Voice medical equipment query system based on mobile equipment
By designing a voice medical equipment query system based on mobile devices, the problem of low efficiency of manual input query in medical device management is solved, fast and accurate medical device information query is realized, and query efficiency and data security are improved.
Patent Information
- Application Number
- CN202411416621.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-11
- Publication Date
- 2025-01-24
AI Technical Summary
The prior art relies on manual input query in medical device management, which is inefficient and prone to errors, especially in emergencies.
Design a voice medical equipment query system based on mobile devices, including mobile application interface, speech recognition module, natural language processing module, database interface module and result display module, which can query hospital medical device information through voice input and provide query results and suggestion.
It improves the efficiency and accuracy of medical professionals' query of medical device information, ensures the security and privacy of data, and meets the actual needs of medical professionals for query and management of medical device information.
Smart Images
Figure CN119361114A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the fields of medical equipment management, information technology, speech recognition technology, natural language processing technology and database technology, and in particular to a voice medical equipment query system based on mobile devices. Background Art
[0002] In the medical industry, effective management of medical devices is crucial. Medical professionals need to be able to quickly and accurately obtain detailed information about medical devices in the hospital, such as specifications, models, usage status, maintenance records, etc.
[0003] The current existing technologies mostly rely on manual input queries, which is not only inefficient but also prone to errors in emergency situations. Therefore, it is of great significance to develop a system that can quickly and accurately query hospital medical device information through voice.
[0004] Based on this, the present invention designs a voice medical equipment query system based on mobile devices to solve the above problems. Summary of the invention
[0005] The purpose of the present invention is to provide a voice medical equipment query system based on mobile devices to solve the above technical problems.
[0006] To achieve the above object, the present invention provides the following technical solution: a voice medical device query system based on a mobile device, comprising: A mobile application interface for receiving user voice input and displaying query results of medical device information; A speech recognition module that converts the user's voice commands into text, optimized for medical terminology; A natural language processing module for parsing the user's query intent and generating query requests for the medical device database; A database interface module for secure data interaction with the hospital's medical device database; a result display module for displaying medical device information, including equipment identification, classification, usage status, maintenance records and compliance, and providing reasonable suggestions.
[0007] Preferably, the speech recognition module integrates a third-party speech recognition service and has an adaptive learning function to improve the recognition accuracy of voice commands in the medical field.
[0008] Preferably, the natural language processing module includes a pre-trained model for medical device information query, which can understand and convert the natural language query of medical professionals into structured query statements.
[0009] Preferably, the database interface module not only supports basic query operations, but also can process complex query requests, such as multi-condition search and data association analysis.
[0010] Preferably, the result display module provides personalized settings and can make corresponding suggestions based on the collected data.
[0011] Preferably, the system also includes a data security and privacy protection mechanism to ensure the security of transmission and storage of medical device information and prevent unauthorized access.
[0012] Preferably, the system is designed with a legal and ethical compliance module to ensure that the operation and data processing of the system comply with the laws, regulations and ethical standards related to medical device management.
[0013] Preferably, the system is continuously optimized through comprehensive system testing and user feedback collection in an actual medical environment to meet the actual needs of medical professionals for medical device information query and management.
[0014] Compared with the prior art, the present invention has the following beneficial effects: A hospital medical device information query system that integrates mobile devices, speech recognition technology, natural language processing technology, and database technology. The system is designed to improve the efficiency and accuracy of medical professionals' queries for medical device information and ensure the security and privacy of data. The system includes a mobile application interface, a speech recognition module, a natural language processing (NLP) module, a database interface module, and a result display module. By using advanced speech recognition technology and NLP models, the system can accurately convert users' natural language queries into executable query statements for the database and update the information display in real time. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0016] Figure 1 This is a schematic diagram of the overall architecture of the system shown in this embodiment; Figure 2 This is a schematic diagram showing the layout of the mobile application interface in this embodiment; Figure 3 A schematic diagram of the NLP model framework is shown for this embodiment. DETAILED DESCRIPTION
[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0018] See also Figure 1-3 The present invention provides a technical solution: a voice medical device query system based on a mobile device, comprising: A mobile application interface for receiving user voice input and displaying query results of medical device information; A speech recognition module that converts the user's voice commands into text, optimized for medical terminology; A natural language processing module for parsing the user's query intent and generating query requests for the medical device database; A database interface module for secure data interaction with the hospital's medical device database; a result display module for displaying medical device information, including equipment identification, classification, usage status, maintenance records and compliance, and providing reasonable suggestions.
