Large language model system for optimizing operation plan and resource allocation

Abstract

The invention discloses a large language model system for optimizing an operation plan and resource allocation. The large language model system comprises a data acquisition and preprocessing module, a large language model training module, an operation plan optimization module, a resource allocation optimization module, a contest support module and a system integration and deployment module, a large amount of patient data can be automatically analyzed and processed by using an optimization system of a large language model, so that an operation plan and resource allocation are more efficient, accurate and consistent, a treatment scheme better meeting patient requirements can be provided, the operation effect and satisfaction degree are improved, and the patient experience is improved. The system can also carry out dynamic adjustment and optimization according to real-time data and feedback so as to cope with changing conditions, the system optimizes operation plans and resource allocation, operation waiting time and idle time of an operating room can be shortened, the utilization efficiency of resources of the operating room can be improved, training and optimization can be carried out by utilizing rich operation data, and the operation efficiency of the operating room can be improved. And standardization of medical practice and continuous quality improvement can be promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and in particular to a large language model system for optimizing surgical planning and resource allocation. Background Art

[0002] The demand for surgery is high, and the distribution of surgical resources is uneven, resulting in insufficient surgical resources in some areas, and long waiting times for surgery are common. In addition, there are large differences in medical resources and technical levels. The equipment and technical level of medical institutions in some areas are low, resulting in patients being unable to receive high-quality surgical treatment. At the same time, surgical costs are also a problem. Some patients cannot afford the cost of surgery due to financial reasons, resulting in delayed treatment. Therefore, it is necessary to further optimize the allocation of surgical resources, improve the level of medical services, and reduce medical costs. Summary of the invention

[0003] The purpose of the present invention is to solve the problems existing in the prior art and to provide a large language model system for optimizing surgical planning and resource allocation.

[0004] A large language model system for optimizing surgical planning and resource allocation includes a data acquisition and preprocessing module, a large language model training module, a surgical planning optimization module, a resource allocation optimization module, a final support module, and a system integration and deployment module.

[0005] The data acquisition and preprocessing module collects historical data of hospital operating rooms, including information such as operation type, operation duration, and operating room resource utilization. The data is then cleaned and processed for use in model training.

[0006] The large language model training module selects the GPT-3 or BERT large language model.

[0007] The surgical plan optimization module can understand the relevant information of surgical plan and resource allocation through the large language training module, and can generate optimized surgical plan and resource allocation suggestions.

[0008] The resource allocation optimization module integrates the trained large language model into the hospital operating room management system, enabling it to process surgical planning and resource allocation needs in real time and generate corresponding optimization suggestions.

[0009] The system integration and deployment module deploys the integrated system into the hospital operating room management system, and continuously optimizes and adjusts it according to the actual application situation to improve the accuracy and practicality of the system. It can help optimize surgical planning and resource allocation, improve surgical efficiency and quality, reduce risks and provide decision support.

[0010] Working principle of the present invention: 1. Core Algorithms and Frameworks: 1. Data collection and preprocessing: Collect surgery-related patient data, including medical records, imaging data, laboratory results, etc. Preprocess patient data, including feature extraction, data cleaning and normalization, for subsequent model use.

[0011] 2. Data storage and management: Store the processed data in a scalable database so that it can be accessed and updated at any time. Design a reliable data management system to track data changes and updates during surgery.

[0012] 3. Model training and optimization: Use patient data and surgical history data to train large language models, such as deep learning-based recurrent neural networks (RNNs) or BERT (BERT). Improve model performance and accuracy by iteratively optimizing model parameters. Use cross-validation and other evaluation techniques to evaluate and compare the performance of different models.

[0013] 3. Surgical planning and resource allocation: Receive the patient's surgical needs information and obtain relevant data from the database. Input the patient information into the trained large language model and generate a recommended surgical plan. Consider the availability of surgical resources, such as operating room time and equipment. Generate the optimal surgical plan and resource allocation strategy based on the recommended plan and resource conditions output by the model.

[0014] 4. Decision Support System: The surgical team can use the decision support tools in the system to analyze and evaluate the risks and benefits of different surgical plans. The system can provide personalized advice and guidance based on the patient's specific situation and health needs.

[0015] 5. Monitoring and Feedback: During surgery, the system can monitor the progress of the surgery and the patient's physiological parameters in real time. Using machine learning and real-time data analysis technology, the system can provide real-time feedback and suggestions to help the surgical team make more accurate decisions. Post-operative data is collected and analyzed to evaluate the effectiveness of surgical planning and resource allocation, and used for future system improvements and optimization.

