WO2023029514A1

WO2023029514A1 - Department triage method, system and device, and storage medium

Info

Publication number: WO2023029514A1
Application number: PCT/CN2022/087997
Authority: WO
Inventors: 周尚思; 赵璐偲
Original assignee: 康键信息技术（深圳）有限公司
Priority date: 2021-08-30
Filing date: 2022-04-20
Publication date: 2023-03-09
Also published as: CN113707285A

Abstract

Provided are a department triage method, system and device, and a storage medium, relating to the fields of smart healthcare and artificial intelligence. The method comprises: first, acquiring complaint information of a patient; then, performing valid complaint determination on the complaint information of the patient, so that invalid information that is unclear or generated due to accidental touch can be filtered out, thereby ensuring the accuracy of a subsequent prediction result; then, extracting valid complaint features, and, on the basis of the valid complaint features, performing prediction by means of a deep learning model, a rule matching model, a drug matching model, and a disease matching model, respectively, obtaining a department prediction result predicted by each prediction model, wherein, relative to a prediction result in a related solution being a single-label prediction result, a multi-label prediction result can be obtained, and the coverage rate and accuracy of the predicted department can be improved; and finally, filtering and sorting all predicted department prediction results, which can further improve the accuracy of the predicted department.

Description

Department triage method, system, device and storage medium

This application claims the priority of the Chinese patent application with the application number 202111004639.3 submitted to the China Patent Office on August 30, 2021, and the invention title is "method, system, equipment and storage medium for departmental triage", the entire contents of which are incorporated by reference in this application.

technical field

The embodiments of the present application relate to the field of smart medical technology, and in particular to a department triage method, system, device and storage medium.

Background technique

Triage is a field of research in medical artificial intelligence and Internet medical care, and it is also the entry path for patients to see a doctor. Mistakes in triage will lead to a chain reaction of errors in the follow-up consultation process.

Traditional triage usually requires patients to actively register to the corresponding department for consultation based on their own judgment of physical discomfort. This has caused a considerable number of patients to register to the wrong department due to lack of medical knowledge, which not only delays the time of consultation but also consumes energy. At present, intelligent triage technology is often used. Intelligent triage usually means that hospitals or Internet medical institutions collect patients' descriptions (such as gender, age, and description information about their own physical abnormalities, etc.) with the consent of patients. ), and then the medical practitioners conduct a professional analysis of the chief complaint, and label the chief complaint to the corresponding department. After collecting a certain amount of labeled data, they use the deep learning algorithm to train the prediction model, and finally based on the model after the training is completed. However, the inventor realized that the prediction results of this scheme are mostly single-label results, not only the coverage of the predicted department is low, but also the accuracy of the predicted department is not high. Moreover, there are a large number of cases of unclear expressions or mistouched input in the chief complaints provided by patients. In related solutions, the unclear or false touches in the chief complaints will be ignored, thereby reducing the accuracy of subsequent prediction results.

technical problem

The following is an overview of the topics described in detail in this article. This summary is not intended to limit the scope of the claims.

The embodiment of the present application provides a department triage method, system, device, and storage medium. The coverage of department prediction results is higher, and the accuracy of department recommendation is also higher.

technical solution

In the first aspect, the embodiment of the present application provides a department triage method, the department triage method includes:

Obtain information about patients' demands;

Extracting effective appeals from the appeal information by using a deep learning entity recognition model, and performing feature extraction on the effective appeals to obtain effective appeal features;

The effective appeal features are input into a plurality of different prediction models to perform department prediction respectively, and the department prediction results output by each of the prediction models are obtained; wherein, the prediction models include a deep learning model, a rule matching model, a drug matching model, and disease matching model;

Filtering and sorting all the department prediction results to obtain the sorting results;

The information of the target department is pushed according to the sorting result.

In the second aspect, the embodiment of the present application provides a department triage system, and the department triage system includes:

An information acquisition unit, configured to acquire patient appeal information;

The first processing unit is configured to extract effective appeals from the appeal information through a deep learning entity recognition model, and perform feature extraction on the effective appeals to obtain effective appeal features;

The department prediction unit is used to input the effective appeal features into a plurality of different prediction models to perform department prediction respectively, and obtain the department prediction results output by each of the prediction models; wherein, the prediction models include deep learning models, rule matching model, drug matching model, and disease matching model;

The second processing unit is configured to filter and sort all the department prediction results to obtain the sorting results;

The department recommending unit is configured to push target department information according to the ranking result.