[0019] The mobile application interface is designed to be intuitive and easy to use. Not only is it easy to navigate, but it also supports quick initiation of medical device information query requests through voice input and displays the query results.
[0020] Preferably, the speech recognition module integrates a third-party speech recognition service and has an adaptive learning function to improve the recognition accuracy of voice commands in the medical field; The speech recognition module integrates advanced speech recognition technology, which can accurately capture and recognize the voice commands of medical professionals, convert speech into text, and provide input for subsequent query processing.
[0021] Preferably, the natural language processing module includes a pre-trained model for medical device information query, which can understand and convert the natural language query of medical professionals into structured query statements; The natural language processing (NLP) module uses a model developed specifically for medical device information queries. The model combines a convolutional neural network (CNN) to extract language features and an attention mechanism module to improve the accuracy of understanding query intent and content, ensuring that the system can accurately convert users' natural language queries into query statements that can be executed by the database.
[0022] Preferably, the database interface module not only supports basic query operations, but also can process complex query requests, such as multi-condition search and data association analysis; The database interface module is designed with a secure database access protocol to enable efficient data interaction with the hospital medical device database, while ensuring the security and privacy of data transmission through data encryption and security measures.
[0023] Preferably, the result display module provides personalized settings and can make corresponding suggestions based on the collected data; The result display module has a dynamic display function, which can update the information display in real time according to the query results, and allow users to personalize according to their personal preferences. It can also make corresponding suggestions based on the collected data, such as if there are n ultrasounds in total, it is recommended to add m.
[0024] Preferably, the system also includes a data security and privacy protection mechanism to ensure the security of transmission and storage of medical device information and prevent unauthorized access.
[0025] Preferably, the system is designed with a legal and ethical compliance module to ensure that the operation and data processing of the system comply with the laws, regulations and ethical standards related to medical device management.
[0026] Preferably, the system is continuously optimized through comprehensive system testing and user feedback collection in an actual medical environment to meet the actual needs of medical professionals for medical device information query and management.
[0027] During the development process, the APP was built using programming languages and frameworks suitable for mobile devices to ensure the performance and compatibility of the application. The speech recognition function was implemented by integrating third-party services or SDKs, while the NLP module parsed user queries by developing or integrating pre-trained models. Cloud storage or local storage mechanisms were developed to achieve real-time synchronization of data. The query algorithm was optimized for the processing power and resource limitations of mobile devices. In addition, the APP performance and user experience were continuously optimized through user testing and feedback collection.
[0028] In subsequent optimization, the speech recognition algorithm was optimized according to the characteristics of mobile devices to improve the accuracy of recognition. The NLP model was also adjusted and optimized according to the user's query habits to better understand the user's intentions. Data security and privacy protection were placed in an important position, and the security of data transmission and storage was ensured through technical means. Finally, the user interface design took into account the screen size and operating habits of mobile devices to achieve a more friendly user interaction experience. Specific embodiments
[0029] First, the system interface is started. The user needs to open the mobile application, and the system displays the main interface, including the navigation bar, voice input button, and user guide. Then the user clicks the voice input button to initiate a query request, saying the query request, such as "query the current status of all ultrasound devices." The voice recognition module is activated, capturing the voice and converting it into text: "query the current status of all ultrasound devices." Then the NLP module parses the text, understands the query intent, and generates a database query statement: for example, "SELECT * FROM MedicalDevices WHERE Type = 'Ultrasound'." The database interface module executes the query statement and retrieves the hospital medical device database. The result display module receives and decrypts the data and displays a list of ultrasound devices, including device number, status, and quantity. Finally, the system displays the results according to the user's preferences, analyzes the data, and makes suggestions: "There are 5 ultrasounds in total, and it is recommended to add 2 to meet the demand." The user reviews the query results and suggestions and can provide feedback through the interface or proceed directly to the next step.
