WO2022068160A1 - Artificial intelligence-based critical illness inquiry data identification method and apparatus, device, and medium - Google Patents

Abstract

An artificial intelligence-based critical illness inquiry data identification method and apparatus, a device, and a medium, relating to artificial intelligence. The method comprises: obtaining inquiry session data corresponding to a target user identifier (S202); inputting the inquiry session data into a prediction model, and outputting, by the prediction model, a model identification result corresponding to the inquiry session data, wherein the prediction model is trained according to multi-dimensional feature data and a training tag corresponding to the multi-dimensional feature data, the multi-dimensional feature data is extracted according to historical inquiry data, and the multi-dimensional feature data comprises entity feature data and entity relationship feature data (S204); according to a tag hit by the inquiry session data in an expert knowledge base, determining an expert identification result corresponding to the inquiry session data (S206); and combining the model identification result and the expert identification result to obtain a target identification result of whether the inquiry session data is critical illness inquiry data (S208). By adoption of the method, the identification accuracy of the critical illness inquiry data can be improved. In addition, the present invention also relates to a blockchain technology, and the inquiry session data of users can be stored in a blockchain.

Description

Method, device, equipment and medium for identification of critically ill interrogation data based on artificial intelligence

This application claims the priority of the Chinese patent application with the application number CN202011065413.X and the title of "Artificial Intelligence-based Intensive Interrogation Data Recognition Method and Device" submitted to the Chinese Patent Office on September 30, 2020, the entire contents of which are approved by Reference is incorporated in this application.

technical field

The present application relates to the technical field of artificial intelligence, and in particular, to a method, device, equipment and medium for identifying data of critical care consultation based on artificial intelligence.

Background technique

With the development of Internet technology and medical technology, the application of Internet technology in the medical industry is becoming more and more common. For example, users can self-report symptoms, consult symptoms, learn about drugs, and seek medical guidance through online consultation applications or online consultation websites. Through multiple rounds of inquiries such as the cause and symptoms, the doctor can make a judgment on whether the current consultation data is serious consultation data.

However, with the surge in the number of users who initiate online consultations, in order to reduce the heavy workload of manual identification by doctors, the medical field has begun to adopt the mode of identification by expert systems. The inventor realizes that although the expert system can reduce the workload of relevant personnel, due to the diversity of online user consultation data, the expert system has great challenges in identifying whether the current consultation data is critically ill consultation data.

SUMMARY OF THE INVENTION

An artificial intelligence-based method for identifying data of critical care inquiries, the method comprising:

Obtain the consultation session data corresponding to the target user ID;

Inputting the consultation session data into a prediction model, and outputting a model identification result corresponding to the consultation session data through the prediction model; wherein the prediction model is based on the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data Obtained through training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

Combining the model identification result and the expert identification result, a target identification result of whether the consultation session data is critical consultation data is obtained.

An artificial intelligence-based critical care data identification device, the device comprising:

an acquisition module, used to acquire the consultation session data corresponding to the target user ID;

The first identification module is used to input the data of the consultation session into a prediction model, and output a model identification result corresponding to the data of the consultation session through the prediction model; wherein, the prediction model is based on the multi-dimensional feature data and the The training labels corresponding to the multi-dimensional feature data are obtained by training, the multi-dimensional feature data is extracted and obtained according to the historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

a second identification module, configured to determine an expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

A decision-making module, configured to combine the model identification result and the expert identification result to obtain a target identification result of whether the consultation session data is critical consultation data.

A computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

Obtain the consultation session data corresponding to the target user ID;

Inputting the consultation session data into a prediction model, and outputting a model identification result corresponding to the consultation session data through the prediction model; wherein the prediction model is based on the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data Obtained through training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

Combining the model identification result and the expert identification result, a target identification result of whether the consultation session data is critical consultation data is obtained.

A computer storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Obtain the consultation session data corresponding to the target user ID;

Inputting the consultation session data into a prediction model, and outputting a model identification result corresponding to the consultation session data through the prediction model; wherein the prediction model is based on the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data Obtained through training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

Combining the model identification result and the expert identification result, a target identification result of whether the consultation session data is critical consultation data is obtained.

Description of drawings

1 is an application scenario diagram of an artificial intelligence-based critical care data identification method in one embodiment;

Fig. 2 is a schematic flowchart of an artificial intelligence-based critical care data identification method in one embodiment;

Fig. 3 is a flow diagram of using a predictive model in one embodiment;

Fig. 4 is a flow chart of training a prediction model in one embodiment;

Fig. 5 is a structural block diagram of an artificial intelligence-based critical care data identification device in one embodiment;

6 is a structural block diagram of an artificial intelligence-based critical interrogation data identification device in another embodiment;

Fig. 7 is the internal structure diagram of the computer device in one embodiment;

FIG. 8 is an internal structure diagram of a computer device in another embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

The artificial intelligence-based critical care data identification method provided in this application can be applied to the application environment shown in FIG. 1 . The terminal 102 communicates with the server 104 through the network through the network. Specifically, the terminal 102 can obtain the consultation session data corresponding to the target user identifier, send the consultation session data to the server 104, and the server 104 inputs the consultation session data into the prediction model, and outputs the data corresponding to the consultation session through the prediction model. The model identification results of the 2000; wherein, the prediction model is trained according to the multi-dimensional feature data and the corresponding training labels of the multi-dimensional feature data, the multi-dimensional feature data is extracted according to the historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data; server; 104 then determine the expert identification result corresponding to the consultation session data according to the tag hit in the expert knowledge base by the consultation session data; the server 104 then combines the model identification result and the expert identification result to obtain whether the consultation session data is a critical consultation The target recognition result of the data. The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

In another embodiment, the terminal 102 or the server 104 may also be independently used to execute the artificial intelligence-based method for identifying data of critical care inquiries. This application is not limited here.

In one embodiment, as shown in FIG. 2 , an artificial intelligence-based method for recognizing data for critical care consultation is provided, and the method is applied to a computer device as an example for description, and the computer device may specifically be the terminal in FIG. 1 . or server. The artificial intelligence-based method for identifying data of critically ill inquiries specifically includes the following steps:

Step 202: Obtain the consultation session data corresponding to the target user identifier.