[0016] 2. Data Processing and Optimization Data collection and preprocessing: Collect surgically relevant patient data, including medical records, imaging data, laboratory results, etc. Preprocess patient data, including feature extraction, data cleaning and normalization, for subsequent model use.

[0017] Model training and optimization: Use the processed data to train a large language model, which can be a deep learning-based recurrent neural network (RNN), Bertrand Transformer (BERT), or other models suitable for the task.

[0018] Set the input and output of the model, such as patient feature data as input and surgical plan and resource allocation as output. Train the model using the training dataset and iteratively optimize the model parameters to improve performance and accuracy. Adjust the model's hyperparameters, such as learning rate, batch size, number of layers, etc., to achieve better performance.

[0019] Surgical planning and resource allocation: Receive the patient’s surgical needs information and obtain relevant data from the database. Input the patient information into the trained large language model and generate recommended surgical plans and resource allocation. Consider the availability of surgical resources, such as operating room time and equipment. Generate the optimal surgical plan and resource allocation strategy based on the recommended plan and resource situation output by the model.

[0020] Monitoring and optimization: During surgery, the system can monitor surgical progress and the patient's physiological parameters in real time.

[0021] Using machine learning and real-time data analysis technology, the system can provide real-time feedback and suggestions to help the surgical team make more accurate decisions. Post-operative data is collected and analyzed to evaluate the effectiveness of surgical planning and resource allocation, and used for future system improvements and optimization.

[0022] 3. Model fine-tuning and application specialization: Data preparation: Collect data related to surgical planning and resource allocation tasks, including surgery type, physician availability, operating room resources, etc. Preprocess the data, including data cleaning, labeling, and feature extraction, to meet training requirements.

[0023] Model fine-tuning: Use a large, trained language model as the base model. Combine the prepared training data with the base model input for further training. Adjust model parameters to suit the requirements of a specific task through optimization algorithms such as backpropagation and gradient descent. Adjust fine-tuning hyperparameters such as learning rate, batch size, and number of training rounds to achieve better performance.

[0024] Customization for specific applications: According to the specific surgical planning and resource allocation tasks, define the loss function and evaluation indicators suitable for the task to guide the learning and optimization of the model. You can use the transfer learning method to combine the general knowledge that has been learned with the knowledge of specific tasks to improve performance and generalization ability. According to specific needs, make customized modifications to the model architecture, such as increasing or decreasing the number of nodes in a specific layer, adjusting the network structure, etc.

[0025] Functional verification and debugging: Use specialized models to verify and test surgical planning and resource allocation tasks. Analyze the results and performance of the model output, and compare and debug with actual needs. Further fine-tuning and optimization can be performed based on actual feedback and results to achieve better results. In the process of model fine-tuning and application specialization, the quality and diversity of data are very important. At the same time, appropriate optimization algorithms and hyperparameter settings, as well as effective evaluation methods, also play an important role in the performance and effect of the model. Regularly tuning and improving the model, combined with feedback from actual application scenarios, can continuously improve the accuracy and usability of the model.

[0026] 4. Real-time Information Processing and Response Data flow collection and transmission: Set up a data stream collection system to obtain real-time information from various information sources (such as surgical equipment, monitoring equipment, medical record system, etc.) and transmit the collected real-time information to the real-time processing system through a suitable communication protocol.

[0027] Real-time processing system design: Design an efficient real-time processing system that can accept data streams from multiple data sources and perform real-time data processing and analysis. Use stream processing technologies such as Apache Kafka, Apache Flink, or Apache Storm to support high-volume, low-latency real-time data processing. Design a suitable data stream processing pipeline to perform data filtering, transformation, aggregation, and calculation operations as needed.

[0028] Real-time information processing: Use the trained large language model in the real-time processing system and input the real-time data into the model for reasoning and analysis. Use the model results to predict, optimize and adjust surgical plans and resource allocation. Real-time machine learning technology can be used to continuously update the model and adapt to the data to improve the accuracy of prediction and decision-making.

[0029] Real-time response and notification: Integrate the output of the model into the response system to generate corresponding decisions, alarms or recommendations based on real-time processing results. Implement instant alarm and notification mechanisms so that the surgical team can obtain important information and support in a timely manner. Optimized surgical plans and resource allocations can be sent to relevant personnel so that they can take timely actions.

[0030] Monitoring and tuning: Set up monitoring and logging in real-time information processing systems to detect and resolve problems in a timely manner.

[0031] Continuously monitor the performance and effectiveness of the system, perform performance tuning and resource optimization to ensure the stability and efficiency of the system.

