Patient health education and consultation system based on large language model

Abstract

The invention discloses a patient health education and consultation system based on a large language model. The system comprises a data collection module, a model establishment and optimization module, a result output module and a feedback module, through integration of a plurality of data sources and cooperation with professional doctors, the system can provide more comprehensive and accurate medical knowledge and clinical experience and cover more diseases and treatment schemes; more targeted health education and consultation contents can be provided according to personal information and specific situations of the user, and the personalized requirements of the user are met; an interpretable large language model is adopted, better interpretation and visualization of an inference process can be provided, and understanding and trust of a user on system output are improved; users are encouraged to participate and feed back, the performance and functions of the system can be continuously improved and optimized, and the user experience and satisfaction degree are improved; through cooperation and guidance with professional doctors, the accuracy and reliability of the system can be improved, and the quality and credibility of health education and consultation contents provided by the system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and in particular to a patient health education and consultation system based on a large language model. Background Art

[0002] At present, the structure or method of the technical solution adopted for the patient health education and consultation system is as follows: 1. Data collection and organization: The system needs to collect and organize a large amount of medical knowledge and clinical experience data, including case records, disease diagnosis, and treatment plans. These data can come from multiple sources such as medical literature, clinical practice, and doctor experience.

[0003] 2. Data preprocessing: The collected data needs to be preprocessed, including text cleaning, word segmentation, noise removal, etc. These steps help improve the training effect of subsequent models.

[0004] 3. Model training: Use large language models, such as BERT, GPT, etc., to train the preprocessed data. These models are usually neural network models based on deep learning, which can learn the semantics and contextual information of the text.

[0005] 4. User input processing: When patients input relevant symptoms, diseases or health problems, the system will process the user input, including word segmentation, entity recognition, etc. These steps help understand the user's needs and problems.

[0006] 5. Text generation and recommendation: Based on the trained large language model, the system can generate corresponding health education and consultation content. This content can be introductions to specific diseases, treatment recommendations, preventive measures, etc. The system can also recommend relevant medical knowledge and resources based on the user's needs and circumstances.

[0007] 6. Results display and feedback: The system displays the generated health education and consultation content to the user and provides corresponding feedback and explanation. The user can further interact with the system to ask more questions or obtain more information.

[0008] The above technical solutions often have the following problems and defects: 1. Incomplete knowledge coverage: Although these systems can collect and organize a large amount of medical knowledge and clinical experience, due to the vast amount of knowledge in the medical field and its constant updating, the system may not cover all diseases and treatment options. Therefore, the health education and consultation content provided by the system may be incomplete or outdated.

[0009] 2. Limitations of semantic understanding and contextual understanding: Although large language models can learn the semantics and contextual information of text, they still have limitations in understanding and reasoning. When dealing with complex medical problems, the system may not be able to accurately understand the user's intentions or provide accurate answers.

[0010] 3. Lack of personalized and contextual processing: Systems based on large language models are usually general and cannot be customized to meet the individual needs and specific situations of each user. This may result in the health education and consultation content provided by the system not being personalized enough or not meeting the specific situation of the user.

[0011] 4. Privacy and security issues: These systems usually require users to provide personal health information in order to better understand and answer users' questions. However, privacy and security issues have always been the focus of attention, and the system needs to ensure that users' personal information is protected and handled securely.

[0012] 5. Lack of professional judgment of human doctors: Although systems based on large language models can provide a certain degree of health education and consulting services, they cannot replace the professional judgment and clinical experience of human doctors. When dealing with complex or serious health problems, the participation and guidance of professional doctors are still needed.

[0013] 6. Data quality and reliability: Large language model-based systems rely on a large amount of medical data for training and learning, but the quality and reliability of the data is a key issue. If the data contains errors, biases, or inaccuracies, the system may produce misleading health education and consultation content.

