value and the true value.

[0120] Mean Square Error (MSE):

[0121]

[0122] Among them, (y_i) is the true value and (\hat{y}_i) is the predicted value.

[0123] Cross Entropy Loss:

[0124]

[0125] (3) Optimization method:

[0126] Use optimization algorithms to minimize the loss function. Common optimization methods include gradient descent, Adam, etc.

[0127] Gradient Descent:

[0128]

[0129] Among them, (\theta) is the model parameter, (\eta) is the learning rate, and J is the loss function.

[0130] Adam optimization algorithm:

[0131] m t =β1m t―1 +(1―β1)g t

[0132]

[0133] Model optimization and iteration: The system needs to optimize and iterate the language model, recommendation algorithm, etc. to continuously improve the performance and accuracy of the system. This includes adjusting model parameters, improving algorithms, and even updating model structures to adapt to changing medical needs and new data.

[0134] Privacy and security: When processing patient health data, the system needs to strictly comply with privacy protection laws and regulations, anonymize patient data, and take security measures to protect the security and privacy of the data.

[0135] 3. Model fine-tuning and application specialization

[0136] Dataset screening: Filter out follow-up-related data from the medical field, such as patient medical records, diagnosis reports, follow-up records, etc. These data can be used to fine-tune the language model to better understand text information in the medical field.

[0137] Model structure adjustment: According to the characteristics of medical follow-up, the structure of the language model may need to be adjusted to better adapt to text processing tasks in the medical field. For example, you can consider adding specific layers or mechanisms related to medical knowledge to improve the performance of the model in the medical field.

[0138] Parameter adjustment: For follow-up tasks in the medical field, the parameters of the language model can be fine-tuned to make it more suitable for the characteristics of medical text data. For example, hyperparameters such as learning rate and regularization parameters can be adjusted.

[0139] Transfer learning: Use pre-trained language models to perform transfer learning on medical follow-up tasks. You can fine-tune the pre-trained model or use part of the pre-trained model for medical follow-up tasks.

[0140] Validation and Adjustment: The fine-tuned language model is validated and adjusted using a validation dataset in the medical field to ensure its performance and stability on the medical follow-up task.

[0141] Application specialization: The fine-tuned language model can be specialized for the specific needs of medical follow-up. For example, the model can be customized according to the characteristics of different diseases and the needs of patients to better support medical follow-up tasks.

[0142] 4. Real-time Information Processing and Response

[0143] Real-time data reception: The system needs to be able to receive various data from medical follow-up in real time, including patients' medical records, diagnostic reports, follow-up records, etc. These data can be obtained through medical information systems, sensor devices, or patients' own input.

[0144] Real-time information processing: After receiving the data, the system needs to be able to process the text information in real time, including natural language processing, data cleaning, entity recognition, sentiment analysis, etc. This can be done with the help of a large language model for semantic understanding and information extraction to better understand the patient's needs and conditions.

[0145] Real-time decision-making and response: The system needs to be able to make real-time decisions and responses based on the processed information. For example, it can automatically generate intelligent follow-up recommendations, medication recommendations, lifestyle guidance, etc. based on the patient's symptoms and follow-up records, and respond to the patient's needs and problems in a timely manner.

[0146] Real-time communication and interaction: The system needs to support real-time medical information exchange and interaction. Real-time communication between doctors and patients can be achieved by integrating instant messaging tools, voice recognition technology, etc., to better support medical follow-up tasks.

[0147] Real-time monitoring and feedback: The system needs to be able to monitor the patient's condition and feedback information in real time, including patient feedback during follow-up, physiological parameter monitoring data, etc. Through real-time analysis and processing of the large language model, the patient's condition can be better understood and the follow-up plan can be adjusted in a timely manner.

[0148] 5. Interactive and contextual understanding

[0149] Contextual understanding capability: The system needs to be able to understand and analyze the patient's context and contextual information in order to better grasp the patient's needs and conditions. This can be achieved by introducing context-aware dialogue management technology and dialogue state tracking technology, so that the system can understand the patient's needs and questions at different stages and be able to respond and interact reasonably.

[0150] Interactive design: The system needs to support interactive dialogue with patients. This can be achieved by introducing dialogue management technology and multi-round dialogue design to better understand the patient's implicit meaning and emotional needs, and enable more intelligent communication and interaction.

