Medical industry intelligent diagnosis system based on large model

Abstract

The invention provides a medical industry intelligent diagnosis system based on a large model, which belongs to the field of intelligent medical application and comprises a data acquisition and preprocessing module, a feature extraction and selection module, a model training and optimization module and a diagnosis reasoning and explaining module. And the diagnosis accuracy and reliability are obviously improved. According to the method, the accuracy and efficiency of diagnosis are improved, personalized medical treatment and rare disease diagnosis are supported, medical resource configuration is optimized, the satisfaction degree of patients is improved, and important data support is provided for medical research and innovation.

Description

Technical Field

[0001] The present invention relates to the fields of large model technology, artificial intelligence and machine learning, and intelligent medical applications, and particularly relates to an intelligent medical diagnosis system based on a large model. Background Art

[0002] In recent years, the digitalization and intelligentization process in the medical industry has accelerated, especially the wide application of electronic health records (EHRs), medical images, and genomic data. However, traditional diagnostic methods are difficult to handle complex cases and rare diseases due to insufficient data processing capabilities and lack of model generalization capabilities. The progress of artificial intelligence (AI) and large models has provided new ways to solve these problems. In particular, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have demonstrated powerful data processing and feature extraction capabilities in the fields of image recognition and natural language processing. In addition, the maturity of interpretive AI technology enables large models to not only provide accurate diagnostic results but also provide detailed explanations and justifications for doctors, enhancing the transparency and reliability of clinical decision-making. Currently, designing an intelligent diagnostic system based on a large model can meet the urgent needs of the medical industry for efficient, accurate, and interpretive diagnoses, providing strong support for improving the quality of medical services. Summary of the Invention

[0003] In view of the above problems, the present invention provides an intelligent medical diagnosis system based on a large model, aiming to improve the accuracy and efficiency of medical diagnosis, support personalized medicine and rare disease diagnosis, while ensuring data security, optimizing resource allocation, and enhancing patient satisfaction.

[0004] The technical solution of the present invention is as follows:

[0005] An intelligent medical diagnosis system based on a large model, comprising:

[0006] A data acquisition and preprocessing module. This module is the cornerstone of the intelligent diagnosis system and is responsible for comprehensively, accurately, and securely acquiring and preprocessing medical data. By integrating multiple data interfaces and supporting standardized protocols, data is obtained from different sources. The preprocessing process covers data cleaning, missing value handling, etc., and uses NLP technology to structurally process text data. At the same time, privacy protection technologies such as data de-identification and encryption are integrated to ensure data security and compliance.

[0007] A feature extraction and selection module. This module aims to extract key features from the preprocessed multi-source heterogeneous data to improve model performance. Deep learning models, word vectors, and other technologies are used to extract data features such as images and texts, and methods such as the filter method and the wrapper method are used for feature selection to remove redundant features. Feature engineering further optimizes the feature set, such as discretization, one-hot encoding, etc., providing an accurate data basis for model training.

[0008] Model Training and Optimization Module. This module focuses on optimizing and training models to efficiently and accurately process medical data. Appropriate machine learning and deep learning models are selected according to the data characteristics, and large-scale data training is carried out using parallel computing technology. Techniques such as cross-validation and early stopping are used to prevent overfitting. At the same time, hyperparameter tuning and model compression are performed to improve the robustness and inference speed of the model, ensuring that the system has excellent diagnostic capabilities and generalization performance.

[0009] Diagnostic Inference and Explanation Module. This module provides real-time diagnostic and explanatory functions, enhancing system transparency and trust. Diagnostic inferences are made based on the trained model to generate preliminary diagnostic results. Interpretive AI technologies are integrated to provide detailed explanations for the diagnostic results to help clinicians verify and adjust the diagnosis. In addition, a feedback mechanism is designed to collect feedback data to optimize the model, forming a closed-loop learning system. It can also be integrated with the HIS to record diagnostic results and support subsequent medical decisions.

[0010] Furthermore,

[0011] The data acquisition and preprocessing module includes:

[0012] Data Acquisition Unit, which integrates multiple data interfaces, supports protocols such as HL7 and FHIR, and acquires multi-source medical data;

[0013] Data Preprocessing Unit, which performs operations such as data cleaning, missing value handling, standardization, and NLP processing on text data;

[0014] Privacy Protection Unit, which uses techniques such as de-identification and encryption to ensure data security and meet compliance requirements.

