CN112394924B - Method, device, electronic equipment and medium for generating questioning model - Google Patents
Method, device, electronic equipment and medium for generating questioning model Download PDFInfo
- Publication number
- CN112394924B CN112394924B CN201910760050.2A CN201910760050A CN112394924B CN 112394924 B CN112394924 B CN 112394924B CN 201910760050 A CN201910760050 A CN 201910760050A CN 112394924 B CN112394924 B CN 112394924B
- Authority
- CN
- China
- Prior art keywords
- sample
- information
- patient
- model
- symptom
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 66
- 208000024891 symptom Diseases 0.000 claims abstract description 95
- 238000012549 training Methods 0.000 claims abstract description 32
- 230000004044 response Effects 0.000 claims abstract description 14
- 239000013598 vector Substances 0.000 claims description 53
- 230000006870 function Effects 0.000 claims description 17
- 238000012795 verification Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 9
- 238000003062 neural network model Methods 0.000 claims description 6
- 230000000306 recurrent effect Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 201000010099 disease Diseases 0.000 claims description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 2
- 230000004060 metabolic process Effects 0.000 claims description 2
- 230000000877 morphologic effect Effects 0.000 claims description 2
- 231100000915 pathological change Toxicity 0.000 claims description 2
- 230000036285 pathological change Effects 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 5
- 206010011224 Cough Diseases 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 4
- 230000018109 developmental process Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 208000004998 Abdominal Pain Diseases 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000008961 swelling Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 206010000087 Abdominal pain upper Diseases 0.000 description 1
- 208000002193 Pain Diseases 0.000 description 1
- 206010036790 Productive cough Diseases 0.000 description 1
- 206010037660 Pyrexia Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002192 cholecystectomy Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000002350 laparotomy Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 206010029410 night sweats Diseases 0.000 description 1
- 230000036565 night sweats Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/35—Creation or generation of source code model driven
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The embodiment of the disclosure discloses a method, a device, electronic equipment and a medium for generating a question model. One embodiment of the method comprises the following steps: acquiring a sample set; selecting a sample from the sample set and performing the training steps of: inputting basic information of a sample patient in sample case information of the selected sample into an initial model to obtain symptom information of the sample patient; analyzing the symptom information of the patient of the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value; comparing the loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be a questioning model. The embodiment realizes the generation of the questioning model.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method, an apparatus, an electronic device, and a medium for generating a question model.
Background
Under the impact of the Internet, intelligent office work has become an important means for enterprises to improve the working efficiency and the industrial competitiveness. The intelligent model is a knowledge-based software development model that is integrated with an expert system. The model applies a rule-based system, adopts a generalization and reasoning mechanism, helps software personnel complete development work, and enables maintenance to be performed at the system specification level. In the implementation process, a knowledge base is established, and the model, software engineering knowledge and knowledge in a specific field are respectively stored in the database. The expert system formed by the generation rules based on the software engineering knowledge is combined with other expert systems containing the knowledge rules of the application field to form the development system of the software of the application field.
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 to disclose a method, an apparatus, an electronic device, and a medium for generating a question model.
In a first aspect, some embodiments of the present disclosure provide a method of generating a question model, the method comprising: acquiring a sample set, wherein a sample in the sample set comprises sample case information, and the sample case information comprises basic information of a sample patient and symptom information of the sample patient; selecting samples from the sample set, and performing the following training steps: inputting basic information of a sample patient in sample case information of the selected sample into an initial model to obtain symptom information of the sample patient; analyzing the symptom information of the patient in the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value; comparing the loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be a questioning model.
In some embodiments, the method comprises: and in response to determining that the initial model is not trained, adjusting model parameters of the initial model, and re-selecting samples from the sample set, and continuing to execute the training step by adopting the initial model with the model parameters adjusted.
In some embodiments, the basic information of the sample patient in the sample case information corresponds to the symptom information of the sample patient one by one; and the acquiring a sample set includes: acquiring basic information of a sample patient; and acquiring symptom information of the sample patient according to the basic information of the sample patient.
