Nursing treatment or medicine automatic reminding method and system based on artificial intelligence

Abstract

The invention discloses a nursing treatment or medicine automatic reminding method and system based on artificial intelligence, and the method comprises the steps: receiving a diagnosis and treatment item of a target patient inputted by a medical worker through a medical terminal and an attached time identifier, and the time identifier is a to-be-reminded time point of the diagnosis and treatment item of the target patient; classifying the diagnosis and treatment items by using a pre-trained diagnosis and treatment identification model based on artificial intelligence to obtain categories of the diagnosis and treatment items; placing the diagnosis and treatment items and the time identifiers thereof in reminding queues of corresponding categories; and automatically reminding the diagnosis and treatment item according to the current time point and the time identifier. According to the embodiment of the invention, a reminding mechanism of diagnosis and treatment items can be optimized through an intelligent technical means, the accuracy of information transmission is improved, the working efficiency of medical staff is greatly improved, and the occurrence rate of medical errors is reduced, so that a patient is ensured to obtain safer and more effective nursing service.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and in particular to an artificial intelligence-based nursing treatment or automatic drug reminder method and system. Background Art

[0002] With the rapid development of the medical industry and the continuous advancement of technology, patients' care and treatment needs are becoming increasingly complex and diverse. In the modern medical environment, the workload of medical staff has increased. How to effectively manage the multiple diagnosis and treatment tasks of patients and ensure that every patient can receive care, treatment and medication on time has become an important issue that needs to be solved urgently.

[0003] Currently, many medical institutions rely on traditional paper records or simple spreadsheets to manage patients' diagnosis and treatment items. This method is not only prone to information omissions, but may also cause medical staff to be unable to obtain patients' treatment information in a timely manner, thereby affecting treatment effects and increasing medical safety risks. In addition, as the number of patients in hospitals increases, the number of patients that medical staff need to track and manage at the same time is also increasing. Traditional methods cannot meet this demand and are prone to omissions. Summary of the invention

[0004] The purpose of the present invention is to provide an artificial intelligence-based nursing treatment or medicine automatic reminder method and system to solve the deficiencies in the prior art. It can optimize the reminder mechanism of diagnosis and treatment items through intelligent technical means, improve the accuracy of information transmission, greatly improve the work efficiency of medical staff, and reduce the incidence of medical errors, thereby ensuring that patients receive safer and more effective nursing services.

[0005] One embodiment of the present application provides an artificial intelligence-based nursing treatment or drug automatic reminder method, the method comprising:

[0006] Receive the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the time mark attached thereto, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient;

[0007] Using a pre-trained artificial intelligence-based diagnosis and treatment recognition model, the diagnosis and treatment items are classified to obtain categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication;

[0008] Placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier;

[0009] According to the current time point and the time mark, the diagnosis and treatment items are automatically reminded.

[0010] Optionally, the automatic reminder of the diagnosis and treatment item according to the current time point and the time mark includes:

[0011] Get the reminder priorities corresponding to each category of reminder queues;

[0012] The diagnosis and treatment items are automatically reminded according to the current time point, the time mark and the reminder priority.

[0013] Optionally, the automatic reminder of the diagnosis and treatment item according to the current time point, the time mark and the reminder priority includes:

[0014] Determine whether the current time point reaches the reminder time point corresponding to any time mark in the reminder queue;

[0015] If the corresponding reminder time point is reached, find the diagnosis and treatment items corresponding to the reminder time point;

[0016] If a diagnosis and treatment item is found, a reminder message of the diagnosis and treatment item is sent to a corresponding patient care terminal, wherein the patient care terminal is managed by a medical staff;

[0017] If multiple diagnosis and treatment items are found, the reminder information of each diagnosis and treatment item is sent cyclically to the corresponding patient care terminal according to the reminder priority of the reminder queue of each diagnosis and treatment item, wherein each diagnosis and treatment item corresponds to different reminder information, and the reminder information includes: patient information and corresponding diagnosis and treatment item.

[0018] Optionally, the method further includes:

[0019] If no response information is received within a preset time interval after the reminder information is sent, a superior reminder information of the reminder information is sent to the superior nursing terminal of the patient nursing terminal.

