Medical advice reminding method and device, readable storage medium and terminal equipment

Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to a medical advice reminding method and device, a computer readable storage medium and terminal equipment. The method comprises the following steps: acquiring target audio data; wherein the target audio data is audio data including doctor's advice information; performing text conversion on the target audio data to obtain text data corresponding to the target audio data; performing information extraction on the text data to obtain the medical advice information; and performing doctor's advice reminding according to the doctor's advice information. Through the method, text conversion can be performed on the target audio data including the doctor's advice information to obtain the text data corresponding to the target audio data, and information extraction can be performed from the text data to obtain the doctor's advice information, so that a user does not need to manually input the doctor's advice information, the operation difficulty is greatly reduced, and the user experience is improved.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and in particular, relates to a method, device, computer-readable storage medium and terminal device for reminding medical advice. Background Art

[0002] After leaving the hospital, patients may still need to take medication or return to the hospital for follow-up visits regularly. When patients forget, they are prone to omissions, mistakes in medication, or missed follow-up visits, which will delay the patient's physical recovery time, leading to worsening of the disease, and even life-threatening in severe cases. At present, some manufacturers will add doctor's order reminder functions to terminal devices such as smart watches and smart medicine boxes to remind users to take medication. However, existing doctor's order reminder methods require users to manually enter doctor's order information such as medication information and medication time, which is more complicated to operate and has a poor user experience. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a medical order reminder method, device, computer-readable storage medium and terminal device to solve the problem that the existing medical order reminder method requires the user to manually enter medical order information such as medication information and medication time, which is more complicated to operate and leads to poor user experience.

[0004] A first aspect of an embodiment of the present application provides a method for reminding a doctor's order, which may include:

[0005] Acquire target audio data; wherein the target audio data is audio data including doctor's advice information;

[0006] Convert the target audio data into text to obtain text data corresponding to the target audio data;

[0007] Extracting information from the text data to obtain the medical advice information;

[0008] A medical advice reminder is provided according to the medical advice information.

[0009] In a specific implementation of the first aspect, extracting information from the text data to obtain the medical advice information may include:

[0010] Using a preset medical advice information extraction model to extract information from the text data to obtain the medical advice information;

[0011] The training process of the medical advice information extraction model includes:

[0012] Obtaining a preset training sample set for extracting medical order information; wherein the training sample set for extracting medical order information includes a preset number of training samples, each training sample including a section of sample text data and corresponding sample medical order information;

[0013] The sample text data of each training sample in the medical order information extraction training sample set is used as input, and the sample medical order information of each training sample in the medical order information extraction training sample set is used as expected output, and the initial artificial intelligence model is trained to obtain the medical order information extraction model.

[0014] In a specific implementation of the first aspect, the medical advice information may include medication time and medication information;

[0015] The performing of a medical advice reminder according to the medical advice information may include:

[0016] Determine a medication reminder time according to the medication time; wherein the medication reminder time is earlier than or equal to the medication time;

[0017] At the medication reminder time, a medication reminder is performed according to the medication information.

[0018] In a specific implementation of the first aspect, after the medication reminder is performed according to the medication information, the method may further include:

[0019] Acquire a target image acquired at the medication time;

[0020] Medication behavior recognition is performed according to the target image to obtain a medication behavior recognition result.

[0021] In a specific implementation of the first aspect, the doctor's advice information may include a follow-up visit time and follow-up visit information;

[0022] The performing of a medical advice reminder according to the medical advice information may include:

[0023] Determine a follow-up visit reminder time according to the follow-up visit time; wherein the follow-up visit reminder time is earlier than or equal to the follow-up visit time;

[0024] At the follow-up reminder time, a follow-up reminder is made according to the follow-up information.

[0025] In a specific implementation of the first aspect, the follow-up consultation information may include a follow-up consultation address;

[0026] After the follow-up consultation reminder is made according to the follow-up consultation information, the following may also be included:

[0027] Obtaining the positioning coordinates collected at the time of the follow-up visit;

[0028] The follow-up visit behavior is identified according to the positioning coordinates and the follow-up visit address to obtain a follow-up visit behavior identification result.