In some embodiments, the method further comprises: acquiring basic information of a questioning object; inputting the basic information into the questioning model to generate symptom information of the questioning object.
In some embodiments, the method further comprises: transmitting the basic information and symptom information of the questioning object to terminal equipment with a verification function, and acquiring verification information of the terminal equipment, wherein the verification information is used for representing whether the basic information and the symptom information pass verification or not; and transmitting the basic information and the symptom information to a storage device in response to the authentication information being information that passes authentication.
In some embodiments, the method further comprises: performing word segmentation operation on the symptom information to obtain at least one word; for each word in the at least one word, performing word embedding on the word to obtain a word vector; for each word vector in the obtained word vectors, inputting the word vector into a time recurrent neural network model to obtain a first semantic vector; acquiring a medical term semantic vector set corresponding to a preset medical term set; for each first semantic vector of the obtained at least one first semantic vector, determining a cosine distance between the first semantic vector and each medical term semantic vector of the medical term semantic vector set; determining the minimum cosine distance of the determined at least one cosine distance as the target cosine distance of the first semantic vector; generating structured data based on each medical term corresponding to each target cosine distance; and sending the structured data to a storage device.
In a second aspect, some embodiments of the present disclosure provide a method for assisting a physician in asking questions, the method comprising: acquiring basic information of a questioning object; inputting the basic information into a question model trained by the method according to the embodiment of the method for generating the question model, and generating symptom information of the question object. .
In a third aspect, some embodiments of the present disclosure provide an apparatus for generating a questioning model, comprising: an acquisition unit configured to acquire a sample set, wherein a sample in the sample set includes sample case information including basic information of a sample patient and symptom information of the sample patient; a training unit configured to select samples from the sample set, and perform the following training steps: inputting basic information of a sample patient in sample case information of the selected sample into an initial model to obtain symptom selection information of the sample patient; analyzing the symptom selection information of the patient in the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value; comparing the loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be a questioning model.
In a fourth aspect, some embodiments of the present disclosure provide an electronic device comprising: 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 the method as in any of the first aspects.
In a fifth aspect, embodiments of the present 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 of the first aspects.
According to the method, the device, the electronic equipment and the computer readable medium for generating the questioning model, basic information of a sample patient in sample case information of a selected sample is input into an initial model, symptom information of the sample patient is obtained, the symptom information of the sample patient is compared with symptom information of the sample patient in corresponding sample case information, whether the initial model is trained is determined to be completed according to a comparison result, and therefore the questioning model is generated.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is an architecture diagram of an exemplary system in which some embodiments of the present disclosure may be applied;
FIG. 2 is a flow chart of some embodiments of a method for generating a questioning model, according to some embodiments of the disclosure;
FIG. 3 is a schematic illustration of one application scenario of a method for generating a questioning model, according to some embodiments of the disclosure;
FIG. 4 is a flow chart of some embodiments of a method for assisting a physician in asking questions, in accordance with some embodiments of the present disclosure;
FIG. 5 is a flow chart of some embodiments of an apparatus for generating a questioning model, according to some embodiments of the disclosure;
fig. 6 is a schematic structural diagram of an electronic 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 should be understood that the present 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 so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
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 a method for generating a questioning model or an apparatus for generating a questioning model of embodiments of the disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and servers 105, 106. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the servers 105, 106. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may cause the terminal devices 101, 102, 103 to interact with the servers 105, 106 via the network 104 to receive or send messages or the like. Various communication client applications can be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting image recognition, including but not limited to smartphones, tablet computers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
Server 105 may be a server that provides various services, such as a data server that stores training samples. The data server may store a set of samples. The sample set may include patient basic information, condition information, physician's diagnostic conclusion information, and treatment method information.
The server 106 may be a server providing various services, such as a background server providing support for the symptom information application generated on the terminal devices 101, 102, 103. The background server may train the model to be trained using the sample set stored in the data server 105 to obtain a questioning model (e.g., a secondarily trained model). The background server can also input information submitted by the terminal device into the questioning model to generate symptom information of the patient, and feed the symptom information (such as a second recognition result) back to the terminal device.
