Personalized patient nutrition and lifestyle suggestion system based on large language model

Abstract

The invention discloses a personalized patient nutrition and lifestyle suggestion 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 follow-up visit module, the data quality and reliability are high, and the accuracy and reliability of suggestion generation of the system are improved; an individualized feedback and adjustment mechanism enables the system to better meet personal requirements and preferences of patients; according to the method, cultural and regional differences are considered, personalized suggestions more suitable for specific cultures and regions are generated, improvement of interpretability and understandability helps patients to understand and accept the suggestions generated by the system, a long-term user participation and continuous support mechanism is introduced, and long-term participation and health behavior change of users are promoted.

Description

Technical Field

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

[0002] The structure or method of the current technical solutions used in computerized advice on patient nutrition and lifestyle is as follows: 1. Data Collection and Preprocessing: The system needs to collect data such as the patient's personal information, health status, and dietary habits. This data can be obtained through questionnaires, medical records, sensors, and other methods. The data is then preprocessed, including data cleaning and feature extraction.

[0003] 2. Model Training: The system uses large language models (such as BERT and GPT) for training. The goal is to predict the most appropriate nutrition and lifestyle recommendations based on the patient's personal information and health status. During training, methods such as supervised learning and reinforcement learning can be used.

[0004] 3. Personalized Recommendation Generation: After the system is trained, it can generate personalized nutrition and lifestyle recommendations based on patient input. Patients can provide information through conversations with the system or by completing questionnaires, and the system generates corresponding recommendations based on this information.

[0005] 4. Evaluation and Optimization: The system needs to be evaluated and optimized to improve the accuracy and reliability of recommendations. Cross-validation and user feedback can be used to evaluate the system's performance, and the model can be optimized and improved based on the evaluation results.

[0006] 5. Prediction and Recommendation: The system can use a trained model to predict the patient's nutritional and lifestyle needs based on their input and provide corresponding recommendations. These recommendations may include dietary advice, exercise plans, lifestyle adjustments, etc.

[0007] 6. Feedback and Adjustment: Patients can perform actions based on the system's recommendations and provide feedback. The system can then adjust and optimize based on patient feedback to provide more accurate and personalized recommendations.

[0008] 7. Monitoring and tracking: The system can monitor the patient's progress and health status and make adjustments as needed. This can be achieved through the patient's health data, lifestyle records, etc. Monitoring and tracking can help the system understand the patient's changes and needs to provide more precise recommendations.

[0009] 8. Education and support: The system can provide relevant education and support to help patients understand and implement recommendations. This can include education on nutrition knowledge, healthy habits, and support in areas such as online consultation and community support.

[0010] The above technical solution has the following problems and defects: 1. Data quality and reliability: The accuracy and reliability of the system are limited by the quality of the data used. If the data collected is inaccurate or incomplete, the recommendations generated by the system may be inaccurate or inappropriate for the patient's actual situation.

[0011] 2. Explainability and Comprehensibility: Recommendations generated by large language models are often black-box and difficult to interpret and understand. Patients may struggle to understand the reasoning and rationale behind the system-generated recommendations, which can affect their acceptance and implementation of the recommendations.

[0012] 3. Uncertainty and Risk Management: Personalized recommendation systems face the challenges of uncertainty and risk management. System-generated recommendations may contain errors and risks, and it is necessary to consider how to manage and reduce these uncertainties and risks.

[0013] 4. Lack of integration of AI and human expertise: Current systems primarily rely on automated processing using large language models and lack integration with human expertise. This can result in the system being unable to provide more in-depth and comprehensive advice and address some complex health issues.

[0014] 5. Lack of long-term effect evaluation: Current systems often lack long-term effect evaluation, making it impossible to verify their actual impact on patients' health status and lifestyle. Long-term effect evaluation can help improve the accuracy and effectiveness of the system.

[0015] 6. Lack of individualized feedback and adjustments: Current systems often lack the ability to provide individualized feedback and adjustments to patients. System-generated recommendations may not be adjusted and optimized in a timely manner based on patient feedback and actual circumstances, resulting in poor recommendations.