[0029] In a specific implementation of the first aspect, performing follow-up visit behavior recognition according to the positioning coordinates and the follow-up visit address to obtain a follow-up visit behavior recognition result may include:

[0030] Determine a coordinate range interval corresponding to the follow-up visit address;

[0031] If the positioning coordinates are within the coordinate range, then determining that the follow-up visit behavior recognition result is that the follow-up visit has been completed;

[0032] If the positioning coordinates are outside the coordinate range, the follow-up visit behavior identification result is determined to be an incomplete follow-up visit.

[0033] A second aspect of the embodiment of the present application provides a medical advice reminder device, which may include:

[0034] An audio acquisition module, used to acquire target audio data; wherein the target audio data is audio data including medical advice information;

[0035] A text conversion module, used for performing text conversion on the target audio data to obtain text data corresponding to the target audio data;

[0036] An information extraction module, used to extract information from the text data to obtain the medical advice information;

[0037] The doctor's order reminder module is used to make a doctor's order reminder based on the doctor's order information.

[0038] In a specific implementation of the second aspect, the text conversion module may include:

[0039] An information extraction submodule, used to extract information from the text data using a preset medical advice information extraction model to obtain the medical advice information;

[0040] Furthermore, the information extraction submodule may include:

[0041] A training sample set acquisition unit, used to acquire a preset training sample set for extracting medical order information; wherein the training sample set for extracting medical order information includes a preset number of training samples, each training sample includes a section of sample text data and corresponding sample medical order information;

[0042] The initial model training unit is used to train the initial artificial intelligence model with the sample text data of each training sample in the medical order information extraction training sample set as input and the sample medical order information of each training sample in the medical order information extraction training sample set as expected output to obtain the medical order information extraction model.

[0043] In a specific implementation of the second aspect, the medical advice information includes medication time and medication information;

[0044] The doctor's advice reminder module may include:

[0045] A medication time determination submodule, used to determine a medication reminder time according to the medication time; wherein the medication reminder time is earlier than or equal to the medication time;

[0046] The medication reminder submodule is used to provide medication reminders according to the medication information at the medication reminder time.

[0047] In a specific implementation of the second aspect, the medical advice reminder device may further include:

[0048] A target image acquisition module, used to acquire a target image acquired at the medication time;

[0049] The medication behavior recognition module is used to perform medication behavior recognition based on the target image to obtain a medication behavior recognition result.

[0050] In a specific implementation of the second aspect, the doctor's advice information may include a follow-up visit time and follow-up visit information;

[0051] The doctor's advice reminder module may also include:

[0052] A follow-up reminder determination submodule is used to determine a follow-up reminder time according to the follow-up time; wherein the follow-up reminder time is earlier than or equal to the follow-up time;

[0053] The follow-up reminder submodule is used to make a follow-up reminder at the follow-up reminder time according to the follow-up information.

[0054] In a specific implementation of the second aspect, the follow-up consultation information may include a follow-up consultation address;

[0055] The medical advice reminder device may also include:

[0056] A positioning coordinate acquisition module, used to acquire the positioning coordinates collected during the follow-up visit;

[0057] The follow-up visit behavior recognition module is used to perform follow-up visit behavior recognition according to the positioning coordinates and the follow-up visit address to obtain a follow-up visit behavior recognition result.

[0058] In a specific implementation of the second aspect, the follow-up visit behavior recognition module may include:

[0059] A coordinate range interval determination submodule, used to determine the coordinate range interval corresponding to the follow-up visit address;

[0060] A first follow-up visit behavior recognition result determination submodule is used to determine that the follow-up visit behavior recognition result is that the follow-up visit has been completed if the positioning coordinates are within the coordinate range interval;

[0061] The second follow-up visit behavior recognition result determination submodule is used to determine that the follow-up visit behavior recognition result is an incomplete follow-up visit if the positioning coordinates are outside the coordinate range interval.

[0062] A third aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of any of the above-mentioned medical advice reminder methods are implemented.