It should be noted that the method for generating a question model and the method for assisting a doctor in asking questions of the embodiments of the present disclosure are generally performed by the server 106.
The servers 105 and 106 may be hardware or software. When the servers 105 and 106 are hardware, the servers may be realized as a distributed server cluster composed of a plurality of servers, or may be realized as a single server. When the server is software, it may be implemented as a plurality of software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention 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 a method for generating a question model is shown, according to some embodiments of the present disclosure. The method for generating the questioning model comprises the following steps:
in step 201, a sample set is acquired.
In some embodiments, the execution body of the method for generating the questioning model (e.g., server 106 shown in FIG. 1) may obtain the sample set in a variety of ways. For example, the executing entity may obtain an existing sample set stored therein from a database server (e.g., database server 105 shown in fig. 1) through a wired connection or a wireless connection. As another example, a user may collect samples through a terminal (e.g., terminals 101, 102, 103 shown in fig. 1). In this way, the executing body may receive samples collected by the terminal and store the samples locally, thereby generating a sample set.
Here, the sample set may include at least one sample. Wherein the sample may include sample case information including basic information of a sample patient and symptom information of a sample doctor. The symptom information of the patient of the sample here may be symptom information characterizing the sample patient in the case information. Here, the basic information of the sample patient may include sex, age information, preliminary symptom information of the patient, and the like. For example, "men, 40 years old, stomachache". Symptom information refers to subjective abnormal sensations or some objective pathological changes in a patient caused by a series of abnormal changes in function, metabolism and morphological structure in the body during the course of a disease. Such as coughing, night sweats, fever in the afternoon, and the like. Here, the symptom information of the patient of the sample is presented in the form of a choice question. It will be appreciated that the basic information of the sample patient and the symptom information of the sample patient may be manually set in advance, or may be obtained by the execution subject or other device executing a certain setting program. The execution subject may also be the rest of the device or system, as desired.
Step 202, selecting a sample from a sample set.
In some embodiments, the executing subject may pick samples from the set of samples obtained in step 201, and perform the training steps of steps 203 through 207. The selection manner and the selection number of the samples are not limited in this disclosure. For example, at least one sample may be selected randomly, or a sample from which the sample case information is better in integrity (i.e., the patient will have all the examinations done).
And 203, inputting basic information of the sample patient in the sample case information of the selected sample into an initial model to obtain symptom information of the sample patient.
In some embodiments, the executing subject may input the basic information of the sample patient in the sample case information of the sample selected in step 202 into the initial model. The symptom information of the patient in the sample can be obtained by detecting and analyzing the basic information area of the patient in the sample case information. The symptom information of the sample patient may be symptom information characterizing the sample patient in the sample case information.
In some embodiments, the initial model may be an existing variety of neural network models created based on machine learning techniques. The neural network model may have various neural network structures (e.g., deep convolutional neural network VGGNet, deep residual network ResNet, etc.) that exist. The storage location of the initial model is likewise not limited in some embodiments of the present disclosure.
As an example, when basic information of a sample patient in sample case information of a selected sample is input into an initial model, the initial model automatically generates symptoms and attributes of symptoms related to the basic information based on the basic information. For example, the symptom may be "abdominal pain". The nature of the symptoms may be the specific location of the pain. For example, "upper right".
And 204, analyzing the symptom information of the patient in the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value.
In some embodiments, the execution subject may find out the basic information of the sample patient in the sample case information and the symptom information of the corresponding sample patient in the sample. The execution subject analyzes the symptom information of the patient of the sample acquired from step 203 and the symptom information of the found sample patient to determine a loss value. For example, the symptom information of the patient in the sample and the symptom information of the patient in the corresponding sample may be input as parameters to a specified loss function (loss function), and the loss value between the two may be calculated. Here, the symptom information may be "upper right abdominal pain". Here, we can acquire case information from a hospital, and the case required to be acquired cannot be a difficult case or a specific case, and all symptoms are contained in the case as much as possible.
