CN113313174A - Information display method and terminal equipment - Google Patents
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Abstract
The embodiment of the disclosure discloses an information display method and terminal equipment. One embodiment of the method comprises: acquiring a target document and a target data pair set input by a user to acquire the target document and the target data pair set; detecting whether an operation authorization signal is received from a target terminal device; in response to the detected operation authorization signal, acquiring a problem word set, wherein the problem words in the problem word set are words representing disease symptoms; inputting the problem word set into a predetermined prediction model to obtain a target graph; and pushing the target graph to a target terminal device with a display function, and controlling the target terminal device to display the target graph. The embodiment generates a target map including disease symptoms from the acquired question word set using a predetermined prediction model.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to an information display method and terminal equipment.
Background
With the development of artificial intelligence technology, methods such as classification clustering, identification analysis and prediction have become basic technologies for artificial intelligence application. Meanwhile, smart medical treatment has received increasing attention as a main component of artificial intelligence application.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an information display method and a terminal device to solve one or more of the technical problems mentioned in the above background section.
In a first aspect, some embodiments of the present disclosure provide an information display method, including: acquiring a target document and a target data pair set input by a user to acquire the target document and the target data pair set; detecting whether an operation authorization signal is received from a target terminal device; in response to detecting the operation authorization signal, acquiring a problem word set, wherein the problem words are words representing disease symptoms; inputting the problem word set into a predetermined prediction model to obtain a target graph; and pushing the target graph to a target terminal device with a display function, and controlling the target terminal device to display the target graph.
In some embodiments, the predetermined predictive model includes a first number of pre-trained neural networks, wherein the pre-trained neural networks generate output features using the following equation:
wherein W is a weight parameter of the pre-trained neural network, A is an input of the pre-trained neural network, A is a relationship matrix in the problem map,
i is an identity matrix of the same size as a,
degree matrix of A, D node matrix of the problem map, and sigma nonlinear excitationThe function of the activity is a function of the activity,
l represents the number of layers of the pre-trained neural network in the pre-determined prediction model, H is an output feature, H(l+1)And representing the output characteristics of the neural network pre-trained by the l < th > layer.
In a second aspect, some embodiments of the present disclosure provide an information display apparatus, the apparatus including: the detection unit is configured to detect whether an operation authorization signal is received from the target terminal device, wherein the operation authorization signal is a signal generated by a user executing a target operation on the target control; the receiving unit is configured to respond to the detection of the operation authorization signal and obtain a problem word set, wherein the problem words are words representing disease symptoms; a generating unit configured to input the problem word set into a predetermined prediction model to obtain a target graph; a control unit configured to push the target map to a target terminal device having a display function, and control the target terminal device to display the target map.
In a third aspect, some embodiments of the present disclosure provide a terminal device, including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as in any one of the first aspects.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements a method as in any one of the first aspect.
The above embodiments of the present disclosure have the following beneficial effects: by the information display method of some embodiments of the present disclosure, a target map including disease symptoms can be generated from the acquired question word set using a predetermined prediction model.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is an architectural diagram of an exemplary system in which some embodiments of the present disclosure may be applied;
FIG. 2 is a flow diagram of some embodiments of an information display method according to the present disclosure;
FIG. 3 is an exemplary authorization prompt box;
FIG. 4 is a flow diagram for one embodiment of training steps for training a predictive model according to the present disclosure;
FIG. 5 is a flow diagram of some embodiments of an information display device according to the present disclosure;
fig. 6 is a schematic block diagram of a terminal device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the information display method of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as an information processing application, an information generation application, a data analysis application, and the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various terminal devices having a display screen, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the above-listed terminal apparatuses. Which may be implemented as multiple software or software modules (e.g., to provide input of a set of terms, etc.), or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as a server that stores a set of question words input by the terminal apparatuses 101, 102, 103, and the like. The server may process the received set of problem terms and feed back the processing results (e.g., target graph) to the terminal device.
It should be noted that the information display method provided by the embodiment of the present disclosure may be executed by the server 105 or by the terminal device.
It should be noted that the server 105 may also directly store the question word set locally, and the server 105 may directly extract the local question word set and obtain the target information set after processing, in which case, the exemplary system architecture 100 may not include the terminal devices 101, 102, and 103 and the network 104.