The target user identifier is used to uniquely identify a user. Target user identifiers, such as application account numbers or hospital medical card numbers, etc. A consultation session is a process of consultation interaction between at least two users. The at least two users include a user corresponding to a patient role and a user corresponding to a doctor role. The consultation session data is the data generated during the consultation interaction. The user corresponding to the doctor role can be the doctor himself or an artificial intelligence robot.

Specifically, an online consultation application or an online consultation website may run on the terminal, and the online consultation application or the online consultation website may provide a consultation entrance. The user inputs the consultation session data based on the consultation portal through the terminal to conduct an online consultation.

In one embodiment, the consultation session data may be voice data, text data, or image data, or the like.

In one embodiment, the consultation session data may include consultation session data corresponding to the patient role and consultation session data corresponding to the doctor role. Among them, the consultation session data corresponding to the patient's role, such as user basic information, symptom description information, symptom photos, medical examination reports or past history information, etc. It can be understood that the inquiring user may or may not be the patient himself, such as in the scene of inquiring for children or the elderly. Consultation session data corresponding to the doctor's role, such as disease description information, symptom analysis information, etiology analysis information, or reply information for user inquiry data, etc.

In one embodiment, the consultation session data may be dialogue data of one or more rounds of question and answer in a consultation process. In this way, when the consultation session data provided by the user for the first time can identify whether the consultation session data is critical consultation data, it can be identified as soon as possible for corresponding processing; when the amount of information is insufficient, the user can be guided to provide more information to combine these The information is more accurate in the identification of critical care data.

Among them, the critically ill consultation data is the consultation session data involving acute and critical illnesses. "Emergency and critical illness" is a medical term, which usually means that the patient's disease is an urgent and endangered disease, and medical treatment should be carried out as soon as possible, otherwise it may cause serious harm to the patient's body or cause death. For example, the consultation session data includes the clinical symptoms of acute and critical illness. The clinical symptoms of acute and critical illness such as "fainting", "difficulty breathing", etc.

Step 204, input the data of the consultation session into the prediction model, and output the model identification result corresponding to the data of the consultation session through the prediction model; wherein, the prediction model is obtained by training according to the multi-dimensional feature data and the corresponding training labels of the multi-dimensional feature data, and the multi-dimensional feature data is obtained according to The historical consultation data is extracted, and the multi-dimensional feature data includes entity feature data and entity relationship feature data.

The prediction model is a pre-trained machine learning model used to identify whether the consultation session data is critical consultation data. The machine learning model can be a neural network model, a support vector machine, or a logistic regression model. Neural network models such as Convolutional Neural Networks, Backpropagation Neural Networks, Feedback Neural Networks, Radial Basis Neural Networks, or Self-Organizing Neural Networks.

Entity characteristics are data reflecting the characteristics of the entity itself. For example, the consultation session data "drinking during dinner, abdominal pain is unbearable now" includes two entities, the first entity is "drinking", and the second entity is "abdominal pain". An entity-relationship feature refers to data that reflects the relationship between at least two entities. For example, the entity relationship between "drinking alcohol" and "abdominal pain" in the consultation session data "drinking during dinner, abdominal pain is unbearable" is "causal relationship", that is, the cause of abdominal pain is drinking. Here, on the one hand, it is considered that the entities in the consultation session data are an important basis for identifying critical consultation data, and on the other hand, it is also considered that the relationship between different entities will also affect the identification results, even the same entity in different contexts There will be different semantics, which will also affect the recognition results. For example, in the consultation session data "Auntie is here, the abdominal pain is unbearable", "Auntie" refers to menstruation, not relatives. Here, the computer equipment can integrate various aspects of information when designing the input data of the prediction model, for example, the two feature dimensions of the data, the entity dimension and the entity relationship dimension, can be used as the input data of the prediction model. The prediction model can learn the effective information of these two feature dimensions during training, and improve the model's ability to recognize critical care data.

Specifically, the computer equipment can input the consultation session data into the prediction model, process the consultation session data through a plurality of neurons included in the prediction model, obtain a model identification result corresponding to the consultation session data, and then output the model identification through the prediction model. result. Among them, the neuron is the most basic structure in the neural network. Under normal circumstances, most neurons are in an inhibitory state, but when the neuron receives input information, causing its potential to exceed a threshold, then the neuron will It is activated and is in an "excited" state, and then the output information is propagated to other neurons. A connection line connecting neurons corresponds to a weight (the value of which is called a weight), and usually different connection lines correspond to different weights. The threshold value of each neuron and the weight of the connection relationship between each neuron are determined when the prediction model is trained by the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data. Neurons include input neurons, output neurons and hidden neurons.

In one embodiment, the artificial intelligence-based method for identifying data of critical care consultation further includes a training step of the prediction model, and the training step specifically includes: collecting historical consultation data and training labels corresponding to the historical consultation data; historical consultation data Corresponding training labels are used to indicate whether the historical consultation data is critical consultation data; entity feature data and entity relationship feature data are extracted from the historical consultation data to generate multi-dimensional feature data corresponding to the historical consultation data; The multi-dimensional feature data corresponding to the data and the historical consultation data are jointly input to the prediction model to be trained to obtain the prediction recognition result; the prediction model is trained based on the prediction recognition result of the prediction model and the training label.

In one embodiment, the multidimensional feature data also includes intent feature data. At this time, the training steps of the prediction model specifically include: collecting historical consultation data and training labels corresponding to the historical consultation data; training labels corresponding to the historical consultation data are used to indicate whether the historical consultation data is critical consultation data; Extract entity feature data, entity relationship feature data and intent feature data from the diagnostic data to generate multi-dimensional feature data corresponding to the historical consultation data; input the historical consultation data and the multi-dimensional feature data corresponding to the historical consultation data together to be trained The prediction model is obtained, and the prediction recognition result is obtained; the prediction model is trained based on the prediction recognition result of the prediction model and the training label.

Among them, the intent feature is the data reflecting the expressed intent. The intent feature includes feature data reflecting the doctor's inquiring or answering intent and the user's inquiring or responding intent. For example, the consultation session data "Do you have unbearable abdominal pain right now" expresses the symptom confirmation intention; the consultation session data "Yes, I have unbearable abdominal pain now" expresses the symptom confirmation intention, and so on.