In some embodiments, the loss function is typically used to measure the degree of inconsistency between a predicted value of the model (e.g., symptom information of a sample patient) and a true value (e.g., symptom information of a sample patient). It is a non-negative real-valued function. In general, the smaller the loss function, the better the robustness of the model. The loss function can be set according to actual requirements.
Step 205, comparing the loss value with a target value.
In some embodiments, the executing body compares the loss value obtained from step 204 with a target value. The target value may generally be used to represent an ideal case of a degree of inconsistency between the predicted value (i.e., the symptom information of the patient in the sample) and the true value (i.e., the symptom information of the patient in the sample). That is, when the loss value reaches the target value, the predicted value may be considered to be close to or approximate to the true value. The target value may be set according to actual requirements. It should be noted that, if a plurality of (at least two) samples are selected in step 202, the execution body may compare the loss value of each sample with the target value. So that it can be determined whether the loss value of each sample reaches the target value.
Step 206, determining whether the initial model is trained according to the comparison result.
In some embodiments, based on the comparison in step 205, the executing subject may determine whether the initial model is trained. As an example, if multiple samples are selected in step 202, the executing body may determine that the initial model training is complete in the event that the loss value for each sample reaches the target value. For another example, the executing entity may count the proportion of samples for which the loss value reaches the target value to the selected samples. And when the ratio reaches a predetermined sample ratio (e.g., 95%), it can be determined that the initial model training is complete.
In some embodiments, if the executing subject determines that the initial model has been trained, then execution may continue with step 207. If the execution subject determines that the initial model is not trained, the relevant parameters in the initial model may be adjusted. For example, using back propagation techniques to modify the weights in the various convolutional layers in the initial model. And may return to step 202 to re-select samples from the sample set. So that the training steps described above can be continued.
It should be noted that the selection manner is not limited in the disclosure. For example, in the case where there are a large number of samples in the sample set, the execution subject may select samples from which not have been selected.
In response to determining that the initial model training is complete, the initial model is determined to be a questioning model, step 207.
In some embodiments, if the executing subject determines that the initial model training is complete, the initial model (i.e., the initial model that is trained) may be used as the questioning model.
Alternatively, the executing body may store the generated question model locally, or may send it to the terminal or the database server.
In some optional implementations of some embodiments, the basic information of the sample patient in the sample case information corresponds to symptom information of the sample patient one-to-one; and the acquiring a sample set includes: acquiring basic information of a sample patient; and acquiring symptom information of the sample patient according to the basic information of the sample patient.
In some alternative implementations of some embodiments, in some embodiments, the method further comprises: transmitting the basic information and symptom information of the questioning object to terminal equipment with a verification function, and acquiring verification information of the terminal equipment, wherein the verification information is used for representing whether the basic information and the symptom information pass verification or not; and transmitting the basic information and the symptom information to a storage device in response to the authentication information being information that passes authentication. . As an example, the basic information may be "male, 35 years old, cough". The symptom information may be "throat redness and swelling". Then the storage device is up to the age of "men, 35 years old, coughing, throat redness and swelling".