[0015] The feature extraction and selection module includes:

[0016] Feature Extraction Unit, which uses models such as CNN to extract image features, techniques such as word vectors to extract text features, and wavelet transforms to extract physiological data features;

[0017] Feature Selection Unit, which selects key features using methods such as the filter method and the wrapper method to reduce the data dimension;

[0018] Feature Engineering Unit, which performs operations such as discretization, one-hot encoding, and feature interaction processing to optimize the feature set.

[0019] The model training and optimization module includes:

[0020] Model Selection Unit, which selects appropriate machine learning or deep learning models according to the data characteristics;

[0021] The model training unit processes large-scale data using parallel computing technology and adopts techniques such as cross-validation to prevent overfitting;

[0022] The model optimization unit performs hyperparameter tuning and model compression to improve the robustness and inference speed of the model.

[0023] The intelligent diagnosis system according to claim 1, wherein the diagnosis reasoning and explanation module comprises:

[0024] The diagnosis reasoning unit generates diagnosis results in real time based on the trained model;

[0025] The result explanation unit integrates interpretive AI technology to provide detailed explanations for the diagnosis results;

[0026] The feedback mechanism unit collects feedback data to optimize the model and integrates with the HIS to record the diagnosis results.

[0027] Furthermore,

[0028] The modules work together through data interfaces and communication protocols.

[0029] Furthermore,

[0030] It further includes a user interface module to facilitate users to input data, view results and provide feedback.

[0031] Furthermore,

[0032] It further includes a data storage module with data backup and recovery functions.

[0033] Furthermore,

[0034] It further includes a system management module to implement functions such as user management and permission management.

[0035] Furthermore,

[0036] It further includes an interface module with other medical information systems to achieve data sharing and integration.

[0037] The beneficial effects of the present invention are

[0038] Improve diagnosis accuracy: Through the deep learning ability of the large model, extract key features from complex data to improve the accuracy of diagnosis.

[0039] Shorten the diagnosis time: Utilize automated data processing and analysis techniques to reduce the time required for manual diagnosis and accelerate the speed of disease assessment.

[0040] Support personalized medicine: Combine multiple data sources of patients, such as genomic information and lifestyle habits, to provide personalized diagnosis suggestions and treatment plans.

[0041] Reduce misdiagnosis rate: The model trained with large-scale data reduces misdiagnosis caused by human factors and improves the consistency and reliability of diagnosis.

[0042] Enhance diagnostic interpretability: Using interpretable AI technology, provide clear explanations for each diagnostic result to help doctors and patients understand the diagnostic basis.

[0043] Support the diagnosis of rare and complex diseases: By training a large-scale and diverse dataset, improve the system's ability in diagnosing rare and complex diseases.

[0044] Optimize resource allocation: Through automated diagnosis, relieve the pressure on medical resources and optimize the allocation of doctors' working hours and energy.

[0045] Improve patient satisfaction: Provide faster and more accurate diagnostic results, reduce patient waiting time, and improve patient satisfaction with medical services.

[0046] Data security and privacy protection: Through advanced encryption technology and data de-identification measures, ensure the security and privacy of patient data.

[0047] Promote medical research and innovation: Accumulate and analyze a large amount of medical data, provide new insights and innovation opportunities for medical research, and promote the development of medical technology. Description of the Drawings

[0048] Figure 1 is the overall workflow diagram of the present invention;

[0049] Figure 2 is the schematic diagram of the workflow of the data collection and preprocessing module;

[0050] Figure 3 is the schematic diagram of the workflow of the feature extraction and selection module;

[0051] Figure 4 is the schematic diagram of the workflow of the model training and optimization module;

[0052] Figure 5 is the schematic diagram of the workflow of the diagnostic reasoning and interpretation module. Detailed Embodiments

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] The present invention provides an intelligent diagnosis system for the medical industry based on large models, which consists of the following four main technical modules:

[0055] Module 1: Data Acquisition and Preprocessing Module

[0056] This module ensures the comprehensiveness, accuracy, and security of medical data, covering data acquisition, cleaning, and privacy protection, and is the foundation for the subsequent modules of the system.

[0057] Data Acquisition: Medical data comes from a wide range of sources, including electronic health records (EHRs), medical images, genomic data, laboratory test results, patient medical histories, etc. Data acquisition is the first step of the system, and this process needs to ensure the comprehensiveness and accuracy of the data. To achieve this, the system integrates multiple data interfaces and supports standardized medical data transmission protocols such as HL7 and FHIR to seamlessly obtain data from different medical institutions and devices.

