Medical behavior risk control prompting system based on AI large model
Through the medical behavior risk control prompt system based on AI big model, the problems of irregular medication use and over-examination of medical staff are solved, the standardization of medical behavior and resource optimization are achieved, and the safety and quality of medical care are improved.
Patent Information
- Application Number
- CN202510355733.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-25
- Publication Date
- 2025-07-08
AI Technical Summary
During the medical process, it is difficult to detect irregular medication and overexamination problems caused by medical staff due to fatigue or insufficient experience, resulting in waste of resources and burden on patients.
The medical behavior risk control prompt system based on AI big models, including data layer, model layer and display layer, build and train AI big models through a deep learning framework, collect, store and manage medical data, provide risk assessment, real-time prompts and behavioral norms guidance, and combine it with hospital information system for data sharing and interaction.
Improve medical safety, standardize medical behavior, reduce medical accident rate, optimize resource allocation, and improve medical quality and information management level.
Smart Images
Figure CN120280127A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of medical technology, and in particular to a medical behavior risk control prompt system based on an AI big model. Background Art
[0002] Medical practice refers to any diagnosis and treatment for the purpose of treating, correcting or preventing human diseases, injuries, defects or health care, or any disposition or medication for the purpose of treatment based on the results of the diagnosis or examination, or any part of the behavior or part thereof.
[0003] At present, when treating patients, when medical staff are too tired or lack experience, some problems of irregular medication may occur. For example, doctors prescribe multiple blood tests, CT scans and other examination items for patients with common colds that exceed the routine. Doctors are reminded to confirm the necessity of the examination to avoid excessive examinations that increase the burden on patients and waste medical resources. In terms of medication: the course of treatment of a drug is significantly longer than the conventional treatment course of similar diseases, or multiple drugs with similar effects are used in combination. This situation is sometimes difficult for patients or doctors to detect in time. Summary of the invention
[0004] The purpose of the present invention is to provide a medical behavior risk control prompt system based on an AI big model to solve the problems raised in the above background technology.
[0005] To achieve the above objectives, the present invention provides the following technical solutions: a medical behavior risk control prompt system based on an AI big model, comprising:
[0006] Data layer, model layer, application layer, presentation layer;
[0007] The output end of the data layer is connected to the model layer, the output end of the model layer is connected to the application layer, and the output end of the application layer is connected to the presentation layer;
[0008] Data layer: responsible for the collection, storage and management of medical data;
[0009] Model layer: Build and train large AI models based on deep learning frameworks;
[0010] Application layer: provides various functional modules of the system, including risk assessment, real-time prompts, and behavioral norms guidance;
[0011] Display layer: Provides a user interface for medical staff and hospital managers to easily view risk warning information and analyze report content.
[0012] Preferably, the data layer includes a collection end, a storage end, and a management end, and the output ends of the collection end and the management end are connected to the storage end.
[0013] Preferably, the collection end collects various medical data such as patients' basic information, diagnosis records, examination and test results, and treatment plans in real time from the electronic medical record system, picture archiving and communication system, and laboratory information management system of the hospital, and integrates and preprocesses them to provide a high-quality data basis for subsequent analysis.
[0014] Preferably, the information collected by the collection end is stored in the storage end, and the management end sorts out the information stored in the storage end and arranges it in a list.
[0015] Preferably, the data layer adopts distributed database technology to achieve efficient storage and rapid retrieval of massive medical data.
[0016] Preferably, the application layer includes a risk assessment unit, a real-time reminder unit, a behavior guidance unit, and a communication module. The output ends of the risk assessment unit, real-time reminder unit, and behavior guidance unit are connected to the communication module.
[0017] Preferably, the risk assessment unit uses an AI large model to deeply analyze the integrated medical data, identify potential risk factors in medical behaviors, and through learning a large amount of historical medical data, the model can predict the possible risk situations of patients at different treatment stages and give corresponding risk scores;
[0018] When it is detected that there is a risk in a medical behavior, the real-time reminder unit will immediately send real-time risk control prompt information to medical staff, and the prompt information is pushed to relevant personnel through various channels such as the hospital's internal information system and mobile terminal, reminding them to take timely measures for risk prevention and handling;
[0019] The medical industry norms and guidelines built into the behavior guidance unit, combined with the analysis results of the AI large model, provide specific medical behavior specification suggestions and operation guidance for medical staff.