In some embodiments, the method further comprises: performing word segmentation operation on the symptom information to obtain at least one word; for each word in the at least one word, performing word embedding on the word to obtain a word vector; for each word vector in the obtained word vectors, inputting the word vector into a time recurrent neural network model to obtain a first semantic vector; acquiring a medical term semantic vector set corresponding to a preset medical term set; for each first semantic vector of the obtained at least one first semantic vector, determining a cosine distance between the first semantic vector and each medical term semantic vector of the medical term semantic vector set; determining the minimum cosine distance of the determined at least one cosine distance as the target cosine distance of the first semantic vector; generating structured data based on each medical term corresponding to each target cosine distance; and sending the structured data to a storage device. As an example, the word segmentation operation is to segment a piece of text into individual words. Word embedding (Word Embedding) or distributed vector (Distributional Vectors) generally refers to techniques for converting words in natural language into vectors or matrices that can be understood by a computer. A time recurrent neural network (aka. Recurrent neural network, RNN) is a class of neural networks that is adept at processing sequence data, whose elements are connected to form a directed loop. The first semantic vector is a vector obtained by a word vector through a time recurrent neural network model. The cosine distance can also be called cosine similarity, which is to measure the difference between two individuals by using the cosine value of the included angle of two vectors in the vector space, calculate the included angle of the two vectors by cosine theorem, and determine whether the directions of the two vectors are consistent. If the two vectors are oriented in the same direction, i.e., the closer the angle is to zero, the closer the two vectors are. Therefore, in this embodiment, each medical term corresponding to each term in the target text can be determined by calculating the cosine distance between the first semantic vector and the medical term semantic vector. Medical terms refer to a common standard language between doctors during a medical procedure. Such as a laparotomy, a cholecystectomy. The method for acquiring the semantic vector of the medical term of each medical term can be that a medical professional is asked to sort out various medical standardized terms manually in advance or by means of an artificial intelligence technology, the medical standardized terms are firstly input into a database, and then a search index is established to generate a medical term set. And carrying out distributed vector on the medical terms to obtain medical term semantic vectors corresponding to the medical terms. The medical term set stores medical terms and medical term semantic vectors corresponding to the medical terms. Structured data is typically data that a computer can retrieve. There are many ways to send the structured data described above to a storage device. For example, the transmission is performed by a wireless or wired connection server. For another example, storage and transmission may be aided by a storable tool such as a hard disk or a U disk. The storage device may be a large data platform that we have deployed themselves.
In some alternative implementations of some embodiments, the method further comprises: acquiring basic information of a questioning object; inputting the basic information into the questioning model to generate symptom information of the questioning object. As an example, the input information may be "male, 20 years old, cough", and the generated symptom information may be whether or not there is "expectoration" in the form of a choice question.
With further reference to fig. 3, fig. 3 is a schematic illustration of one application scenario of a method for generating a challenge model in accordance with some embodiments of the present disclosure. In the application scenario of fig. 3, a model training class application may be installed on the terminal 31 used by the user. After the user opens the application and uploads the sample set or the storage path of the sample set, the server 32 that provides background support for the application may run a method for generating a challenge model, comprising:
First, a sample set may be acquired. Wherein the samples in the sample set may include sample case information 321, the sample case information 321 including basic information 322 of the sample patient and symptom information 323 of the sample patient. Thereafter, samples may be selected from the sample set, and the following training steps are performed: inputting basic information 322 of a sample patient of the selected sample into the initial model 320 to obtain symptom information 323' of the patient of the sample; analyzing the symptom information 323' of the patient of the sample with the symptom information 323 of the patient of the corresponding sample to determine a loss value 324; comparing the loss value 324 with a target value; determining whether the training of the initial model 320 is completed according to the comparison result; in response to determining that the initial model 320 training is complete, the initial model 320 is taken as the questioning model 320'.
At this time, the server 32 may also transmit prompt information indicating that model training is completed to the terminal 31. The prompt may be voice and/or text information. Thus, the user can acquire the questioning model at the preset storage position.
In the method for generating a question model in this embodiment, by acquiring a sample set, a sample may be selected therefrom for training of the initial model. Wherein the samples in the sample set may include sample case information including basic information of the sample patient and symptom information of the sample patient. Thus, the basic information of the selected sample patient is input into the initial model, and the symptom information of the sample patient can be obtained. Thereafter, a loss value may be determined based on the symptom information of the sample patient. The loss value may then be compared to a target value. Finally, it may be determined whether the training of the initial model is completed based on the comparison result. If it is determined that the training of the initial model is completed, the trained initial model may be used as a questioning model. Thus, a model can be obtained that can be used to assist a physician in asking questions. And is helpful to enrich the generation mode of the model.