[0151] Emotion recognition and response: The system needs to have the ability to recognize emotions and understand and analyze the patient's emotional state in order to better communicate and respond emotionally. This can be achieved with the help of the emotion analysis capabilities of large language models, so that the system can more intelligently understand and respond to the patient's emotional needs.

[0152] Context persistence: The system needs to be able to persist the conversation context so that it can better understand and respond to the patient's needs in multiple rounds of conversation. This can be achieved by introducing conversation history management and context storage technology, so that the system can better track and understand the patient's previous communication content and needs, so as to provide more personalized and targeted responses.

[0153] 6. System Integration and Interface Design

[0154] Data interface design: The system needs to be able to exchange and share data with the patient information management system, diagnosis system, and medical record system of the medical institution. Therefore, it is necessary to design a suitable data interface to ensure that the system can obtain and update the patient's medical information and can feed back the follow-up results and suggestions to the medical record system.

[0155] Dialogue interface design: The system needs to be able to interact with patients in natural language, so a suitable dialogue interface needs to be designed so that the system can understand the patient's needs and questions and be able to respond and communicate intelligently. This may involve the design of speech recognition interface, natural language processing interface and dialogue management interface.

[0156] Multi-platform adaptation: Considering that patients may use different devices for follow-up communication, the system needs to have the ability to adapt to multiple platforms and be able to interact stably and smoothly on different devices. Therefore, it is necessary to design appropriate interfaces and adaptation solutions to ensure that the system can effectively connect and communicate on different devices.

[0157] Security and privacy protection: In system integration and interface design, issues such as data security and privacy protection need to be considered to ensure that the system can safely exchange data and transmit information, and protect patients' privacy information from being leaked or abused.

[0158] 7. Scalability and maintainability

[0159] Modular design: Divide the system into multiple independent modules, each of which is responsible for a specific function or task. This design can make the system easier to expand and maintain, because when modifying the system or adding new functions, you only need to focus on specific modules without affecting the entire system.

[0160] Use standardized interfaces: The modules in the system use standardized interfaces for communication, which ensures that the coupling between modules is low and facilitates the expansion and maintenance of the system. For example, using a RESTful API interface can make the system more flexible and scalable.

[0161] Introduce automated testing: Establish a comprehensive automated testing framework, including unit testing, integration testing, and end-to-end testing, to ensure that each module and function of the system can be tested quickly and accurately, which helps to ensure the maintainability and scalability of the system.

[0162] Adopt appropriate technical architecture: Choosing appropriate technical architecture and design patterns, such as microservice architecture, event-driven architecture, etc., can improve the scalability and maintainability of the system, making it easier for the system to expand new features and handle larger loads.

[0163] Continuous integration and continuous delivery: Introducing continuous integration and continuous delivery tools ensures that every modification to the system can be built, tested, and deployed quickly and automatically, helping to ensure the stability and maintainability of the system.

[0164] Beneficial effects of the present invention:

[0165] Personalized services: The intelligent system based on the large language model can understand and analyze the user's description of the condition, provide users with personalized medical advice and health management plans, and improve the quality and efficiency of medical services.

[0166] Data-driven decision making: The system can analyze users’ personal health data, identify potential health risks and provide preventive measures to help users better manage their health.

[0167] Doctor assistance: The system can present the user's questions and condition descriptions to doctors, helping them make diagnoses and treatment recommendations, thereby improving their work efficiency and accuracy.

[0168] Provide convenient services: Users can interact with the system through mobile apps and web pages to obtain medical advice and health management services anytime and anywhere.

[0169] Data security and privacy protection: The system ensures that the user's personal health data is fully protected through the security and privacy protection module, in compliance with relevant privacy laws and regulations.

[0170] Stronger natural language processing capabilities: The cardiovascular disease early warning and intervention system based on a multimodal large language model can more accurately understand the patient's natural language input, including spoken and written language, and can better identify the patient's needs and problems.

[0171] Intelligent recommendations and suggestions: The system can provide personalized health advice and treatment plans based on the patient's health status and history, helping patients better manage their health.

[0172] Real-time data analysis: The system can analyze the patient's health data in real time, including physical signs, biochemical indicators, etc., to detect abnormal conditions and issue warnings in a timely manner.