[0020] Preferably, the communication module is integrated with other information systems of the hospital based on the API interface to achieve data sharing and interaction.
[0021] Compared with the prior art, the beneficial effects of the present invention are:
[0022] This solution is enhanced by AI technology to improve medical safety: promptly detect and prevent medical risks, reduce the incidence of medical accidents, and ensure the safety of patients' lives. Standardize medical behaviors: provide accurate behavior standardization suggestions for medical staff, and promote the standardization and regularization of medical behaviors. Improve medical quality: discover potential quality problems through in-depth analysis and mining of medical data, and provide strong support for the quality improvement of hospitals. Optimize the allocation of medical resources: reduce unnecessary medical examinations and treatments, rationally use medical resources, and reduce medical costs. Promote the development of medical informatization: promote the integration and upgrading of hospital information systems, and improve the informatization management level of hospitals. Brief Description of the Drawings
[0023] Figure 1 It is the system logic block diagram of the present invention;
[0024] Figure 2 It is the system logic block diagram of the data layer of the present invention;
[0025] Figure 3 It is the system logic block diagram of the application layer of the present invention. Detailed Embodiments
[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. 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.
[0027] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention.
[0028] Embodiment 1:
[0029] Please refer to Figures 1-3 , the present invention provides a technical solution: a medical behavior risk control prompt system based on an AI large model, including: a data layer, a model layer, an application layer, and a display layer;
[0030] Among them, the output end of the data layer is connected to the model layer, the output end of the model layer is connected to the application layer, and the output end of the application layer is connected to the presentation layer; Data layer: responsible for the collection, storage, and management of medical data; Model layer: based on a deep learning framework, construct and train an AI large model; Application layer: provide various functional modules of the system, including risk assessment, real-time prompt, and behavior specification guidance; Presentation layer: provide a user interface for medical staff and hospital managers, facilitating the viewing of risk prompt information and analysis report content.
[0031] Analysis of the above content: The medical behavior risk control prompt system based on an AI large model is an intelligent system that uses advanced artificial intelligence technology, especially the powerful data analysis and learning ability of the large model, to comprehensively and real-time monitor and risk assess medical behaviors. The system aims to standardize medical behaviors, reduce medical risks, improve medical quality, and safeguard the safety and rights of patients.
[0032] Data layer: responsible for the collection, storage, and management of medical data. Adopt distributed database technology to achieve efficient storage and rapid retrieval of massive medical data.
[0033] Model layer: based on a deep learning framework, construct and train an AI large model. The model can adopt advanced architectures such as Transformer to improve the understanding and analysis ability of medical data.
[0034] The Transformer architecture mainly consists of an input part, multiple encoder layers, multiple decoder layers, and an output part. The input part includes a source text embedding layer and a position encoding layer. The encoder part is stacked by multiple encoder layers, and each encoder layer contains a multi-head self-attention sub-layer and a feed-forward fully connected sub-layer. The decoder part is stacked by multiple decoder layers, and each decoder layer contains a masked multi-head self-attention sub-layer, a multi-head attention sub-layer (from encoder to decoder), and a feed-forward fully connected sub-layer.
[0035] The working principle of Transformer is based on the self-attention mechanism. This mechanism allows the model to consider all positions in the input sequence simultaneously when processing sequence data, thus better capturing long-distance dependencies. The self-attention mechanism measures the similarity between them by calculating the dot product of the Query, Key, and Value matrices, and calculates the attention weights through scaled dot-product attention, and finally obtains a weighted output.
[0036] Application layer: provide various functional modules of the system, including risk assessment, real-time prompt, behavior specification guidance, etc. Integrate with other information systems in the hospital through API interfaces to achieve data sharing and interaction.
[0037] Presentation layer: Provide a friendly user interface for medical staff and hospital administrators, facilitating their viewing of risk warning information, analysis reports, etc. The interface design emphasizes simplicity, intuitiveness, and ease of use.
[0038] The training method for the AI large model in this model layer is as follows:
[0039] Data preparation
[0040] Data collection: Collect medical data from multiple channels such as hospital information systems and medical insurance databases, covering patient basic information, medical records, examination and test reports, expense details, etc.
[0041] Data annotation: Organize medical field experts and professional annotators to annotate the data according to the rules and standards of medical behavior risk control. For example, mark medical behaviors as categories such as normal and risky, or divide them into levels according to the degree of risk.
[0042] Data partitioning: Divide the annotated data into a training set, a validation set, and a test set according to a certain ratio. Generally, the training set accounts for 60%-80%, the validation set accounts for 10%-20%, and the test set accounts for 10%-20%.
[0043] Feature engineering
[0044] Feature extraction: Based on medical knowledge and business experience, extract features related to medical behavior risk control, such as patient age, gender, disease diagnosis code, types and dosages of medications, frequencies of examination items, etc.
[0045] Feature selection: Use methods such as information gain, mutual information, and correlation analysis to screen out features that contribute significantly to model prediction, remove redundant and irrelevant features, and reduce the complexity of the model.
[0046] Feature transformation: Perform standardization, normalization, or discretization on some features to improve the training effect and stability of the model. For example, discretize continuous expense data into different expense intervals.
[0047] Model selection and training
[0048] Select an algorithm: According to the characteristics and requirements of medical behavior risk control, select a suitable machine learning algorithm, such as logistic regression, decision tree, support vector machine for classification, and linear regression, random forest for regression.
[0049] Parameter initialization: Set initial values for the parameters of the selected model, usually using random initialization or setting according to experience.
[0050] Model Training: Use the training set data to train the model. During the training process, adjust the parameters to minimize the loss function or maximize the objective function of the model. For example, in logistic regression, the gradient descent method can be used to update the parameters.
[0051] Model Evaluation and Tuning
[0052] Evaluation Metrics: Adopt metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve to evaluate the model performance on the validation set.
[0053] Model Tuning: According to the evaluation results, adjust the model parameters or select different feature combinations. Grid search, random search, etc. can be used to find the optimal parameter combination.
[0054] Model Fusion: Integrate multiple different machine learning models, such as using voting method, averaging method, or stacking method, etc., to improve the generalization ability and accuracy of the model.
[0055] Example Two:
[0056] Please refer to Figures 1-3 , the present invention provides a technical solution based on Example One: The data layer includes a collection end, a storage end, and a management end. The output ends of the collection end and the management end are connected to the storage end. The collection end collects various medical data of patients such as basic information, diagnosis records, examination and test results, and treatment plans in real time from the data sources of the hospital's electronic medical record system, picture archiving and communication system, and laboratory information management system, and performs integration and preprocessing to provide a high-quality data basis for subsequent analysis. The information collected by the collection end is stored in the storage end, and the management end sorts and arranges the information stored in the storage end in a list. The data layer adopts distributed database technology to achieve efficient storage and rapid retrieval of massive medical data.
[0057] Analysis of the above content: Based on the collection end, data collection and integration can be achieved: The system can collect various medical data of patients such as basic information, diagnosis records, examination and test results, and treatment plans in real time from multiple data sources such as the hospital's electronic medical record system (EMR), picture archiving and communication system (PACS), and laboratory information management system (LIS), and perform integration and preprocessing to provide a high-quality data basis for subsequent analysis.
[0058] Based on the display layer, data analysis and reporting: Regular statistical analysis is performed on the medical behavior risk data to generate detailed risk reports and trend analysis charts. Hospital managers can understand the overall medical risk situation of the hospital through these reports, discover potential problems and risk points, and provide a basis for formulating targeted improvement measures and management decisions.
[0059] Model training and optimization: Continuously collect new medical data, continuously train and optimize the AI model to improve the accuracy and adaptability of the model. At the same time, the system also supports the evaluation and monitoring of the model's performance, timely discovering problems with the model and making adjustments.
[0060] Embodiment three:
[0061] See also Figures 1-3 , the present invention provides a technical solution based on the first embodiment: the application layer includes a risk assessment unit, a real-time reminder unit, a behavior guidance unit and a communication module, and the output ends of the risk assessment unit, the real-time reminder unit and the behavior guidance unit are connected to the communication module. The risk assessment unit uses the AI big model to conduct in-depth analysis of the integrated medical data to identify potential risk factors in medical behavior. By learning a large amount of historical medical data, the model can predict the risk situations that may occur to patients at different treatment stages and give corresponding risk scores;
[0062] When a risk is detected in a medical behavior, the real-time reminder unit will immediately send real-time risk control reminder information to medical staff. The reminder information will be pushed to relevant personnel through the hospital's internal information system and mobile terminals, reminding them to take timely measures to prevent and deal with risks.