[0020] Another embodiment of the present application provides an artificial intelligence-based nursing treatment or drug automatic reminder system, the system comprising:

[0021] A receiving module, used to receive the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the accompanying time mark, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient;

[0022] A classification module, used to classify the diagnosis and treatment items using a pre-trained artificial intelligence-based diagnosis and treatment recognition model to obtain the categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication;

[0023] An arrangement module, used for placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier;

[0024] The reminder module is used to automatically remind the diagnosis and treatment items according to the current time point and the time mark.

[0025] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0026] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0027] Compared with the prior art, the present invention provides an artificial intelligence-based nursing treatment or drug automatic reminder method, which receives the diagnosis and treatment items of a target patient and the accompanying time stamps entered by medical staff through a medical terminal, wherein the time stamp is the time point at which the diagnosis and treatment items of the target patient are to be reminded; the diagnosis and treatment items are classified using a pre-trained artificial intelligence-based diagnosis and treatment recognition model to obtain the category of the diagnosis and treatment items; the diagnosis and treatment items and their time stamps are placed in a reminder queue of the corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time stamp; and the diagnosis and treatment items are automatically reminded according to the current time point and the time stamp, thereby optimizing the reminder mechanism of the diagnosis and treatment items through intelligent technical means, improving the accuracy of information transmission, greatly improving the work efficiency of medical staff, and reducing the incidence of medical errors, thereby ensuring that patients receive safer and more effective nursing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A hardware structure block diagram of a computer terminal for an artificial intelligence-based nursing treatment or drug automatic reminder method provided in an embodiment of the present invention;

[0029] Figure 2 A flowchart of an artificial intelligence-based nursing treatment or drug automatic reminder method provided in an embodiment of the present invention;

[0030] Figure 3 A structural schematic diagram of an artificial intelligence-based nursing treatment or automatic medicine reminder system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0032] The embodiment of the present invention first provides an artificial intelligence-based nursing treatment or automatic medicine reminder method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0033] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal for a nursing treatment or drug automatic reminder method based on artificial intelligence provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0034] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the artificial intelligence-based nursing treatment or automatic drug reminder method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] See also Figure 2 , an embodiment of the present invention provides an artificial intelligence-based nursing treatment or drug automatic reminder method, which may include the following steps:

[0037] S201, receiving the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the time mark attached thereto, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient;

[0038] In modern medical care, it is crucial to ensure that patients receive the correct diagnosis and treatment services and take medication on time. Medical staff can effectively organize and manage the patient's care plan by entering the target patient's diagnosis and treatment items and the accompanying time stamps through the medical terminal. This process ensures the accurate transmission of information and avoids medical errors caused by human omissions. The introduction of time stamps provides a dimension of time management, allowing the system to remind medical staff to provide corresponding care and treatment on time. By clarifying the time points, medical staff can optimize workflows, improve work efficiency, and enhance patient safety and comfort.

[0039] Specifically, the user interface of the medical terminal can allow medical staff to quickly enter diagnosis and treatment items through touch screen, voice input and shortcut options. The interface should include multiple input boxes for recording patient information, diagnosis and treatment items, time stamps, etc. Or directly scan the patient's barcode and enter the corresponding information.

[0040] When the user enters a time stamp, the system will monitor the input content in real time. By using regular expressions and rule engines, it can parse time expressions in different formats (such as "today at 3 pm", "tomorrow at 12:30 pm", "two hours later", etc.). This process ensures that various time expressions can be recognized and converted into a unified time stamp format.

[0041] To ensure the accuracy of the information, the system will perform correlation analysis based on the context of the current input. For example, if the input "Change dressing for patient A" is received, the system will automatically associate the patient's history to confirm whether he is suitable for the project and verify the rationality of the time stamp.

[0042] During the process of inputting information, the system will provide real-time feedback and display the entered content, ensuring that medical staff can check and confirm the accuracy of the information in a timely manner. This mechanism not only reduces medical accidents caused by incorrect information, but also enhances the user experience.