[0058] Data Preprocessing: Since medical data usually has heterogeneity, preprocessing is one of the key steps. The preprocessing process includes data cleaning, missing value handling, data standardization and normalization, outlier detection, etc. For example, EHR data may contain inconsistent units or formats, and the system will automatically convert this data into a unified format. For text data, such as doctors' notes or reports, the system uses natural language processing (NLP) techniques for structured processing, including word segmentation, part-of-speech tagging, named entity recognition, etc., for subsequent processing.

[0059] Privacy Protection: Considering the sensitivity of medical data, the data acquisition and preprocessing module integrates data de-identification and encryption technologies. During data storage and transmission, the system uses advanced encryption methods to ensure data security. In addition, this module also includes access control and logging functions to ensure that only authorized personnel can access the data and record all data operations to meet compliance requirements.

[0060] Module 2: Feature Extraction and Selection Module

[0061] This module improves the accuracy and efficiency of the model by extracting and selecting key features, providing an accurate data basis for intelligent diagnosis.

[0062] Feature Extraction: After preprocessing, the system extracts useful features from multi-source heterogeneous data. For image data, such as X-ray films or MRIs, deep learning models, such as convolutional neural networks (CNNs), are used to extract image features, such as edges, shapes, and textures. For text data, techniques such as word vectors and TF-IDF are used to extract text features. In addition, for the feature extraction of physiological data such as electrocardiograms (ECGs) or electroencephalograms (EEGs), signal processing techniques such as wavelet transform are used to extract time-domain and frequency-domain features.

[0063] Feature Selection: Feature selection is an important step in reducing data dimensionality, improving model efficiency and accuracy. The system employs a variety of feature selection methods, including filter methods, wrapper methods, and embedded methods. For example, redundant features are filtered out through correlation analysis, the importance of features is evaluated using models such as decision trees, and the most important features are selected through regularization methods such as LASSO. Feature selection not only improves the computational efficiency of the model but also reduces the risk of overfitting.

[0064] Feature Engineering: Feature engineering further optimizes the feature set to make it more suitable for model training. For example, the system can discretize numerical features, perform one-hot encoding on categorical features, or perform feature interaction processing (such as polynomial feature generation). Feature engineering also includes feature scaling and normalization to ensure that different features have an equal impact on model training.

[0065] Module Three: Model Training and Optimization Module

[0066] This module optimizes and trains the model to efficiently and accurately process medical data, ensuring that the system has excellent diagnostic capabilities and generalization performance.

[0067] Model Selection: An intelligent diagnosis system based on large models can select a variety of machine learning and deep learning models, such as random forests, support vector machines (SVMs), neural networks, etc. When selecting a model, the system considers the characteristics of the data and the specific requirements of the task. For example, for structured data, random forests or gradient-boosted decision trees (GBDTs) can be selected; for image data, convolutional neural networks (CNNs) are chosen; for sequence data, such as gene sequences or time series data, recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) are selected.

[0068] Model Training: Model training is the key to the system's performance. The system uses large-scale parallel computing technologies, such as distributed training and GPU acceleration, to process large-scale datasets. During the training process, the system uses cross-validation techniques to evaluate the generalization ability of the model and uses techniques such as early stopping and learning rate adjustment to prevent overfitting. To improve the robustness of the model, the system also uses data augmentation techniques to generate more diverse training samples.

[0069] Model Optimization: Model optimization includes hyperparameter tuning and model compression. The system uses methods such as grid search, random search, and Bayesian optimization to find the best combination of hyperparameters. For models deployed in resource-constrained environments, such as mobile devices or embedded systems, the system uses techniques such as model quantization, pruning, and knowledge distillation to reduce the model size and improve the inference speed.

[0070] Module Four: Diagnostic Inference and Explanation Module

[0071] This module provides real-time diagnosis and interpretation functions, enhances system transparency and trust, and continuously optimizes system performance through a feedback mechanism.

[0072] Diagnostic reasoning: After the model training is completed, the system enters the actual application stage, namely diagnostic reasoning. The system receives input data, including patients' symptom descriptions, imaging data, laboratory results, etc., and then conducts reasoning based on the trained model. The reasoning process is real-time, and the system generates preliminary diagnostic results, including a list of possible diseases, an assessment of disease severity, recommended further tests, etc.

