CN113724898B - Intelligent inquiry method, device, equipment and storage medium - Google Patents
Intelligent inquiry method, device, equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of AI and discloses an intelligent inquiry method, which comprises the following steps: acquiring face information and sound information of a user; determining user information to be diagnosed based on the face information, and acquiring historical diagnosis information of a user according to the user information; generating prompt information according to the historical diagnosis information, wherein the prompt information is used for indicating a user to input physical symptoms; and receiving physical symptom information input by a user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining physical diagnosis results and medical treatment guiding information of the user, and displaying the physical diagnosis results and the medical treatment guiding information. The medical treatment method and the medical treatment device can comprehensively diagnose the physical condition of the user and give out reasonable medical treatment guiding information while reducing the medical treatment process of the user.
Description
Technical Field
The application relates to the technical field of AI (advanced technology attachment), in particular to an intelligent inquiry method, device, equipment and storage medium.
Background
With the development and application of artificial intelligence technology, great convenience is brought to the life of people. However, there are also major drawbacks in disease diagnosis. The disease diagnosis process is influenced by various factors, such as the current physical state of a user, the history case information and the like, and the data collected by the existing machine during automatic disease diagnosis are single, so that judgment can be made only for special physical symptoms, the physical health condition of the user can not be comprehensively estimated, and reasonable medical guide is provided.
Disclosure of Invention
The application provides an intelligent inquiry method, an intelligent inquiry device, intelligent inquiry equipment and an intelligent inquiry storage medium, wherein the physical symptom information and the sound information of a user are analyzed through an AI disease diagnosis model to obtain the physical diagnosis result and the medical treatment guiding information of the user, and the comprehensive diagnosis can be carried out on the physical health of the user and the reasonable medical treatment guiding information can be given while the medical treatment process of the user is reduced.
In a first aspect, the present application provides an intelligent inquiry method, the method comprising:
acquiring face information and sound information of a user;
Determining the identity information of the user based on the face information, and acquiring the historical diagnosis information of the user according to the identity information;
Generating prompt information according to the historical diagnosis information, wherein the prompt information is used for indicating a user to input physical symptoms;
And receiving physical symptom information input by the user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying a physical diagnosis result and medical treatment guiding information of the user.
In a second aspect, the present application further provides an intelligent inquiry apparatus, including:
the first acquisition module is used for acquiring face information and sound information of a user;
the second acquisition module is used for determining the identity information of the user based on the face information and acquiring the historical diagnosis information of the user according to the identity information;
the generation module is used for generating prompt information according to the historical diagnosis information, and the prompt information is used for indicating a user to input physical symptoms;
the obtaining module is used for receiving the physical symptom information input by the user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying the physical diagnosis result and the medical treatment guiding information of the user.
In a third aspect, the present application also provides an intelligent inquiry apparatus, including:
A memory and a processor;
the memory is used for storing a computer program;
The processor is configured to execute the computer program and implement the steps of the intelligent inquiry method as described in the first aspect above when the computer program is executed.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement the steps of the intelligent inquiry method as described in the first aspect above.