It should be noted that the terminal apparatuses 101, 102, and 103 may also have an information display application installed therein, and in this case, the processing method may also be executed by the terminal apparatuses 101, 102, and 103. At this point, the exemplary system architecture 100 may also not include the server 105 and the network 104.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (for example, for providing an information display service), or may be implemented as a single software or software module. And is not particularly limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of some embodiments of an information display method according to the present disclosure is shown. The information display method comprises the following steps:
In some embodiments, an executing body of the information generating method (e.g., a server shown in fig. 1) may detect whether an operation authorization signal is received from the target terminal device. The operation authorization signal is a signal generated by a user executing a target operation on the target control. The target terminal device may be a terminal device logged with an account corresponding to the user. The terminal equipment can be a mobile phone or a computer. The target control may be contained in an authorization prompt box. And the target control is displayed in the authorization prompt box, and the authorization prompt box is displayed on the target terminal equipment.
In response to detecting the operation authorization signal, a question word set is obtained, step 202.
In some embodiments, an execution subject of the information display method (e.g., a server shown in fig. 1) may acquire the problem word set in response to receiving the operation authorization signal. Wherein, the problem words in the problem word set are words representing disease symptoms. Specifically, the question word may be "expectoration", the question word may also be "sore throat", and the question word may also be "chest distress". The set of problem words may be "expectoration, chest distress, lasting for one week". The set of problem words includes a first number of problem words. Specifically, the set of question words may be user-entered.
The operation authorization signal may be a signal generated by the user corresponding to the question word performing a target operation on the target control. The target control may be contained in an authorization prompt box. The authorization prompt box can be displayed on the target terminal equipment. The target terminal device may be a terminal device logged with an account corresponding to the user. The terminal equipment can be a mobile phone or a computer. The target operation may be a "click operation" or a "slide operation". The target control may be a "confirm button".
As an example, the authorization prompt box described above may be as shown in fig. 3. The authorization prompt box may include: a prompt information display section 301 and a control 302. The prompt information display section 301 may be configured to display prompt information. The above-mentioned prompt information may be "whether or not to allow the acquisition of the question word". The control 302 may be a "confirm button" or a "cancel button".
In some embodiments, the execution subject inputs the set of problem words into a predetermined prediction model to obtain the target graph.
Optionally, the set of problem words is converted into a problem graph. Specifically, the problem map is map structure data. The graph structure data includes nodes and edges. Wherein the nodes correspond to disease symptoms and the edges characterize the relationship between the nodes. Specifically, the vocabulary characterizing the disease symptoms in the problem word set may be determined as nodes in the problem map. The edges in the problem map are empty.
Optionally, the problem map is input into a predetermined prediction model to obtain a target map. The target graph comprises each symptom information corresponding to the question word set. The target map may be used for follow-up physician-assisted interrogation. Optionally, the predetermined predictive model comprises a first number of pre-trained neural networks. The pre-trained neural network generates output features using the following equation:
wherein, W is the weight parameter of the pre-trained neural network, and A is the input of the pre-trained neural network. A is a relation matrix in the problem graph,
i is an identity matrix of the same size as a,
is the degree matrix of a. D is a node matrix in the problem map, sigma is a nonlinear activation function,
l represents the number of layers of the pre-trained neural network in the pre-determined predictive model. H is an output characteristic, H(l+1)And representing the output characteristics of the neural network pre-trained by the l < th > layer.
Specifically, output features of a first number of pre-trained neural networks of the predetermined predictive model are determined as target features. And converting the target characteristics into graph structure data to obtain a target graph.
And step 204, pushing the target graph to the target terminal equipment with the display function, and controlling the target terminal equipment to display the target graph.
In some embodiments, the execution body pushes the target graph to a target terminal device having a display function, and controls the target terminal device to display the target graph. The target terminal device may be a device in communication connection with the execution main body, and may perform display-related operations according to the received target graph. For example, the node set in the target graph output by the execution subject may include [ expectoration "," pharyngalgia "," headache "," aggravation "," 2 days "continuously ]. The target terminal device may display the node emphasis in the target graph to prompt further diagnosis or treatment for the disease symptoms. The target graph with high accuracy can be generated through the predetermined prediction model, so that the accuracy of the display related operation is improved.
One embodiment presented in fig. 2 has the following beneficial effects: acquiring a target document and a target data pair set input by a user to acquire the target document and the target data pair set; detecting whether an operation authorization signal is received from a target terminal device; in response to detecting the operation authorization signal, acquiring a problem word set, wherein the problem words are words representing disease symptoms; inputting the problem word set into a predetermined prediction model to obtain a target graph; and pushing the target graph to a target terminal device with a display function, and controlling the target terminal device to display the target graph. The embodiment generates a target map including disease symptoms from the acquired question word set using a predetermined prediction model.
With continued reference to FIG. 4, a flow 400 of one embodiment of the training steps of the predetermined predictive model according to the present disclosure is shown. The training step may include the steps of:
In some embodiments, the execution subject of the training step may be the same as or different from the execution subject of the information display method (e.g., the terminal device shown in fig. 1). If the two parameters are the same, the executing agent of the training step can store the model structure information of the trained prediction model and the parameter values of the model parameters in the local after the prediction model is obtained through training. If the difference is not the same, the execution main body of the training step can send the model structure information of the trained prediction model and the parameter values of the model parameters to the execution main body of the information display method after the prediction model is obtained through training.
In some embodiments, the agent performing the training step may obtain the sample set locally or remotely from other terminal devices networked with the agent. Wherein the samples in the sample set include a sample input graph and a sample output graph corresponding to the sample input graph. Optionally, a sample input graph set is generated, wherein the sample input graph set is an empty set. A set of historic case documents is obtained, wherein the set of historic case documents includes a second number of historic case documents. Based on the set of historic case documents, an initial graph set is generated. The initial graph set comprises a second number of initial graphs, the initial graphs comprise initial node sets and initial edge sets, the initial nodes are disease symptom features in the history case documents, the initial edges represent relations among different initial nodes, and the weights of the initial edges are accumulated connection times among different initial nodes.
Optionally, for each initial graph in the initial graph set, a graph set corresponding to the initial graph is generated based on the initial graph, so as to obtain a set of graph sets. For each initial graph in the initial set of graphs, a first process graph is generated. Wherein the first process diagram is the same as the initial diagram. And generating an initial corresponding graph set, wherein the initial corresponding graph set is an empty set. The number of generations is determined, where the number of generations may be any positive integer. In response to the number of generation times not being 0, performing the following generating steps:
the first process diagram is determined as the second process diagram. And randomly deleting a third number of nodes in the second process diagram to obtain a third process diagram. The edges in the third process graph are updated. And putting the third process diagram into the initial corresponding diagram set. The value of the number of generations is decremented by 1. And determining the initial corresponding graph set as the graph set corresponding to the initial graph.
Placing each graph in the set of graph sets into a sample input graph set. Specifically, for the following historical medical record document' patients 2020-05 do not intend to find the lump with the size of one jujube at the right neck, and have no pain, ulceration, fever, palpitation, character, appetite change, hoarseness, dysphagia, dyspnea, drinking water choking cough, headless neck pain and thoracic outlet syndrome, the patients 2020-05-25 go to the hospital for a doctor, and the thyroid ultrasonic examination shows that: the bilateral thyroid lobes have multiple solid and mixed echogenic masses, and nodular goiter is suspected. In order to further diagnose, the clinic receives the hospitalization for nodular goiter. Since the spontaneous illness, patients have good mental state, good physical condition, good appetite and food intake, good sleeping condition, no obvious change of weight, normal defecation and normal urination. ", it can be translated into a structured paragraph" [ { "thyroid ultrasound": [ "seen": "echo ball": [ "position": "bilateral thyroid lobe", "nature": "substantivity, mixability" ], "conclusion": "nodular goiter" ] }, "mental": "good", "physical strength": "good", "appetite": "good", "food intake": "good", "body weight": "unchanged", "stool": "normal", "urinate": "Normal" ] ". An initial graph may be generated using the structured paragraph, where the content elements in the structured paragraph may be considered as initial nodes in the initial graph and the initial edges may be null. Determining the initial graph as G, and using the above generation steps, generating a subgraph set { G1, G2, G3} corresponding to G, where nodes in G1 may include [ appetite is good "," stool is normal ], nodes in G2 may include [ nodular goiter "," urine is normal ], and nodes in G3 may include [ thyroid bilateral lobe "," nodular goiter "," appetite is good ].
Optional contents in the above step 401, namely: the technical content of constructing the sample set in a sampling mode serves as an invention point of the embodiment of the disclosure, and the problems that the information in the problem word set is limited to be incomplete, the medical information related to available disease symptoms is less, and the algorithm is deviated by assisting the inquiry by using an artificial intelligence model under the condition that the available historical data is limited are solved. The factors that lead to algorithm bias tend to be as follows: the available structured medical cases are limited, and few sample data can be used for training the prediction model, so that the prediction accuracy of the prediction model is influenced. If the above factors are solved, the effect of improving the level of auxiliary diagnosis can be achieved. To achieve this, the present disclosure introduces a sampling approach to generate the sample set. First, a historical case document set is structured to obtain an initial graph set. Then, by using the generating step, sampling processing is performed on the initial atlas, and an atlas corresponding to the initial atlas is generated, so as to obtain an atlas set. And finally, determining the set of the processed image set as a sample input image set. By the sampling generation method, a set of the image set can be generated according to the initial image set, wherein medical disease and disease information is included respectively, so that the problem of insufficient training sample data is solved, and the training requirement of a high-quality prediction model is met.
In some embodiments, the performing agent of this training step may first determine the model structure of the initial predictive model. Alternatively, the initial predictive model may include a first number of initial neural networks.
The executing agent of this training step may then initialize the model parameters of the initial predictive model. In practice, the model parameters (e.g., weight parameters and bias parameters) of the initial prediction model may be initialized with some different small random numbers. The small random number is used for ensuring that the model does not enter a saturation state due to overlarge weight value, so that training fails, and the difference is used for ensuring that the model can be normally learned.
In some embodiments, the performing subject of the training step may train the input graph of the samples included in the sample set as the input of the initial prediction model, and the output graph of the pre-obtained samples corresponding to the input sample input graph as the expected output of the initial prediction model by using a machine learning method to obtain the prediction model.
Specifically, the sample input graph of the selected sample is input to the initial prediction model to obtain an output graph of the selected sample. The output map of the selected sample is compared with the corresponding sample output map. And determining whether the initial prediction model reaches a preset optimization target according to the comparison result. Specifically, the optimization goal may be less than a predetermined threshold, or the optimization goal may be reaching a predetermined number of iterations. In response to determining that the initial predictive model meets the optimization goal, the initial predictive model is treated as a pre-trained predictive model.
And in response to determining that the initial prediction model is not trained, adjusting relevant parameters in the initial prediction model, reselecting samples from the sample set, and performing the training step again by using the adjusted initial prediction model as the initial prediction model.
One embodiment presented in fig. 4 has the following beneficial effects: the sample input image set is generated by the sampling method, so that the training effect of the prediction model can be improved, the prediction accuracy of the prediction model is improved, the subsequent auxiliary medical diagnosis level is improved, and the misdiagnosis condition is reduced.
With further reference to fig. 5, as an implementation of the above-described method for the above-described figures, the present disclosure provides some embodiments of an information display apparatus, which correspond to those of the method embodiments described above for fig. 2, and which may be applied in particular to various terminal devices.
As shown in fig. 5, an information display apparatus 500 of some embodiments includes: a detection unit 501, a receiving unit 502, a generation unit 503, and a control unit 504. Wherein the detecting unit 501 is configured to detect whether an operation authorization signal is received from the target terminal device. The operation authorization signal is a signal generated by a user executing a target operation on the target control. The receiving unit 502 is configured to obtain a set of problem words in response to detecting the operation authorization signal, wherein the problem words are words representing disease symptoms. A generating unit 503 configured to input the question word set into a predetermined prediction model to obtain a target graph. A control unit 504 configured to push the target map to a target terminal device having a display function, and control the target terminal device to display the target map.