[0014] 7. Explanation and transparency: Large language models are usually black-box models, and it is difficult to explain their decision-making and reasoning processes. This makes it difficult for users to understand the basis and reasons for the health education and consultation content provided by the system. In order to increase user trust and acceptance, the system needs to provide better explanation and transparency.

[0015] 8. Multilingual and cross-cultural adaptability: The needs of health education and counseling are not limited to one language and cultural background. Therefore, the system needs to be multilingual and cross-culturally adaptable and able to provide health education and counseling services for different language and cultural backgrounds.

[0016] 9. Patient participation and feedback: In order to improve the effectiveness of the system and user experience, patient participation and feedback are crucial. The system needs to be able to receive and process patient feedback to continuously improve and optimize the performance and functionality of the system.

[0017] 10. Legal and ethical issues: Patient health education and consultation systems based on large language models need to comply with relevant laws and ethical norms, such as protecting user privacy, ensuring information security, and avoiding misleading health advice.

[0018] Causes of problems or defects: 1. Incomplete knowledge coverage: The knowledge in the medical field is huge and constantly updated, making it difficult for the system to cover all diseases and treatment options. This is because medical knowledge is updated very quickly, and the system cannot update and organize the latest knowledge in a timely manner. In addition, professional knowledge and experience in the medical field also need to be accumulated through the practice and research of human doctors, and large language models cannot completely replace this accumulation process of human professional experience.

[0019] 2. Limitations of semantic understanding and contextual understanding: Although large language models can learn the semantics and contextual information of text, they still have limitations in understanding and reasoning. This is because the meaning and context of language are often complex and ambiguous, and large language models may not be able to accurately understand the user's intentions or provide accurate answers. In addition, the training data of large language models may also be biased, causing the model to make errors in understanding and reasoning.

[0020] 3. Lack of personalized and contextual processing: Systems based on large language models are usually general and cannot be customized for each user's personalized needs and specific contexts. This is because the training data of large language models usually comes from large-scale public texts, which cannot fully reflect the personalized needs and specific contexts of each user. In addition, personalized processing requires more user data and algorithm support, and these resources may be limited.

[0021] 4. Privacy and security issues: Large language model-based systems require users to provide personal health information in order to better understand and answer users' questions. However, privacy and security issues have always been the focus of attention, and the system needs to ensure that users' personal information is protected and handled safely. This is because medical data is sensitive information, and once leaked or abused, it may cause damage to users' privacy and rights.

[0022] 5. Lack of professional judgment of human doctors: Systems based on large language models cannot replace the professional judgment and clinical experience of human doctors. This is because decision-making and judgment in the medical field often require comprehensive consideration of multiple factors, including the patient's specific conditions, medical history, laboratory tests, etc. Although large language models can provide a certain degree of health education and consulting services, they cannot completely replace the professional knowledge and experience of human doctors. Summary of the invention

[0023] The purpose of the present invention is to solve the problems described in the above-mentioned background technology and to provide a patient health education and consultation system based on a large language model.

[0024] A patient health education and consultation system based on a large language model includes a data collection module, a model building and optimization module, a result output module and a feedback module.

[0025] The data collection module integrates multi-source data. By integrating multiple data sources and cooperating with professional doctors, the system can provide more comprehensive and accurate medical knowledge and clinical experience, covering more diseases and treatment options.

[0026] The model building and optimization module introduces a personalized and situational processing mechanism, which can provide more targeted health education and consulting content according to the user's personal information and specific situations to meet the user's personalized needs.

[0027] The result output module adopts an interpretable large language model, which can provide better explanation and visualization of the reasoning process, and increase users' understanding and trust in the system output.

[0028] The feedback module establishes a model feedback reward mechanism, expands longitudinal data input, and establishes a professional clinical diagnosis feedback optimization model.

[0029] Working process and working principle of the present invention: Multi-source data integration: The system integrates multiple data sources, including medical literature, clinical practice, doctor experience, etc. This ensures that the system has comprehensive and accurate medical knowledge and covers more diseases and treatment options.