[0043] After all the information is entered, the system will perform intelligent verification. Through the set logical rules, it will determine whether the time mark is within a reasonable range (such as not earlier than the current time) and generate a prompt message. If it is found that the input does not meet the conditions, the medical staff will be notified immediately to make changes.

[0044] After confirmation, the system will structure and store the entered diagnosis and treatment items and time stamps according to unified standards. By establishing a dedicated database, it ensures that the subsequent reminder system can quickly and accurately retrieve and process relevant information.

[0045] During the entire input process, the system will ensure the encryption and storage security of data transmission, comply with relevant privacy regulations, and ensure that the patient's sensitive information will not be leaked. This measure not only enhances the confidence of medical staff in using it, but also meets the compliance requirements of the medical industry.

[0046] S202, using a pre-trained artificial intelligence-based diagnosis and treatment recognition model to classify the diagnosis and treatment items to obtain categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication;

[0047] In the process of medical care and treatment, accurate classification of diagnosis and treatment items is one of the key factors to ensure that medical staff can perform their tasks efficiently. By clearly classifying various diagnosis and treatment items into nursing, treatment and medication, medical staff can learn about patients' needs more quickly, thereby optimizing resource allocation and reducing the risk of misoperation. Classification not only helps medical staff clarify the priority of various tasks, but also promotes the automation of information management and improves the efficiency and safety of the entire medical process. Specifically, one implementation method may include:

[0048] 1. Data collection and preprocessing:

[0049] -Collect historical medical records, including classified diagnosis and treatment items and their description information. These data will be used as training sets to build and optimize classification models. -Cleaning the data, removing irrelevant information and noise data, and achieving text standardization, including removing redundant spaces and converting to a unified format.

[0050] 2. Feature extraction:

[0051] -Use natural language processing techniques such as Bag of Words and TF-IDF (Term Frequency-Inverse Document Frequency) to extract key features from the text. These features will help the model understand the text content.

[0052] -Further introduce word embedding technology, such as Word2Vec or GloVe, to convert text into vector representation and capture the semantic relationship between words.

[0053] 3. Model construction:

[0054] -Build a multi-layer neural network model, including input layer, hidden layer and output layer. The input layer is responsible for receiving feature vectors, the hidden layer is responsible for learning the complex associations between features, and the output layer corresponds to the classification results of the diagnosis and treatment items.

[0055] - Apply activation functions (such as ReLU or Sigmoid) at each layer of the network to introduce non-linearity, allowing the model to learn more complex representations.

[0056] 4. Model training:

[0057] - Use the labeled training data to perform back propagation training on the model and adjust the model parameters by minimizing the cross entropy loss function. The optimization algorithm can be Adam or SGD (stochastic gradient descent) to improve training efficiency and effect.

[0058] - Use cross-validation technology to divide the data set into training set, validation set and test set to monitor model performance and prevent overfitting.

[0059] 5. Model verification and tuning:

[0060] -Evaluate model performance on the validation set, using indicators such as accuracy, recall, and F1-score for multi-dimensional evaluation to ensure the reliability of the classification effect.

[0061] -According to the verification results, adjust the hyperparameters (such as learning rate, batch size, etc.) and retrain the model until satisfactory results are achieved.

[0062] 6. Classification implementation:

[0063] -Embed the trained model into the medical terminal system so that it can classify the diagnosis and treatment items in real time when the medical staff enters them.

[0064] -When medical staff enter a diagnosis and treatment item, the system will automatically identify and output the category of the item (nursing, treatment or medication) by calling the intelligent classification model, and return the classification result to the user.

[0065] 7. Real-time learning and feedback mechanism:

[0066] -During the operation of the system, user feedback on classification results is collected to continuously optimize the model. Whenever a new diagnosis and treatment item is entered and classified, the system will automatically update the training data and regularly retrain and optimize the model.

[0067] -Such a dynamic learning mechanism ensures that the model can adapt to rapid changes in the medical field and provide higher classification accuracy.

[0068] 8. System integration and response mechanism:

[0069] -Integrate the classification results into the overall care management system to ensure that the classification information can be effectively utilized by subsequent automatic reminders and information management modules.