In this way, considering that the intentions of doctors and users to ask and answer can also be used as the basis for identifying critical care data, it is possible to introduce intention features when the input of the prediction model is involved, so that the entity dimension, entity relationship dimension and intention dimension The fusion of these three feature dimensions can enable the prediction model to learn the effective information of these three feature dimensions during training, improve the model's ability to recognize critical care data, and expand the direction of critical care data identification.

For the specific content of the prediction model training step, reference may be made to the specific description in the subsequent embodiments.

In one embodiment, the computer device may convert the consultation session data into a data format that can be processed by the prediction model, and then input the converted data into the prediction model. The data format that the prediction model can handle, such as vector format or matrix format, etc.

Step 206: Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base.

Among them, the knowledge base refers to the set of rules applied in the design of the expert system, including the facts and data related to the rules, and the whole of them constitutes the knowledge base. The knowledge base is related to a specific expert system, and in this application, the expert knowledge base is related to the medical consultation expert system. The rules in the expert knowledge base are obtained by extracting the frequently occurring data in the critical consultation data in the historical consultation process. The facts linked by the rules include whether the consultation session data in the historical consultation process is the critical consultation data. The data contacted by the rule includes labels such as frequently occurring disease symptoms in the consultation session data during the historical consultation process.

Specifically, the computer device may be driven by a rule engine, and based on the rule set of the expert knowledge base, determine the tags hit by the consultation session data in the expert knowledge base. When the hit label is the label corresponding to the critical care consultation data, the expert identification result corresponding to the consultation session data is obtained as the consultation session data is the critical consultation data; the hit label is not the label corresponding to the critical consultation data When , the expert identification result corresponding to the consultation session data is obtained as that the consultation session data is not critical consultation data.

Step 208 , combining the model identification result and the expert identification result, obtain the target identification result of whether the consultation session data is critical consultation data.

Specifically, the model recognition result includes two types of recognition results, that the consultation session data is critical-care consultation data and the consultation session data is not critical-care consultation data. The expert identification result also includes two kinds of identification results, which are that the consultation session data is critically ill consultation data, and that the consultation session data is not critically ill consultation data. If both the model identification result and the expert identification result are both the consultation session data and the critical care consultation data, it can be obtained that the consultation session data is the target identification result of the critical care consultation data. If at least one of the model identification result and the expert identification result is that the consultation session data is not the critically ill consultation data, then the target identification result that the consultation session data is not the critically ill consultation data can be obtained.

In one embodiment, the expert identification result may also include a situation in which it is not identified whether the consultation session data is critical consultation data. At this time, the model recognition result can be used as the target recognition result.

The above artificial intelligence-based critical consultation data identification method, after obtaining the user's consultation session data, on the one hand, the consultation session data is input into the prediction model to obtain the model identification result, and on the other hand, based on the expert knowledge base, the query session data is obtained. Identify the data of the consultation session to obtain the expert identification result, and then combine the model identification result and the expert identification result to obtain the final identification result. Since the prediction model is trained based on multi-dimensional feature data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data, the prediction model can learn information of different dimensions during the training process, and combine the contextual semantic environment to better Understand the logic of language, thereby improving the predictive model's ability to recognize critical care data; in this way, the combination of model prediction and expert system for critical care data identification can make up for the lack of relying only on expert systems and improve critical care data. recognition accuracy.

In one embodiment, after obtaining the target identification result of whether the consultation session data is critical care consultation data, the computer device may perform an operation corresponding to the target identification result according to the target identification result.

In a specific embodiment, the artificial intelligence-based method for identifying critical-care consultation data further includes: when obtaining a target identification result that the consultation session data is critical-care consultation data, identifying the consultation session to which the consultation session data belongs Access to the doctor's terminal; when it is obtained that the target identification result of the consultation session data is not the critically ill consultation data, continue to advance the consultation session to which the consultation session data belongs.

Specifically, FIG. 3 shows a flowchart of using a predictive model in one embodiment. Referring to this figure, after the computer equipment obtains the consultation session data, the consultation session data can be input into the prediction model and the expert system in parallel. On the one hand, the expert system is used to identify whether the consultation session data is critical consultation data, and the expert identification result is obtained. Then, the decision maker combines the model identification results and the expert identification results to obtain the target identification results of whether the consultation session data is critical consultation data. When it is obtained that the consultation session data is the target identification result of the critically ill consultation data, the consultation session to which the consultation session data belongs is connected to the doctor's terminal, and the doctor manually intervenes through the doctor's terminal to review the final identification result of the critically-ill consultation data. , when the identification is correct, it can be further processed in a timely manner, such as giving medical advice. When it is obtained that the consultation session data is not the target recognition result of the critically ill consultation data, the consultation session to which the consultation session data belongs is continued, for example, the artificial intelligence robot continues to interact with the user for consultation.

In this embodiment, when different target recognition results are obtained, the corresponding next step is immediately performed, so that the user who is in urgent need of help can be effectively answered when the consultation session data is critical Session data is not critical consultation data, and online consultations can be continued in an orderly manner.

In one embodiment, the consultation session data is stored on the blockchain. It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned consultation session data, the above-mentioned consultation session data may also be stored in a node of a blockchain.

For the specific content of the training steps of the prediction model involved in the foregoing embodiments, reference may be made to the specific descriptions in the following embodiments.

In one embodiment, the artificial intelligence-based method for identifying data of critical care consultation further includes: collecting historical consultation data and training labels corresponding to the historical consultation data; and the training labels corresponding to the historical consultation data are used to represent the historical consultation data Whether it is critical consultation data; extract entity feature data and entity relationship feature data from historical consultation data to generate multi-dimensional feature data corresponding to historical consultation data; The data is jointly input to the prediction model to be trained, and the prediction recognition result is obtained; the prediction model is trained based on the prediction recognition result of the prediction model and the training label.

In one embodiment, the multi-dimensional feature data further includes intent feature data; the artificial intelligence-based method for identifying critical care data further includes: collecting historical consultation data and training labels corresponding to the historical consultation data; The training label is used to indicate whether the historical consultation data is critical consultation data; entity feature data, entity relationship feature data and intent feature data are extracted from the historical consultation data to generate multi-dimensional feature data corresponding to the historical consultation data; The multi-dimensional feature data corresponding to the consultation data and the historical consultation data are jointly input to the prediction model to be trained to obtain the prediction recognition result; the prediction model is trained based on the prediction recognition result of the prediction model and the training label.

Specifically, the computer device may first collect historical consultation data and training labels corresponding to the historical consultation data. The training label may be the result of manual labeling, indicating whether the historical consultation data is critical consultation data. The computer device can then extract entity feature data and entity relationship feature data from the historical consultation data, and generate multidimensional feature data corresponding to the historical consultation data, the multidimensional feature data includes at least two feature dimensions, and the multidimensional feature data and historical The interview data are collectively used as input data for the predictive model to be trained. The computer equipment can also extract entity feature data, entity relationship feature data and intention feature data from historical consultation data, and generate multidimensional feature data corresponding to the historical consultation data, where the multidimensional feature data includes at least three feature dimensions, and the multidimensional feature data Feature data and historical interview data are used together as input data for the predictive model to be trained. Wherein, generating multi-dimensional feature data based on multiple feature data may be splicing or fusing the multiple feature data.

After that, the computer equipment can obtain the prediction recognition result output by the prediction model to be trained, and then construct a training loss function according to the difference between the prediction recognition result and the training label, and use the back-propagation algorithm to optimize in the direction of minimizing the training loss function. For the parameters of the prediction model, the weights and thresholds of each hidden neuron and input/output neurons can be obtained after training, and then the model parameter file of the trained prediction model can be obtained. The computer device may store the model parameter file in the expert knowledge base.

In this embodiment, when designing the input data of the prediction model, the computer device fuses various information, for example, the two feature dimensions of the data, the entity dimension and the entity relationship dimension, are fused as the input data of the prediction model. This enables the prediction model to learn at least the effective information of these two feature dimensions during training, and improves the model's ability to recognize critical care data.

In addition, it is also considered that the intentions of doctors and users to ask and answer can also be used as the basis for identifying critical care data. Therefore, when designing the input data of the prediction model, intention features can be introduced, so that the entity dimension, entity relationship dimension and intention The fusion of these three feature dimensions can enable the prediction model to learn at least the effective information of these three feature dimensions during training, improve the model's ability to recognize critical care data, and expand the direction of critical care data identification. .

It can be understood that in the actual use process, there are many ways of feature extraction, mainly from the following aspects:

In one embodiment, extracting entity feature data, entity relationship feature data, and intent feature data from historical consultation data, and generating multidimensional feature data corresponding to the historical consultation data, includes: using a rule engine to drive the extraction of historical consultation data corresponding to Named entity recognition of historical consultation data using sequence annotation model, and extraction of entity relationship feature data between named entities based on the identified named entities; semantic model to extract intent feature data in historical consultation data ; According to entity feature data, entity relationship feature data and intention feature data, multi-dimensional feature data corresponding to historical consultation data is generated.

(1) In terms of entity feature data extraction, in a specific embodiment, a rule engine is used to drive the extraction of entity feature data corresponding to historical consultation data, including: performing word segmentation on historical consultation data to obtain word segmentation results; based on expert knowledge base The provided entity label is driven by a rule engine to extract the entity label according to the word segmentation result, and obtain the entity feature data corresponding to the historical consultation data.

Specifically, the computer equipment can perform word segmentation on the historical consultation data to obtain the word segmentation result, and use the rule engine to extract the entity label from the word segmentation result according to the rules in the rule set of the expert knowledge base and the entity label provided from the expert knowledge base, and obtain Entity feature data. For example: entity label: entity feature data → patient: infant, symptom: vomiting, weight loss, etc. Among them, the rules engine is a component embedded in the application that accepts data input, interprets the rules, and makes decisions based on the rules.

In this way, when extracting entity feature data, the effective data in the expert knowledge base is effectively used, the efficiency and effectiveness of feature extraction are improved, and unnecessary training time for long-tail words is avoided.

(2) In the aspect of entity relationship feature data extraction, in a specific embodiment, the computer equipment can group historical consultation data according to consultation sessions, and generate consultations from historical consultation data of one consultation session according to the response sequence. Then, the sequence labeling model is used to identify the named entities of the medical data sequence, and based on the identified named entities, the entity relationship feature data between the named entities is extracted. For example, named entities (drinking, inducement, abdominal pain), extract the entity relationship: the inducement of abdominal pain is drinking. In this way, when performing named entity recognition and entity relationship extraction, the sequence-based processing method can effectively combine context information, and can perform more accurate relationship extraction between entities, which has a wide range of practical significance.

(3) In the aspect of intention feature data extraction, in a specific embodiment, using a semantic model to extract the intention feature data in the historical consultation data, including: based on the intention label provided by the expert knowledge base, using the semantic model to extract the historical query data. The intent label corresponding to the medical consultation data is obtained, and the intent characteristic data corresponding to the historical medical consultation data is obtained.

Specifically, in the stage of training the prediction model, the computer device can also use the semantic model in the third aspect to extract the intent features of the historical consultation data. Among them, the computer equipment can obtain the historical consultation data as samples, manually annotate the intent category labels (intent labels provided by the expert knowledge base), and train the semantic model with supervision. After that, the trained semantic model can be used to extract the historical consultation. Intent characteristics of the data.

In one embodiment, the artificial intelligence-based method for identifying critical illness data further includes: screening out historical critical consultation data from historical critical consultation data; counting the occurrence frequency of entity keywords in the historical critical consultation data; Entity keywords whose frequency is higher than the preset threshold are used as entity labels to join the expert knowledge base; the consultation intention corresponding to the historical critical consultation data is determined, and the intention label is obtained; the intention label is added to the expert knowledge base.

Specifically, the computer device can also establish an expert knowledge base. The computer equipment can first obtain the critically ill patient data identified in the historical consultation data, and obtain the critically ill sample data set; and then count the occurrence frequency of entity keywords such as incentives, objects, and symptoms included in the critically ill sample data set in the critically ill sample data set, Entity keywords whose occurrence frequency is higher than a preset threshold are filtered and used as entity labels to join the expert knowledge base. In addition, the computer equipment can also determine the consultation intention corresponding to the historical critical care consultation data, obtain the intention label, and add the intention label to the expert knowledge base.

In this way, when the multi-dimensional features of the training samples are extracted when training the prediction model, the labels accurately summarized by the expert system can be used to extract the multi-dimensional features of the training samples, which avoids unnecessary training time for long-tail words and greatly improves the The training time for machine learning model training.

By way of example, FIG. 4 shows a flowchart of training a prediction model in one embodiment. Referring to this figure, it can be seen that the computer equipment can first perform data preparation, that is, collect historical consultation data and the corresponding training labels of the historical consultation data, and then, on the one hand, based on the expert system, based on the various data provided by the expert knowledge base from Entity feature data is extracted from historical consultation data. On the one hand, entity relationship feature data is extracted from historical consultation data based on semantic understanding, and intent feature data is extracted from historical consultation data by combining various data and semantic understanding provided by expert knowledge bases. The computer equipment then fuses the feature data extracted from the three aspects, and then combines the historical consultation data as input data to construct a neural network structure and conduct training to obtain a prediction model for identifying critical consultation data.

It should be understood that, although the steps in the flowcharts of the above embodiments are sequentially displayed according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. These steps or stages The order of execution of the steps is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the steps or stages in the other steps.

In one embodiment, as shown in FIG. 5 , an artificial intelligence-based critical care data identification device is provided, including: an acquisition module 501 , a first identification module 502 , a second identification module 503 and a decision module 504 , wherein :

Obtaining module 501 is used to obtain the consultation session data corresponding to the target user identifier;

The first identification module 502 is configured to input the data of the consultation session into a prediction model, and output a model identification result corresponding to the data of the consultation session through the prediction model; The corresponding training labels of the multi-dimensional feature data are obtained by training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

The second identification module 503 is configured to determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

The decision module 504 is configured to combine the model identification result and the expert identification result to obtain a target identification result of whether the consultation session data is critical consultation data.

In one embodiment, the first identification module 502 is further configured to input the consultation session data into the prediction model, process the consultation session data through a plurality of neurons included in the prediction model, and obtain a model identification result corresponding to the consultation session data ; Output the model recognition result through the prediction model; wherein, the threshold of each neuron and the weight of the connection relationship between each neuron are determined when the prediction model is trained through the multi-dimensional feature data and the corresponding training labels of the multi-dimensional feature data, and the multi-dimensional feature data also Include intent feature data.

As shown in FIG. 6, in one embodiment, the multi-dimensional feature data further includes intention feature data; the artificial intelligence-based critical consultation data identification device further includes: a training module 505 for collecting historical consultation data and corresponding historical consultation data Training label; the training label corresponding to the historical consultation data is used to indicate whether the historical consultation data is critical consultation data; entity feature data, entity relationship feature data and intent feature data are extracted from the historical consultation data to generate historical consultation data. Corresponding multi-dimensional feature data; input the historical consultation data and the multi-dimensional feature data corresponding to the historical consultation data into the prediction model to be trained to obtain the prediction and recognition results; based on the prediction and recognition results of the prediction model and the training labels to train the prediction model .

In one embodiment, the training module 505 is further configured to use a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data; use a sequence labeling model to perform named entity recognition on the historical consultation data, and extract names based on the identified named entities The entity relationship feature data between entities; the semantic model is used to extract the intent feature data in the historical consultation data; according to the entity feature data, entity relationship feature data and intent feature data, multi-dimensional feature data corresponding to the historical consultation data is generated.

In one embodiment, the training module 505 is also used to perform word segmentation on the historical consultation data to obtain word segmentation results; based on the entity labels provided by the expert knowledge base, the rule engine is used to drive the extraction of entity labels according to the word segmentation results, and the results of the historical consultation data are obtained. Corresponding entity feature data; based on the intent labels provided by the expert knowledge base, the semantic model is used to extract the intent labels corresponding to the historical consultation data, and the intent feature data corresponding to the historical consultation data is obtained.

In one embodiment, the training module 505 is further configured to filter out historical critical care consultation data from historical consultation data; count the occurrence frequency of entity keywords in the historical critical consultation data; filter entities whose occurrence frequency is higher than a preset threshold Keywords are used as entity labels to be added to the expert knowledge base; the consultation intentions corresponding to the historical critical consultation data are determined, and the intention labels are obtained; the intention labels are added to the expert knowledge base.

In one embodiment, the consultation session data is stored in the blockchain; the decision-making module 504 is further configured to, when obtaining the target identification result that the consultation session data is the critically-ill consultation data, store the consultation session data to which the consultation session data belongs. The session is connected to the doctor's terminal; when it is obtained that the target identification result of the consultation session data is not the critical care consultation data, the consultation session to which the consultation session data belongs is continued.

The above-mentioned artificial intelligence-based critical consultation data identification device, after acquiring the user's consultation session data, on the one hand, input the consultation session data into the prediction model to obtain the model identification result, and on the other hand, based on the expert knowledge base to answer the question. Identify the data of the consultation session to obtain the expert identification result, and then combine the model identification result and the expert identification result to obtain the final identification result. Since the prediction model is trained based on multi-dimensional feature data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data, the prediction model can learn information of different dimensions during the training process, and combine the contextual semantic environment to better Understand the logic of the language, thereby improving the predictive model's ability to recognize the critical care data based on artificial intelligence; in this way, the combination of model prediction and expert system to identify critical data can make up for the deficiency of relying only on the expert system, and improve the performance based on Accuracy of AI-based critical care data identification.

For the specific limitations of the artificial intelligence-based critical care data identification device, please refer to the above definition of the artificial intelligence-based critical care data identification method, which will not be repeated here. Each module in the above-mentioned artificial intelligence-based critical care data identification device may be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided, and the computer device can be a server, and its internal structure diagram can be as shown in FIG. 7 . The computer device includes a processor, memory, a network interface, and a database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The nonvolatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used to store the identification data of the critical care consultation data based on artificial intelligence. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an artificial intelligence-based method for recognizing data of critical care inquiries can be realized.

In one embodiment, a computer device is provided, and the computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 8 . The computer equipment includes a processor, memory, a network interface, a display screen, and an input device connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The nonvolatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an artificial intelligence-based method for recognizing data of critical care inquiries can be realized. The display screen of the computer equipment may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment may be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer equipment , or an external keyboard, trackpad, or mouse.

Those skilled in the art can understand that the structure shown in FIG. 7 or 8 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. A device may include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: acquiring consultation session data corresponding to a target user identifier; The consultation session data is input into the prediction model, and the model identification result corresponding to the consultation session data is output through the prediction model; wherein, the prediction model is trained according to the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data, and the multi-dimensional feature data is based on the historical consultation data. The extracted multi-dimensional feature data includes entity feature data and entity relationship feature data; according to the tags hit by the consultation session data in the expert knowledge base, the expert identification result corresponding to the consultation session data is determined; combined with the model identification result and the expert identification As a result, a target identification result of whether the consultation session data is critical consultation data is obtained.

In one embodiment, inputting the consultation session data into a prediction model, and outputting a model recognition result corresponding to the consultation session data through the prediction model includes: inputting the consultation session data into the prediction model, and using a plurality of neurons included in the prediction model Process the data of the consultation session to obtain the model recognition result corresponding to the data of the consultation session; output the model recognition result through the prediction model; among them, the threshold of each neuron and the weight of the connection relationship between each neuron are obtained through the multi-dimensional feature data. and the training labels corresponding to the multi-dimensional feature data are determined when training the prediction model, and the multi-dimensional feature data also includes intent feature data.

In one embodiment, the multidimensional feature data also includes intent feature data. When the processor executes the computer program, the following steps are also implemented: collecting historical consultation data and training labels corresponding to the historical consultation data; the training labels corresponding to the historical consultation data are used to indicate whether the historical consultation data is critical consultation data; Entity feature data, entity relationship feature data, and intent feature data are extracted from the consultation data to generate multi-dimensional feature data corresponding to historical consultation data; The trained prediction model is used to obtain the prediction recognition result; the prediction model is trained based on the prediction recognition result of the prediction model and the training label.

In one embodiment, extracting entity feature data, entity relationship feature data, and intent feature data from historical consultation data, and generating multidimensional feature data corresponding to the historical consultation data, includes: using a rule engine to drive the extraction of historical consultation data corresponding to Named entity recognition of historical consultation data using sequence annotation model, and extraction of entity relationship feature data between named entities based on the identified named entities; semantic model to extract intent feature data in historical consultation data ; According to entity feature data, entity relationship feature data and intention feature data, multi-dimensional feature data corresponding to historical consultation data is generated.

In one embodiment, using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data includes: performing word segmentation on the historical consultation data to obtain a word segmentation result; As a result, entity labels are extracted to obtain entity feature data corresponding to historical consultation data. Using the semantic model to extract the intent feature data in the historical consultation data, including: based on the intent labels provided by the expert knowledge base, using the semantic model to extract the intent labels corresponding to the historical consultation data, and obtaining the intent features corresponding to the historical consultation data data.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps: screening out historical critical illness consultation data from historical consultation data; counting the occurrence frequency of entity keywords in the historical critical illness consultation data; screening the occurrence frequency higher than Entity keywords with preset thresholds are used as entity labels to be added to the expert knowledge base; the consultation intention corresponding to the historical critical consultation data is determined to obtain the intention label; the intention label is added to the expert knowledge base.

In one embodiment, the consultation session data is stored in the blockchain; when the processor executes the computer program, the processor further implements the following steps: when obtaining the target identification result that the consultation session data is critical care consultation data, then the consultation session The consultation session to which the data belongs is connected to the doctor's terminal; when it is obtained that the consultation session data is not the target identification result of the critical care consultation data, the consultation session to which the consultation session data belongs continues to be promoted.

In one embodiment, a computer storage medium is provided, the computer storage medium may be volatile or non-volatile, and a computer program is stored thereon, and when the computer program is executed by a processor, the following Steps: obtaining the consultation session data corresponding to the target user identification; inputting the consultation session data into the prediction model, and outputting the model identification result corresponding to the consultation session data through the prediction model; wherein, the prediction model is based on the multi-dimensional feature data and the multi-dimensional feature data. Corresponding training labels are trained, multi-dimensional feature data is extracted from historical consultation data, and multi-dimensional feature data includes entity feature data and entity relationship feature data; The expert identification result corresponding to the session data; combined with the model identification result and the expert identification result, the target identification result of whether the consultation session data is critical consultation data is obtained.

In one embodiment, inputting the consultation session data into a prediction model, and outputting a model recognition result corresponding to the consultation session data through the prediction model includes: inputting the consultation session data into the prediction model, and using a plurality of neurons included in the prediction model Process the data of the consultation session to obtain the model recognition result corresponding to the data of the consultation session; output the model recognition result through the prediction model; among them, the threshold of each neuron and the weight of the connection relationship between each neuron are obtained through the multi-dimensional feature data. and the training labels corresponding to the multi-dimensional feature data are determined when training the prediction model, and the multi-dimensional feature data also includes intent feature data.

In one embodiment, the multi-dimensional feature data further includes intention feature data; when the computer program is executed by the processor, the following steps are further implemented: collecting historical consultation data and training labels corresponding to the historical consultation data; training labels corresponding to the historical consultation data It is used to indicate whether the historical consultation data is critical consultation data; extract entity feature data, entity relationship feature data and intention feature data from the historical consultation data to generate multi-dimensional feature data corresponding to the historical consultation data; The multi-dimensional feature data corresponding to the data and the historical consultation data are jointly input to the prediction model to be trained to obtain the prediction recognition result; the prediction model is trained based on the prediction recognition result of the prediction model and the training label.

In one embodiment, extracting entity feature data, entity relationship feature data, and intent feature data from historical consultation data, and generating multidimensional feature data corresponding to the historical consultation data, includes: using a rule engine to drive the extraction of historical consultation data corresponding to Named entity recognition of historical consultation data using sequence annotation model, and extraction of entity relationship feature data between named entities based on the identified named entities; semantic model to extract intent feature data in historical consultation data ; According to entity feature data, entity relationship feature data and intention feature data, multi-dimensional feature data corresponding to historical consultation data is generated.

In one embodiment, using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data includes: performing word segmentation on the historical consultation data to obtain a word segmentation result; As a result, entity labels are extracted to obtain entity feature data corresponding to historical consultation data. Using the semantic model to extract the intent feature data in the historical consultation data, including: based on the intent labels provided by the expert knowledge base, using the semantic model to extract the intent labels corresponding to the historical consultation data, and obtaining the intent features corresponding to the historical consultation data data.

In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: screening out historical critical illness consultation data from historical consultation data; counting the occurrence frequency of entity keywords in the historical critical illness consultation data; screening for high occurrence frequency The entity keywords at the preset threshold are used as entity labels to join the expert knowledge base; the consultation intention corresponding to the historical critical consultation data is determined, and the intention label is obtained; the intention label is added to the expert knowledge base.

In one embodiment, the consultation session data is stored in the blockchain; when the computer program is executed by the processor, the following steps are further implemented: when the target identification result that the consultation session data is the critically ill consultation data is obtained, then the consultation session data is obtained. The consultation session to which the session data belongs is connected to the doctor's terminal; when it is obtained that the consultation session data is not the target identification result of the critical care consultation data, the consultation session to which the consultation session data belongs continues to be promoted.

The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other medium used in the various embodiments provided in this application may include non-volatile and/or volatile memory. Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.

The above-mentioned embodiments only represent several embodiments of the present application, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the appended claims.

Claims (20)

1. An artificial intelligence-based method for identifying data for critical care inquiries, wherein the method comprises:

   Obtain the consultation session data corresponding to the target user ID;

   Inputting the consultation session data into a prediction model, and outputting a model identification result corresponding to the consultation session data through the prediction model; wherein the prediction model is based on the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data Obtained through training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

   Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

   Combining the model identification result and the expert identification result, a target identification result of whether the consultation session data is critical consultation data is obtained.
2. The method according to claim 1, wherein the inputting the medical consultation session data into a prediction model, and outputting a model identification result corresponding to the medical consultation session data through the prediction model, comprises:

   Inputting the consultation session data into a prediction model, and processing the consultation session data through a plurality of neurons included in the prediction model to obtain a model identification result corresponding to the consultation session data;

   outputting the model identification result through the prediction model;

   The threshold of each of the neurons and the weight of the connection relationship between the neurons are determined when the prediction model is trained by using the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data, and the multi-dimensional feature data Also includes intent feature data.
3. The method of claim 1, wherein the multi-dimensional feature data further comprises intent feature data; the method further comprises:

   Collect historical consultation data and a training label corresponding to the historical consultation data; the training label corresponding to the historical consultation data is used to indicate whether the historical consultation data is critical illness consultation data;

   Extract entity feature data, entity relationship feature data and intent feature data from the historical consultation data, and generate multidimensional feature data corresponding to the historical consultation data;

   The historical consultation data and the multidimensional feature data corresponding to the historical consultation data are jointly input into the prediction model to be trained to obtain a prediction and identification result;

   The prediction model is trained based on the prediction recognition result of the prediction model and the training label.
4. The method according to claim 3, wherein the extracting entity feature data, entity relationship feature data and intention feature data from the historical consultation data to generate multi-dimensional feature data corresponding to the historical consultation data, comprising: :

   Using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data;

   Perform named entity recognition on the historical inquiry data by using a sequence annotation model, and extract entity relationship feature data between the named entities based on the identified named entities;

   Extract the intent feature data in the historical consultation data by using a semantic model;

   According to the entity feature data, entity relationship feature data and intention feature data, multi-dimensional feature data corresponding to the historical consultation data is generated.
5. The method according to claim 4, wherein said using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data comprises:

   Perform word segmentation on the historical consultation data to obtain a word segmentation result;

   Based on the entity labels provided by the expert knowledge base, a rule engine is used to drive the extraction of entity labels according to the word segmentation results, to obtain entity feature data corresponding to the historical consultation data;

   The use of the semantic model to extract the intent feature data in the historical consultation data includes:

   Based on the intent labels provided by the expert knowledge base, a semantic model is used to extract the intent labels corresponding to the historical consultation data to obtain the intent feature data corresponding to the historical consultation data.
6. The method of claim 5, wherein the method further comprises:

   Selecting historical critical illness consultation data from the historical consultation data;

   Count the frequency of occurrence of entity keywords in the historical critical consultation data;

   Screening entity keywords with a frequency higher than a preset threshold as entity tags to join the expert knowledge base;

   Determine the consultation intention corresponding to the historical critical consultation data, and obtain the intention label;

   The intent tag is added to the expert knowledge base.
7. The method of any one of claims 1-6, wherein the consultation session data is stored in a blockchain;

   The method also includes:

   When obtaining the target identification result that the consultation session data is critical care consultation data, connecting the consultation session to which the consultation session data belongs to the doctor terminal;

   When it is obtained that the object identification result that the consultation session data is not the critical care consultation data, the consultation session to which the consultation session data belongs is continued to be promoted.
8. An artificial intelligence-based critical care data identification device, wherein the device comprises:

   an acquisition module, used to acquire the consultation session data corresponding to the target user ID;

   The first identification module is used to input the data of the consultation session into a prediction model, and output a model identification result corresponding to the data of the consultation session through the prediction model; wherein, the prediction model is based on the multi-dimensional feature data and the The training labels corresponding to the multi-dimensional feature data are obtained by training, the multi-dimensional feature data is extracted and obtained according to the historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

   a second identification module, configured to determine an expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

   A decision-making module, configured to combine the model identification result and the expert identification result to obtain a target identification result of whether the consultation session data is critical consultation data.
9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the following steps when executing the computer program:

   Obtain the consultation session data corresponding to the target user ID;

   Inputting the consultation session data into a prediction model, and outputting a model identification result corresponding to the consultation session data through the prediction model; wherein the prediction model is based on multidimensional feature data and training labels corresponding to the multidimensional feature data Obtained through training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

   Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

   Combining the model identification result and the expert identification result, a target identification result of whether the consultation session data is critical consultation data is obtained.
10. The computer device according to claim 9, wherein the inputting the medical consultation session data into a prediction model, and outputting a model identification result corresponding to the medical consultation session data through the prediction model, comprises:

    Inputting the consultation session data into a prediction model, and processing the consultation session data through a plurality of neurons included in the prediction model to obtain a model identification result corresponding to the consultation session data;

    outputting the model identification result through the prediction model;