[0173] Automated follow-up and follow-up: The system can automatically send follow-up reminders and follow-up messages to help doctors better manage patients' follow-up plans and improve the timeliness and efficiency of follow-up.

[0174] Intelligent knowledge base: The system can integrate rich medical knowledge and clinical experience to provide doctors with more comprehensive reference materials, helping them to better serve patients.

[0175] In summary, the cardiovascular disease early warning and intervention system based on the multimodal large language model has significant improvements in understanding patient needs, personalized recommendations, data analysis, and knowledge base, and can better improve the efficiency and quality of medical follow-up. BRIEF DESCRIPTION OF THE DRAWINGS

[0176] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0177] See also Figure 1 Shown is an embodiment of the present invention.

[0178] A cardiovascular disease early warning and intervention system based on a multimodal large language model includes a user interface layer, a multimodal information module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module, and a security and privacy protection module.

[0179] The user interface layer includes a mobile app and a web page, through which users can interact with the system, including filling out follow-up questionnaires, obtaining medical advice, etc.

[0180] The multimodal information module is used to convert the user's voice input into text form so that the system can process and analyze it.

[0181] The natural language processing module is based on a large language model and is used to understand and process text information input by the user, including answering questions raised by the user, analyzing the user's description of the condition, etc.

[0182] The medical knowledge base includes medical literature, clinical guidelines, physician experience, etc., which are used to systematically provide medical knowledge and advice.

[0183] The data management module is used to manage the user's personal health data, including medical records, examination reports, medication records, etc.

[0184] The intelligent recommendation module provides users with personalized medical advice and health management solutions based on their personal health data and medical knowledge base.

[0185] The doctor assistance module is used to present the user's questions and condition descriptions to the doctor, assisting the doctor in making diagnosis and treatment recommendations;

[0186] The data analysis module is used to analyze the user's health data, discover potential health risks and provide preventive measures.

[0187] The security and privacy protection module ensures that the user's personal health data is fully protected and complies with relevant privacy laws and regulations.

[0188] 1. User interface layer

[0189] Mobile App and Web Page:

[0190] User interaction: Users can fill out follow-up questionnaires, obtain medical advice, etc. through the interface.

[0191] User interaction formula:

[0192] UI=Interface(Uinput,Rresponse)UI=\text{Interface}(U_{\text{input}}, R_{\text{response}})UI=Interface(Uinput,Rresponse)

[0193] UI=Interface(UI input , R response )

[0194] Among them, UI stands for user interface, input Represents user input, R response Indicates the system response.

[0195] 2. Multimodal Information Module

[0196] Speech to text:

[0197] Voice input: Convert the user's voice input into text.

[0198] Speech recognition formula:

[0199] Ttext=ASR(Aaudio)T_{\text{text}}=\text{ASR}(A_{\text{audio}})Ttext=ASR(Aaudio)

[0200] T text =ASR(A audio )

[0201] Among them, T text is the recognized text, ASR is the automatic speech recognition function, A audio It is voice input.

[0202] 3. Natural Language Processing Module

[0203] Text understanding and processing:

[0204] Text understanding: Understand the text information input by the user based on the large language model.

[0205] Text processing: answering users’ questions, analyzing users’ descriptions of their medical conditions, etc.

[0206] Natural language processing formula:

[0207] RNLP=NLP(Ttext)R_{\text{NLP}}=\text{NLP}(T_{\text{text}})RNLP=NLP(Ttext)

[0208] RNLP =NLP(T text )

[0209] Among them, R NLP is the result of natural language processing, NLP is the natural language processing function, T text is the input text.

[0210] 4. Medical Knowledge Base

[0211] Medical knowledge provided:

[0212] Knowledge base: including medical literature, clinical guidelines, physician experience, etc.

[0213] Knowledge base query formula:

[0214] Aknowledge=Query(Quser,Kmedical)A_{\text{knowledge}}=\text{Query}(Q_{\text{user}},K_{\text{medical}})Aknowledge=Query(Quser,Kmedical)

[0215] A knowledge =Query(Q user , K medical )

[0216] Among them, A knowledge is the answer to the knowledge base, Q user It is a user problem, K medical It is a medical knowledge base.