[0070] -By setting up a classification response mechanism, when the classification results change, the system will update the corresponding reminder queue in real time to ensure that medical staff can always obtain the latest patient treatment information.

[0071] Through the above steps, the intelligent diagnosis and treatment classification algorithm based on multi-level neural network can provide efficient, accurate and real-time learning solutions in the automatic classification process. This not only improves the work efficiency of medical staff, but also has important significance in ensuring patient safety and treatment effects.

[0072] S203, placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier;

[0073] Place the diagnosis and treatment items and their time stamps in the reminder queue of the corresponding category to ensure that each diagnosis and treatment item can be reminded in the predetermined time sequence. This process not only improves the organizational efficiency of patient care, but also brings the transparency of the medical process to a new level. By classifying and sorting various diagnosis and treatment items, medical staff can quickly identify priority tasks to avoid delays and omissions, while also optimizing time management and improving patient safety and treatment outcomes.

[0074] Specifically, after receiving the input diagnosis and treatment items, the system first uses the pre-trained classification model to classify them to determine whether they belong to the "nursing", "treatment" or "medication" category. Parse the time stamp of each diagnosis and treatment item to ensure that the input time format is unified and understandable. For example, convert "3 pm" into a corresponding timestamp to facilitate subsequent arrangement. Create a separate reminder queue for each classification category (nursing, treatment, medication). At initialization, the queue is empty and the system is waiting to receive input data.

[0075] Linked lists or priority queues can be used as data structures to store reminder queues of various categories. Priority queues can be automatically sorted according to time stamps to facilitate subsequent processing. Each queue node contains information including: the name of the diagnosis and treatment project, time stamp and its classification category. When a new diagnosis and treatment project is received, the system will find a suitable position in the corresponding reminder queue for insertion by comparing the current time with the time stamp of the project to be inserted. If a priority queue structure is used, the nature of the heap can be used to ensure that the time stamps are always arranged in order from early to late during insertion. As new projects are continuously added, the system will maintain the order of the queue in real time. After each insertion operation, the system will check and adjust the order of the queue to ensure that the ascending order of the time stamp is always maintained. If the time stamp of a diagnosis and treatment project is modified, the system will remove it and insert it back to the corresponding position according to the new time. In classification, in addition to time stamps, the concept of priority can also be introduced. For example, the project that needs to be processed urgently can be marked as high priority, so that when there are multiple projects at the same time point, the system can give priority to reminding the high priority project. Regularly feedback the current reminder queue status to medical staff, including the upcoming reminder projects, to ensure that they can have a clear expectation of the upcoming tasks. Medical staff can proactively adjust, delete or modify queues to adapt to actual clinical conditions, thereby managing the patient's treatment process more flexibly.

[0076] Through the implementation of the above-mentioned intelligent classification and dynamic scheduling algorithm, not only an efficient solution is provided in classification and time arrangement, but also changes and challenges in the medical process can be flexibly responded to in actual operation.

[0077] S204, automatically reminding the diagnosis and treatment items according to the current time point and the time mark.

[0078] Automatically reminding medical treatment items based on the current time point and the time mark is a key step to ensure that medical staff perform nursing and treatment tasks on time. Through accurate time management, the system can effectively communicate the medical treatment items to be reminded to the corresponding medical staff in a timely manner, thereby avoiding unnecessary medical risks caused by omissions or delays.

[0079] Specifically, you can obtain the reminder priorities corresponding to each category of reminder queues;

[0080] During the design phase, a unified priority is set for each reminder queue, which determines the reminder priority of the queue among all queues. You can assign a corresponding priority to each reminder queue based on the urgency and importance of each category of diagnosis and treatment items. For example, the type of items that require urgent care can be marked as high priority, while routine treatment items can be marked as low priority.

[0081] The diagnosis and treatment items are automatically reminded according to the current time point, the time mark and the reminder priority.

[0082] The system continuously monitors the current time and compares it with the time stamps of the items in the queue. When the current time approaches or reaches a certain time stamp, the system will consider the priority of the queue and trigger the corresponding reminder. By combining time points and unified priorities, the system can ensure that medical staff receive important reminders at the right time, thereby improving efficiency and reducing potential errors.

[0083] Specifically, according to the current time point, the time mark and the reminder priority, the diagnosis and treatment items are automatically reminded, and it can be determined whether the current time point reaches the reminder time point corresponding to any time mark in the reminder queue;

[0084] The timer mechanism can be used to continuously check the current time and compare it with the time stamp in the reminder queue. When the current time point is found to be consistent or basically consistent with a time stamp, the system considers that the item corresponding to the time stamp can be reminded. This real-time judgment mechanism ensures the timely transmission of information, avoids affecting the treatment effect of patients due to delays, and provides medical staff with an opportunity to respond quickly.

[0085] If the corresponding reminder time point is reached, find the diagnosis and treatment items corresponding to the reminder time point;

[0086] After recognizing that the current time reaches a certain time mark, the system quickly locates the diagnosis and treatment project with the time mark in the corresponding reminder queue. The system extracts project-related information for subsequent notification. This step ensures that the reminder information sent is accurate, helps medical staff quickly understand the tasks that need to be performed, and reduces unnecessary query time and information errors.

[0087] If a diagnosis and treatment item is found, a reminder message of the diagnosis and treatment item is sent to a corresponding patient care terminal, wherein the patient care terminal is managed by a medical staff;

[0088] After finding the diagnosis and treatment items to be reminded, the system will use the message push mechanism to send the information to the corresponding patient care terminal. The content sent should include a clear description of the project, patient identity and time information to ensure that medical staff obtain detailed context. This operation ensures the timely transmission of information, reduces misoperation caused by missing information, and improves the transparency and efficiency of the entire medical process.

[0089] If multiple diagnosis and treatment items are found, the reminder information of each diagnosis and treatment item is sent cyclically to the corresponding patient care terminal according to the reminder priority of the reminder queue of each diagnosis and treatment item, wherein each diagnosis and treatment item corresponds to different reminder information, and the reminder information includes: patient information and corresponding diagnosis and treatment item.

[0090] When the system finds multiple items to be reminded, it needs to sort the priorities of these items. The priority of the item is the priority of the queue in which the item is located. In order of priority from high to low, the reminder information of each item is sent in sequence. The interval between two reminder messages should not be too long to avoid message confusion caused by simultaneous reminders and too delayed reminders in sequence.

[0091] Furthermore, if no response information is received within a preset time interval after the reminder information is sent, a higher-level reminder information of the reminder information is sent to the higher-level nursing terminal of the patient care terminal.

[0092] Specifically, when sending a reminder message, a preset time interval may be set first. The time interval is a reasonable waiting time, which is usually derived based on historical data analysis to ensure that medical staff have enough time to respond after receiving the reminder.

[0093] After sending the reminder message, the system will start a status monitoring mechanism, which continuously monitors whether a response message is received from the patient care terminal through a timer or asynchronous event monitoring. This mechanism can track the response status in real time to ensure that the next step can be responded to quickly.

[0094] The system sets a logical judgment to detect the reception status of the response information when the preset time interval arrives. If the response information is received, the system will end the operation process; if the response information is not received, it will proceed to the next step. When it is determined that the response information is not received, the system will generate a higher-level reminder information based on the original reminder information. At this time, the higher-level reminder information usually contains the same content as the original reminder information, but may be accompanied by a higher-level emergency mark or priority for emphasis.

[0095] The system will send the generated upper-level reminder information to the upper-level nursing terminal of the patient's nursing terminal through an established communication protocol (such as HTTP request, WebSocket or other message push mechanism). This process must also ensure the integrity of the information and the reliability of the transmission. After completing the sending of the upper-level reminder information, the system will record all the details of the event, including the sending time, patient information, original reminder content, response status, and the status of the upper-level reminder. This data will provide a basis for subsequent analysis and improvement.

[0096] Through this series of steps, not only the medical staff's ability to respond to important reminder information is improved, but also data support is provided for the continuous optimization of medical services, ensuring the timeliness and safety of patient care.

[0097] It can be seen that the medical treatment items of the target patient and the accompanying time stamps entered by the medical staff through the medical terminal are received, and the time stamp is the time point to be reminded of the medical treatment items of the target patient; the medical treatment items are classified by using the pre-trained artificial intelligence-based medical treatment recognition model to obtain the category of the medical treatment items; the medical treatment items and their time stamps are placed in a reminder queue of the corresponding category, wherein the medical treatment items in the reminder queue are arranged according to the time stamp; and the medical treatment items are automatically reminded according to the current time point and the time stamp, so that the reminder mechanism of the medical treatment items can be optimized through intelligent technical means, the accuracy of information transmission can be improved, the work efficiency of medical staff can be greatly improved, and the incidence of medical errors can be reduced, thereby ensuring that patients receive safer and more effective nursing services.

[0098] Another embodiment of the present invention provides an artificial intelligence-based nursing treatment or drug automatic reminder system, see Figure 3 , the system may include:

[0099] The receiving module 301 is used to receive the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the time mark attached thereto, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient;

[0100] The classification module 302 is used to classify the diagnosis and treatment items using a pre-trained diagnosis and treatment recognition model based on artificial intelligence to obtain the categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication;

[0101] Arrangement module 303, used to place the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier;

[0102] The reminder module 304 is used to automatically remind the diagnosis and treatment items according to the current time point and the time mark.

[0103] It can be seen that the medical treatment items of the target patient and the accompanying time stamps entered by the medical staff through the medical terminal are received, and the time stamp is the time point to be reminded of the medical treatment items of the target patient; the medical treatment items are classified by using the pre-trained artificial intelligence-based medical treatment recognition model to obtain the category of the medical treatment items; the medical treatment items and their time stamps are placed in a reminder queue of the corresponding category, wherein the medical treatment items in the reminder queue are arranged according to the time stamp; and the medical treatment items are automatically reminded according to the current time point and the time stamp, so that the reminder mechanism of the medical treatment items can be optimized through intelligent technical means, the accuracy of information transmission can be improved, the work efficiency of medical staff can be greatly improved, and the incidence of medical errors can be reduced, thereby ensuring that patients receive safer and more effective nursing services.

[0104] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0105] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0106] S201, receiving the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the time mark attached thereto, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient;

[0107] S202, using a pre-trained artificial intelligence-based diagnosis and treatment recognition model to classify the diagnosis and treatment items to obtain categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication;

[0108] S203, placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier;

[0109] S204, automatically reminding the diagnosis and treatment items according to the current time point and the time mark.

[0110] It can be seen that the medical treatment items of the target patient and the accompanying time stamps entered by the medical staff through the medical terminal are received, and the time stamp is the time point to be reminded of the medical treatment items of the target patient; the medical treatment items are classified by using the pre-trained artificial intelligence-based medical treatment recognition model to obtain the category of the medical treatment items; the medical treatment items and their time stamps are placed in a reminder queue of the corresponding category, wherein the medical treatment items in the reminder queue are arranged according to the time stamp; and the medical treatment items are automatically reminded according to the current time point and the time stamp, so that the reminder mechanism of the medical treatment items can be optimized through intelligent technical means, the accuracy of information transmission can be improved, the work efficiency of medical staff can be greatly improved, and the incidence of medical errors can be reduced, thereby ensuring that patients receive safer and more effective nursing services.

[0111] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0112] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0113] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0114] S201, receiving the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the time mark attached thereto, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient;

[0115] S202, using a pre-trained artificial intelligence-based diagnosis and treatment recognition model to classify the diagnosis and treatment items to obtain categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication;

[0116] S203, placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier;

[0117] S204, automatically reminding the diagnosis and treatment items according to the current time point and the time mark.

[0118] It can be seen that the medical treatment items of the target patient and the accompanying time stamps entered by the medical staff through the medical terminal are received, and the time stamp is the time point to be reminded of the medical treatment items of the target patient; the medical treatment items are classified by using the pre-trained artificial intelligence-based medical treatment recognition model to obtain the category of the medical treatment items; the medical treatment items and their time stamps are placed in a reminder queue of the corresponding category, wherein the medical treatment items in the reminder queue are arranged according to the time stamp; and the medical treatment items are automatically reminded according to the current time point and the time stamp, so that the reminder mechanism of the medical treatment items can be optimized through intelligent technical means, the accuracy of information transmission can be improved, the work efficiency of medical staff can be greatly improved, and the incidence of medical errors can be reduced, thereby ensuring that patients receive safer and more effective nursing services.