[0063] The built-in medical industry norms and guidelines of the behavior guidance unit, combined with the analysis results of the AI big model, provide medical staff with specific medical behavior norms and operational guidance. The communication module is integrated with other information systems of the hospital based on the API interface to achieve data sharing and interaction.
[0064] Analysis of the above content: Clinical diagnosis and treatment process: When doctors prescribe medical advice and develop treatment plans, the system conducts risk assessment and prompts on medical behaviors in real time to help doctors make more scientific and reasonable decisions.
[0065] Drug management: Conduct risk monitoring on drug procurement, storage, and use to prevent drug waste, expiration, and misuse.
[0066] Medical quality management: Hospital managers can use the system's data analysis function to conduct a comprehensive assessment of medical quality, identify problems and make timely corrections, and continuously improve the hospital's medical service level.
[0067] Medical insurance cost control: By regulating and monitoring medical behavior, we can reduce unreasonable medical expenses and improve the efficiency of the use of medical insurance funds.
[0068] The foregoing has shown and described the basic principles, main features and advantages of the present invention. For a person skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic features of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes that fall within the meaning and scope of the equivalent elements of the claims in the present invention, and any reference signs in the claims should not be regarded as limiting the claims involved.
[0069] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A medical behavior risk control prompt system based on the large AI model, characterized in that, It includes: Data layer, model layer, application layer, and display layer; Among them, the output end of the data layer is connected to the model layer, the output end of the model layer is connected to the application layer, and the output end of the application layer is connected to the display layer; Data layer: Responsible for the collection, storage, and management of medical data; Model layer: Based on the deep learning framework, build and train an AI large model; Application layer: Provide various functional modules of the system, including risk assessment, real-time reminder, and behavior specification guidance; Display layer: Provide a user interface for medical staff and hospital administrators, facilitating the viewing of risk reminder information and analysis report content.
2. The medical behavior risk control prompt system based on the AI large model according to claim 1, characterized in that: The data layer includes a collection end, a storage end, and a management end, and the output ends of the collection end and the management end are connected to the storage end.
3. The medical behavior risk control prompt system based on the AI large model according to claim 2, characterized in that: The collection end real-time collects various medical data of patients, such as basic information, diagnosis records, examination and test results, and treatment plans, from the hospital's electronic medical record system, picture archiving and communication system, and laboratory information management system data sources, and integrates and preprocesses them to provide a high-quality data basis for subsequent analysis.
4. The medical behavior risk control prompt system based on the AI large model according to claim 3, wherein: The information collected by the collection end is stored in the storage end, and the management end sorts out the information stored in the storage end and arranges it in a list.
5. The medical behavior risk control prompt system based on the AI large model according to claim 2, wherein: The data layer adopts distributed database technology to achieve efficient storage and rapid retrieval of massive medical data.
6. The medical behavior risk control prompt system based on the AI large model according to claim 1, characterized in that: The application layer includes a risk assessment unit, a real-time reminder unit, a behavior guidance unit, and a communication module, and the output ends of the risk assessment unit, the real-time reminder unit, and the behavior guidance unit are connected to the communication module.
7. The medical behavior risk control prompt system based on the AI large model according to claim 6, characterized in that: The risk assessment unit uses the AI large model to deeply analyze the integrated medical data, identify potential risk factors in medical behaviors. Through learning a large amount of historical medical data, the model can predict the possible risk situations of patients at different treatment stages and give corresponding risk scores; When it detects that a medical behavior has risks, the real-time reminder unit will immediately send real-time risk control reminder information to medical staff, and the reminder information is pushed to relevant personnel through various channels such as the hospital's internal information system and mobile terminals, reminding them to take timely measures for risk prevention and handling; The behavior guidance unit incorporates medical industry norms and guidelines, and combines with the analysis results of the AI large model to provide specific medical behavior specification suggestions and operation guidance for medical staff.
8. The medical behavior risk control prompt system based on the AI large model according to claim 6, wherein: The communication module is integrated with other information systems of the hospital based on API interfaces to achieve data sharing and interaction.