[0030] Secondly, we first conducted extensive and in-depth data collection, accumulated a large number of text query statements related to medical devices, and performed meticulous preprocessing on them, including text cleaning, word segmentation, building vocabulary indexes, and sequence vectorization. Subsequently, we designed and constructed a natural language processing model that includes a convolutional neural network and an attention mechanism, in which the convolution layer is responsible for extracting local features of the text, while the attention mechanism enhances the model's ability to identify key information in the input text. The model was trained and verified through carefully set hyperparameters and optimization algorithms, and the model performance was rigorously tested and optimized through a series of evaluation indicators such as accuracy and F1 score. Finally, after repeated tuning and verification, the model demonstrated excellent generalization capabilities on an independent test set and was successfully deployed in an actual NLP system, achieving efficient and accurate processing of medical professionals' query statements.
[0031] Third, during the initialization process of this model, the basic framework of the model is first set by calling the constructor of the base class nn.Module. Then, based on the provided configuration parameters, the model decides whether to use the pre-trained word embedding matrix to initialize the self.embedding layer. If there is no pre-trained matrix, a new embedding layer is created to achieve the function of mapping text indexes to word vectors. Next, a convolutional layer list self.convs is constructed, each convolutional layer is designed with a different convolution kernel size to capture features of different granularities in the word vector. In addition, the model adds a new linear layer self.attention, which is specifically used for subsequent calculation of attention weights. Finally, according to the requirements of the model design, the original fully connected layer is appropriately adjusted or retained.
[0032] During the forward propagation of the model, the index of the input text is first converted to a word vector through an embedding layer and the dimension is increased to accommodate the convolution operation. Then, through the convolution layer and pooling operation, the model extracts and refines the features. Using the conv_and_pool method, the output of each convolution layer is max-pooled after applying the ReLU activation function to obtain the most representative features. The model then implements attention weighting through the defined attention mechanism method, where the linear layer self.attention is used to calculate the unnormalized attention score and normalized through the softmax function to obtain the attention weight of each feature. These weights are multiplied by the features to achieve weighted summation and generate a comprehensive context vector, allowing the model to focus on the parts of the text that are more important to the current task.
[0033] Ultimately, the output of the model can be the classification result after processing by the fully connected layer, or it can be the context vector generated directly by the attention mechanism, depending on the model design and task requirements. This model structure that incorporates the attention mechanism is particularly suitable for natural language processing tasks that require a deep understanding of text information, and can significantly improve the performance and generalization ability of the model.
[0034] In the description of the present invention, it is to be understood that the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that an article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such article or device. In the absence of further restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the article or device comprising the elements.
[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A voice medical equipment query system based on mobile devices, characterized in that: include: A mobile application interface for receiving user voice input and displaying query results of medical device information; A speech recognition module that converts the user's voice commands into text, optimized for medical terminology; A natural language processing module for parsing the user's query intent and generating query requests for the medical device database; A database interface module for secure data interaction with the hospital's medical device database; a result display module for displaying medical device information, including equipment identification, classification, usage status, maintenance records and compliance, and providing reasonable suggestions.
2. A voice medical device query system based on mobile device according to claim 1, characterized in that: The speech recognition module integrates a third-party speech recognition service and has an adaptive learning function to improve the recognition accuracy of voice commands in the medical field.
3. A voice medical device query system based on mobile device according to claim 1, characterized in that: The natural language processing module includes a pre-trained model for medical device information query, which can understand and convert the natural language query of medical professionals into structured query statements.
4. A voice medical device query system based on mobile device according to claim 1, characterized in that: The database interface module not only supports basic query operations, but also can process complex query requests, such as multi-condition search and data association analysis.
5. According to the voice medical device query system based on mobile device as described in claim 1, it is characterized in that: The result display module provides personalized settings and can make corresponding suggestions based on the collected data.
6. The voice medical device query system based on mobile device according to claim 1, characterized in that: The system also includes data security and privacy protection mechanisms to ensure the security of transmission and storage of medical device information and prevent unauthorized access.
7. The voice medical device query system based on mobile device according to claim 1, characterized in that: The system is designed with a legal and ethical compliance module to ensure that the system's operation and data processing comply with laws, regulations and ethical standards related to medical device management.
8. The voice medical device query system based on mobile device according to claim 1, characterized in that: The system is continuously iterated and optimized through comprehensive system testing and user feedback collection in actual medical environments to meet the actual needs of medical professionals for medical device information query and management.