[0119] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. An artificial intelligence-based nursing treatment or drug automatic reminder method, characterized in that: The method comprises: Receive the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the time mark attached thereto, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient; Using a pre-trained artificial intelligence-based diagnosis and treatment recognition model, the diagnosis and treatment items are classified to obtain categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication; Placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier; According to the current time point and the time mark, the diagnosis and treatment items are automatically reminded.

2. The method according to claim 1, characterized in that The automatic reminder of the diagnosis and treatment items according to the current time point and the time mark includes: Get the reminder priorities corresponding to each category of reminder queues; The diagnosis and treatment items are automatically reminded according to the current time point, the time mark and the reminder priority.

3. The method according to claim 2, characterized in that The automatic reminder of the diagnosis and treatment item according to the current time point, the time mark and the reminder priority includes: Determine whether the current time point reaches the reminder time point corresponding to any time mark in the reminder queue; If the corresponding reminder time point is reached, find the diagnosis and treatment items corresponding to the reminder time point; If a diagnosis and treatment item is found, a reminder message of the diagnosis and treatment item is sent to a corresponding patient care terminal, wherein the patient care terminal is managed by a medical staff; If multiple diagnosis and treatment items are found, the reminder information of each diagnosis and treatment item is sent cyclically to the corresponding patient care terminal according to the reminder priority of the reminder queue of each diagnosis and treatment item, wherein each diagnosis and treatment item corresponds to different reminder information, and the reminder information includes: patient information and corresponding diagnosis and treatment item.

4. The method according to claim 3, characterized in that The method further comprises: If no response information is received within a preset time interval after the reminder information is sent, a superior reminder information of the reminder information is sent to the superior nursing terminal of the patient nursing terminal.

5. An artificial intelligence-based nursing treatment or drug automatic reminder system, characterized in that: The system comprises: A receiving module, used to receive the diagnosis and treatment items of the target patient entered by the medical staff through the medical terminal and the accompanying time mark, wherein the time mark is the time point to be reminded of the diagnosis and treatment items of the target patient; A classification module, used to classify the diagnosis and treatment items using a pre-trained artificial intelligence-based diagnosis and treatment recognition model to obtain the categories of the diagnosis and treatment items, wherein the categories of the diagnosis and treatment items include: nursing, treatment, and medication; An arrangement module, used for placing the diagnosis and treatment items and their time identifiers in a reminder queue of a corresponding category, wherein the diagnosis and treatment items in the reminder queue are arranged according to the time identifier; The reminder module is used to automatically remind the diagnosis and treatment items according to the current time point and the time mark.

6. The system according to claim 5, characterized in that The reminder module is specifically used for: Get the reminder priorities corresponding to each category of reminder queues; The diagnosis and treatment items are automatically reminded according to the current time point, the time mark and the reminder priority.

7. The system according to claim 6, characterized in that The reminder module is specifically used for: Determine whether the current time point reaches the reminder time point corresponding to any time mark in the reminder queue; If the corresponding reminder time point is reached, find the diagnosis and treatment items corresponding to the reminder time point; If a diagnosis and treatment item is found, a reminder message of the diagnosis and treatment item is sent to a corresponding patient care terminal, wherein the patient care terminal is managed by a medical staff; If multiple diagnosis and treatment items are found, the reminder information of each diagnosis and treatment item is sent cyclically to the corresponding patient care terminal according to the reminder priority of the reminder queue of each diagnosis and treatment item, wherein each diagnosis and treatment item corresponds to different reminder information, and the reminder information includes: patient information and corresponding diagnosis and treatment item.

8. The system according to claim 7, characterized in that The system further comprises: The sending module is used to send the upper-level reminder information of the reminder information to the upper-level nursing terminal of the patient nursing terminal if no response information is received within a preset time interval after the reminder information is sent.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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