The application discloses an intelligent inquiry method, a device, equipment and a storage medium, which are characterized in that firstly, face information and sound information of a user are obtained, identity information of the user is determined based on the face information, and historical diagnosis information of the user is obtained according to the identity information of the user; generating prompt information for indicating the user to input physical symptoms according to the historical diagnosis information; and finally, receiving physical symptom information input by a user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining physical diagnosis results and medical treatment guiding information of the user, and displaying the physical diagnosis results and medical treatment guiding information. The medical treatment method and the medical treatment device can comprehensively diagnose the physical condition of the user and give out reasonable medical treatment guiding information while reducing the medical treatment process of the user.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an application scenario schematic diagram of an intelligent inquiry method provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart of an intelligent inquiry method according to an embodiment of the present application;
FIG. 3 is a flowchart of a specific implementation of S203 in FIG. 2;
FIG. 4 is a flowchart of a specific implementation of S204 in FIG. 2;
fig. 5 is a schematic structural diagram of an intelligent inquiry apparatus according to an embodiment of the present application;
fig. 6 is a schematic block diagram of an intelligent inquiry apparatus according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The embodiment of the application provides an intelligent inquiry method, device, equipment and storage medium. The intelligent inquiry method provided by the embodiment of the application comprises the steps of firstly, determining the identity information of a user based on face information and sound information of the user, and acquiring historical diagnosis information of the user according to the identity information of the user; generating prompt information for indicating the user to input physical symptoms according to the historical diagnosis information; and finally, receiving physical symptom information input by a user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining physical diagnosis results and medical treatment guiding information of the user, and displaying the physical diagnosis results and medical treatment guiding information. The medical treatment method and the medical treatment device can comprehensively diagnose the physical condition of the user and give out reasonable medical treatment guiding information while reducing the medical treatment process of the user.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic application scenario diagram of an intelligent inquiry method according to an embodiment of the present application. In this embodiment, the intelligent inquiry method is applied to the robot 102. Specifically, assuming that the user 101 wants to know the physical health condition of the user and hopes to obtain effective medical instruction information through the robot 102, the robot 102 needs to obtain face information and sound information of the user 101 first, then judges identity information of the user 101 according to the obtained face information, further obtains historical diagnosis information of the user 101 according to the identity information, further generates prompt information for indicating that the user inputs physical symptoms according to the obtained historical diagnosis information, and then analyzes the physical symptom information and the obtained sound information input by the user according to a preset AI disease diagnosis model to obtain and display the physical diagnosis result and medical instruction information of the user 101.
The robot 102 is an intelligent machine capable of autonomous working, has basic characteristics of sensing, decision making, executing and the like, can assist or even replace human beings to complete complex and heavy work, mainly performs various tasks through programmable actions, and has a programming capability. In the present embodiment, the robot 102 structurally includes a head and a stand, and is provided with an image pickup device for acquiring face information of a user and a voice acquisition device for acquiring sound information at its head (both of the image pickup device and the voice acquisition device are not shown in the drawings); a processor with programming capability is arranged in the robot 102, and is used for determining the identity information of the user based on the face information and acquiring the historical diagnosis information of the user according to the identity information. It should be noted that the present application is not limited to the structure of the robot 102, and this embodiment is merely an exemplary representation.
In addition, the present embodiment also does not specifically limit the image pickup apparatus and the voice acquisition apparatus. Optionally, the historical diagnosis information of the user may be stored in the robot 102 in advance in association with the identity information of the user, specifically, a storage space may be provided in the robot 102, the historical diagnosis information of the user may also be stored in the cloud in advance in association with the identity information of the user, and the robot 102 may obtain the historical diagnosis information of the user from the cloud.
In addition, the robot 102 is further provided with a display screen 1021, for example, a display screen 1021 is provided on the head of the robot 102 for displaying prompt information generated according to historical diagnostic information; corresponding to the display screen 1021, an input window 1022 is further provided for inputting physical symptoms by the user, wherein the relative position of the display screen 1021 and the input window 1022 is not limited herein. Optionally, the input window 1022 may also set a drop down menu or selection item for the user to select physical symptom information.
In particular, the operation of the robot 102 may be described below with respect to various embodiments of the intelligent interrogation method.
Referring to fig. 2, fig. 2 is a schematic flowchart of an intelligent inquiry method according to an embodiment of the present application. The intelligent inquiry method can be executed by intelligent inquiry equipment, the intelligent inquiry equipment can be a server or a terminal, and the server can be a single server or a server cluster. The terminal may be a handheld terminal, a notebook computer, a wearable device, a robot, or the like.
As shown in fig. 2, fig. 2 is a flowchart illustrating an implementation of the intelligent inquiry method according to an embodiment of the present application.
The method specifically comprises the following steps: step S201 to step S204. The details are as follows:
S201, face information and sound information of a user are acquired.
In an embodiment of the present application, the face information of the user includes a face image of the user acquired by the image capturing apparatus, and the sound information includes an audio signal emitted by the user within a preset time period acquired by the sound acquiring apparatus. Specifically, the image pickup apparatus and the sound collection apparatus are both disposed on the intelligent inquiry apparatus, and the embodiment of the present application does not limit the image pickup apparatus and the sound collection apparatus at all, and may be any existing image pickup apparatus and sound collection apparatus.