In the third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor, and when the processor executes the computer program, the following is realized: as described above Departmental triage method.

In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to execute: the above-mentioned department triage method.

Beneficial effect

The first aspect of the embodiment of the present application provides a departmental triage method. This method first obtains the patient's appeal information; and then judges the patient's appeal information to effectively appeal, and can filter out unclear or invalid information caused by mistouching , so as to ensure the accuracy of the follow-up prediction results; then extract the effective appeal features, and based on the effective appeal features, use the deep learning model, the rule matching model, the drug matching model and the disease matching model to make predictions respectively, and get each Compared with the prediction results of related schemes, the department prediction results predicted by the prediction model are single-label prediction results. This method uses the deep learning model, rule matching model, drug matching model and disease matching model to make predictions respectively, and can obtain multi-label The prediction results not only improve the coverage of the predicted departments, but also improve the accuracy of the predicted departments; finally, this method also filters and sorts the predicted results of all the predicted departments, which can further improve the accuracy of the predicted departments.

It can be understood that the beneficial effects of the above-mentioned second aspect to the fourth aspect compared with the related technology are the same as those of the above-mentioned first aspect compared with the related technology. Please refer to the relevant description in the above-mentioned first aspect. This will not be repeated here.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings that need to be used in the embodiments or related technical descriptions. Obviously, the drawings in the following description are only the embodiments of the present application For some embodiments of the present invention, those skilled in the art can also obtain other drawings based on these drawings without paying creative efforts.

FIG. 1 is a schematic diagram of a system architecture for implementing a department triage method provided by an embodiment of the present application;

Fig. 2 is a logic block diagram of a kind of department triage method provided by one embodiment of the present application;

Fig. 3 is a schematic flowchart of a department triage method provided by an embodiment of the present application.

Embodiments of the present invention

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the embodiments of the present application with unnecessary detail.

It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than in the flowchart. The terms "first", "second" and the like in the specification and claims and the above drawings are used to distinguish similar objects, and not necessarily used to describe a specific sequence or sequence.

It should also be understood that references to "one embodiment" or "some embodiments" described in the description of the embodiments of the present application mean that specific features described in conjunction with the embodiments of the present application are included in one or more embodiments of the embodiments of the present application. , structure or characteristics. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in other embodiments," etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean "one or more but not all embodiments" unless specifically stated otherwise. The terms "including", "comprising", "having" and variations thereof mean "including but not limited to", unless specifically stated otherwise.

The embodiment of the present application can acquire and process related data based on medical artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application of using digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. system. Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning. The embodiments of the present application mainly relate to natural language processing technology and machine learning/deep learning technology in medical artificial intelligence.

Traditional triage usually requires patients to actively register to the corresponding department for consultation based on their own judgment of physical discomfort. This has caused a considerable number of patients to register to the wrong department due to lack of medical knowledge, which not only delays the time of consultation but also consumes energy. At present, intelligent triage technology is often used. Intelligent triage usually means that hospitals or Internet medical institutions collect patients' descriptions (such as gender, age, and description information about their own physical abnormalities, etc.) with the consent of patients. ), and then the medical practitioners conduct a professional analysis of the chief complaint, and label the chief complaint to the corresponding department. After collecting a certain amount of labeled data, they use the deep learning algorithm to train the prediction model, and finally based on the model after the training is completed. However, the prediction results of this scheme are mostly single-label results, not only the coverage of the predicted department is low, but also the accuracy of the predicted department is not high. Moreover, there are a large number of cases of unclear expressions or mistouched input in the chief complaints provided by patients. In related solutions, the unclear or false touches in the chief complaints will be ignored, thereby reducing the accuracy of subsequent prediction results.

In order to solve the above-mentioned technical defects, the embodiment of the present application first obtains the patient's appeal information; and then judges the effective appeal of the patient's appeal information, which can filter out unclear or invalid information caused by mistaken touch, so as to ensure the accuracy of subsequent prediction results degree; then extract the effective appeal features, and based on the effective appeal features, use multiple different prediction models to predict the departments respectively, and obtain the prediction results of multiple departments. Compared with the prediction results of related schemes, the prediction results of single labels, The embodiment of this application can obtain multi-label prediction results, and can realize multi-label refined prediction, which not only improves the coverage of the prediction department, but also greatly improves the accuracy of the prediction department; Filtering and sorting the prediction results of each department can further improve the accuracy of the prediction department.