With continued reference to fig. 4, a flow 400 of some embodiments of a method for assisting a physician in asking questions is shown, in accordance with some embodiments of the present disclosure. The method for assisting a physician in asking questions may comprise the steps of:
Step 401, obtaining basic information of a questioning object.
In some embodiments, the subject of execution of the method for assisting a physician in asking questions (e.g., server 106 shown in FIG. 1) may obtain a sample set in a variety of ways. For example, the executing entity may obtain an existing sample set stored therein from a database server (e.g., database server 105 shown in fig. 1) through a wired connection or a wireless connection. As another example, a user may collect samples through a terminal (e.g., terminals 101, 102, 103 shown in fig. 1).
In some embodiments, the questioning object may be any user, such as a user using a terminal, or other user present within the information collection range, etc. The basic information may be sex, age information and patient preliminary symptom information and/or patient preliminary symptom information, etc. Symptom information is typically a condition, duration, trend, etc.
Step 402, inputting the basic information into a question model to generate question information of a question object.
In some embodiments, the execution subject may input the basic information acquired in step 401 into a question model, thereby generating question information of a question object. The question information may include whether it is uncomfortable, specific question information in case of discomfort, etc.
In some embodiments, the questioning model may be generated using the method described above in connection with the embodiment of FIG. 2. The specific generation process may be referred to in the description of the embodiment of fig. 2, and will not be described herein.
It should be noted that the method for assisting a doctor in asking questions according to the embodiments of the present disclosure may be used to test the question model generated in the above embodiments. And then the questioning model can be continuously optimized according to the test result. The method may be a practical application method of the question model generated in each of the above embodiments. The questioning model generated by the embodiments is adopted to conduct questioning, which is beneficial to improving accuracy of questioning. For example, the number of the found questioning objects is large, the found basic information is accurate, and the like.
With continued reference to FIG. 5, as an implementation of the method illustrated in the above figures, the present disclosure provides one embodiment of an apparatus for generating a challenge model. The embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device can be applied to various electronic devices.
As shown in fig. 5, the embodiment discloses an apparatus 500 for generating a question model may include: an obtaining unit 501 configured to obtain a sample set, wherein a sample in the sample set includes sample case information including basic information of a sample patient and symptom information of the sample patient; a training unit 502 configured to select samples from the sample set, and perform the following training steps: inputting basic information of a sample patient in sample case information of the selected sample into an initial model to obtain symptom selection information of the sample patient; analyzing the symptom selection information of the patient in the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value; comparing the loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be a questioning model.
Optionally, the apparatus 500 may further include: an adjustment unit 503 is configured to, in response to determining that the initial model is not trained, adjust relevant parameters in the initial model, and re-select samples from the sample set, continue performing the training step.
It will be appreciated that the elements described in the apparatus 500 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the apparatus 500 and the units contained therein, and are not described in detail herein.
Referring now to fig. 6, a schematic diagram of an electronic device (e.g., server in fig. 1) 600 suitable for use in implementing some embodiments of the present disclosure is shown. The server illustrated in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure in any way.
As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may 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 means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 6 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some 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 shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 609, or from storage device 608, or from ROM 602. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.
It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 some embodiments of 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 some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a sample set, wherein a sample in the sample set comprises sample case information, and the sample case information comprises basic information of a sample patient and symptom information of the sample patient; selecting samples from the sample set, and performing the following training steps: inputting basic information of a sample patient in sample case information of the selected sample into an initial model to obtain symptom information of the sample patient; analyzing the symptom information of the patient in the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value; comparing the loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be a questioning model.
Further, the one or more programs, when executed by the electronic device, may further cause the electronic device to: acquiring case information of a questioning object; the case information is input into the question model, and symptom information of the question object is generated. Wherein the questioning model may be generated using the method for generating a questioning model as described in the above embodiments.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in 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" programming 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The embodiment of the disclosure discloses a method for generating a questioning model, which realizes the generation of the questioning model for assisting a doctor in questioning.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application referred to in the present application is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept described above.