[0016] 7. Cultural and regional differences: Systems based on large language models may not fully account for cultural and regional differences. Factors such as dietary habits and lifestyles vary across cultures and regions, and the system needs to better adapt and account for these differences.

[0017] 8. Lack of long-term user engagement and ongoing support: Personalized patient nutrition and lifestyle recommendations require long-term user engagement and ongoing support. However, current systems often lack mechanisms to guide long-term user engagement and provide ongoing support.

[0018] 9. Lack of personalized behavior change strategies: Personalized patient nutrition and lifestyle recommendation systems need to better integrate behavioral science and psychology to provide personalized behavior change strategies. This can help patients better understand and implement recommendations, promoting healthy behavior changes.

[0019] The reasons that lead to the above problems or defects are often: 1. Data quality and reliability: Data quality and reliability are fundamental to personalized patient nutrition and lifestyle recommendation systems. If data collection is inaccurate, incomplete, or unreliable, the resulting recommendations will be compromised. This can be due to inaccurate data collection methods, missing or erroneous data, or unreliable data sources.

[0020] 2. Explainability and Comprehensibility: Recommendations generated by large language models are often black-box and difficult to interpret and understand. This can make it difficult for patients to understand the basis and reasoning behind the system's recommendations, thus affecting their acceptance and implementation of the recommendations.

[0021] 3. Uncertainty and Risk Management: Personalized recommendation systems face the challenges of uncertainty and risk management. System-generated recommendations may contain errors and risks, and it is necessary to consider how to manage and mitigate these uncertainties and risks. This may involve model uncertainty handling, risk assessment, and management.

[0022] 4. Lack of long-term user engagement and sustained support: Personalized recommendation systems require long-term user engagement and support, but current systems often lack mechanisms to guide long-term user engagement and provide sustained support. This may be due to factors such as imperfect system design and low user willingness to participate.

[0023] 5. Lack of individualized feedback and adjustments: Personalized recommendation systems lack the ability to provide individualized feedback and adjustments to patients, possibly due to imperfect system design or a lack of effective feedback mechanisms. The system needs to be able to make timely adjustments and optimizations based on patient feedback and actual conditions to provide more accurate and personalized recommendations.

[0024] 6. Cultural and regional differences: Personalized recommendation systems may not fully account for cultural and regional differences, resulting in recommendations that are not applicable to specific cultures and regions. This may be due to factors such as dataset limitations and model training methods. Summary of the Invention

[0025] The purpose of the present invention is to solve the problems existing in the existing background technology and provide a personalized patient nutrition and lifestyle recommendation system based on a large language model.

[0026] A personalized patient nutrition and lifestyle recommendation system based on a large language model includes a data collection module, a model building and optimization module, a result output module and a follow-up module.

[0027] The data collection module improves the strict quality control of quantitative sources, cooperates with medical institutions, health experts and patients, and adopts multiple data collection methods, such as questionnaires, sensor data, medical records, etc., to obtain more accurate, comprehensive and reliable data; cleans and verifies the collected data, removes erroneous, missing and duplicate data, and ensures the accuracy and completeness of the data; introduces a data quality assessment and monitoring mechanism, regularly assesses the quality of the data, and promptly discovers and resolves data quality issues.

[0028] The model building and optimization module establishes an algorithm based on the patient's needs and optimizes and adjusts it based on feedback. It establishes personalized models and algorithms based on the patient's personal characteristics, health status and needs, takes into account the patient's physiological, psychological and social factors, and combines the patient's feedback and actual situation to adjust and optimize suggestions in real time, providing individualized feedback and adjustment mechanisms to meet the patient's personal needs and preferences. It introduces machine learning and deep learning technologies, uses the patient's historical data and real-time data to train and optimize the model, and improves the accuracy and personalization of the suggestions; comprehensively considers regional cultural characteristics, collects and integrates relevant data from different cultures and regions, including eating habits, lifestyle, disease incidence, etc., to establish cultural and regional specific models and algorithms, and generates personalized suggestions for specific cultures and regions based on the characteristics of different cultures and regions, taking into account local eating habits, food availability and other factors.