[0030] Pre-training and fine-tuning: The system uses pre-trained large language models, such as BERT, GPT, etc., to train the integrated data. Then, by fine-tuning the model, it is optimized for specific health education and consulting tasks. This can improve the model's understanding and reasoning ability in the medical field.

[0031] Personalized and contextualized processing: The system introduces a personalized and contextualized processing mechanism to provide targeted health education and consulting content based on the user's personal information, medical history, laboratory tests, etc. This can increase the practicality and user experience of the system, allowing users to obtain more personalized and accurate advice.

[0032] Explanation and transparency: Use interpretable large language models, such as XLNet, ALBERT, etc., to provide better explanations and visualization of the reasoning process. This can increase users’ understanding and trust in the health education and consultation content provided by the system.

[0033] User participation and feedback: The system encourages user participation and feedback, such as through user surveys, evaluations, and feedback, etc. This can collect user needs and opinions and continuously improve and optimize the performance and functions of the system.

[0034] Cooperation and guidance with professional doctors: The system cooperates and guides with professional doctors, who provide professional medical knowledge and experience to verify and correct the system's output. This ensures the accuracy and credibility of the health education and consultation content provided by the system.

[0035] Privacy and security protection: The system takes strict privacy and security measures to ensure that users' personal health information is protected and processed safely. This includes data encryption, access control, anonymization, etc.

[0036] Multilingual and cross-cultural adaptability: The system is multilingual and cross-cultural, and can provide health education and consulting services for different languages ​​and cultural backgrounds. This can meet the needs of different regions and user groups.

[0037] Patient education and self-help functions: The system provides patient education and self-help functions, such as providing a health knowledge base, disease management tools, health risk assessment, etc. This can help users proactively manage their own health and improve their health literacy.

[0038] Continuous Improvement and Updates: The system is regularly updated and improved to keep up with the latest knowledge and technology in the medical field. This ensures that the system always provides the latest and accurate health education and consultation content.

[0039] 1. Core Algorithms and Frameworks 1. Core Algorithms and Frameworks: 1) The core algorithm and framework of the newly designed system adopts a generative dialogue model based on a large language model.

[0040] 2) The neural network structure adopts the Transformer model, which consists of an encoder and a decoder. The encoder is used to encode the input sequence, and the decoder is used to generate the output sequence.

[0041] 3) Each encoder and decoder in the Transformer model consists of multiple layers of self-attention layers and feedforward neural network layers.

[0042] 2. Training methods: 1) The training method adopts two stages: pre-training and fine-tuning.

[0043] 2) In the pre-training stage, the model is trained using large-scale unlabeled text data, and learns the statistical laws and semantic representation of the language through self-supervised learning.

[0044] 3) In the fine-tuning phase, the model is trained using labeled task-specific data, such as the dialogue generation task, to further optimize the performance of the model.

[0045] 3. Pre-training strategy: 1) The pre-training strategy adopts methods such as Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).

[0046] 2) In MLM, the model randomly masks a part of the input text and then predicts the masked part.

[0047] 3) In NSP, the model determines whether two sentences are continuous.

[0048] 4. Consider other possible algorithmic or structural alternatives. Here are some common alternatives and their advantages and disadvantages: 1) Statistical Machine Translation (SMT): Advantages: It has a certain degree of maturity and explainability in machine translation tasks, and can be trained using a large amount of bilingual parallel corpus.

[0049] Disadvantages: Limited applicability for generative dialogue tasks, may have difficulties in processing long texts and complex semantics.

[0050] 2) Sequence-to-Sequence Model: Advantages: It can handle generative tasks such as machine translation and text summarization, and can generate coherent natural language sequences.

[0051] Disadvantages: There may be problems with gradient vanishing or gradient exploding when processing long sequences, and the ability to model long-distance dependencies is limited.

[0052] 3) Variants of pre-trained language models: Advantages: Through pre-training on large-scale unlabeled data, it learns the statistical laws and semantic representation of language, and has strong semantic understanding and context modeling capabilities.