S202, determining the identity information of the user based on the face information, and acquiring the historical diagnosis information of the user according to the identity information.
Optionally, the face information is identified according to a preset face recognition model, a target object matched with the face information is obtained, and the identity information of the target object is obtained, namely the identity information of the user. The preset face recognition model is not specifically limited in this embodiment, and may be an existing face recognition model such as a deep learning network model and a neural network model.
In addition, in the embodiment of the application, the facial information can be analyzed by adopting a geometric feature comparison algorithm or a template comparison algorithm to determine the identity information of the user.
The identity information of the user comprises information which can uniquely identify the identity of the user, such as a name, a telephone number, an identity card number or a driving license number.
Illustratively, the obtaining the historical diagnosis information of the user according to the identification information includes: and determining the historical diagnosis information stored in association with the identity information by taking the identity information as an association field, wherein the historical diagnosis information stored in association with the identity information is the historical diagnosis information of the user.
It should be noted that, in the embodiment of the present application, the identification information and the history diagnosis information of the user may be stored in the intelligent inquiry device in an associated manner, or may be stored in the cloud end in an associated manner.
And S203, generating prompt information according to the historical diagnosis information, wherein the prompt information is used for indicating a user to input physical symptoms.
Wherein, the historical diagnostic information may include: medical records, diagnosis result information, diagnosis reference data and user portrait information of a user in a specific time period. Specifically, the user portrait information is user feature information such as age, sex, height, body shape, occupation, etc., which is discriminated from information registered when the user has previously had a doctor, and in this embodiment, the user portrait information is stored in a preset database.
Illustratively, as shown in FIG. 3, FIG. 3 is a flow chart of a specific implementation of S203 in FIG. 2. As can be seen from fig. 3, in the present embodiment, S203 includes S2031 to S2033. The details are as follows:
s2031, preprocessing the historical diagnosis information to obtain characteristic variables.
In particular, the characteristic variable is a variable constituted by a characteristic associated with the user physical state data. For example, the user physical state data is blood glucose data higher than a normal index value, the characteristic associated with the blood glucose data higher than the normal index value is diabetes, and for example, the user physical state data is weight higher than the normal index value, the characteristic associated with the weight higher than the normal index value is obesity; illustratively, in this embodiment, the preprocessing the historical diagnostic information to obtain the feature variable includes: extracting target diagnosis information with occurrence frequency larger than a preset frequency from the historical diagnosis information, and acquiring user physical state data associated with the target diagnosis information; features associated with the acquired user physical state data are determined, constituting the feature variables.
S2032, calculating the characteristic variable by using a preset judgment rule to obtain a calculation result.
The calculating the characteristic variable by using a preset judging rule to obtain a calculation result comprises the following steps: judging the characteristic variables by utilizing a pre-trained decision tree model to obtain the probability of each preset disease associated with the physical state data of the user; wherein the probability of each preset disease associated with the user physical state data is the calculated result.
Specifically, the pre-trained decision tree model comprises a strategy layer, a decision layer and a result, wherein the strategy layer, the decision layer and the result form a tree diagram for carrying out sequence decision on the characteristic variable, and the maximum expectation of the characteristic variable is used as a decision criterion to obtain the probability of each preset disease associated with the user physical state data.
And S2033, obtaining prompt information according to the calculation result, wherein the prompt information is used for indicating a user to input physical symptoms.
Specifically, according to the probability of each preset disease associated with the physical state data of the user, a disease category corresponding to the maximum probability value is obtained, after the basic category corresponding to the maximum probability value is determined, the physical symptoms of the user associated with the disease category are determined, and the prompt information is generated. For example, according to the probability magnitude of each preset disease associated with the user physical state data, determining that the disease type corresponding to the maximum probability value is a stomach disease, determining the user physical symptoms associated with the stomach disease includes: meal volume, meal frequency, food category, physical symptoms caused by the corresponding food categories, etc.
S204, receiving the physical symptom information input by the user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying the physical diagnosis result and the medical treatment guiding information of the user.
The AI traditional Chinese medicine diagnosis model comprises a data processing network layer, a neural network layer, a training network layer and a detection network layer.
Illustratively, as shown in FIG. 4, FIG. 4 is a flow chart of a specific implementation of S204 in FIG. 2. As can be seen from fig. 4, in the present embodiment, S204 includes S2041 to S2044, which are described in detail below:
S2041, inputting the physical symptom information and the sound information into the AI traditional Chinese medicine diagnosis model, performing data expansion processing on the physical symptom information through the data processing network layer, and randomly dividing the expanded data into a training sample set and a test sample set.
Specifically, the data processing network layer expands the physical symptom information to obtain physical symptom information similar to the physical symptom information; specifically, the physical symptom information includes at least one of tongue information (such as tongue coating, thickness, color, etc.), blood pressure data, body temperature data, daily eating habit data, heart rate data, body weight value, etc. For example, if the physical symptom information includes a weight value of 50KG, the data network layer obtains a plurality of weight values floating up and down around the weight value, and obtains a plurality of weight values after expansion.
And S2042, building the AI traditional Chinese medicine diagnosis model based on the training sample set and the neural network layer, and training the AI traditional Chinese medicine diagnosis model based on the training sample set and the sound information through the training network layer.
In this implementation, the voice information of the user is added into the training sample, which is favorable for the AI traditional Chinese medicine diagnosis model to diagnose the basic related to the voice according to the voice information of the user and can improve the accuracy of diagnosis.
In particular, diseases that may be caused by changes in sound include laryngeal tumors, laryngitis, pulmonary diseases, vocal cord diseases, etc., which are often difficult to accurately diagnose by common physical symptom data.
Illustratively, training, by the training network layer, the AI TCM diagnostic model based on the training sample set and the sound information includes: training a main network of the AI traditional Chinese medicine diagnosis model based on the training sample set, obtaining a first prediction result of a first classification output function (also called an expected failure function), and determining whether the first prediction result of the first classification output function is the same as a preset disease; if the first predicted result is the same as the preset disease, retraining the main network of the AI traditional Chinese medicine diagnosis model based on the sound data, and monitoring a second predicted result of a second classification output function (also called an expected full function) until the second predicted result is the same as the preset disease.
The main network of the AI traditional Chinese medicine diagnosis model is a bayesian network, and can simulate the relations among hundreds of diseases, risk factors and symptoms. Furthermore, two classification output functions, a first classification output function and a second classification output function, are connected to the bayesian network. Wherein the first classification output function is a diagnosis of the inverse facts, called the expected failure function, and the second classification output function is a diagnosis of the facts, called the expected sufficiency function. The bayesian network represents the prediction result among the disease, symptom and risk factors as binary nodes, which are either on (representing true) or off (representing false), and outputs the prediction result through the first classification output function and the second classification output function respectively.
Specifically, the first classification output function may be expressed as:
The second classification output function may be expressed as:
wherein, in the first classification function and the second classification function, E dis (D, epsilon): a first prediction result representing a first class output function, E suff (D, epsilon): and representing a second prediction result of the second classification output function, wherein epsilon is a fact evidence, S+ is a evidence-corroborated fact state, S '+ is an evidence-corroborated fact state, D represents a predicted disease, F represents a preset disease, and S' is a negative fact symptom evidence state.
S2043, verifying the trained AI traditional Chinese medicine diagnosis model based on the test sample set and the sound information through the detection network layer, and obtaining the trained AI traditional Chinese medicine diagnosis model after verification is passed.
Specifically, the test sample set and the sound information are input into an AI traditional Chinese medicine diagnosis model after training, the test sample set and the sound information are analyzed in the AI traditional Chinese medicine diagnosis model after training, if the probability values of the first prediction result output by the first classification function and the second prediction result output by the second classification function are both larger than the preset probability value, the verification is determined to be passed, otherwise, the verification is not passed. It will be appreciated that when the verification fails, training of the AI TCM diagnostic model based on the training sample set and the sound information needs to be continued until the verification passes.
S2044, analyzing the physical symptom information and the sound information by using the trained AI traditional Chinese medicine diagnosis model, obtaining and displaying the physical diagnosis result and the medical treatment guiding information of the user.
Specifically, the trained AI traditional Chinese medicine diagnosis model is utilized to analyze the physical symptom information and the sound information to obtain the probability value of the preset disease obtained by the user, and the physical diagnosis result and the medical treatment guiding information of the user are generated according to the probability value of the preset disease obtained by the user.
Optionally, a AIDE menu key is set on the intelligent inquiry equipment corresponding to each disease, and the prompt information is information required by disease diagnosis corresponding to the AIDE key. Specifically, the AIDE menu key is arranged on a display of the intelligent inquiry equipment.
Optionally, the prompt information may be sent to a voice broadcasting end, so that the voice broadcasting end sends out a voice prompt according to the prompt information.
The characteristic variable is calculated by using a preset judging rule to obtain a calculation result; after obtaining the prompt information according to the calculation result, the method may further include:
Acquiring information required by disease diagnosis input by a user based on AIDE menus; determining service prompt information according to the information required by disease diagnosis and a pre-established disease diagnosis pre-judging model; and sending the service prompt information to a user portrait information display end for display so as to prompt the user to enter a corresponding service queue.
That is, after the user selects and inputs the information required for disease diagnosis according to the IVR menu, the robot determines the disease type of the diagnosis or the type of the diagnosis channel required for the user according to the information required for disease diagnosis input by the user and the pre-established disease diagnosis pre-determined model (for example, the pre-established disease diagnosis pre-determined model is a probability information matrix model), obtains service prompt information, and displays the service prompt information on the user portrait information display end, so that after the customer service and the user see the displayed service prompt information, the disease diagnosis progress of the user can be known in time.
As can be seen from the above analysis, in the intelligent inquiry method provided in this embodiment, face information and sound information of a user are obtained, identity information of the user is determined based on the face information, and history diagnosis information of the user is obtained according to the identity information of the user; generating prompt information for indicating the user to input physical symptoms according to the historical diagnosis information; and finally, receiving physical symptom information input by a user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining physical diagnosis results and medical treatment guiding information of the user, and displaying the physical diagnosis results and medical treatment guiding information. The medical treatment method and the medical treatment device can comprehensively diagnose the physical condition of the user and give out reasonable medical treatment guiding information while reducing the medical treatment process of the user.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an intelligent inquiry apparatus according to an embodiment of the present application. The intelligent inquiry apparatus 500 is used to perform the steps of the intelligent inquiry method shown in the embodiment of fig. 2. The intelligent inquiry apparatus 500 may be a single server or a cluster of servers, or the intelligent inquiry apparatus 500 may be a terminal, which may be a handheld terminal, a notebook computer, a wearable device, a robot, or the like.
As shown in fig. 5, the intelligent inquiry apparatus 500 includes:
A first obtaining module 501, configured to obtain face information and sound information of a user;
A second obtaining module 502, configured to determine identity information of the user based on the face information, and obtain historical diagnostic information of the user according to the identity information;
A generating module 503, configured to generate prompt information according to the historical diagnostic information, where the prompt information is used to instruct a user to input physical symptoms;
The obtaining module 504 is configured to receive physical symptom information input by the user according to the prompt information, input the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtain a physical diagnosis result and medical care guiding information of the user, and display the physical diagnosis result and medical care guiding information.
In an embodiment, the second obtaining module 502 is specifically configured to:
and determining historical diagnosis information stored in association with the identity information by taking the identity information as an association field.
In an embodiment, the generating module 503 includes:
The first obtaining unit is used for preprocessing the historical diagnosis information to obtain characteristic variables;
The second obtaining unit is used for calculating the characteristic variable by utilizing a preset judging rule to obtain a calculation result;
and the third obtaining unit is used for obtaining prompt information according to the calculation result, wherein the prompt information is used for indicating the user to input physical symptoms.
In an embodiment, the first obtaining unit includes:
The acquisition subunit is used for extracting target diagnosis information with the occurrence frequency larger than a preset frequency from the historical diagnosis information and acquiring user physical state data associated with the target diagnosis information;
And a composing subunit for determining a feature associated with the acquired user physical state data, and composing the feature variable.
In an embodiment, the second obtaining unit is specifically configured to:
Judging the characteristic variables by utilizing a pre-trained decision tree model to obtain the probability of each preset disease associated with the physical state data of the user; wherein the probability of each preset disease associated with the user physical state data is the calculated result.
In an embodiment, the preset AI disease diagnosis model is an AI traditional Chinese medicine diagnosis model, and the AI traditional Chinese medicine diagnosis model includes a data processing network layer, a neural network layer, a training network layer and a detection network layer;
The obtaining module 504 includes:
the processing unit is used for inputting the physical symptom information and the sound information into the AI traditional Chinese medicine diagnosis model, respectively carrying out data expansion processing on the physical symptom information and the sound information through the data processing network layer, and randomly dividing the expanded data into a training sample set and a test sample set;
the training unit is used for building the AI traditional Chinese medicine diagnosis model based on the training sample set and the neural network layer, and training the AI traditional Chinese medicine diagnosis model based on the training sample set through the training network layer;
The verification unit is used for verifying the trained AI traditional Chinese medicine diagnosis model based on a test sample set through the detection network layer, and obtaining the trained AI traditional Chinese medicine diagnosis model after the verification is passed;
And a fourth obtaining unit, configured to analyze the physical symptom information and the sound information by using the trained AI traditional Chinese medicine diagnostic model, obtain a physical diagnostic result and medical treatment guiding information of the user, and display the physical diagnostic result and the medical treatment guiding information.
In an embodiment, the training unit comprises:
The acquisition subunit is used for training the main network of the AI traditional Chinese medicine diagnosis model based on the training sample set, acquiring a first prediction result of the first classification output function and determining whether the first prediction result of the first classification output function is the same as a preset disease;
and the detection subunit is used for retraining the main network of the AI traditional Chinese medicine diagnosis model based on the sound data if the first prediction result is the same as the preset disease, and monitoring a second prediction result of a second classification output function until the second prediction result is the same as the preset disease.
It should be noted that, for convenience and brevity of description, the specific working process of the above-described intelligent inquiry apparatus and each module may refer to the corresponding process in the embodiment of the intelligent inquiry method described in the embodiment of fig. 2, which is not described herein.
The intelligent interrogation method described above may be implemented in the form of a computer program which may be run on an apparatus as shown in figure 5.
Referring to fig. 6, fig. 6 is a schematic block diagram of an intelligent inquiry apparatus according to an embodiment of the present application. The intelligent inquiry apparatus 600 includes a processor, a memory, and a network interface connected through a system bus, wherein the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any of a number of intelligent interrogation methods.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a number of intelligent interrogation methods.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the terminal to which the present inventive arrangements are applied, and that a particular intelligent interrogation apparatus 600 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be appreciated that the Processor may be a central processing unit (Central Processing Unit, CPU), it may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in one embodiment the processor is configured to run a computer program stored in the memory to implement the steps of:
acquiring face information and sound information of a user;
Determining the identity information of the user based on the face information, and acquiring the historical diagnosis information of the user according to the identity information;
Generating prompt information according to the historical diagnosis information, wherein the prompt information is used for indicating a user to input physical symptoms;
And receiving physical symptom information input by the user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying a physical diagnosis result and medical treatment guiding information of the user.
In an embodiment, the obtaining the historical diagnosis information of the user according to the identification information includes:
and determining historical diagnosis information stored in association with the identity information by taking the identity information as an association field.
In an embodiment, the generating the prompt information according to the historical diagnostic information includes:
preprocessing the historical diagnosis information to obtain a characteristic variable;
calculating the characteristic variable by using a preset judgment rule to obtain a calculation result;
And obtaining prompt information according to the calculation result, wherein the prompt information is used for indicating a user to input physical symptoms.