[0073] Result interpretation: To enhance system transparency and trust, the system integrates interpretive AI technologies such as LIME and SHAP. The interpretation module provides detailed explanations for each diagnostic result, including which features have the greatest impact on the decision-making and how the model arrives at the conclusion. This is particularly important for clinicians, who can verify and adjust the diagnosis based on the interpretation results. In addition, interpretive AI technologies can also help discover potential biases in the model and promote the continuous improvement of the model.

[0074] Feedback mechanism: The system designs a feedback mechanism that allows clinicians and patients to provide feedback on the accuracy and satisfaction of the diagnostic results. The feedback data is used to further optimize the model, forming a closed-loop learning system. In addition, the system can also be integrated with the hospital information system (HIS) to automatically record the diagnostic results in the patient's electronic health record, providing support for subsequent medical decisions.

[0075] Overall, these four modules constitute a highly integrated, data-driven intelligent diagnostic system, aiming to improve the efficiency and accuracy of medical diagnosis while ensuring data security and transparency. The design of this system not only meets the needs of modern medicine but also has good scalability and adaptability, and can continuously evolve to address emerging medical challenges.

[0076] The above description is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A medical industry intelligent diagnosis system based on a large model, characterized in that: include: The data collection and preprocessing module is responsible for collecting and preprocessing medical data. It integrates several data interfaces and supports standardized protocols to obtain data from different sources. The preprocessing process covers data cleaning, missing value processing, and uses NLP technology to structure text data. At the same time, it integrates data de-identification and encryption privacy protection technologies. The feature extraction and selection module extracts key features from preprocessed multi-source heterogeneous data to improve model performance; uses deep learning models and word vectors to extract data features, uses filtering and packaging methods to select features, and removes redundant features; and uses feature engineering to further optimize feature sets; Model training and optimization module: optimizes and trains models to process medical data; selects appropriate machine learning and deep learning models based on data characteristics, uses parallel computing technology to conduct large-scale data training, and prevents overfitting through cross-validation and early stopping; at the same time, performs hyperparameter tuning and model compression to improve model robustness and inference speed; The diagnostic reasoning and interpretation module provides real-time diagnosis and interpretation functions to enhance system transparency and trust; performs diagnostic reasoning based on trained models to generate preliminary diagnostic results; integrates explanatory AI technology to provide detailed explanations for diagnostic results to help clinicians verify and adjust diagnoses; designs a feedback mechanism to collect feedback data to optimize the model and form a closed-loop learning system that can be integrated with HIS to record diagnostic results and support subsequent medical decisions.

2. The system according to claim 1, characterized in that The data acquisition and preprocessing module includes: The data collection unit integrates several data interfaces, supports HL7 and FHIR protocols, and collects multi-source medical data; Data preprocessing unit, which performs data cleaning, missing value processing, standardization operations, and NLP processing on text data; The privacy protection unit uses de-identification and encryption technology to ensure data security and meet compliance requirements.

3. The system according to claim 1, It is characterized in that Among them, the feature extraction and selection module includes: Feature extraction unit, which uses the model to extract image features, word vector technology to extract text features, and wavelet transform to extract physiological data features; Feature selection unit, which uses filtering and packaging methods to select key features and reduce data dimensions; The feature engineering unit performs discretization, one-hot encoding, feature interaction processing, and optimizes the feature set.

4. The system according to claim 1, characterized in that The model training and optimization module includes: Model selection unit, which selects appropriate machine learning or deep learning models based on data characteristics; The model training unit uses parallel computing technology to process large-scale data and adopts cross-validation technology to prevent overfitting; The model optimization unit performs hyperparameter tuning and model compression to improve model robustness and inference speed.

5. The system according to claim 1, characterized in that The diagnostic reasoning and interpretation module includes: The diagnostic reasoning unit generates diagnostic results in real time based on the trained model; The result interpretation unit integrates explanatory AI technology to provide detailed explanations for diagnostic results; Feedback mechanism unit collects feedback data to optimize the model and integrates with HIS to record diagnosis results.

6. The system according to claim 1, characterized in that The modules work together via data interfaces and communication protocols.

7. The system according to claim 1, characterized in that It also includes a user interface module for users to input data, view results and provide feedback.

8. The system according to claim 1, characterized in that It also includes a data storage module with data backup and recovery functions.

9. The system according to claim 1, characterized in that It also includes a system management module to implement user management and authority management functions.

10. The system according to claim 1, characterized in that It also includes interface modules with other medical information systems to achieve data sharing and integration.

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