[0217] 5. Data Management Module

[0218] Health Data Management:

[0219] Data management: manage users’ personal health data, including medical records, examination reports, medication records, etc. Data management formula:

[0220] Dmanaged=Manage(Dhealth)D_{\text{managed}}=

[0221] \text{Manage}(D_{\text{health}})Dmanaged=Manage(Dhealth)

[0222] D managed =Manage(D health )

[0223] Among them, D managedis the managed data, Manage is the data management function, D health It's health data.

[0224] 6. Intelligent recommendation module

[0225] Personalized medical advice:

[0226] Intelligent recommendation: Provide personalized medical advice and health management solutions based on the user's personal health data and medical knowledge base.

[0227] Intelligent recommendation formula:

[0228] Rrecommend=Recommend(Dmanaged,Kmedical)R_{\text{recommend}}=

[0229] \text{Recommend}(D_{\text{managed}},K_{\text{medical}})Rrecommend

[0230] =Recommend(Dmanaged,Kmedical)

[0231] R recommend =Recommend(D mmanaged , K medical )

[0232] Among them, R recommend is the recommendation result, Recommend is the recommendation function, and D managed is the managed data, K medical It is a medical knowledge base.

[0233] 7. Doctor assistance module

[0234] Doctor-assisted diagnosis:

[0235] ·Assisted diagnosis: present the user's questions and condition descriptions to the doctor to assist the doctor in making diagnosis and treatment recommendations. Doctor assistance formula:

[0236] Dassist=Assist(Uinput,Rrecommend)D_{\text{assist}}=\text{Assist}(U_{\text{input}}, R_{\text{recommend}})Dassist=Assist(Uinput,Rrecommend)

[0237] D assist =Assist(U input , Rrecommend )

[0238] Among them, D assist is the doctor's assistance result, Assist is the auxiliary function, U input is the user input, R recommend It is the recommended result.

[0239] 8. Data Analysis Module

[0240] Health Data Analysis:

[0241] Data analysis: Analyze users’ health data to identify potential health risks and provide preventive measures.

[0242] Data analysis formula:

[0243] Aanalysis=Analyze(Dmanaged)A_{\text{analysis}}=

[0244] \text{Analyze}(D_{\text{managed}})Aanalysis=Analyze(Dmanaged)

[0245] A analysis =Analyze(D managed )

[0246] Among them, A analysis is the analysis result, Analyze is the analysis function, D managed It is the managed data.

[0247] 9. Security and Privacy Protection Module

[0248] Data Protection:

[0249] Privacy protection: Ensure that users’ personal health data is fully protected and in compliance with relevant privacy laws and regulations. Data protection formula:

[0250] Dsecure=Secure(Dmanaged)D_{\text{secure}}=\text{Secure}(D_{\text{managed}})Dsecure=Secure(Dmanaged)

[0251] D secure =Secure(D managed )

[0252] Among them, D secure is the protected data, Secure is the data protection function, D managed It is the managed data.

Claims

1. A cardiovascular disease early warning and intervention system based on a multimodal large model, characterized by: It includes user interface layer, multimodal information module, natural language processing module, medical knowledge base, data management module, intelligent recommendation module, doctor assistance module, data analysis module and security and privacy protection module.

2. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The user interface layer includes a mobile app and a web page, through which users can interact with the system, including filling out follow-up questionnaires, obtaining medical advice, etc.

3. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The multimodal information module is used to convert the user's voice input into text form so that the system can process and analyze it.

4. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The natural language processing module is based on a large language model and is used to understand and process text information input by the user, including answering questions raised by the user, analyzing the user's description of the condition, etc.

5. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The medical knowledge base includes medical literature, clinical guidelines, physician experience, etc., which are used to systematically provide medical knowledge and advice.

6. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The data management module is used to manage the user's personal health data, including medical records, examination reports, medication records, etc.

7. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The intelligent recommendation module provides users with personalized medical advice and health management solutions based on their personal health data and medical knowledge base.

8. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The doctor assistance module is used to present the user's questions and condition descriptions to the doctor, and to assist the doctor in making diagnosis and treatment recommendations.

9. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The data analysis module is used to analyze the user's health data, identify potential health risks and provide preventive measures.

10. The cardiovascular disease early warning and intervention system based on a multimodal large model according to claim 1, characterized in that: The security and privacy protection module ensures that the user's personal health data is fully protected and complies with relevant privacy laws and regulations.

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