    The threshold of each of the neurons and the weight of the connection relationship between the neurons are determined when the prediction model is trained by using the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data, and the multi-dimensional feature data Also includes intent feature data.
11. The computer device according to claim 9, wherein the multi-dimensional feature data further comprises intent feature data; the processor further implements the following steps when executing the computer program:

    Collect historical consultation data and the corresponding training label of the historical consultation data; the corresponding training label of the historical consultation data is used to indicate whether the historical consultation data is critical consultation data;

    Extract entity feature data, entity relationship feature data and intent feature data from the historical consultation data, and generate multidimensional feature data corresponding to the historical consultation data;

    The historical consultation data and the multidimensional feature data corresponding to the historical consultation data are jointly input into the prediction model to be trained to obtain a prediction and identification result;

    The prediction model is trained based on the prediction recognition result of the prediction model and the training label.
12. The computer device according to claim 11, wherein the extracting entity feature data, entity relationship feature data and intention feature data from the historical consultation data to generate multi-dimensional feature data corresponding to the historical consultation data, include:

    Using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data;

    Perform named entity recognition on the historical consultation data using a sequence annotation model, and extract entity relationship feature data between the named entities based on the identified named entities;

    Extract the intent feature data in the historical consultation data by using a semantic model;

    According to the entity feature data, entity relationship feature data and intention feature data, multi-dimensional feature data corresponding to the historical consultation data is generated.
13. The computer device according to claim 12, wherein said using a rule engine to drive the extraction of entity feature data corresponding to said historical consultation data, comprising:

    Perform word segmentation on the historical consultation data to obtain a word segmentation result;

    Based on the entity labels provided by the expert knowledge base, a rule engine is used to drive the extraction of entity labels according to the word segmentation results, to obtain entity feature data corresponding to the historical consultation data;

    The use of the semantic model to extract the intent feature data in the historical consultation data includes:

    Based on the intent labels provided by the expert knowledge base, a semantic model is used to extract the intent labels corresponding to the historical consultation data to obtain intent feature data corresponding to the historical consultation data.
14. The computer device according to claim 13, wherein the processor further implements the following steps when executing the computer program:

    Selecting historical critical illness consultation data from the historical consultation data;

    Count the frequency of occurrence of entity keywords in the historical critical consultation data;

    Screening entity keywords with a frequency higher than a preset threshold as entity tags to join the expert knowledge base;

    Determine the consultation intention corresponding to the historical critical consultation data, and obtain the intention label;

    The intent tag is added to the expert knowledge base.
15. The computer device according to any one of claims 9-14, wherein the consultation session data is stored in a blockchain; the processor further implements the following steps when executing the computer program:

    When obtaining the target identification result that the consultation session data is the critical care consultation data, the consultation session to which the consultation session data belongs is connected to the doctor terminal;

    When it is obtained that the object identification result that the consultation session data is not the critical care consultation data, the consultation session to which the consultation session data belongs is continued to be promoted.
16. A computer storage medium on which a computer program is stored, wherein the computer program implements the following steps when executed by a processor:

    Obtain the consultation session data corresponding to the target user ID;

    Inputting the consultation session data into a prediction model, and outputting a model identification result corresponding to the consultation session data through the prediction model; wherein the prediction model is based on the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data Obtained through training, the multi-dimensional feature data is extracted and obtained according to historical consultation data, and the multi-dimensional feature data includes entity feature data and entity relationship feature data;

    Determine the expert identification result corresponding to the consultation session data according to the tags hit by the consultation session data in the expert knowledge base;

    Combining the model identification result and the expert identification result, a target identification result of whether the consultation session data is critical consultation data is obtained.
17. The computer storage medium according to claim 16, wherein the inputting the medical consultation session data into a prediction model, and outputting a model identification result corresponding to the medical consultation session data through the prediction model, comprises:

    Inputting the consultation session data into a prediction model, and processing the consultation session data through a plurality of neurons included in the prediction model to obtain a model identification result corresponding to the consultation session data;

    outputting the model identification result through the prediction model;

    The threshold of each of the neurons and the weight of the connection relationship between the neurons are determined when the prediction model is trained by using the multi-dimensional feature data and the training labels corresponding to the multi-dimensional feature data, and the multi-dimensional feature data Also includes intent feature data.
18. The computer storage medium according to claim 16, wherein the multi-dimensional feature data further comprises intent feature data; and the computer program further implements the following steps when executed by the processor:

    Collect historical consultation data and a training label corresponding to the historical consultation data; the training label corresponding to the historical consultation data is used to indicate whether the historical consultation data is critical illness consultation data;

    Extract entity feature data, entity relationship feature data and intent feature data from the historical consultation data, and generate multidimensional feature data corresponding to the historical consultation data;

    The historical consultation data and the multidimensional feature data corresponding to the historical consultation data are jointly input into the prediction model to be trained to obtain a prediction and identification result;

    The prediction model is trained based on the prediction recognition result of the prediction model and the training label.
19. The computer storage medium according to claim 18, wherein the entity feature data, entity relationship feature data and intention feature data are extracted from the historical consultation data to generate multi-dimensional feature data corresponding to the historical consultation data ,include:

    Using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data;

    Perform named entity recognition on the historical consultation data using a sequence annotation model, and extract entity relationship feature data between the named entities based on the identified named entities;

    Extract the intent feature data in the historical consultation data by using a semantic model;

    According to the entity feature data, entity relationship feature data and intention feature data, multi-dimensional feature data corresponding to the historical consultation data is generated.
20. The computer storage medium according to claim 19, wherein said using a rule engine to drive the extraction of entity feature data corresponding to the historical consultation data comprises:

    Perform word segmentation on the historical consultation data to obtain a word segmentation result;

    Based on the entity labels provided by the expert knowledge base, a rule engine is used to drive the extraction of entity labels according to the word segmentation results, to obtain entity feature data corresponding to the historical consultation data;

    The use of the semantic model to extract the intent feature data in the historical consultation data includes:

    Based on the intent labels provided by the expert knowledge base, a semantic model is used to extract the intent labels corresponding to the historical consultation data to obtain intent feature data corresponding to the historical consultation data.

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