[0053] Disadvantages: Training requires a lot of computing resources and time, which may not be suitable for scenarios with limited resources and requires additional data collection and labeling.

[0054] 2. Data Processing and Optimization 1. Data preprocessing: 1) Data preprocessing refers to operations such as cleaning, standardization, and conversion of raw data to facilitate model training and reasoning.

[0055] 2) In health education and consultation tasks, data preprocessing can include noise removal, word segmentation, stop word removal, stemming, and other operations.

[0056] 3) You can use common natural language processing tool libraries, such as NLTK, spaCy, etc., to preprocess data.

[0057] 2. Text encoding method: 1) Text encoding is the process of converting text data into a numerical representation acceptable to the model.

[0058] 2) Common text encoding methods include bag-of-words model, TF-IDF, word embedding, etc.

[0059] 3) In health education and consultation tasks, pre-trained word embedding models such as Word2Vec, GloVe, BERT, etc. can be used to convert text into dense vector representations.

[0060] 3. Sequence Modeling: 1) Sequence modeling refers to the serialization of input text so that the model can capture contextual information and semantic relationships.

[0061] 2) Common sequence modeling methods include recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), etc.

[0062] 3) In the newly designed system, the Transformer model can be used as the basis for sequence modeling, which is better able to handle long-distance dependencies and parallel computing.

[0063] 4. Different data processing methods may differ in feasibility and effectiveness. The following is a comparison of some common methods: 1) Data preprocessing: Different data preprocessing methods may be suitable for different tasks and data types. For example, for health education and consultation tasks, word segmentation and stop word removal are common preprocessing steps that can improve the model's ability to understand and generate text.

[0064] 2) Text encoding method: The bag-of-words model and TF-IDF are suitable for simple text classification tasks, but for health education and consultation tasks, the word embedding model is more suitable because it can convert text into a dense vector representation and capture the semantic relationship between words.

[0065] 3) Sequence modeling: Traditional sequence modeling methods such as RNN, LSTM, and GRU may have problems with gradient vanishing or gradient exploding when processing long sequences, while the Transformer model can better handle long-distance dependencies and parallel computing, so it has more advantages in the newly designed system.

[0066] 5. Anonymization and desensitization: Data containing sensitive information can be anonymized and desensitized to protect user privacy.

[0067] 6. Data encryption: For sensitive data that needs to be transmitted, encryption algorithms can be used to encrypt it to ensure the security of the data during transmission.

[0068] 7. Access control: For sensitive data stored in the system, access rights can be set to allow only authorized personnel to access and process the data to prevent unauthorized access and abuse.

[0069] 3. Model fine-tuning and application specialization 1. Fine-tuning process: 1) Fine-tuning refers to further training based on a general pre-trained model using a domain-specific dataset to adapt to the tasks and contexts of a specific domain.

[0070] 2) The fine-tuning process consists of two stages: freezing and fine-tuning. In the freezing stage, most of the model parameters remain unchanged, and only a few parameters are fine-tuned. In the fine-tuning stage, all parameters of the entire model are trained.

[0071] 3) During fine-tuning, a smaller learning rate can be used to avoid over-adjusting the parameters of the general model.

[0072] 2. Dataset used: 1) During fine-tuning, use a domain-specific dataset for training. The dataset can be labeled data in a specific domain or domain-related unlabeled data collected from sources such as the Internet.

[0073] 2) For the medical field, you can use medical literature, clinical records and other datasets for fine-tuning. For the legal field, you can use legal documents, regulations and other datasets for fine-tuning. For the financial field, you can use financial reports, financial data and other datasets for fine-tuning.

[0074] 3. Performance and application scope of the fine-tuned model: 1) The performance of a fine-tuned model in a specific domain will usually improve because it is further trained and optimized on data from that specific domain.