[0063] The fourth aspect of an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-mentioned medical advice reminder methods when executing the computer program.

[0064] A fifth aspect of an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the steps of any one of the above-mentioned medical advice reminder methods.

[0065] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain target audio data; wherein the target audio data is audio data including medical order information; the target audio data is converted into text to obtain text data corresponding to the target audio data; information is extracted from the text data to obtain the medical order information; and medical order reminders are made according to the medical order information. Through the embodiments of the present application, the target audio data including medical order information can be converted into text to obtain text data corresponding to the target audio data, and information can be extracted from the text data to obtain medical order information, thereby eliminating the need for users to manually input medical order information, greatly reducing the difficulty of operation and helping to improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0067] Figure 1 This is a flow chart of an embodiment of a method for reminding a doctor's order in an embodiment of the present application;

[0068] Figure 2A schematic diagram of a communication connection between a terminal device and another terminal device in an embodiment of the present application;

[0069] Figure 3 A schematic diagram of a reminder method;

[0070] Figure 4 A schematic diagram of another reminder method;

[0071] Figure 5 A schematic diagram of the different stages of drug-taking behavior;

[0072] Figure 6 This is the first schematic diagram of the process of identifying the follow-up behavior;

[0073] Figure 7 The second schematic diagram of the follow-up behavior identification process;

[0074] Figure 8 This is a structural diagram of an embodiment of a medical advice reminder device in an embodiment of the present application;

[0075] Fig. 9 This is a schematic block diagram of a terminal device in an embodiment of the present application. DETAILED DESCRIPTION

[0076] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0077] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0078] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0079] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0080] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0081] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0082] After leaving the hospital, patients may still need to take medication or return to the hospital for follow-up visits regularly. When patients forget, they are prone to omissions, mistakes in medication, or missed follow-up visits, which will delay the patient's physical recovery time, leading to worsening of the disease, and even life-threatening in severe cases. At present, some manufacturers will add doctor's order reminder functions to terminal devices such as smart watches and smart medicine boxes to remind users to take medication. However, existing doctor's order reminder methods require users to manually enter doctor's order information such as medication information and medication time, which is more complicated to operate and has a poor user experience.

[0083] In view of this, the embodiments of the present application provide a medical order reminder method, device, computer-readable storage medium and terminal device to solve the problem that the existing medical order reminder method requires the user to manually enter medical order information such as medication information and medication time, which is more complicated to operate and leads to poor user experience.

[0084] It should be noted that the executor of the method of the present application is a terminal device, which may include but is not limited to smart watches (also called smart bracelets), smart phones, notebooks, PDAs and other computing devices.

[0085] See also Figure 1 In the embodiment of the present application, an embodiment of a method for reminding a doctor's order may include:

[0086] Step S101, obtaining target audio data.

[0087] The target audio data is audio data including medical advice information.

[0088] In the embodiments of the present application, the target audio data can be obtained through on-site recording, call recording, and social software voice information.

[0089] In a specific implementation of the embodiment of the present application, the target audio data can be collected by on-site recording. When the user is seeking medical treatment, the preset audio collection device can be used to collect the audio of the doctor's medical advice to obtain audio data including the medical advice information. For example, the executor of the method of the present application is a smart watch. When the doctor prescribes medicine, the user is usually informed of the medical advice information such as the medication time. The audio collection device preset in the smart watch can be used to collect the audio of the medical advice to obtain the target audio data.

[0090] In another specific implementation of the embodiment of the present application, the target audio data can be collected by recording the call. For example, a user can talk to a doctor by telephone consultation. The doctor usually informs the user of medical advice information such as the time to take the medicine during the call. At this time, the user can record the call process to obtain the target audio data. For another example, the user's family or friends can remind the user of information such as the time to take the medicine during the call with the user. At this time, the user can obtain the target audio data by recording the call process.

[0091] In another specific implementation of the embodiment of the present application, the target audio data can be obtained through social software voice information. For example, a user can conduct a remote consultation through an online consultation webpage or application, and the doctor can inform the user of medical advice information such as medication time through a voice message during the online consultation. At this time, the voice information is the target audio data including the medical advice information. For another example, the user's family or friends can send a voice message on the social software to remind the doctor of the medical advice information. At this time, the voice information is the target audio data including the medical advice information.

[0092] It should be noted that the target audio data can be obtained in real time through the above method, or the target audio data can be stored in a preset storage module, and when a medical advice reminder needs to be generated, the target audio data can be obtained from the storage module. This application does not make any specific limitations on this.

[0093] In another specific implementation of the embodiment of the present application, the target audio data can also be obtained from another terminal device that is connected to the terminal device for communication. Figure 2 The execution subject of the method of the present application is a smart watch, which can communicate and connect with a smart phone for interaction; the smart phone can send target audio data to the smart watch, so that the smart watch can issue medical advice reminders.

[0094] Step S102: convert the target audio data into text to obtain text data corresponding to the target audio data.

[0095] In an embodiment of the present application, any artificial intelligence model in the prior art, such as a hidden Markov model, a recurrent neural network, a convolutional neural network, etc., can be used to convert the target audio data into text to obtain text data corresponding to the target audio data.

[0096] Step S103: extract information from the text data to obtain medical advice information.

[0097] In the embodiment of the present application, a preset medical order information extraction model can be used to extract information from text data to obtain medical order information. The medical order information extraction model can be a pre-trained artificial intelligence model for extracting medical order information, including but not limited to any common artificial intelligence model for information extraction in the prior art such as recurrent neural networks (RNN), convolutional neural networks (CNN) and attention mechanisms.

[0098] It should be understood that before using the medical order information extraction model, the initial artificial intelligence model can be trained to obtain the medical order information extraction model in the embodiment of the present application.

[0099] Specifically, a preset training sample set for extracting medical order information may be obtained; wherein the training sample set for extracting medical order information may include a preset number of training samples, and each training sample may include a section of sample text data and corresponding sample medical order information. Afterwards, the sample text data of each training sample in the training sample set for extracting medical order information may be used as input, and the sample medical order information of each training sample in the training sample set for extracting medical order information may be used as expected output to train the initial artificial intelligence model, thereby obtaining a medical order information extraction model.

[0100] It should be noted that in order to make the extraction results of the medical order information extraction model more robust, the medical order information extraction training sample set can include training samples in various medical scenarios. For example, the text data corresponding to the audio data in the consultation scenarios of different departments can be obtained, and the text data can be annotated to obtain sample medical order information such as medication time, drug name, dosage, and medication method.

[0101] In a possible embodiment, when training the medical order information extraction model, the medical order information extraction training sample set can be divided into a training set and a test set according to a preset ratio, and the training set is used for model training, and the test set is used for model testing.

[0102] After training the medical order information extraction model of the embodiment of the present application, the medical order information extraction model can be used to extract information from text data to obtain medical order information; wherein the medical order information may include medication time and medication information, and the medication information may include information such as drug type, medication dosage, medication dosage, and medication method.

[0103] Step S104: Providing a doctor's advice reminder based on the doctor's advice information.

[0104] In the embodiment of the present application, the medication reminder time can be determined according to the medication time. The medication reminder time can be earlier than the medication time, for example, it can be 3 minutes, 5 minutes, 10 minutes, etc. before the medication time; or the medication reminder time can be equal to the medication time.

[0105] Afterwards, you can make medication reminders based on the medication information at the medication reminder time.

[0106] It should be noted that the specific method of medication reminder can be specific and situational according to the actual situation, and this application does not limit this. For example, an alarm can be set according to the medication reminder time, and the medication information can be used as the alarm remark information, such as Figure 3 As shown in the figure, an alarm can be set for 12:00, and medication information such as "Drug A, 1 pill, take with meals" can be used as the alarm note information. For another example, medication reminder information can be popped up in a pop-up window, such as Figure 4 As shown, "Drug B, 10 ml, take after meals" can be used as a prompt message and popped up in a pop-up window.

[0107] In the embodiment of the present application, it is also possible to confirm whether the user has taken the medicine. Specifically, after the medication reminder is given according to the medication information, a target image can be obtained, wherein the target image can be an image collected at the medication time; and medication behavior recognition is performed according to the target image to obtain a medication behavior recognition result.

[0108] In a specific implementation of the embodiment of the present application, the user can be imaged at the medication time by a preset camera acquisition device on the terminal device to obtain a target image. For example, if the execution subject of the method of the present application is a smart watch, the user can be imaged at the medication time by a camera on the smart watch to obtain a target image.

[0109] In another specific implementation of the embodiment of the present application, the user can be imaged at the medication time through a preset camera capture device on another terminal device that is connected to the terminal device in communication, to obtain a target image. For example, the execution subject of the method of the present application is a smart watch, and the smart watch can also communicate with a monitoring device installed at home, and can send an image capture instruction to the monitoring device at the medication time to instruct the monitoring device to capture an image of the user; after receiving the image capture instruction, the monitoring device can capture an image of the user to obtain a target image, and can send the target image to the smart watch, so that the smart watch can obtain the target image.

[0110] It should be understood that medication behavior recognition can be performed on the target image. If it is identified that the user in the target image has taken medication, the medication behavior recognition result can be determined as completed medication; if it is identified that the user in the target image has not taken medication, the medication behavior recognition result can be determined as incomplete medication.

[0111] In order to more accurately identify whether the user is taking the medicine correctly, it is possible to identify whether the user is taking the medicine when it is identified that both the user and the target medicine exist in the target image; wherein the type of the target medicine is the type of medicine in the medication information.

[0112] Specifically, face recognition can be performed on the target image to identify whether there is a user in the target image. If there is no user in the target image, the medication behavior recognition result can be directly determined to be unfinished medication. If there is a user in the target image, a preset drug recognition model can be further used to identify whether there is a target drug in the target image. If there is no target drug in the target image, the medication behavior recognition result can be determined to be unfinished medication. If there is a target drug in the target image, a drug behavior recognition model can be further used to identify whether the user in the target image has medication behavior. If the user in the target image has medication behavior, the medication behavior recognition result can be determined to be completed medication. Otherwise, the medication behavior recognition result can be determined to be unfinished medication.

[0113] Among them, the drug recognition model and the medication behavior recognition model can be obtained by training any artificial intelligence model for image recognition in the existing technology, including but not limited to recurrent neural networks (RNN), convolutional neural networks (CNN) and attention mechanisms.

[0114] For the drug recognition model, a preset drug recognition training sample set can be obtained; wherein the drug recognition training sample set may include a preset number of training samples, each training sample may include a sample image and a corresponding sample label, and the sample label may be the presence or absence of a certain drug. Afterwards, the sample image of each training sample in the drug recognition training sample set may be used as input, and the sample label of each training sample in the drug recognition training sample set may be used as the expected output to train the initial artificial intelligence model and obtain the drug recognition model.

[0115] It should be understood that the whole process of medication may include multiple stages. Here, the first stage may be the preparation stage, such as Figure 5 As shown, the first stage is the stage of preparation actions such as tearing open the package and taking out the medicine; the second stage can be the drug intake stage, that is, the stage of the intake action of putting the medicine into the mouth; the third stage can be the swallowing stage, that is, the stage of the chewing action or swallowing action after the medicine enters the mouth. Here, the second stage can be defined as the key behavior of medication, specifically defined as the drug is placed in the mouth and can be seen, and the image containing the second stage can be defined as the key behavior frame of medication. When identifying whether the user has medication behavior, the medication behavior recognition model can be used to identify the multiple target images collected frame by frame. If the target images are identified as key behavior frames of medication in turn, it can be determined that the user has medication behavior; otherwise, it can be determined that the user does not have medication behavior.

[0116] Before using the medication behavior recognition model to identify whether the user has medication behavior, the medication behavior recognition model can be trained. Specifically, a preset medication behavior recognition training sample set can be obtained; wherein the medication recognition training sample set may include a preset number of training samples, each training sample may include a sample image and a corresponding state label, and the state label may include a key frame state label and a non-key frame state label; wherein the key frame state label may indicate that the sample image is a medication key behavior frame, and the non-key frame state label may indicate that the sample image is not a medication key behavior frame. Afterwards, the sample image of each training sample in the medication recognition training sample set can be used as input, and the state label of each training sample in the medication behavior recognition training sample set can be used as the expected output to train the initial artificial intelligence model and obtain the medication behavior recognition model.

[0117] In a specific implementation of the embodiment of the present application, the medication behavior recognition result can also be sent to another terminal device that is communicatively connected to the terminal device to monitor the user's medication behavior. For example, if the executor of the present application method is a smart watch and the user is an elderly person, the elderly person's smart watch can be communicatively connected to the smart phones of their children; at the medication time, the smart watch can send the medication behavior recognition result to the children's smart phones, and the children can view the follow-up behavior recognition result through the application for managing the smart watch installed on the smart phone, so as to better monitor the elderly person's medication behavior and scientifically manage the elderly person's physical health.

[0118] In another specific implementation method of the embodiment of the present application, the medical order information may include a follow-up visit time and follow-up visit information, wherein the follow-up visit information may include information such as review items and review location; in actual application, the follow-up visit reminder time can be determined according to the follow-up visit time, specifically, the follow-up visit reminder time may be earlier than or equal to the follow-up visit time; and a follow-up visit reminder may be made at the follow-up visit time based on the follow-up visit information.

[0119] It is understandable that the method of confirming the follow-up visit time and information, the follow-up visit reminder time, and the method of follow-up visit reminder can all be set with reference to the relevant process of the aforementioned medication reminder, and this application does not make specific limitations on this.

[0120] In addition, in the above implementation, after the follow-up reminder is given according to the follow-up information, the follow-up behavior of the user can be monitored. Specifically, the positioning coordinates collected by the preset positioning module at the follow-up time can be obtained, and the follow-up behavior can be identified according to the positioning coordinates and the follow-up address to obtain the follow-up behavior identification result. Here, the coordinate range interval corresponding to the follow-up address can be determined according to the follow-up address. For example, if the follow-up address is "XX Hospital", the coordinate range interval corresponding to the follow-up address can be found in the preset map. If the positioning coordinates are within the coordinate range interval, such as Figure 6 As shown, it can be determined that the follow-up behavior recognition result is completed; if the positioning coordinates are outside the coordinate range, such as Figure 7 As shown, it can be determined that the follow-up visit behavior recognition result is an incomplete follow-up visit.

[0121] Considering that the sensor used for positioning in the positioning module may have certain errors, when the user arrives at the follow-up address, the collected positioning coordinates may show that the user is at the edge of the coordinate range interval corresponding to the follow-up address or outside the coordinate range interval corresponding to the follow-up address, which may cause a misjudgment of the follow-up behavior recognition result. Therefore, the coordinate range of the follow-up address in the map can be expanded to a certain extent according to actual needs to obtain the coordinate range interval corresponding to the follow-up address in the embodiment of the present application. For example, the coordinate range of the follow-up address in the map can be expanded by 1.25 times to obtain the coordinate range interval corresponding to the follow-up address in the embodiment of the present application.

[0122] In order to better monitor the user's follow-up behavior, the user's follow-up behavior can be sent to another terminal device that is connected to the terminal device. For example, if the execution subject of the present application method is a smart watch and the user is an elderly person, the elderly person's smart watch can be connected to the smart phones of their children; at the follow-up time, the smart watch can send the follow-up behavior recognition result to the children's smart phones, and the children can view the follow-up behavior recognition result through the application for managing the smart watch installed on the smart phone, so as to better monitor the elderly person's follow-up behavior.

[0123] In summary, the embodiment of the present application obtains target audio data; wherein the target audio data is audio data including medical order information; the target audio data is converted into text to obtain text data corresponding to the target audio data; information is extracted from the text data to obtain the medical order information; and medical order reminders are made according to the medical order information. Through the embodiment of the present application, the target audio data including medical order information can be converted into text to obtain text data corresponding to the target audio data, and information can be extracted from the text data to obtain medical order information, thereby eliminating the need for users to manually input medical order information, greatly reducing the difficulty of operation, and helping to improve user experience.

[0124] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0125] Corresponding to the doctor's advice reminder method described in the above embodiment, Figure 8 A structural diagram of an embodiment of a medical advice reminder device provided in an embodiment of the present application is shown.

[0126] In an embodiment of the present application, a medical advice reminder device may include:

[0127] The audio acquisition module 801 is used to acquire target audio data; wherein the target audio data is audio data including medical advice information;

[0128] A text conversion module 802, configured to perform text conversion on the target audio data to obtain text data corresponding to the target audio data;

[0129] An information extraction module 803 is used to extract information from the text data to obtain the medical advice information;

[0130] The doctor's order reminder module 804 is used to make a doctor's order reminder according to the doctor's order information.

[0131] In a specific implementation of the embodiment of the present application, the text conversion module may include:

[0132] An information extraction submodule, used to extract information from the text data using a preset medical advice information extraction model to obtain the medical advice information;

[0133] Furthermore, the information extraction submodule may include:

[0134] A training sample set acquisition unit, used to acquire a preset training sample set for extracting medical order information; wherein the training sample set for extracting medical order information includes a preset number of training samples, each training sample includes a section of sample text data and corresponding sample medical order information;

[0135] The initial model training unit is used to train the initial artificial intelligence model with the sample text data of each training sample in the medical order information extraction training sample set as input and the sample medical order information of each training sample in the medical order information extraction training sample set as expected output to obtain the medical order information extraction model.

[0136] In a specific implementation of the embodiment of the present application, the medical advice information includes medication time and medication information;

[0137] The doctor's advice reminder module may include:

[0138] A medication time determination submodule, used to determine a medication reminder time according to the medication time; wherein the medication reminder time is earlier than or equal to the medication time;

[0139] The medication reminder submodule is used to provide medication reminders according to the medication information at the medication reminder time.

[0140] In a specific implementation of the embodiment of the present application, the medical advice reminder device may further include:

[0141] A target image acquisition module, used to acquire a target image acquired at the medication time;

[0142] The medication behavior recognition module is used to perform medication behavior recognition based on the target image to obtain a medication behavior recognition result.

[0143] In a specific implementation of the embodiment of the present application, the doctor's advice information may include a follow-up visit time and follow-up visit information;

[0144] The doctor's advice reminder module may also include:

[0145] A follow-up reminder determination submodule is used to determine a follow-up reminder time according to the follow-up time; wherein the follow-up reminder time is earlier than or equal to the follow-up time;

[0146] The follow-up reminder submodule is used to make a follow-up reminder at the follow-up reminder time according to the follow-up information.

[0147] In a specific implementation of the embodiment of the present application, the follow-up consultation information may include a follow-up consultation address;

[0148] The medical advice reminder device may also include:

[0149] A positioning coordinate acquisition module, used to acquire the positioning coordinates collected during the follow-up visit;

[0150] The follow-up visit behavior recognition module is used to perform follow-up visit behavior recognition according to the positioning coordinates and the follow-up visit address to obtain a follow-up visit behavior recognition result.

[0151] In a specific implementation of the embodiment of the present application, the follow-up behavior recognition module may include:

[0152] A coordinate range interval determination submodule, used to determine the coordinate range interval corresponding to the follow-up visit address;

[0153] A first follow-up visit behavior recognition result determination submodule is used to determine that the follow-up visit behavior recognition result is that the follow-up visit has been completed if the positioning coordinates are within the coordinate range interval;

[0154] The second follow-up visit behavior recognition result determination submodule is used to determine that the follow-up visit behavior recognition result is an incomplete follow-up visit if the positioning coordinates are outside the coordinate range interval.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0157] Fig. 9 A schematic block diagram of a terminal device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0158] like Fig. 9 As shown, the terminal device 9 of this embodiment includes: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90. When the processor 90 executes the computer program 92, the steps in the above-mentioned various medical advice reminder method embodiments are implemented, for example Figure 1 Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 8 The functions of modules 801 to 804 are shown.

[0159] Exemplarily, the computer program 92 may be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 92 in the terminal device 9.

[0160] The terminal device 9 may be a computing device such as a smart watch, a smart phone, a notebook, a PDA, etc. Those skilled in the art will appreciate that Fig. 9 It is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 9 may also include input and output devices, network access devices, buses, etc.

[0161] The processor 90 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0162] The memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 9. Further, the memory 91 may also include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store the computer program and other programs and data required by the terminal device 9. The memory 91 may also be used to temporarily store data that has been output or is to be output.

[0163] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0164] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0166] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0169] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0170] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for reminding a doctor's order, characterized in that: include: Acquire target audio data; wherein the target audio data is audio data including doctor's advice information; Convert the target audio data into text to obtain text data corresponding to the target audio data; Extracting information from the text data to obtain the medical advice information; A medical advice reminder is provided according to the medical advice information.

2. The method for reminding medical advice according to claim 1, characterized in that: The extracting information from the text data to obtain the medical advice information includes: Using a preset medical advice information extraction model to extract information from the text data to obtain the medical advice information; The training process of the medical advice information extraction model includes: Obtaining a preset training sample set for extracting medical order information; wherein the training sample set for extracting medical order information includes a preset number of training samples, each training sample including a section of sample text data and corresponding sample medical order information; The sample text data of each training sample in the medical order information extraction training sample set is used as input, and the sample medical order information of each training sample in the medical order information extraction training sample set is used as expected output, and the initial artificial intelligence model is trained to obtain the medical order information extraction model.

3. The method for reminding medical advice according to claim 1, characterized in that: The medical advice information includes medication time and medication information; The step of providing a doctor's advice reminder according to the doctor's advice information includes: Determine a medication reminder time according to the medication time; wherein the medication reminder time is earlier than or equal to the medication time; At the medication reminder time, a medication reminder is performed according to the medication information.

4. The method for reminding medical advice according to claim 3, characterized in that: After the medication reminder is given according to the medication information, the method further includes: Acquire a target image acquired at the medication time; Medication behavior recognition is performed according to the target image to obtain a medication behavior recognition result.

5. The method for reminding medical advice according to claim 1, characterized in that: The medical advice information includes the time and information of the follow-up visit; The step of providing a doctor's advice reminder according to the doctor's advice information includes: Determine a follow-up visit reminder time according to the follow-up visit time; wherein the follow-up visit reminder time is earlier than or equal to the follow-up visit time; At the follow-up reminder time, a follow-up reminder is made according to the follow-up information.

6. The method for reminding medical advice according to claim 5, characterized in that: The follow-up consultation information includes the follow-up consultation address; After the follow-up consultation reminder is made according to the follow-up consultation information, the following is also included: Obtaining the positioning coordinates collected at the time of the follow-up visit; The follow-up visit behavior is identified according to the positioning coordinates and the follow-up visit address to obtain a follow-up visit behavior identification result.

7. The method for reminding medical advice according to claim 6, characterized in that: The performing follow-up visit behavior recognition according to the positioning coordinates and the follow-up visit address to obtain the follow-up visit behavior recognition result includes: Determine a coordinate range interval corresponding to the follow-up visit address; If the positioning coordinates are within the coordinate range, then determining that the follow-up visit behavior recognition result is that the follow-up visit has been completed; If the positioning coordinates are outside the coordinate range, the follow-up visit behavior identification result is determined to be an incomplete follow-up visit.

8. A medical advice reminder device, characterized in that: include: An audio acquisition module, used to acquire target audio data; wherein the target audio data is audio data including medical advice information; A text conversion module, used for performing text conversion on the target audio data to obtain text data corresponding to the target audio data; An information extraction module, used to extract information from the text data to obtain the medical advice information; The doctor's order reminder module is used to make a doctor's order reminder based on the doctor's order information.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the medical advice reminder method according to any one of claims 1 to 7 are implemented.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the medical advice reminder method according to any one of claims 1 to 7 are implemented.

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