The department triage method provided by an embodiment of the present application can be implemented in an electronic device. The terminal/device may be a mobile electronic device or a non-mobile electronic device. Mobile electronic devices can be mobile phones, tablet computers, notebook computers, handheld computers, vehicle electronic devices, wearable devices, super mobile personal computers, netbooks, personal digital assistants, etc.; non-mobile electronic devices can be personal computers, televisions, teller machines or Self-service machines, etc.; the implementation plan of this application does not make specific limitations.

The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (universal serial bus, USB) interface, a charging management module, a power management module, a battery, an antenna, a mobile communication module, a wireless communication module, an audio module, Speakers, receivers, microphones, headphone jacks, sensor modules, buttons, motors, indicators, cameras, displays, and Subscriber Identification Module (SIM) card interfaces, etc.

The embodiments of the present application will be further described below in conjunction with the accompanying drawings.

Referring to FIG. 1 , it is a schematic diagram of a system architecture for performing a department triage method provided by an embodiment of the present application. In the example of FIG. 1 , the system architecture mainly includes but is not limited to an information acquisition unit 100, a first processing unit 200. Department prediction unit 300, second processing unit 400, and department recommendation unit 500, wherein:

The information acquisition unit 100 is used to acquire patient appeal information;

The first processing unit 200 is configured to extract effective appeals from the appeal information through a deep learning entity recognition model, and perform feature extraction on the effective appeals, so as to extract features of effective appeals;

The department prediction unit 300 is used to input the effective appeal features into multiple different prediction models to perform department prediction respectively, and obtain the department prediction results output by each prediction model; wherein, the multiple prediction models include deep learning models, rule matching models, drug matching models, etc. model and disease matching model;

The second processing unit 400 is used to filter and sort the prediction results of all departments to obtain the sorting results;

The department recommendation unit 500 is used to push target department information according to the sorting results.

The system architecture and application scenarios described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute limitations on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the system architecture The evolution of the technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

Those skilled in the art can understand that the system architecture shown in Figure 1 does not constitute a limitation to the embodiment of the present application, and may include more or less components than those shown in the illustration, or combine some components, or different components layout.

In the system architecture shown in FIG. 1 , each unit can call its stored program to implement the department triage method.

Based on the above system architecture, various embodiments of the department triage method according to the embodiment of the present application are proposed.

With reference to Fig. 2, one embodiment of the present application provides a kind of department triage method, and this method comprises the following steps:

Step S100, acquiring patient appeal information.

Here, the patient's appeal information can come from the terminal equipment on the Internet medical platform or the terminal equipment on the hospital treatment platform. said claim information). For example, a patient describes his symptoms, age, gender and other appeal information on the Internet medical platform. Help register."

In some embodiments, before step S100, the departmental triage method also includes the following steps:

Step S1001, obtaining the historical consultation information of the patient;

Step S1002, based on the patient's historical consultation information, push the target department information.

Since the patient's historical consultation information is an important reference data for the patient (in practical applications, doctors will also assist in diagnosis based on the patient's historical consultation information), this embodiment is used in departments that perform multiple prediction models on patients Before the triage prediction, first judge whether the patient has historical consultation information. When the patient has historical consultation information, he can first obtain the historical consultation information on the Internet medical platform or the hospital consultation platform. The historical consultation information includes the patient's Inquire about the relevant data of the department, and then recommend the corresponding department to the patient based on the historical consultation information. For example, if the patient has repeatedly recorded in the historical consultation information that he has consulted with Chinese medicine, the Chinese medicine department will be recommended to the patient first. If the patient does not choose a Chinese medicine department, continue to the next step. It should be noted that step S1002 is to push the target department information to the terminal equipment on the Internet medical platform or the terminal equipment on the hospital consultation platform. department or gastroenterology and so on.

Step S200, extract effective appeals from the appeal information by using the deep learning entity recognition model, and perform feature extraction on the effective appeals to obtain effective appeal features.

In this embodiment, the effective appeal judgment of the patient's appeal information can eliminate invalid or defective information to ensure that useful features can be extracted, thereby ensuring the accuracy of the prediction results of multiple prediction departments obtained in subsequent steps. In some embodiments, judging the effective appeal of the patient's appeal information includes a processing process: extracting the effective appeal from the appeal information through the deep learning entity recognition model, and pushing the completion request of the appeal information (also known as is a questioning mechanism), when the completed appeal information is received, the effective appeal is extracted from the completed appeal information through the deep learning entity recognition model. For example, the patient's appeal information is: "Hello, what I want to consult is: My family member is not feeling well, and I want to take him to the hospital to see a doctor." Which part of the body is uncomfortable", "How long has the discomfort lasted", "Whether you used drugs during the period of discomfort", "Whether there is a history of disease", etc.; if the patient's answer is: "It is my child who has nausea The situation, which appeared about 2 days ago, did not use drugs and had no medical history." After obtaining the appeal information completed by the patient, effective appeals can be extracted through the deep learning entity recognition model. It should be noted that in step S200, the patient's appeal information is the corpus information belonging to the patient. In practical applications, the corpus information often includes multiple entities (corresponding to the appeals described in the embodiments of this application), and the depth The learning entity recognition model usually has an entity library, which contains many entity features. Through the deep learning entity recognition model and entity library, effective appeal judgment can be realized from the patient's appeal information. The related technologies of the deep learning entity recognition model and the entity database are common knowledge, and the structure and principle of the deep learning entity recognition model will not be described in detail here. It is also worth noting that different entity types also correspond to fixed questioning words, which are not limited in this embodiment of the application, and can be set according to actual situations.

After the effective appeal is judged on the patient's appeal information in step S200, the features of the effective appeal are further extracted from the patient's appeal information.

In some embodiments, the processing of extracting effective appeal features from patient appeal information includes, but is not limited to: segmenting patient appeal information, converting traditional Chinese to simplified Chinese, standardizing synonyms (that is, normalizing synonyms) and removing stop words etc., for example: the patient's appeal information put forward by the patient is: "Hello, doctor, I have a little stomachache.";

Then the simplified form is: "Hello, doctor, I have a little stomachache.";

Synonyms normalized to: "Hi Doctor, I have a stomachache.";

After the participle, it is: "【Hello】【,】【Doctor】【,】【I】【a little】【stomachache】【。】";

After removing the stop words, it becomes: "[I] [somewhat] [stomach pain]".

For example: the patient’s request information is: "Hello, what I want to consult is: I am pregnant and want to have a B-ultrasound examination, please register for me." After processing, I get "[B-ultrasound] [wife] [pregnancy] [ registered】". It should be noted that the extracted effective appeal feature is a combination of two-dimensional word vectors.

Step S300, input the effective appeal features into multiple different prediction models to perform department prediction respectively, and obtain the department prediction results output by each prediction model, wherein the prediction models include a deep learning model, a rule matching model, a drug matching model and a disease matching model .

Based on the effective appeal features obtained in the above step S200 as prediction data, this step S300 performs department predictions through a plurality of different prediction models, and each prediction model can correspond to output department prediction results. The multiple prediction models in step S300 include: deep learning model, rule matching model, drug matching model and disease matching model, and each model is introduced as follows:

Step S301, input the effective appeal features into the trained deep learning model, and obtain the department prediction result output by the deep learning model.

In some embodiments, the deep learning model includes Bayesian network model and BERT (Bidirectional Encoder Representations from Transformers) network model, compared with related schemes that use a single network model for training, this embodiment uses a combination of Bayesian network model and BERT network model for training and prediction schemes, which can avoid the limitations of a single model and increase the depth The accuracy of the predictions output by the learned model. Moreover, the Bayesian network model greatly considers the correlation characteristics of the disease, and has great advantages in knowledge reasoning. The deep learning BERT model has made a great breakthrough in semantic reasoning, and has multiple triage results in prediction. has great advantages.

The following briefly introduces the training process of the Bayesian network model and the BERT network model:

When using the Bayesian network model, word segmentation is required, stop words are removed, and TF_IDF (term frequency–inverse document frequency, a commonly used weighting scheme), extract effective features, and then input the features to the Bayesian network model for training. The specific feature processing is as follows: firstly, the English in the input text is unified into lowercase, filtered through the pre-constructed stop vocabulary, and then segmented, and then the contents of the segmented words are synonymously standardized.

When using the BERT network model, the patient's age, gender, symptom description and other appeal information are directly input into the BERT network model for training.

During training, multiple parameter tuning processes are performed to obtain a model with relatively better performance. Adjustable parameters include: learning rate, loss function, word vector dimension, number of iterations, batch data size (Batch Size) for updating gradients, etc. The main method is to record and analyze the learning rate of each time step, the impact of loss rate changes on the accuracy of the model, and the impact of each training parameter on the accuracy of the model.

After obtaining the trained Bayesian network model and BERT network model, input the effective appeal features extracted in step S200 into the trained Bayesian network model and BERT network model, specifically including the following steps:

Step S3011, input the effective appeal features into the Bayesian network model and the BERT network model respectively.

Step S3012, when the output result of the Bayesian network model is the same as the output result of the BERT network model, use the same output result as the department prediction result.

Step S3013, when the output result of the Bayesian network model is different from the output result of the BERT network model, the output results of the Bayesian network model and the output results of the BERT network model are respectively subjected to confidence standardization processing to obtain a processing result; The processing results are sorted, and the top sorted output results are used as department prediction results.

Step S302, using the rule matching model to select departments that match the forward rules and do not match the reverse rules from the effective appeal features as department prediction results.

The rule matching model refers to the selection of departments that match the forward rules and do not match the reverse rules from the effective appeal features through the pre-configured regular expressions as the department prediction results. In the rule matching model, each department corresponds to multiple forward and reverse rules. If the pre-configured regular expression matches the department’s forward rules and does not match the department’s reverse rules from the effective appeal features, Then the department is a department prediction result of the rule matching model.

Step S303 , using the drug matching model to extract drug features from the effective appeal features, and match the department corresponding to the drug feature as the department prediction result.

Step S304 , extracting disease features from the effective appeal features through the disease matching model, and matching the department corresponding to the disease feature as the department prediction result.

The drug matching model and the disease matching model use a similar method, that is, two data tables of drug characteristics and departments, disease characteristics and departments are obtained through data association analysis, because there is a certain relationship between drugs and departments, and the relationship between diseases and departments There is a certain correlation between them. For example, if the drug feature is esomelaxol in the effective appeal feature, it can be seen that the drug is used to treat stomach diseases, and gastroenterology belongs to the department of gastroenterology. Therefore, the department prediction result output by the drug matching model is gastroenterology. The same is true for the disease matching model. For example, the patient's appeal information entered by the patient is: "Hello, what I want to consult is: the child has a fever and coughs all the time, but does not have a runny nose or dizziness", then the extracted effective appeal The features are: [Fever] [Always] [Cough] [Child]. Finally, the department prediction result output by the disease matching model based on the two disease characteristics of fever and cough is Respiratory Medicine.

Step S400 , filtering and sorting the prediction results of all departments to obtain the sorting results.

After obtaining the multi-label results output by the four prediction models in step S300, it is necessary to further filter and sort the prediction results of multiple departments. In some embodiments, step S400 specifically includes the following steps:

Step S401: Filter all department prediction results according to the patient's gender, age, preference, and historical consultation information to obtain filtered department prediction results.

For example: if the gender of the patient is male, and obstetrics and gynecology appear in the department prediction result, the department prediction result of obstetrics and gynecology will be removed. If there are records of multiple consultations with traditional Chinese medicine in the patient's historical consultation information, then the Chinese medicine department label is given more weight, so that it is placed in the front position of multiple prediction results.

Step S402, sort the filtered department prediction results in sequence according to the order of the department prediction results output by the rule matching model, disease matching model, drug matching model and deep learning model.

In this embodiment, if the patient has historical consultation information (as shown in the above steps S1001 and S1002), the department recommended by the historical consultation information is given priority, and then the rule matching model, disease matching model, and drug matching model are sequentially used And the department prediction results output by the deep learning model are sorted and recommended. If there is no historical consultation information for the patient, the department prediction results output by the rule matching model, disease matching model, drug matching model, and deep learning model are used to sort and recommend.

Step S500, pushing target department information according to the sorting result.

In the embodiment of the present application, the sorting results are sent to the terminal device on the Internet medical platform or the terminal device on the hospital consultation platform, so that the terminal device can recommend departments to patients according to the ranking results, and the department with the highest ranking is recommended first.

In some embodiments, after step S500, the department triage method also includes the steps of:

Step S601, acquiring referral information of the patient.

Step S602 , iteratively updating the trained deep learning model according to the patient's referral information to obtain an updated deep learning model.

After the department has been recommended to the patient in step S500, the terminal device will automatically match the doctor of the corresponding department. If the patient is not satisfied with the assignment of the department or the doctor thinks that the matching is wrong, the patient can actively choose to refer the patient or the doctor can choose to transfer the patient. After a referral occurs, referral information will be generated, and then step S601 will obtain the referral information as an error case for analysis and quality inspection (analysis and quality inspection can be performed by a doctor), and then the transfer after analysis and quality inspection The diagnostic information is used as the training data of the Bayesian network model and the BERT network model, and then the Bayesian network model and the BERT network model are iterated and updated through the training data, so that the Bayesian network model and the BERT network model are gradually improved. , and finally improve the accuracy of the prediction results of the Bayesian network model and the BERT network model.

In the embodiment of the departmental triage method provided by this application, the patient’s appeal information is first obtained; then the effective appeal judgment is made on the patient’s appeal information, which can filter out unclear or invalid information caused by mistouching, thereby ensuring the accuracy of subsequent prediction results Accuracy; Then extract the effective appeal features, and based on the effective appeal features, perform department predictions through multiple different prediction models, and obtain multiple department prediction results. Compared with the prediction results of related programs, the prediction results are single-label prediction results , the embodiment of this method can obtain multi-label prediction results, and can realize multi-label refined prediction, which not only improves the coverage of the prediction department, but also greatly improves the accuracy of the prediction department; Filtering and sorting the prediction results of each department can further improve the accuracy of the prediction department.

An embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and operable on the processor.

The processor and memory can be connected by a bus or other means.

As a non-transitory computer-readable storage medium, memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

It should be noted that the electronic device in this embodiment can constitute a part of the system architecture in the embodiment shown in FIG. 1, and these embodiments all belong to the same inventive concept, so these embodiments have the same implementation principle and technical effect, No more details here.

The non-transitory software programs and instructions required to realize the department triage method of the above-mentioned embodiment are stored in the memory, and when executed by the processor, the method of the above-mentioned embodiment is executed, for example, the method step S100 in Fig. 3 described above is executed to S500.

The terminal embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, an embodiment of the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium may be non-volatile or volatile, and the computer-readable storage medium stores the computer Executable instructions, the computer-executable instructions are executed by a processor or a controller, for example, executed by a processor in the above-mentioned electronic device embodiment, so that the above-mentioned processor can execute the department triage method in the above-mentioned embodiment, for example , executing the method steps S100 to S500 in FIG. 3 described above.

As another example, being executed by a processor in the above embodiment of the device connector may cause the above processor to execute the department triage method in the above embodiment, for example, execute the method steps S100 to S500 in FIG. 3 described above.

Those skilled in the art can understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware and an appropriate combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit . Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those of ordinary skill in the art, the term computer storage media includes both volatile and nonvolatile media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. permanent, removable and non-removable media. Computer storage media including, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or Any other medium used to store desired information and which can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

The above is a specific description of the preferred implementation of the embodiment of the present application, but the embodiment of the present application is not limited to the above-mentioned implementation, and those skilled in the art can make various modifications without violating the spirit of the embodiment of the present application. Equivalent modifications or replacements are all included within the scope defined by the claims in the embodiments of the present application.

Claims

A department triage method, wherein, the department triage method comprises:

Obtain information about patients' demands;

Extracting effective appeals from the appeal information by using a deep learning entity recognition model, and performing feature extraction on the effective appeals to obtain effective appeal features;

The effective appeal features are input into a plurality of different prediction models to perform department prediction respectively, and the department prediction results output by each of the prediction models are obtained; wherein, the prediction models include a deep learning model, a rule matching model, a drug matching model, and disease matching model;

Filtering and sorting all the department prediction results to obtain the sorting results;

The information of the target department is pushed according to the sorting result.
The departmental triage method according to claim 1, wherein said inputting said effective appeal features into a plurality of different prediction models to perform departmental predictions respectively, and obtaining departmental prediction results output by each said prediction model, comprising:

Inputting the effective appeal features into the trained deep learning model to obtain department prediction results output by the deep learning model;

Inputting the effective appeal feature into the rule matching model to obtain the department prediction result output by the rule matching model; wherein the rule matching model will match the forward rule and no match according to the effective appeal feature The department with the reverse rule is used as the prediction result of the department;

Input the effective appeal feature into the drug matching model to obtain the department prediction result output by the drug matching model; wherein the drug matching model obtains the corresponding drug feature according to the effective appeal feature, and matches the matching with the The departments corresponding to the above drug characteristics are used as the department prediction results;

Input the effective appeal feature into the disease matching model to obtain the department prediction result output by the disease matching model; wherein the disease matching model obtains the corresponding disease feature according to the effective appeal feature, and matches with the The departments corresponding to the above disease characteristics are used as the department prediction results.
The departmental triage method according to claim 2, wherein the deep learning model includes a trained Bayesian network model and a BERT network model; the input of the effective appeal feature into the trained deep In the learning model, the department prediction results output by the deep learning model are obtained, including:

Input the effective appeal features into the Bayesian network model and the BERT network model respectively;

When the output result of the Bayesian network model is the same as the output result of the BERT network model, the same output result is used as the department prediction result;

When the output result of the Bayesian network model is different from the output result of the BERT network model, the output result of the Bayesian network model and the output result of the BERT network model are respectively subjected to confidence standardization processing , get the processing result;

The processing results are sorted, and the highest sorted output results are used as department prediction results.
The department triage method according to claim 2, wherein the filtering and sorting of all the prediction results of the departments to obtain the sorting results includes:

filtering all the department prediction results according to the patient's gender, age, preference, and historical consultation information to obtain the filtered department prediction results;

The filtered department prediction results are sorted in sequence according to the order of the rule matching model, the disease matching model, the drug matching model and the department prediction results output by the deep learning model.
The departmental triage method according to claim 1, wherein the departmental triage method further comprises:

When no effective appeal is extracted from the appeal information through the deep learning entity recognition model, push a completion request for the appeal information;

When the completed appeal information is received, an effective appeal is extracted from the completed appeal information through the deep learning entity recognition model.
The department triage method according to claim 2, wherein, after pushing the target department information according to the ranking result, the department triage method further comprises:

Obtain patient referral information;

Iteratively updating the trained deep learning model through the referral information to obtain the updated deep learning model.
The departmental triage method according to claim 1, wherein, before the acquisition of the patient's appeal information, the departmental triage method further comprises:

Obtain historical consultation information of patients;

According to the historical consultation information of the patient, the target department information is pushed.
A department triage system, wherein the department triage system includes:

An information acquisition unit, configured to acquire patient appeal information;

The first processing unit is configured to extract effective appeals from the appeal information through a deep learning entity recognition model, and perform feature extraction on the effective appeals to obtain effective appeal features;

The department prediction unit is used to input the effective appeal features into a plurality of different prediction models to perform department prediction respectively, and obtain the department prediction results output by each of the prediction models; wherein, the prediction models include deep learning models, rule matching model, drug matching model, and disease matching model;

The second processing unit is configured to filter and sort all the department prediction results to obtain the sorting results;

The department recommending unit is configured to push target department information according to the ranking result.
An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein, when the processor executes the computer program, one of the department triage methods described above is implemented, The departmental triage methods include:

Obtain information about patients' demands;

Extracting effective appeals from the appeal information by using a deep learning entity recognition model, and performing feature extraction on the effective appeals to obtain effective appeal features;

The effective appeal features are input into a plurality of different prediction models to perform department prediction respectively, and the department prediction results output by each of the prediction models are obtained; wherein, the prediction models include a deep learning model, a rule matching model, a drug matching model, and disease matching model;

Filtering and sorting all the department prediction results to obtain the sorting results;

The information of the target department is pushed according to the sorting result.
The electronic device according to claim 9, wherein said inputting said effective appeal features into a plurality of different prediction models to perform department prediction respectively, and obtaining department prediction results output by each of said prediction models, including:

Inputting the effective appeal features into the trained deep learning model to obtain department prediction results output by the deep learning model;

Inputting the effective appeal feature into the rule matching model to obtain the department prediction result output by the rule matching model; wherein the rule matching model will match the forward rule and no match according to the effective appeal feature The department with the reverse rule is used as the prediction result of the department;

Input the effective appeal feature into the drug matching model to obtain the department prediction result output by the drug matching model; wherein the drug matching model obtains the corresponding drug feature according to the effective appeal feature, and matches the matching with the The departments corresponding to the above drug characteristics are used as the department prediction results;

Input the effective appeal feature into the disease matching model to obtain the department prediction result output by the disease matching model; wherein the disease matching model obtains the corresponding disease feature according to the effective appeal feature, and matches with the The department corresponding to the above disease characteristics is used as the prediction result of the department.
The electronic device according to claim 10, wherein the deep learning model includes a trained Bayesian network model and a BERT network model; the input of the effective appeal features into the trained deep learning model In, the department prediction results output by the deep learning model are obtained, including:

Input the effective appeal features into the Bayesian network model and the BERT network model respectively;