[0075] 2) In the medical field, the fine-tuned model can be used for tasks such as disease diagnosis and drug recommendation, providing more accurate and reliable results.

[0076] 3) In the legal field, the fine-tuned model can be used for tasks such as automatic summarization of legal documents and answering legal questions, providing more accurate and comprehensive solutions.

[0077] 4) In the financial field, the fine-tuned model can be used for tasks such as financial risk assessment and stock forecasting, providing more accurate and timely forecasting and decision support.

[0078] 4. Real-time Information Processing and Response 1. Information Acquisition: 1) Real-time information processing requires obtaining data from a variety of information sources, including sensor data, social media data, real-time streaming data, etc.

[0079] 2) Various technologies and tools can be used to obtain information, such as API interfaces, crawlers, message queues, etc.

[0080] 3) Different acquisition methods and protocols may be required for different types of information sources.

[0081] 2. Information processing: 1) Real-time information processing includes operations such as cleaning, conversion, aggregation and analysis of acquired information to extract useful information and knowledge.

[0082] 2) You can use stream processing technologies, such as Apache Kafka, Apache Flink, etc., to process real-time streaming data.

[0083] 3) Processing delay is an important factor that needs to be considered in real-time information processing. Reasonable processing delay needs to be set according to specific application scenarios and requirements.

[0084] 3. Response mechanism: 1) After real-time information processing, the system needs to make corresponding responses based on the processing results, which can be automated operations, report generation, notification sending, etc.

[0085] 2) Response accuracy is a key indicator in real-time information processing, and the accuracy and reliability of the response results need to be ensured.

[0086] 3) Real-time monitoring and feedback mechanisms can be used to evaluate and optimize the accuracy of response results.

[0087] Alternative processing flows can be adjusted according to specific needs and scenarios. The following are some possible alternative processing flows: Batch Processing: 1) If the latency requirement for real-time information processing is not high, you can consider converting real-time information into batch tasks for regular processing and response.

[0088] 2) Batch processing can reduce the pressure of real-time processing, but the response time will be delayed.

[0089] 2. Distributed processing flow: 1) If the amount of data to be processed in real-time information is large, you can consider using a distributed processing framework, such as Apache Spark, Hadoop, etc., to improve processing efficiency and throughput.

[0090] 2) Distributed processing can decompose tasks into multiple subtasks for parallel processing, thus speeding up the processing.

[0091] 3. Edge computing process: 1) If real-time information processing needs to be performed on edge devices, consider using edge computing technology to send processing tasks to edge devices for processing and response.

[0092] 2) Edge computing processes can reduce data transmission delays and network bandwidth consumption.

[0093] 5. Interactive and contextual understanding 1. Processing of conversation context: 1) Conversation context refers to the historical conversation content and related information in the conversation, including previous questions, answers, intentions, etc.

[0094] 2) The model needs to be able to store and manage the conversation context for subsequent processing and understanding.

[0095] 3) Various techniques and methods can be used to process the conversation context, such as recurrent neural networks (RNNs), attention mechanisms, etc.

[0096] 2. Continuity of long conversations: 1) Long conversations refer to conversations involving multiple rounds. The model needs to be able to maintain the continuity of the conversation, that is, to understand the previous conversation content in subsequent rounds.

[0097] 2) Technologies such as Memory Network can be used to store and retrieve previous conversation information to maintain the coherence and consistency of the conversation.

[0098] 3. Context switching processing: 1) During a conversation, context switching may occur, that is, switching from one topic or scene to another.

[0099] 2) The model needs to be able to identify context switches and perform corresponding processing and responses based on the context after the switch.

[0100] 3) Techniques such as attention mechanisms can be used to identify and handle context switches to ensure that the model can adapt to different contextual environments. 6. System Integration and Interface Design 1. Interface design: 1) Interface design refers to defining the interaction interface between a system and other systems, including input interface and output interface.

[0101] 2) The input interface defines the way and format in which other systems send data to the newly designed system. It can be an API interface, message queue, file transfer, etc.