[0032] 5. Interactive and contextual understanding skills: Dialogue system design: Design a dialog system architecture with dialog functions and context understanding capabilities. You can use rule-based methods, retrieval-based methods, and generation-based methods. Use dialog management technology to track dialog status and context information to better understand user intentions and needs.

[0033] Context-aware: Set up context-awareness function in the dialogue system to understand the context of the current dialogue by collecting and analyzing the previous dialogue history between the user and the system, as well as external environmental information such as context and time.

[0034] With the help of natural language processing (NLP) and information extraction technology, important contextual information is extracted from the conversation history and external environment and used for the next step of conversation understanding and generation.

[0035] Real-time feedback and adaptation: Provide real-time feedback and suggestions during the conversation, and provide users with more accurate and personalized responses based on user input and contextual information. Use machine learning techniques, such as reinforcement learning and sequence-to-sequence models, to optimize the feedback and response mechanisms of the dialogue system to obtain a better interactive experience and dialogue effect. Based on user feedback and system performance, continuously adjust and optimize the dialogue system to improve contextual understanding and interaction capabilities.

[0036] Multimodal support: Support multi-modal input and output, such as text, voice, and images, to more fully understand user input and provide diverse responses. Combine speech recognition, image recognition, and natural language processing technologies to process and analyze multi-modal input data to obtain richer contextual information.

[0037] Continuous learning and updating: Use online learning and incremental learning techniques to enable the dialogue system to continuously learn and update knowledge to adapt to new dialogue scenarios and user needs. Collect user feedback and evaluations as training data to improve and optimize the dialogue system.

[0038] 6. System integration and interface design: Determine system requirements and goals: Understand the requirements and goals of the overall system and clarify the functions and services that each component or subsystem needs to provide.

[0039] Define the interface requirements between various components, including data format, communication protocol, data transmission requirements and reliability, etc.

[0040] Design system architecture: Design the overall system architecture based on system requirements and goals, including organizational structure, relationships between components, and interaction methods. Determine the system integration mode, such as centralized integration, distributed integration, or asynchronous integration.

[0041] Interface design: Design interfaces based on the functions and services of each component, and determine the input parameters and output results of each interface. Define the data exchange format and protocol of the interface to ensure that data between different components can be transmitted correctly and efficiently. Consider the scalability and compatibility of the interface so that new components or services can be connected in the future.

[0042] Interface development and testing: Develop each interface and write corresponding code and logic according to the interface design.

[0043] Implement interface testing to ensure that each interface exchanges data and interacts with functions as expected, and verify their correctness and stability. Perform unit testing and integration testing to ensure that each interface works properly when the entire system is integrated.

[0044] Monitoring and Management: Configure appropriate monitoring and management tools to monitor the performance and availability of each component and interface of the system integration during runtime. Perform diagnosis and troubleshooting based on the monitoring results to promptly handle any interface or integration issues.

[0045] Documentation and Communication: Write interface documents and user manuals to clarify the functions, parameters, and usage of each interface so that other developers or teams can understand and use it. Maintain communication and cooperation, communicate with various components or service providers, and solve interface or integration related problems or demand changes in a timely manner.

[0046] 7. Scalability and maintainability: Modular Design: Split the system into independent modules or components, each of which is responsible for a specific function or task. Modular design allows different parts of the system to be developed, tested, and expanded independently. Use interfaces to define the interactions between modules to reduce dependencies between modules and improve scalability and replaceability.

[0047] Loosely coupled architecture: Use loosely coupled architecture to reduce dependencies and tight coupling between modules. In this way, when modifying or replacing a module, it will not cause too much impact on other modules. Use technologies such as message queues and event-driven architecture to achieve loosely coupled communication and asynchronous processing between modules.

[0048] API Design: Define clear, concise, and easy-to-use API interfaces so that developers can easily use and extend the functionality of the system. Follow consistent naming conventions and design principles to improve the readability, understandability, and maintainability of the API.

[0049] High cohesion and low coupling: Strive for high cohesion within the module, that is, the components and functions within the module are closely related to each other, and a module completes a specific task as much as possible. Avoid excessive coupling between modules so that modules can be modified, tested, and expanded independently.

[0050] Appropriate level of abstraction: Use appropriate levels of abstraction to hide the underlying implementation details, so that the system can be expanded and evolved according to business needs without large-scale refactoring. Use techniques such as design patterns and architectural patterns to support the flexibility and scalability of the system architecture.