Claims (4)
1. A method for assisting a physician in asking questions, comprising:
Basic information of a questioning object is obtained, and the basic information of a sample patient comprises sex and age information of the patient and preliminary symptom information of the patient;
inputting the basic information into a question model, generating symptom information of the question object, wherein the generated symptom information appears in the form of a selection question, and the symptom information refers to subjective abnormal feeling or some objective pathological changes of a patient caused by a series of abnormal changes of functions, metabolism and morphological structures in a body in a disease process;
transmitting the basic information and symptom information of the questioning object to terminal equipment with a verification function, and acquiring verification information of the terminal equipment, wherein the verification information is used for representing whether the basic information and the symptom information pass verification or not;
transmitting the basic information and symptom information to a storage device in response to the verification information being verified information;
Performing word segmentation operation on the symptom information to obtain at least one word;
For each word in the at least one word, carrying out word embedding on the word to obtain a word vector;
inputting each word vector in the obtained word vectors into a time recurrent neural network model to obtain a first semantic vector;
Acquiring a medical term semantic vector set corresponding to a preset medical term set;
For each first semantic vector of the obtained at least one first semantic vector, determining a cosine distance of the first semantic vector from each medical term semantic vector of the set of medical term semantic vectors; determining a minimum cosine distance of the determined at least one cosine distance as a target cosine distance of the first semantic vector;
Generating structured data based on each medical term corresponding to each target cosine distance;
Transmitting the structured data to a storage device;
the method comprises the following steps of:
Acquiring a sample set, wherein a sample in the sample set comprises sample case information, and the sample case information comprises basic information of a sample patient and symptom information of the sample patient;
Selecting a sample from the sample set and performing the training steps of: inputting basic information of a sample patient in sample case information of the selected sample into an initial model to obtain symptom information of the sample patient; analyzing the symptom information of the patient of the sample and the symptom information of the sample patient in the corresponding sample case information to determine a loss value; comparing the loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, determining the initial model as a questioning model;
and in response to determining that the initial model is not trained, adjusting model parameters of the initial model, and re-selecting samples from the sample set, and continuing to execute the training step by adopting the model parameters as the initial model after adjustment.
2. The method of claim 1, wherein the basic information of the sample patient and the symptom information of the sample patient in the sample case information are in one-to-one correspondence; and
The acquiring a sample set includes:
Acquiring basic information of a sample patient;
and acquiring symptom information of the sample patient according to the basic information of the sample patient.
3. An electronic device, comprising:
One or more processors;
a storage device having one or more programs stored thereon,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-2.
4. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910760050.2A CN112394924B (en) | 2019-08-16 | 2019-08-16 | Method, device, electronic equipment and medium for generating questioning model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910760050.2A CN112394924B (en) | 2019-08-16 | 2019-08-16 | Method, device, electronic equipment and medium for generating questioning model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112394924A CN112394924A (en) | 2021-02-23 |
| CN112394924B true CN112394924B (en) | 2024-06-07 |
Family
ID=74602895
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910760050.2A Active CN112394924B (en) | 2019-08-16 | 2019-08-16 | Method, device, electronic equipment and medium for generating questioning model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112394924B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313174A (en) * | 2021-06-01 | 2021-08-27 | 北京大数医达科技有限公司 | Information display method and terminal equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897568A (en) * | 2017-02-28 | 2017-06-27 | 北京大数医达科技有限公司 | The treating method and apparatus of case history structuring |