[0102] 3) The output interface defines the way and format in which the newly designed system returns data to other systems. It can also be an API interface, message queue, file transfer, etc.

[0103] 2. Data exchange format: 1) Data exchange format refers to the data format used when exchanging data between systems. Common data exchange formats include JSON, XML, CSV, etc.

[0104] 2) In interface design, the data exchange format needs to be clearly defined to ensure that the system can correctly parse and process the received data.

[0105] 3. System compatibility: 1) System compatibility refers to the ability of the newly designed system to seamlessly integrate and work together with other systems.

[0106] 2) In interface design, it is necessary to consider the compatibility between systems, including the system's hardware and software environment, operating system, programming language, etc.

[0107] 3) Standardized interfaces and protocols, such as RESTful API, SOAP, etc., can be used to improve the compatibility and interoperability of the system.

[0108] 4. Integration method: 1) Integration method refers to the way and method of integrating the newly designed system with other systems.

[0109] 2) Direct integration can be adopted, that is, data can be directly exchanged with other systems through API interfaces or message queues.

[0110] 3) Indirect integration can also be adopted, that is, to realize data exchange and integration between systems through intermediate layers such as middleware or data bus.

[0111] 7. Scalability and maintainability 1. Scalability: 1) Scalability refers to the ability of a system to easily expand and upgrade its functions.

[0112] In model design, a modular and component-based approach can be used to divide the system into multiple independent modules or components to facilitate the addition and modification of new functions.

[0113] 2) A plug-in architecture can be used to support dynamic loading and unloading of functional modules, thereby achieving flexible expansion of the system.

[0114] 2. Update the training dataset: 1) Updating the training data set means that during the model training process, as new data is generated, the model needs to be updated and retrained.

[0115] 2) The system’s data interface and data storage method can be designed to facilitate easy updating and replacement of training data sets.

[0116] 3) You can use the incremental training method to train only the newly added data to improve training efficiency and reduce resource consumption.

[0117] 3. Maintainability: 1) Maintainability refers to the system's ability to easily perform error correction, performance optimization, and code maintenance.

[0118] 2) In model design, a clear code structure and naming conventions can be adopted to facilitate code understanding and maintenance.

[0119] 3) You can use logging and error tracking mechanisms to quickly locate and fix errors.

[0120] 4) It can perform performance analysis and optimization to identify and resolve system performance bottlenecks.

[0121] 4. Continuous Integration and Deployment: 1) Continuous integration and deployment refers to the rapid update and deployment of the system through automated processes and tools.

[0122] 2) Version control systems and continuous integration tools can be used to automate code management, building, and testing.

[0123] 3) Containerization technologies, such as Docker, can be used to facilitate rapid deployment and expansion of the system.

[0124] Beneficial effects of the present invention: 1. Comprehensive knowledge: By integrating multiple data sources and collaborating with professional doctors, the system can provide more comprehensive and accurate medical knowledge and clinical experience, covering more diseases and treatment options.

[0125] 2. Personalized and contextualized processing: The introduction of personalized and contextualized processing mechanisms can provide more targeted health education and consulting content based on the user's personal information and specific situations to meet the user's personalized needs.

[0126] 3. Explanation and transparency: Using an interpretable large language model can provide better explanation and visualization of the reasoning process, increasing users' understanding and trust in the system output.

[0127] 4. User participation and feedback: Encouraging user participation and feedback can continuously improve and optimize the performance and functions of the system and enhance user experience and satisfaction.

[0128] 5. Cooperation and guidance with professional doctors: Cooperation and guidance with professional doctors can improve the accuracy and reliability of the system and ensure the quality and credibility of the health education and consultation content provided by the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0130] See also Figure 1 Shown is an embodiment of the present invention.

[0131] A patient health education and consultation system based on a large language model includes a data collection module, a model building and optimization module, a result output module and a feedback module.