When the output result of the Bayesian network model is the same as the output result of the BERT network model, the same output result is used as the department prediction result;

When the output result of the Bayesian network model is different from the output result of the BERT network model, the output result of the Bayesian network model and the output result of the BERT network model are respectively subjected to confidence standardization processing , get the processing result;

The processing results are sorted, and the highest sorted output results are used as department prediction results.
The electronic device according to claim 10, wherein the filtering and sorting of all the prediction results of departments to obtain the sorting results includes:

filtering all the department prediction results according to the patient's gender, age, preference, and historical consultation information to obtain the filtered department prediction results;

The filtered department prediction results are sorted in sequence according to the order of the rule matching model, the disease matching model, the drug matching model and the department prediction results output by the deep learning model.
The electronic device according to claim 9, wherein the department triage method further comprises:

When no effective appeal is extracted from the appeal information through the deep learning entity recognition model, push a completion request for the appeal information;

When the completed appeal information is received, an effective appeal is extracted from the completed appeal information through the deep learning entity recognition model.
The electronic device according to claim 10, wherein, after pushing the target department information according to the sorting result, the department triage method further comprises:

Obtain patient referral information;

Iteratively updating the trained deep learning model through the referral information to obtain the updated deep learning model.
A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute a department triage method, the department triage method comprising:

Obtain information about patients' demands;

Extracting effective appeals from the appeal information by using a deep learning entity recognition model, and performing feature extraction on the effective appeals to obtain effective appeal features;

The effective appeal features are input into a plurality of different prediction models to perform department prediction respectively, and the department prediction results output by each of the prediction models are obtained; wherein, the prediction models include a deep learning model, a rule matching model, a drug matching model, and disease matching model;

Filtering and sorting all the department prediction results to obtain the sorting results;

The information of the target department is pushed according to the sorting result.
The computer-readable storage medium according to claim 15, wherein the input of the effective appeal features into a plurality of different prediction models to perform department predictions respectively, and obtain department prediction results output by each of the prediction models, including:

Inputting the effective appeal features into the trained deep learning model to obtain department prediction results output by the deep learning model;

Inputting the effective appeal feature into the rule matching model to obtain the department prediction result output by the rule matching model; wherein the rule matching model will match the forward rule and no match according to the effective appeal feature The department with the reverse rule is used as the prediction result of the department;

Input the effective appeal feature into the drug matching model to obtain the department prediction result output by the drug matching model; wherein the drug matching model obtains the corresponding drug feature according to the effective appeal feature, and matches the matching with the The departments corresponding to the above drug characteristics are used as the department prediction results;

Input the effective appeal feature into the disease matching model to obtain the department prediction result output by the disease matching model; wherein the disease matching model obtains the corresponding disease feature according to the effective appeal feature, and matches with the The department corresponding to the above disease characteristics is used as the prediction result of the department.
The computer-readable storage medium according to claim 16, wherein the deep learning model includes a trained Bayesian network model and a BERT network model; the input of the effective appeal features into the trained In the deep learning model, the department prediction results output by the deep learning model are obtained, including:

Input the effective appeal features into the Bayesian network model and the BERT network model respectively;

When the output result of the Bayesian network model is the same as the output result of the BERT network model, the same output result is used as the department prediction result;

When the output result of the Bayesian network model is different from the output result of the BERT network model, the output result of the Bayesian network model and the output result of the BERT network model are respectively subjected to confidence standardization processing , get the processing result;

The processing results are sorted, and the highest sorted output results are used as department prediction results.
The computer-readable storage medium according to claim 16, wherein the filtering and sorting of all the prediction results of departments to obtain the sorting results includes:

filtering all the department prediction results according to the patient's gender, age, preference, and historical consultation information to obtain the filtered department prediction results;

The filtered department prediction results are sorted in sequence according to the order of the rule matching model, the disease matching model, the drug matching model and the department prediction results output by the deep learning model.
The computer-readable storage medium according to claim 15, wherein the department triage method further comprises:

When no effective appeal is extracted from the appeal information through the deep learning entity recognition model, push a completion request for the appeal information;

When the completed appeal information is received, an effective appeal is extracted from the completed appeal information through the deep learning entity recognition model.
The computer-readable storage medium according to claim 16, wherein, after pushing the target department information according to the sorting result, the department triage method further comprises:

Obtain patient referral information;

Iteratively updating the trained deep learning model through the referral information to obtain the updated deep learning model.