It will be understood that the elements described in the apparatus 500 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 500 and the units included therein, and are not described herein again.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a terminal device of an embodiment of the present disclosure. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM603 are connected to each other via a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: a storage portion 606 including a hard disk and the like; and a communication section 607 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 607 performs communication processing via a network such as the internet. Drivers 608 are also connected to the I/O interface 605 as needed. A removable medium 609 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 608 as necessary, so that a computer program read out therefrom is mounted into the storage section 606 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 607 and/or installed from the removable medium 609. The above-described functions defined in the method of the present disclosure are performed when the computer program is executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (10)
1. An information display method comprising:
detecting whether an operation authorization signal is received from a target terminal device, wherein the operation authorization signal is a signal generated by a user executing a target operation on a target control;
in response to the detection of the operation authorization signal, acquiring a problem word set, wherein the problem words in the problem word set are words representing disease symptoms;
inputting the problem word set into a predetermined prediction model to obtain a target graph;
and pushing the target graph to target terminal equipment with a display function, and controlling the target terminal equipment to display the target graph.
2. The method of claim 1, wherein the target control is displayed in an authorization prompt displayed on the target terminal device.
3. The method of claim 2, wherein the inputting the set of problem words into a predetermined predictive model to obtain a target graph comprises:
converting the question word set into a question graph;
and inputting the question graph into a predetermined prediction model to obtain the target graph, wherein the target graph comprises each symptom information corresponding to the question word set.
4. The method of claim 3, wherein the predetermined predictive model includes a first number of pre-trained neural networks, wherein the pre-trained neural networks generate output features using the equation:
wherein W is a weight parameter of the pre-trained neural network, A is an input of the pre-trained neural network, A is a relationship matrix in the problem map,
i is an identity matrix of the same size as a,
is a degree matrix of A, D is a node matrix in the problem map, sigma is a nonlinear activation function,
l represents the number of layers of the pre-trained neural network in the pre-determined prediction model, H is an output feature, H(l+1)Representing the output characteristics of a layer 1 pre-trained neural network.
5. The method of claim 4, wherein the pre-trained predictive model is obtained by:
determining the structure of an initial prediction model and initializing the parameters of the initial prediction model;
obtaining a sample set, wherein a sample comprises a sample input graph and a sample output graph corresponding to the sample input graph;
selecting samples from the sample set, and performing the following training steps:
inputting a sample input graph of a selected sample into an initial prediction model to obtain an output graph of the selected sample;
comparing the output graph of the selected sample with the corresponding sample output graph;
determining whether the initial prediction model reaches a preset optimization target or not according to the comparison result;
in response to determining that the initial predictive model meets the optimization goal, treating the initial predictive model as the pre-trained predictive model.
6. The method of claim 5, wherein the method further comprises:
and in response to determining that the initial prediction model is not trained, adjusting relevant parameters in the initial prediction model, reselecting samples from the sample set, and performing the training step again by using the adjusted initial prediction model as the initial prediction model.
7. The method of claim 6, wherein before inputting the set of problem words into a predetermined predictive model to obtain a target graph, the method further comprises:
generating the sample input graph set, wherein the sample input graph set is an empty set;
obtaining a set of historic case documents, wherein the set of historic case documents comprises a second number of historic case documents;
generating an initial graph set based on the historical case document set, wherein the initial graph set comprises a second number of initial graphs, the initial graphs comprise an initial node set and an initial edge set, the initial nodes are symptoms of diseases in the historical case documents, the initial edges represent relations between different initial nodes, and the weights of the initial edges are accumulated connection times between different initial nodes;
for each initial graph in the initial graph set, generating a graph set corresponding to the initial graph based on the initial graph to obtain a set of graph sets;
placing each graph in the set of graph sets into the sample input graph set.
8. The method of claim 7, wherein generating the atlas corresponding to the initial graph based on the initial graph comprises:
generating a first process diagram, wherein the first process diagram is the same as the initial diagram;
generating an initial corresponding graph set, wherein the initial corresponding graph set is an empty set;
determining the generation times, wherein the generation times are non-zero positive integers;
the following generation steps are performed:
determining the first process diagram as a second process diagram;
randomly deleting a third number of nodes in the second process diagram to obtain a third process diagram;
updating edges in the third process graph;
placing the third process diagram into the initial corresponding diagram set;
subtracting 1 from the value of the generation times;
and in response to the value of the generation times being 0, determining the initial corresponding graph set as the graph set corresponding to the initial graph.
9. The method of claim 8, wherein generating the atlas corresponding to the initial graph based on the initial graph further comprises:
in response to determining that the value of the number of generations is greater than 0, performing the generating step again.
10. A terminal device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
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