[0132] The data collection module integrates multi-source data. By integrating multiple data sources and cooperating with professional doctors, the system can provide more comprehensive and accurate medical knowledge and clinical experience, covering more diseases and treatment options.

[0133] The model building and optimization module introduces a personalized and situational processing mechanism, which can provide more targeted health education and consulting content according to the user's personal information and specific situations to meet the user's personalized needs.

[0134] The result output module adopts an interpretable large language model, which can provide better explanation and visualization of the reasoning process, and increase users' understanding and trust in the system output.

[0135] The feedback module establishes a model feedback reward mechanism, expands longitudinal data input, and establishes a professional clinical diagnosis feedback optimization model.

[0136] Working process and working principle of the present invention: Multi-source data integration: The system integrates multiple data sources, including medical literature, clinical practice, doctor experience, etc. This ensures that the system has comprehensive and accurate medical knowledge and covers more diseases and treatment options.

[0137] Pre-training and fine-tuning: The system uses pre-trained large language models, such as BERT, GPT, etc., to train the integrated data. Then, by fine-tuning the model, it is optimized for specific health education and consulting tasks. This can improve the model's understanding and reasoning ability in the medical field.

[0138] Personalized and contextualized processing: The system introduces a personalized and contextualized processing mechanism to provide targeted health education and consulting content based on the user's personal information, medical history, laboratory tests, etc. This can increase the practicality and user experience of the system, allowing users to obtain more personalized and accurate advice.

[0139] Explanation and transparency: Use interpretable large language models, such as XLNet, ALBERT, etc., to provide better explanations and visualization of the reasoning process. This can increase users’ understanding and trust in the health education and consultation content provided by the system.

[0140] User participation and feedback: The system encourages user participation and feedback, such as through user surveys, evaluations, and feedback, etc. This can collect user needs and opinions and continuously improve and optimize the performance and functions of the system.

[0141] Cooperation and guidance with professional doctors: The system cooperates and guides with professional doctors, who provide professional medical knowledge and experience to verify and correct the system's output. This ensures the accuracy and credibility of the health education and consultation content provided by the system.

[0142] Privacy and security protection: The system takes strict privacy and security measures to ensure that users' personal health information is protected and processed safely. This includes data encryption, access control, anonymization, etc.

[0143] Multilingual and cross-cultural adaptability: The system is multilingual and cross-cultural, and can provide health education and consulting services for different languages ​​and cultural backgrounds. This can meet the needs of different regions and user groups.

[0144] Patient education and self-help functions: The system provides patient education and self-help functions, such as providing a health knowledge base, disease management tools, health risk assessment, etc. This can help users proactively manage their own health and improve their health literacy.

[0145] Continuous Improvement and Updates: The system is regularly updated and improved to keep up with the latest knowledge and technology in the medical field. This ensures that the system always provides the latest and accurate health education and consultation content.

Claims

1. A patient health education and consultation system based on a large language model, characterized by: It includes data collection module, model building and optimization module, result output module and feedback module.

2. A patient health education and consultation system based on a large language model according to claim 1, characterized in that: The data collection module integrates multi-source data. By integrating multiple data sources and cooperating with professional doctors, the system can provide more comprehensive and accurate medical knowledge and clinical experience, covering more diseases and treatment options.

3. A patient health education and consultation system based on a large language model according to claim 1, characterized in that: The model building and optimization module introduces a personalized and situational processing mechanism, which can provide more targeted health education and consulting content according to the user's personal information and specific situations to meet the user's personalized needs.

4. A patient health education and consultation system based on a large language model according to claim 1, characterized in that: The result output module adopts an interpretable large language model, which can provide better explanation and visualization of the reasoning process, and increase users' understanding and trust in the system output.

5. A patient health education and consultation system based on a large language model according to claim 1, characterized in that: The feedback module establishes a model feedback reward mechanism, expands longitudinal data input, and establishes a professional clinical diagnosis feedback optimization model.

Images