[0029] The result output module combines clinical practice, optimizes the readability and operability of the output results, and improves interpretability and comprehensibility: introduces interpretable machine learning and deep learning models, such as decision trees, rule reasoning, etc., to provide interpretable suggestions and explain the basis and reasons for the system to generate suggestions; designs user-friendly interfaces and visualization tools to display the basis and reasons for the system to generate suggestions, helping patients understand and accept the suggestions.

[0030] The follow-up module establishes a model feedback reward mechanism, expands longitudinal data input, designs attractive user participation mechanisms such as personalized goal setting and reward mechanisms, and encourages users to participate in and continue to use the system for a long time; provides continuous support and guidance such as regular reminders, progress tracking, and personalized suggestion updates to help users maintain healthy behaviors and promote changes in healthy behaviors.

[0031] Working principle of the present invention: 1. Core Algorithms and Frameworks 1. Basic Architecture: 1) Neural Network Architecture: The Transformer architecture is used as the foundation, including a multi-layer self-attention mechanism and a feedforward neural network. The self-attention mechanism is used to capture the contextual relationships in the input sequence, and the feedforward neural network is used to perform nonlinear transformations on features.

[0032] 2) Input representation: The patient’s personal characteristics, health status, and needs are encoded into a vector representation and concatenated with the text input as the input of the model.

[0033] 3) Output layer: A softmax layer is used to convert the output of the model into a probability distribution, indicating the likelihood of different suggestions.

[0034] 2. Training methods: 1) Supervised learning: Use labeled data for training, take the input sequence and the corresponding recommendations as training samples, and optimize the model parameters by maximizing the probability of predicting the recommendations.

[0035] 2) Loss function: The cross entropy loss function is used to measure the difference between the model prediction results and the true labels.

[0036] 3) Optimization algorithm: The Adam optimization algorithm is used, combined with momentum and adaptive learning rate adjustment to accelerate model convergence and optimization.

[0037] 3. Pre-training strategy: 1) Unsupervised pre-training: Use large-scale unlabeled data for pre-training, and learn the representation and contextual relationship of the input sequence through methods such as autoencoders or language models.

[0038] 2) Fine-tuning training: Based on pre-training, fine-tuning training is performed using labeled data to further optimize the model parameters to adapt them to the needs of specific tasks.

[0039] 4. Advantages and disadvantages of alternatives: Alternative 1: Traditional rule-based approach Advantages: The rules are highly interpretable, easy to understand and adjust; they do not require a large amount of labeled data.

[0040] Disadvantages: Rule writing is cumbersome and difficult to cover all situations; it is difficult to model complex relationships and patterns; it lacks adaptive capabilities.

[0041] Alternative 2: Sequence-to-Sequence Model (Seq2Seq) Advantages: Able to handle complex relationships between input and output; suitable for generative tasks such as machine translation.

[0042] Disadvantages: Requires a large amount of labeled data for training; may have difficulty processing long sequences; generated recommendations may lack interpretability.

[0043] Alternative 3: BERT (Bidirectional Encoder Representations from Transformers) Advantages: Through pre-training and fine-tuning, it can learn richer semantic representations; it is suitable for a variety of natural language processing tasks.

[0044] Disadvantages: Requires a lot of computing resources and time for pre-training; further model adjustment and optimization may be required for the generation of personalized recommendations.

[0045] 2. Data Processing and Optimization 1. Data preprocessing: 1) Cleaning and filtering: Clean and filter the raw data to remove noise, invalid information, and duplicate data to improve data quality.

[0046] 2) Standardization and normalization: Standardize and normalize the data so that the data has the same scale and distribution to facilitate model training and optimization.

[0047] 3) Feature selection and extraction: Select appropriate features according to task requirements and perform feature extraction to reduce data dimensions and redundant information, and improve the efficiency and generalization ability of the model.

[0048] 2. Text encoding method: 1) Word-level encoding: Split the text into words and map each word to a unique encoding for easier model processing. Common encoding methods include one-hot encoding, bag-of-words model, and word embedding.

[0049] 2) Character-level encoding: Segment the text into characters and map each character to a unique encoding to capture more fine-grained semantic information. Character-level encoding is suitable for processing unknown words and multilingual scenarios.

[0050] 3. Sequence Modeling: 1) Sequential modeling: The text sequence is used as the input of the model, and sequential modeling is performed through models such as recurrent neural networks (RNNs) or Transformers to capture the contextual relationships and semantic information in the text.

[0051] 2) Bidirectional modeling: Based on sequential modeling, a bidirectional model is introduced to simultaneously consider forward and backward contextual information to better understand the semantics and relevance in the text.

[0052] 4. Feasibility and effectiveness of different methods: 1) Data Preprocessing: Appropriate data preprocessing methods can improve model training performance and generalization capabilities, but they need to be selected and adjusted based on the specific task and data characteristics. Different preprocessing methods may have different effects on model performance and effectiveness.

[0053] 2) Text Encoding Methods: Word-level encoding and character-level encoding each have their advantages and disadvantages. Word-level encoding can capture richer semantic information, but may face issues with large vocabularies and unknown words. Character-level encoding can handle unknown words and multiple languages, but may require larger models and computing resources.

[0054] 3) Sequence Modeling: Sequential modeling and bidirectional modeling each have their advantages and disadvantages. Sequential modeling is suitable for processing long text sequences and time series data, but may suffer from gradient vanishing and information loss issues. Bidirectional modeling can better capture contextual relationships, but may require a larger model and computational overhead.

[0055] 5. Data privacy and security issues: During data processing, attention should be paid to protecting patient privacy and data security. The following measures can be taken: 1) Anonymization and desensitization: Sensitive information is anonymized and desensitized to protect patient privacy.

[0056] Data encryption: Encrypt data to prevent unauthorized access and disclosure.

[0057] 2) Access control: Limit access rights to data to ensure that only authorized personnel can access and use the data.

[0058] 3) Data security audit: Establish a data security audit mechanism to monitor and record the use and access of data to ensure data security.

[0059] 3. Model Fine-tuning and Application Specialization 1. Data collection and preprocessing: 1) Collect relevant data in the medical field, including medical literature, clinical records, medical records, etc.

[0060] 2) Clean, standardize and normalize the data to remove noise and invalid information to improve data quality.

[0061] 2. Model selection and pre-training: 1) Select a basic model suitable for the medical field, such as BERT or GPT.

[0062] 2) Use a large-scale general corpus for pre-training to learn common semantic representations and language patterns.

[0063] 3. Fine-tuning process: 1) Based on the pre-trained model, fine-tune it using a medical dataset.

[0064] 2) Mixing medical datasets with general corpora to maintain the model’s ability to understand general semantics.

[0065] 3) Fine-tune using appropriate loss functions and optimization algorithms to maximize adaptability to task requirements in the medical field.

[0066] 4. Model performance evaluation: 1) Use the medical evaluation dataset to evaluate the performance of the fine-tuned model.

[0067] 2) Evaluation indicators can include accuracy, recall rate, F1 value, etc. Select appropriate evaluation indicators based on specific task requirements.

[0068] 5. Application scope: 1) The fine-tuned model can be applied to various tasks in the medical field, such as disease diagnosis, drug recommendation, clinical decision support, etc.

[0069] 2) The model can be further optimized and adjusted according to specific task requirements to improve the performance and effect of the model on specific tasks.

[0070] 4. Real-time Information Processing and Response 1. Information Acquisition: 1) Diverse information sources: The system can obtain real-time data from multiple information sources, including sensor data, medical device data, patient monitoring data, medical literature, etc.

[0071] 2) Data collection and transmission: Establish a data collection and transmission mechanism to ensure the accuracy and timeliness of real-time data. Technologies such as sensor networks and cloud platforms can be used to achieve data collection and transmission.

[0072] 2. Information Processing: 1) Real-time Data Processing: Design efficient real-time data processing algorithms and processes to process real-time data from various information sources. Stream processing technologies such as Apache Kafka and Apache Flink can be used to process and analyze data in real time.

[0073] 2) Data aggregation and integration: Aggregate and integrate data from different information sources to obtain more comprehensive and accurate information. Data integration and fusion technologies such as data warehouses and data lakes can be used.

[0074] 3. Response Mechanism: 1) Real-time decision-making and recommendations: Based on the results of real-time processing, the system can generate real-time decisions and recommendations, such as disease diagnosis and treatment plan recommendations. Machine learning and deep learning models can be used to support the generation of real-time decisions and recommendations.

[0075] 2) Real-time notifications and reminders: Based on the results of real-time processing, the system can send real-time notifications and reminders to doctors, patients, or other relevant personnel to promote timely actions and decisions.

[0076] 3. Considering issues such as information processing delay and accuracy, the following alternative processing procedures can be explored: 1) Batch processing: Real-time data is collected and stored, and then batch processed periodically to reduce the latency of real-time processing. Distributed computing frameworks such as Apache Hadoop and Apache Spark can be used to process and analyze batch data.

[0077] 2) Edge computing: Perform real-time data processing and analysis on edge devices or edge servers to reduce data transmission and processing delays. Edge computing platforms such as Microsoft Azure IoT Edge and AWS Greengrass can be used to support edge computing and real-time response.

[0078] 5. Interactive and contextual understanding skills 1. Context Modeling: 1) Conversation history: The system can maintain a history of conversations, including the user’s questions, the system’s answers, and other contextual information in the conversation.

[0079] 2) Context vector representation: The conversation history is converted into a context vector representation to capture the semantic and contextual information of the conversation.

[0080] 2. Context switch processing: 1) Context switch detection: The system can determine whether a context switch has occurred by detecting keywords or indicator words in the user's questions and answers.

[0081] 2) Context switch processing: When a context switch occurs, the system can re-establish a new context vector representation and answer and process based on the new context.

[0082] 3. Continuity of long conversations: 1) Contextual Memory: The system can maintain a continuous understanding of long conversations through methods such as memory networks or attention mechanisms. Past conversation information is stored as memory and, when needed, introduced to assist in understanding and answering current questions.

[0083] 2) Conversation state tracking: The system can maintain the state of the conversation, including the user's intention, conversation goal, etc., to maintain consistency and coherence in long conversations.

[0084] 3. Different context handling strategies may produce different effects: 1) Fixed-window context processing strategy: Only the most recent dialogue rounds are considered, ignoring earlier context information. This strategy can reduce computational and storage complexity, but may lose some important context information.

[0085] 2) Global context-based processing strategy: Considering the entire conversation history and incorporating all contextual information. This strategy can more comprehensively understand the semantics and context of the conversation, but may increase computational and storage overhead.

[0086] 6. System Integration and Interface Design 1. Interface design: 1) Determine the function and purpose of the interface: clarify the specific function and purpose of each interface, such as data exchange, function call, etc.

[0087] 2) Define the input and output of the interface: clarify the parameters and return values ​​of each interface, including data type, format, etc.

[0088] 3) Design the interface calling method: Determine the interface calling method, such as using RESTful API, SOAP, etc.

[0089] 4) Consider interface version management: Design a reasonable interface version management mechanism to facilitate interface upgrades and evolution.

[0090] 2. Data exchange format: 1) Select a suitable data exchange format: Based on system requirements and compatibility considerations, select a suitable data exchange format, such as JSON, XML, Protobuf, etc.

[0091] 2) Define the structure of the data exchange format: Clarify the structure of each data exchange format, including fields, types, constraints, etc.

[0092] 3) Consider data serialization and deserialization: Design data serialization and deserialization mechanisms to facilitate data transmission and parsing between systems.

[0093] 3. System compatibility: 1) Analyze the interfaces and data formats of existing systems: Understand the interfaces and data formats of existing systems in detail to facilitate adaptation and compatibility.

[0094] 2) Design an adapter or middle layer: If there are differences between the new system and the existing system, design an adapter or middle layer to perform data format conversion and interface adaptation.

[0095] 4. How the model is integrated with other systems: 1) Database Integration: Design the database interface, including operations such as reading, writing, and updating data. Database operations can be implemented using SQL statements or an ORM framework.

[0096] 2) User Interface Integration: Design the user interface, including obtaining user input and displaying results. User interface integration can be achieved using a front-end framework or API calls.

[0097] 5. Security design: Design the security mechanism of the interface, including identity authentication, permission control, data encryption, etc., to ensure the security of the interface.

[0098] 6. Error handling design: Design a reasonable error handling mechanism, including error codes, exception handling, logging, etc., to detect and solve problems in a timely manner.

[0099] 7. Performance optimization design: Consider the performance optimization of the interface, including caching mechanisms, request merging, asynchronous processing, etc., to improve the system's response speed and throughput.

[0100] 7. Scalability and Maintainability 1. Scalability: 1) Model Architecture Design: A modular and pluggable architecture is used to facilitate the addition of new functions or modules to the system. Each function or module should have a clear interface and independent implementation to facilitate expansion and replacement.

[0101] 2) Dataset Design: Design flexible dataset interfaces and data processing flows to facilitate adding new training datasets or updating existing ones. Data pipelines or data streams can be used to dynamically adapt the data processing and training process to new data.

[0102] 3) Model training and updating: Design a scalable model training and updating mechanism to facilitate the addition of new training algorithms or model structures. Techniques such as incremental learning or transfer learning can be used to enable model updates and optimizations without interrupting system operation.

[0103] 2. Maintainability: 1) Error Correction: Design a reasonable error handling mechanism, including error logging, exception handling, and error codes. When a system error occurs, the error information can be captured and recorded in a timely manner for troubleshooting and repair.

[0104] 2) Performance Optimization: Regularly evaluate and optimize performance, including model inference speed and memory usage. Use performance analysis tools and tuning techniques, such as model pruning and quantization, to improve system performance and efficiency.

[0105] 3) Model Monitoring and Maintenance: Establish a model monitoring system to regularly check the model's performance and accuracy. If the model degrades or becomes inaccurate, timely adjustments and corrections can be made. At the same time, the model's training dataset should be regularly updated to maintain its robustness and adaptability.

[0106] Beneficial effects of the present invention: 1. High data quality and reliability improve the accuracy and reliability of system-generated recommendations.

[0107] 2. Individualized feedback and adjustment mechanisms enable the system to better meet the patient's individual needs and preferences.

[0108] 3. Consider cultural and regional differences and generate personalized recommendations that are more suitable for specific cultures and regions.

[0109] 4. Improved explainability and comprehensibility help patients understand and accept system-generated recommendations.

[0110] 5. Introduce long-term user engagement and continuous support mechanisms to promote long-term user engagement and healthy behavior changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 Flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0112] See also Figure 1 Shown is an embodiment of the present invention.

[0113] A personalized patient nutrition and lifestyle recommendation system based on a large language model includes a data collection module, a model building and optimization module, a result output module and a follow-up module.

[0114] The data collection module improves the strict quality control of quantitative sources, cooperates with medical institutions, health experts and patients, and adopts multiple data collection methods, such as questionnaires, sensor data, medical records, etc., to obtain more accurate, comprehensive and reliable data; cleans and verifies the collected data, removes erroneous, missing and duplicate data, and ensures the accuracy and completeness of the data; introduces a data quality assessment and monitoring mechanism, regularly assesses the quality of the data, and promptly discovers and resolves data quality issues.