In an embodiment, the feature variable is a variable formed by a feature associated with the physical state data of the user, and the preprocessing the historical diagnostic information to obtain the feature variable includes:
Extracting target diagnosis information with the occurrence frequency larger than a preset frequency from the historical diagnosis information, and acquiring user physical state data associated with the target diagnosis information;
features associated with the acquired user physical state data are determined, constituting the feature variables.
In an embodiment, the calculating the feature variable by using a preset judgment rule to obtain a calculation result includes:
Judging the characteristic variables by utilizing a pre-trained decision tree model to obtain the probability of each preset disease associated with the physical state data of the user; wherein the probability of each preset disease associated with the user physical state data is the calculated result.
In an embodiment, the preset AI disease diagnosis model is an AI traditional Chinese medicine diagnosis model, and the AI traditional Chinese medicine diagnosis model includes a data processing network layer, a neural network layer, a training network layer and a detection network layer;
Inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying the physical diagnosis result and the medical instruction information of the user, wherein the method comprises the following steps:
Inputting the physical symptom information and the sound information into the AI traditional Chinese medicine diagnosis model, respectively carrying out data expansion processing on the physical symptom information and the sound information through the data processing network layer, and randomly dividing the expanded data into a training sample set and a test sample set;
Building the AI traditional Chinese medicine diagnosis model based on the training sample set and the neural network layer, and training the AI traditional Chinese medicine diagnosis model based on the training sample set through the training network layer;
Verifying the trained AI traditional Chinese medicine diagnosis model based on a test sample set through the detection network layer, and obtaining the trained AI traditional Chinese medicine diagnosis model after verification is passed;
and analyzing the physical symptom information and the sound information by using the trained AI traditional Chinese medicine diagnosis model to obtain and display the physical diagnosis result and the medical treatment guiding information of the user.
In an embodiment, the building the AI traditional Chinese medicine diagnosis model based on the training sample set and the neural network layer, and training the AI traditional Chinese medicine diagnosis model based on the training sample set through the training network layer, includes:
Training a main network of the AI traditional Chinese medicine diagnosis model based on the training sample set, obtaining a first prediction result of a first classification output function, and determining whether the first prediction result of the first classification output function is the same as a preset disease;
if the first prediction result is the same as the preset disease, retraining the main network of the AI traditional Chinese medicine diagnosis model based on the sound data, and monitoring a second prediction result of a second classification output function until the second prediction result is the same as the preset disease.
In an embodiment of the present application, a computer readable storage medium is further provided, where the computer readable storage medium stores a computer program, where the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the intelligent inquiry method provided in the embodiment of fig. 1 of the present application.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like, which are provided on the computer device.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (7)
1. An intelligent inquiry method, characterized in that the method comprises:
acquiring face information and sound information of a user;
Determining the identity information of the user based on the face information, and acquiring the historical diagnosis information of the user according to the identity information;
extracting target diagnosis information with occurrence frequency larger than a preset frequency from the historical diagnosis information, and acquiring user physical state data associated with the target diagnosis information;
determining features associated with the acquired user physical state data, and forming feature variables, wherein the feature variables are variables formed by the features associated with the user physical state data;
judging the characteristic variables by utilizing a pre-trained decision tree model to obtain the probability of each preset disease associated with the user physical state data, wherein the probability of each preset disease associated with the user physical state data is a calculation result;
generating prompt information according to the calculation result, wherein the prompt information is used for indicating a user to input physical symptoms;
And receiving physical symptom information input by the user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying a physical diagnosis result and medical treatment guiding information of the user.
2. The intelligent inquiry method according to claim 1, wherein the acquiring the historical diagnosis information of the user according to the identification information includes:
and determining historical diagnosis information stored in association with the identity information by taking the identity information as an association field.
3. The intelligent inquiry method according to claim 1, wherein the preset AI disease diagnosis model is an AI traditional Chinese medicine diagnosis model, and the AI traditional Chinese medicine diagnosis model comprises a data processing network layer, a neural network layer, a training network layer and a detection network layer;
Inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying the physical diagnosis result and the medical instruction information of the user, wherein the method comprises the following steps:
Inputting the physical symptom information and the sound information into the AI traditional Chinese medicine diagnosis model, respectively carrying out data expansion processing on the physical symptom information and the sound information through the data processing network layer, and randomly dividing the expanded data into a training sample set and a test sample set;
Building the AI traditional Chinese medicine diagnosis model based on the training sample set and the neural network layer, and training the AI traditional Chinese medicine diagnosis model based on the training sample set through the training network layer;
Verifying the trained AI traditional Chinese medicine diagnosis model based on a test sample set through the detection network layer, and obtaining the trained AI traditional Chinese medicine diagnosis model after verification is passed;
and analyzing the physical symptom information and the sound information by using the trained AI traditional Chinese medicine diagnosis model to obtain and display the physical diagnosis result and the medical treatment guiding information of the user.
4. The intelligent inquiry method according to claim 3, wherein the building the AI TCM diagnostic model based on the training sample set and the neural network layer and training the AI TCM diagnostic model based on the training sample set through the training network layer includes:
Training a main network of the AI traditional Chinese medicine diagnosis model based on the training sample set, obtaining a first prediction result of a first classification output function, and determining whether the first prediction result of the first classification output function is the same as a preset disease;
if the first prediction result is the same as the preset disease, retraining the main network of the AI traditional Chinese medicine diagnosis model based on the sound data, and monitoring a second prediction result of a second classification output function until the second prediction result is the same as the preset disease.
5. An intelligent inquiry apparatus, comprising:
the first acquisition module is used for acquiring face information and sound information of a user;
the second acquisition module is used for determining the identity information of the user based on the face information and acquiring the historical diagnosis information of the user according to the identity information;
The generation module is used for extracting target diagnosis information with occurrence frequency larger than preset frequency from the historical diagnosis information, acquiring user physical state data related to the target diagnosis information, determining characteristics related to the acquired user physical state data, and forming characteristic variables, wherein the characteristic variables are variables formed by the characteristics related to the user physical state data, judging the characteristic variables by utilizing a decision tree model which is trained in advance to obtain the probability of each preset disease related to the user physical state data, wherein the probability of each preset disease related to the user physical state data is a calculation result, and generating prompt information according to the calculation result, wherein the prompt information is used for indicating a user to input physical symptoms;
the obtaining module is used for receiving the physical symptom information input by the user according to the prompt information, inputting the physical symptom information and the sound information into a preset AI disease diagnosis model for analysis, obtaining and displaying the physical diagnosis result and the medical treatment guiding information of the user.
6. An intelligent inquiry apparatus, comprising:
A memory and a processor;
the memory is used for storing a computer program;
The processor for executing the computer program and for implementing the steps of the intelligent inquiry method according to any one of claims 1 to 4 when the computer program is executed.
7. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to implement the steps of the intelligent inquiry method according to any one of claims 1 to 4.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013155002A1 (en) * | 2012-04-09 | 2013-10-17 | Richard Franz | Wireless telemedicine system |
| CN107451407A (en) * | 2017-07-31 | 2017-12-08 | 广东工业大学 | A kind of traditional Chinese medical science intelligent diagnosing method, system and traditional medical system |
| CN111681779A (en) * | 2020-04-22 | 2020-09-18 | 北京捷通华声科技股份有限公司 | Medical diagnosis system |
| CN112967822A (en) * | 2021-04-19 | 2021-06-15 | 广东兴睿科技有限公司 | Remote medical inquiry method, system and readable storage medium |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013155002A1 (en) * | 2012-04-09 | 2013-10-17 | Richard Franz | Wireless telemedicine system |
| CN107451407A (en) * | 2017-07-31 | 2017-12-08 | 广东工业大学 | A kind of traditional Chinese medical science intelligent diagnosing method, system and traditional medical system |
| CN111681779A (en) * | 2020-04-22 | 2020-09-18 | 北京捷通华声科技股份有限公司 | Medical diagnosis system |
| CN112967822A (en) * | 2021-04-19 | 2021-06-15 | 广东兴睿科技有限公司 | Remote medical inquiry method, system and readable storage medium |
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