[0051] Automated testing and integration: Develop automated test cases to verify the functionality and performance of the system and ensure the stability of the system after expansion or modification. Implement continuous integration and continuous deployment (CI / CD) pipelines to ensure that the integration and deployment process of modifications or new functions is fast, reliable, and controllable.

[0052] Beneficial effects of the present invention: Automation and intelligence: Traditional surgical planning and resource allocation usually rely on the experience and manual decision-making of the surgical team. The optimization system using large language models can automatically analyze and process large amounts of patient data and use machine learning algorithms to provide intelligent decision support. This makes surgical planning and resource allocation more efficient, accurate, and consistent.

[0053] Personalization: Traditional surgical planning and resource allocation are usually based on general guidelines without considering individual differences of patients. The optimization system based on the large language model can personalize surgical planning and resource allocation according to the specific situation and needs of each patient. This helps to provide treatment plans that better meet the needs of patients and improve the effectiveness and satisfaction of surgery.

[0054] Comprehensive consideration of multiple factors: Surgical planning and resource allocation involve multiple factors, such as the type of surgery, the difficulty of the surgery, the doctor's available time, the operating room equipment, etc. With the help of the large language model system, these factors can be comprehensively considered and comprehensive optimization decisions can be made. At the same time, the system can also be dynamically adjusted and optimized based on real-time data and feedback to cope with changing situations.

[0055] Improve efficiency and reduce risks: The system optimizes surgical planning and resource allocation, which can reduce surgical waiting time and operating room idle time, and improve the utilization efficiency of operating room resources. In addition, the system can reduce surgical risks and provide safer and more reliable surgical plans through comprehensive risk assessment and professional knowledge.

[0056] Data-driven decision making: The large language model system can be trained and optimized using rich surgical data. By learning and analyzing a large amount of surgical data, the system can discover potential patterns and trends and provide more accurate and reliable decision support. This helps promote the standardization of medical practices and continuous quality improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0058] See also Figure 1 Shown is an embodiment of the present invention.

[0059] A large language model system for optimizing surgical planning and resource allocation includes a data acquisition and preprocessing module, a large language model training module, a surgical planning optimization module, a resource allocation optimization module, a final support module, and a system integration and deployment module.

[0060] The data acquisition and preprocessing module collects historical data of hospital operating rooms, including information such as operation type, operation duration, and operating room resource utilization. The data is then cleaned and processed for use in model training.

[0061] The large language model training module selects the GPT-3 or BERT large language model.

[0062] The surgical plan optimization module can understand the relevant information of surgical plan and resource allocation through the large language training module, and can generate optimized surgical plan and resource allocation suggestions.

[0063] The resource allocation optimization module integrates the trained large language model into the hospital operating room management system, enabling it to process surgical planning and resource allocation needs in real time and generate corresponding optimization suggestions.

[0064] The system integration and deployment module deploys the integrated system into the hospital operating room management system, and continuously optimizes and adjusts it according to the actual application situation to improve the accuracy and practicality of the system. It can help optimize surgical planning and resource allocation, improve surgical efficiency and quality, reduce risks and provide decision support.

Claims

1. A large language model system for optimizing surgical planning and resource allocation, characterized by: It includes data acquisition and preprocessing module, large language model training module, surgical plan optimization module, resource allocation optimization module, final support module and system integration and deployment module.

2. A large language model system for optimizing surgical planning and resource allocation according to claim 1, characterized in that: The data acquisition and preprocessing module collects historical data of hospital operating rooms, including information such as operation type, operation duration, and operating room resource utilization. The data is then cleaned and processed for use in model training.

3. A large language model system for optimizing surgical planning and resource allocation according to claim 1, characterized in that: The large language model training module selects the GPT-3 or BERT large language model.

4. The large language model system for optimizing surgical planning and resource allocation according to claim 1, characterized in that: The surgical plan optimization module can understand the relevant information of surgical plan and resource allocation through the large language training module, and can generate optimized surgical plan and resource allocation suggestions.

5. The large language model system for optimizing surgical planning and resource allocation according to claim 1, characterized in that: The resource allocation optimization module integrates the trained large language model into the hospital operating room management system, enabling it to process surgical planning and resource allocation needs in real time and generate corresponding optimization suggestions.

6. A large language model system for optimizing surgical planning and resource allocation according to claim 1, characterized in that: The system integration and deployment module deploys the integrated system into the hospital operating room management system, and continuously optimizes and adjusts it according to the actual application situation to improve the accuracy and practicality of the system. It can help optimize surgical planning and resource allocation, improve surgical efficiency and quality, reduce risks and provide decision support.

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