[0115] The model building and optimization module establishes an algorithm based on the patient's needs and optimizes and adjusts it based on feedback. It establishes personalized models and algorithms based on the patient's personal characteristics, health status and needs, takes into account the patient's physiological, psychological and social factors, and combines the patient's feedback and actual situation to adjust and optimize suggestions in real time, providing individualized feedback and adjustment mechanisms to meet the patient's personal needs and preferences. It introduces machine learning and deep learning technologies, uses the patient's historical data and real-time data to train and optimize the model, and improves the accuracy and personalization of the suggestions; comprehensively considers regional cultural characteristics, collects and integrates relevant data from different cultures and regions, including eating habits, lifestyle, disease incidence, etc., to establish cultural and regional specific models and algorithms, and generates personalized suggestions for specific cultures and regions based on the characteristics of different cultures and regions, taking into account local eating habits, food availability and other factors.

[0116] The result output module combines clinical practice, optimizes the readability and operability of the output results, and improves interpretability and comprehensibility: introduces interpretable machine learning and deep learning models, such as decision trees, rule reasoning, etc., to provide interpretable suggestions and explain the basis and reasons for the system to generate suggestions; designs user-friendly interfaces and visualization tools to display the basis and reasons for the system to generate suggestions, helping patients understand and accept the suggestions.

[0117] The follow-up module establishes a model feedback reward mechanism, expands longitudinal data input, designs attractive user participation mechanisms such as personalized goal setting and reward mechanisms, and encourages users to participate in and continue to use the system for a long time; provides continuous support and guidance such as regular reminders, progress tracking, and personalized suggestion updates to help users maintain healthy behaviors and promote changes in healthy behaviors.

Claims

1. A personalized patient nutrition and lifestyle recommendation system based on a large language model, characterized by: It includes data collection module, model building and optimization module, result output module and follow-up module.

2. A personalized patient nutrition and lifestyle recommendation system based on a large language model according to claim 1, characterized in that: The data collection module improves the quality control of the data sources, collaborates with medical institutions, health experts and patients, and adopts multiple data collection methods such as questionnaires, sensor data, medical records, etc. to obtain more accurate, comprehensive and reliable data; Clean and verify the collected data to remove erroneous, missing and duplicate data to ensure the accuracy and completeness of the data; introduce data quality assessment and monitoring mechanisms, conduct regular quality assessments on the data, and promptly identify and resolve data quality issues.

3. The personalized patient nutrition and lifestyle recommendation system based on a large language model according to claim 1, characterized in that: The model building and optimization module establishes an algorithm based on the patient's needs and optimizes and adjusts it based on feedback. It establishes personalized models and algorithms based on the patient's personal characteristics, health status and needs, takes into account the patient's physiological, psychological and social factors, and combines the patient's feedback and actual situation to adjust and optimize suggestions in real time, providing individualized feedback and adjustment mechanisms to meet the patient's personal needs and preferences. It introduces machine learning and deep learning technologies, uses the patient's historical data and real-time data to train and optimize the model, and improves the accuracy and personalization of the suggestions; comprehensively considers regional cultural characteristics, collects and integrates relevant data from different cultures and regions, including eating habits, lifestyles, disease incidence, etc., to establish cultural and regional specific models and algorithms, and generates personalized suggestions for specific cultures and regions based on the characteristics of different cultures and regions, taking into account local eating habits, food availability and other factors.

4. The personalized patient nutrition and lifestyle recommendation system based on a large language model according to claim 1, characterized in that: The result output module combines clinical practice, optimizes the readability and operability of the output results, and improves interpretability and comprehensibility: it introduces interpretable machine learning and deep learning models, such as decision trees and rule reasoning, to provide interpretable suggestions and explain the basis and reasons for the system's generated suggestions; Design user-friendly interfaces and visualization tools to demonstrate the basis and reasons for the system-generated recommendations to help patients understand and accept them.

5. The personalized patient nutrition and lifestyle recommendation system based on a large language model according to claim 1, characterized in that: The follow-up module establishes a model feedback reward mechanism, expands longitudinal data input, designs attractive user participation mechanisms such as personalized goal setting and reward mechanisms, and encourages users to participate in and continue to use the system for a long time; provides continuous support and guidance such as regular reminders, progress tracking, and personalized suggestion updates to help users maintain healthy behaviors and promote changes in healthy behaviors.

Images