| CN107247868A (en) * | 2017-05-18 | 2017-10-13 | 深思考人工智能机器人科技（北京）有限公司 | A kind of artificial intelligence aids in interrogation system |
| CN108764280A (en) * | 2018-04-17 | 2018-11-06 | 中国科学院计算技术研究所 | A kind of medical data processing method and system based on symptom vector |
| CN109299239A (en) * | 2018-09-29 | 2019-02-01 | 福建弘扬软件股份有限公司 | ES-based electronic medical record retrieval method |
| CN109492128A (en) * | 2018-10-30 | 2019-03-19 | 北京字节跳动网络技术有限公司 | Method and apparatus for generating model |
| CN109741804A (en) * | 2019-01-16 | 2019-05-10 | 四川大学华西医院 | A kind of information extracting method, device, electronic equipment and storage medium |
| CN109785928A (en) * | 2018-12-25 | 2019-05-21 | 平安科技（深圳）有限公司 | Diagnosis and treatment proposal recommending method, device and storage medium |
-
2019
- 2019-08-16 CN CN201910760050.2A patent/CN112394924B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897568A (en) * | 2017-02-28 | 2017-06-27 | 北京大数医达科技有限公司 | The treating method and apparatus of case history structuring |
| CN107247868A (en) * | 2017-05-18 | 2017-10-13 | 深思考人工智能机器人科技（北京）有限公司 | A kind of artificial intelligence aids in interrogation system |
| CN108764280A (en) * | 2018-04-17 | 2018-11-06 | 中国科学院计算技术研究所 | A kind of medical data processing method and system based on symptom vector |
| CN109299239A (en) * | 2018-09-29 | 2019-02-01 | 福建弘扬软件股份有限公司 | ES-based electronic medical record retrieval method |
| CN109492128A (en) * | 2018-10-30 | 2019-03-19 | 北京字节跳动网络技术有限公司 | Method and apparatus for generating model |
| CN109785928A (en) * | 2018-12-25 | 2019-05-21 | 平安科技（深圳）有限公司 | Diagnosis and treatment proposal recommending method, device and storage medium |
| CN109741804A (en) * | 2019-01-16 | 2019-05-10 | 四川大学华西医院 | A kind of information extracting method, device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| A Machine Learning-Based Method for Autism Diagnosis Assistance in Children;Sushama Rani Dutta等;《2017 International Conference on Information Technology (ICIT)》;20180802;第36-41页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112394924A (en) | 2021-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107729929B (en) | Method and device for acquiring information | |
| JP2020522817A (en) | Semantic analysis method, device, and storage medium | |
| CN108197652B (en) | Method and apparatus for generating information | |
| WO2021179630A1 (en) | Complications risk prediction system, method, apparatus, and device, and medium | |
| US20190370387A1 (en) | Automatic Processing of Ambiguously Labeled Data | |
| CN109243600B (en) | Method and apparatus for outputting information | |
| CN112397195B (en) | Method, apparatus, electronic device and medium for generating physical examination model | |
| WO2021180244A1 (en) | Disease risk prediction system, method and apparatus, device and medium | |
| CN113223735B (en) | Diagnosis method, device, equipment and storage medium based on dialogue characterization | |
| US20190051405A1 (en) | Data generation apparatus, data generation method and storage medium | |
| US20200111576A1 (en) | Producing a multidimensional space data structure to perform survival analysis | |
| CN115830001B (en) | Intestinal tract image processing method and device, storage medium and electronic equipment | |
| CN112309565A (en) | Method, apparatus, electronic device, and medium for matching drug information and disorder information | |
| WO2023110477A1 (en) | A computer implemented method and a system | |
| CN113066531B (en) | Risk prediction method, risk prediction device, computer equipment and storage medium | |
| CN112394924B (en) | Method, device, electronic equipment and medium for generating questioning model | |
| US20200253548A1 (en) | Classifying a disease or disability of a subject | |
| CN112397194B (en) | Method, device and electronic equipment for generating patient disease attribution interpretation model | |
| CN112086155A (en) | Diagnosis and treatment information structured collection method based on voice input | |
| WO2023084254A1 (en) | Diagnosic method and system | |
| US20210183515A1 (en) | Methods and systems for confirming an advisory interaction with an artificial intelligence platform | |
| CN113360612A (en) | AI diagnosis method, device, storage medium and equipment based on inquiry request | |
| CN115803821A (en) | Intelligent triage method and device, storage medium and electronic equipment | |
| CN112397163B (en) | Method, apparatus, electronic device and medium for generating case input model | |
| CN112397196A (en) | Method and device for generating image inspection recommendation model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |