WO2022227203A1 - Procédé, appareil et dispositif de triage basés sur une représentation de dialogue, et support de stockage - Google Patents

Procédé, appareil et dispositif de triage basés sur une représentation de dialogue, et support de stockage Download PDF

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WO2022227203A1
WO2022227203A1 PCT/CN2021/097183 CN2021097183W WO2022227203A1 WO 2022227203 A1 WO2022227203 A1 WO 2022227203A1 CN 2021097183 W CN2021097183 W CN 2021097183W WO 2022227203 A1 WO2022227203 A1 WO 2022227203A1
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dialogue
vector
target
triage
data
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Chinese (zh)
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孙行智
胡岗
朱昭苇
刘卓
唐蕊
姚海申
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks

Definitions

  • the present application relates to the field of big data, and in particular, to a method, device, device and storage medium for triage based on dialogue representation.
  • Triage is to assign patients to different departments for medical treatment according to their condition, which is of great significance for improving the efficiency of medical treatment.
  • Triage is the initial link for patients to seek medical treatment, and the selection of appropriate departments is directly related to the treatment effect or whether effective treatment can be obtained.
  • the inventors realized that the division of labor in medicine was gradually refined, and different departments specialized in the diagnosis and treatment of certain diseases. Most patients do not have profound medical knowledge, so it is difficult to identify their own conditions and choose the most appropriate department according to their own conditions.
  • the number of triage desk staff in hospitals is limited, and the volume of hospital admissions is huge, resulting in a heavy workload for triage desk staff.
  • the triage desk faces the general practice environment, which further increases the possibility of misdiagnosis, which will lead to more secondary referrals and affect the efficiency of medical treatment. Therefore, how to improve the accuracy of triage has become a technical problem that those skilled in the art need to face.
  • the main purpose of this application is to improve the accuracy of triage based on dialogue representation, and to solve the technical problem of low accuracy of triage based on dialogue representation.
  • the present application provides a method for triage based on dialogue representation, and the method for triage based on dialogue representation comprises the following steps:
  • Data cleaning is performed on the consultation data to obtain target consultation data, wherein the target consultation data includes the main complaint information of the user;
  • the present application also provides a triage device based on dialogue representation, and the triage device based on dialogue representation includes the following modules:
  • a data cleaning module configured to perform data cleaning on the consultation data to obtain target consultation data, wherein the target consultation data includes the main complaint information of the user;
  • an intention recognition module configured to perform intention recognition on the target consultation data to obtain sentence pairs included in the target consultation data, wherein the target consultation data includes at least one sentence pair;
  • the feature extraction module is used to call the preset target BERT network model to perform feature extraction on the sentence pair and the main complaint information, and obtain the sentence pair vector of the sentence pair and the main complaint vector of the main complaint information;
  • a first calculation module used for calculating the Euclidean distance between the main complaint vector and the sentence pair vector, and determining the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogues based on the Euclidean distance;
  • the identification module is used to input the dialogue representation vector of each round of dialogue into the preset triage model for identification, and obtain triage information.
  • the present application also provides a triage device based on dialogue representation
  • the triage device based on dialogue representation includes: a memory and at least one processor, wherein instructions are stored in the memory, and the a memory and the at least one processor are interconnected by wires;
  • the at least one processor invokes the instructions in the memory, so that when the dialogue-characterization-based triage device is executed, the steps of the above-mentioned dialogue-characterization-based triage method include:
  • Data cleaning is performed on the consultation data to obtain target consultation data, wherein the target consultation data includes the main complaint information of the user;
  • the triage model is used to identify and obtain triage information.
  • the present application also provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when it is run on a computer, the above-mentioned dialogue-based dialogue is realized when the computer is executed.
  • the steps of the triage method of characterization include:
  • Data cleaning is performed on the consultation data to obtain target consultation data, wherein the target consultation data includes the main complaint information of the user;
  • the triage model is used to identify and obtain triage information.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the data; Sentence pairs contained in the interrogation data; call the preset target BERT network model to perform feature extraction on the sentence pairs and main complaint information to obtain sentence pair vectors and main complaint vectors; calculate the Euclidean distance between the main complaint vectors and the sentence pair vectors respectively, and The dialogue representation vector corresponding to each round of dialogue is determined based on the Euclidean distance; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • 1 is a schematic diagram of the first embodiment of the triage method based on dialogue representation of the present application
  • FIG. 2 is a schematic diagram of a second embodiment of the triage method based on dialogue representation of the present application
  • FIG. 3 is a schematic diagram of a third embodiment of the triage method based on dialogue representation of the present application.
  • FIG. 4 is a schematic diagram of the fourth embodiment of the triage method based on dialogue representation of the present application.
  • FIG. 5 is a schematic diagram of the fifth embodiment of the triage method based on dialogue representation of the present application.
  • FIG. 6 is a schematic diagram of the first embodiment of the triage device based on dialogue representation of the present application.
  • FIG. 7 is a schematic diagram of a second embodiment of the triage device based on dialogue representation of the present application.
  • FIG. 8 is a schematic diagram of an embodiment of the triage device based on dialogue representation in the present application.
  • the embodiments of the present application provide a method, device, device, and storage medium for triage based on dialogue representation.
  • the consultation data entered by the object to be triaged in each round of dialogue is obtained and the consultation is performed.
  • the data is cleaned to obtain the target consultation data;
  • the intent recognition is performed on the target consultation data to obtain the sentence pairs contained in the target consultation data;
  • the preset target BERT network model is called to perform feature extraction on the sentence pairs and the main complaint information, and the sentence pairs are obtained.
  • the first embodiment of the triage method based on the dialogue representation in the embodiment of the present application includes:
  • the multiple rounds of dialogues generated by the object to be triaged when visiting a doctor are acquired, and the consultation data in the multiple rounds of dialogues are extracted.
  • the object to be triaged may issue a triage request instruction to the dialogue robot (doctor) in the form of voice.
  • the dialogue robot receives the voice of the object to be triaged, it can determine whether it includes preset keywords, and the keywords include but are not limited to words such as "triage", "diagnosis guide", "department” and so on. , if the voice of the subject to be triaged includes the keyword, it can be determined that the voice is a triage request instruction issued by the subject to be triaged.
  • the object to be triaged can also issue a triage request instruction to the dialogue robot through the physical button or virtual button in the designated human-computer interaction interface.
  • the specific button displayed therein can be clicked.
  • the dialogue robot can carry out N rounds of dialogue with the object to be triaged according to the preset dialogue flow, and obtains information about the number of rounds of the object to be triaged in each round. Conversational statements within a conversation.
  • N is a positive integer, and its specific value can be set according to the actual situation, for example, it can be set to 2, 3, 5 or other values.
  • the dialogue robot can conduct N rounds of dialogue with the object to be triaged. For example, in the first round of dialogue, the dialogue robot can ask: “Where are you uncomfortable?", and then obtain the dialogue sentence answered by the subject to be triaged in response to the question. In the second round of dialogue, the dialogue The robot can ask: "What main symptoms do you have", and then obtain the dialogue sentence answered by the subject to be triaged for this question, and in the Nth round of dialogue, the dialogue robot can ask: "What accompanying symptoms do you have?" , and then obtain the dialogue sentence answered by the subject to be triaged for the question.
  • the user's consultation data is obtained through multiple rounds of interactions between the object to be triaged and the robot (doctor), wherein the consultation data includes the user's basic information and main complaint content.
  • the execution body of the embodiment of the present application is a server end with a remote diagnosis function.
  • the user terminal may be a smart terminal such as a PC terminal, a mobile phone, a tablet computer, a smart watch, a smart bracelet, etc. used by the patient, and the user enters the consultation data through the user terminal, wherein the consultation data
  • the information can include: the patient's basic information and main complaints.
  • the content of the main complaint may include: time of illness (onset time, duration or duration of each attack), patient symptoms, patient identity information, patient underlying conditions (for example: what chronic disease the patient has suffered in addition to the current symptoms) )Wait.
  • Data cleaning is the process of re-examining and verifying data with the purpose of removing duplicate information, correcting existing errors, and providing data consistency.
  • data cleaning includes data desensitization, data verification, and data conversion.
  • data desensitization is used to encrypt sensitive data in source business data.
  • the data includes an individual's ID card number, etc., and the ID card number can be encrypted.
  • Data verification is used to query whether there is dirty data in the source business data, and delete the dirty data to eliminate the impact of dirty data on actuarial results.
  • the server sets a dirty data determination method for each type of data, and detects whether it is dirty data according to a preset determination method. For example, you can set the character length range or numerical size range of each type of data, etc.
  • Data verification is the process of uniformly converting data with multiple different representations into the same preset representation.
  • Data cleaning is to "wash out” the "dirty”, which refers to the last process of finding and correcting identifiable errors in data files, including checking data consistency, dealing with invalid and missing values, etc.
  • the data in the data warehouse is a collection of data oriented to a certain topic, these data are extracted from multiple business systems and contain historical data, so it is inevitable that some data are wrong data, and some data are interrelated with each other. Conflicts, these erroneous or conflicting data that we obviously don't want, are called “dirty data”.
  • the task of data cleaning is to filter those data that do not meet the requirements, and hand over the filtered results to the business department to confirm whether it is filtered out or corrected by the business unit before extracting.
  • the data that does not meet the requirements are mainly divided into three categories: incomplete data, wrong data, and duplicate data.
  • intent recognition is performed on the target consultation data to obtain sentence pairs included in the target consultation data.
  • Train and classify the target consultation data construct a convolutional neural network, take the target consultation data after replacing the entity as input, map the question after word segmentation, each word to word embedding, and the spliced entity embedding and entity location Embedding together, as the input of the neural network, the annotated intent is used as the classification label to train the classification problem.
  • the subsequent multi-round dialogue process is configured. After identifying the intent and entity, the subsequent process configuration is performed according to the obtained value.
  • the processing method of functionalizing the intent and parameterizing the entity is adopted, that is, the subsequent operation for a certain intent is regarded as a function, and the entity to be extracted is regarded as the parameter of this function, which is adjusted according to different parameters. The return result of the function.
  • the preset target BERT network model is invoked to perform feature extraction on the sentence pair and the main complaint information, and the sentence pair vector of the sentence pair and the main complaint vector of the main complaint information are obtained.
  • the full name of the BERT network model in this embodiment is Pre-training of DeepBidirectional Transformers for Language Understanding.
  • Pre-training means that BERT is a pre-training model that learns a large amount of prior language, syntax, word meaning and other information for downstream tasks through unsupervised training of a large number of corpora in the early stage.
  • Bidirectional shows that BERT adopts a two-way language model, which can better integrate the knowledge of the context.
  • BERT is a deep bidirectional pretrained language understanding model using Transformers as feature extractors.
  • Symptom recognition is named entity recognition, and its essence is a serialized labeling task.
  • the above process of semantic encoding is the process of vectorizing disease information, specifically: the pre-training model BERT vectorizes each character in the disease information, obtains the character vector of each character, and gives each character Mark the position vector to obtain a character position vector, combine each character vector and its corresponding character mark position vector to obtain the encoding vector of each character, and combine the encoding vectors of each character to obtain the disease code.
  • a graph neural network is used to perform representation learning on the target consultation data, so as to obtain sentence pair vectors for obtaining all sentence pairs and a main complaint vector for main complaint information contained in the consultation data.
  • representation learning is a collection of techniques that use computers to learn a feature, and is to convert data into a form of learning that can be learned and developed by machines.
  • the Euclidean distance between the main complaint vector and the sentence pair vector is calculated, and based on the Euclidean distance, a dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue is determined.
  • a sentence pair is also called a question-and-answer pair, which means that the dialogue between the doctor and the patient is organized into several sentence pairs, that is, a question-and-answer format. If a character speaks several times in a row, the consecutive speeches are spliced together as a whole speech. For example, in each sentence pair, the doctor's speech is the first sentence, and the patient's speech is the next sentence, such as: ⁇ CLS>: [How long has the headache been accompanied by dizziness?
  • ⁇ SEP> It has been more than a month without dizziness.
  • the former means the doctor's speech, and the latter means the patient's speech.
  • This sentence takes the vector representation of ⁇ CLS> for the final representation. Measure the Euclidean distance between the main complaint vector and each sentence pair vector.
  • Similarity Measurement the similarity measurement between different samples during classification
  • the method usually adopted in this case is to calculate the "distance" (Distance) between the samples.
  • the dialogue representation vector of each round of dialogue is input into a preset triage model for identification, and triage information is obtained.
  • the triage model is a model for automatically determining the corresponding department for the user according to the user's symptoms.
  • Different softmax classifiers are used in the fully connected layer of the prediction model, and the output result of the fully connected layer is input into the softmax classifier to obtain the prediction result of the claim settlement data set.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data; Sentence pairs contained in the data; call the preset target BERT network model to perform feature extraction on sentence pairs and main complaint information, and obtain sentence pair vectors and main complaint vectors; The distance is used to determine the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and the triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • the second embodiment of the triage method based on dialogue representation in the embodiment of the present application includes:
  • the cleaning requirements of the consultation data are obtained. Understandably, real-world data is often multi-dimensional, incomplete, noisy, and inconsistent.
  • the purpose of data cleaning is to fill in missing values, smooth noise and identify outliers, and correct inconsistencies in the data. Wait.
  • the electronic device after acquiring the data to be cleaned that needs to be cleaned, the electronic device further acquires the cleaning requirement of the data to be cleaned.
  • the cleaning requirement describes the cleaning effect that the data to be cleaned needs to be cleaned.
  • the original data to be cleaned contains data of multiple dimensions, and these dimensions are often not independent, that is to say, maybe one of them
  • the cleaning requirement of the data to be executed may be to reduce the dimension of the data to be cleaned to a specified dimension.
  • a target cleaning rule for performing data cleaning on the consultation data is determined according to the consultation data and cleaning requirements. All possible cleaning rules can be integrated in advance, and the sample data to be cleaned corresponding to each cleaning rule and its cleaning effect can be collected at the same time; then, the cleaning rule features that can characterize the cleaning rule, and the sample data that can characterize the cleaning effect and its cleaning effect are obtained. Then, take each joint feature as training input, take the cleaning rule feature corresponding to each joint feature as target output, and carry out model training according to the preset training algorithm, so as to obtain which cleaning rule to be used for training. Cleaning rule classification model for cleaning data.
  • the electronic device can input the data to be cleaned and the cleaning requirement into the cleaning rule classification model, so that the cleaning rule classification model outputs A cleaning rule that can perform data cleaning on the data to be cleaned and the cleaning effect meets the cleaning requirements, and the cleaning rule is used as the target cleaning rule for data cleaning of the data to be cleaned.
  • data cleaning is performed on the consultation data according to the target cleaning rule to obtain the target consultation data.
  • the data to be cleaned can be cleaned according to the target cleaning rule, so that the cleaning effect of the data to be cleaned meets the aforementioned cleaning requirements, and finally the required data is obtained.
  • the data to be cleaned that needs to be cleaned, and the cleaning requirements of the data to be cleaned are obtained first, and then according to the obtained data to be cleaned, the cleaning requirements and the pre-trained cleaning rule classification model, a classification model for the data to be cleaned is determined.
  • the target cleaning rules for data cleaning are performed on the cleaned data, and finally the data to be cleaned is cleaned according to the determined target cleaning rules, and the target consultation data that meets the requirements are obtained.
  • all question and answer sentences in the target consultation data and object identifiers of objects corresponding to the question and answer sentences are acquired according to preset characterization rules.
  • the target consultation data refers to the online consultation record between the robot (doctor) and the patient, which may include multiple question and answer sentences between the doctor and the patient, that is, may include the question or answer sentences corresponding to the doctor or the patient.
  • a question and answer sentence is a sentence corresponding to each conversation between the doctor or the patient in the dialogue between the doctor and the patient.
  • the object identifier is an identifier used to distinguish a doctor and a patient in the target consultation data, and may include a doctor identifier and a patient identifier.
  • the doctor identification may refer to the unified doctor identification indication applicable to all target consultation data, for example, the patient identification refers to the unified patient identification indication applicable to all target consultation data.
  • intention identification is performed on each question and answer sentence in the target consultation data, respectively, to obtain an intention identification sequence corresponding to the consultation data.
  • the intent identifier refers to an identifier that identifies the intent of each question and answer sentence, for example, a medication inquiry, a registered inquiry, a medication response, a registered response, and the like.
  • the intent identifiers of the patient's question may be represented by P 1 -P n
  • the intent identifier of the patient's answer is represented by P A
  • the intent identifier of the doctor's question is denoted as D 1 -D n
  • the intent identifier of the doctor's answer is denoted by P 1 -P n .
  • D A1 ⁇ D An Denoted as D A1 ⁇ D An .
  • the intent identifier sequence refers to a sequence composed of multiple intent identifiers, for example, P 1 , D A1 , D A1 , P 2 , P 2 , D A2 and so on.
  • the server may perform intention identification on each question and answer sentence according to the object identifier corresponding to each question and answer sentence, and generate each corresponding intent identifier, and may obtain the corresponding target consultation data according to a plurality of target consultation data of each intent identifier sequence.
  • the intent identifier of the target answer sentence corresponding to the question is determined according to the intent identifier sequence.
  • Each question sentence intent identifier corresponds to the number of answer sentence intent identifiers of each initial answer sentence, and based on each quantity, a target answer sentence intent identifier corresponding to each question sentence intent identifier is determined.
  • the server may generate a corresponding target sentence pair according to the question sentence intent identifier and the corresponding target answer sentence intent identifier. For example, if the server determines that the intent identifier of the corresponding question sentence P 1 corresponds to the intent identifier of the target answer sentence as D A1 , the server may generate a pair of target sentences as P 1 -D A1 .
  • the target sentence pair is not limited to a binary combination relationship composed of a question sentence intent identifier and a target answer sentence intent identifier, but may also be a ternary combination relationship.
  • the target consultation data includes a plurality of question and answer sentences and object identifiers corresponding to the question and answer sentences, and based on the object identifiers, intention recognition is performed on each question and answer sentence in the target consultation data.
  • the target sentence pair can be determined after the intention recognition processing of the question and answer sentences of multiple target medical consultation data, so that the generation of the target sentence pair can cover a plurality of different forms of question and answer sentences, and the target sentence pair can be improved. coverage and accuracy.
  • the intent recognition is performed on each question and answer sentence in the target consultation data, respectively, to obtain a sequence of intent identifiers corresponding to the target consultation data, which may include: determining the target consultation according to the object identifiers Each question and answer sentence corresponding to each object in the data; each question and answer sentence of each object is identified separately, and each question and answer sentence is determined to be a question sentence of the corresponding object or the answer sentence of the corresponding object; the question sentence of each object or the answer sentence of each object Perform intention identification respectively to obtain the corresponding intention identification; according to the obtained intention identification, obtain the intention identification sequence corresponding to the target consultation data.
  • the target consultation data may include multiple question and answer sentences between doctors and patients, and each question and answer sentence includes a corresponding object identifier, such as a doctor identifier or a patient identifier.
  • Steps 201, 209-211 in this embodiment are similar to steps 101, 104-106 in the first embodiment, and are not repeated here.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data; Sentence pairs contained in the data; call the preset target BERT network model to perform feature extraction on sentence pairs and main complaint information, and obtain sentence pair vectors and main complaint vectors; The distance is used to determine the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and the triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • the third embodiment of the triage method based on dialogue representation in the embodiment of the present application includes:
  • a pre-training task is established, and a preset vectorization model is invoked to process historical user consultation data into several word vectors.
  • the pre-training task is mainly used to perform the loop iterative calculation of steps S305-S306.
  • the word2vec model is a model for generating word vectors.
  • the symptom data is ⁇ cough: three days; bloodshot ⁇ , and word vectors emb1 (cough), emb2 (three days), and emb3 (bloodshot) can be obtained after transformation by the word2vec model.
  • emb1 is the first word vector
  • emb2 and emb3 are the second word vectors.
  • Symptom samples refer to the training data used to train the initial BERT network model, generally historical symptom data in a certain area.
  • each word vector can be input into the initial BERT network model as input data, and the training representation vector can be generated, and the corresponding loss value can be calculated.
  • the loss value can be obtained by calculating the loss function.
  • the loss function is defined as:
  • L(Vs, sym (n) ) is the loss value of the nth symptom; sym (n) represents the nth symptom in the symptom list; Vs represents the overall characterization vector; it is the nth symptom in the characterization vector.
  • the loss term of is the loss term of other symptoms in the representation vector.
  • the loss value of the initial BERT network model is calculated according to the training characterization vector, and the loss value of the initial BERT network model is calculated according to the training characterization vector, and the obtained loss value can be used to adjust model parameters and determine whether the model is convergence. If the loss value is outside the preset range, the model parameters of the initial BERT network model are adjusted, and the training representation vector of the symptom sample is recalculated, so as to perform iterative calculation when the model does not converge.
  • the several word vectors are input into the initial BERT network model, and the training representation vector output by the initial BERT network model is obtained, so as to perform the training step of the initial BERT network model.
  • the loss value of the initial BERT network model is calculated according to the training characterization vector, and the obtained loss value can be used to adjust model parameters and judge whether the model converges. If the loss value is outside the preset range, the model parameters of the initial BERT network model are adjusted, and the training representation vector of the symptom sample is recalculated, so as to perform iterative calculation when the model does not converge. If the loss value is within the preset range, the pre-training task is trained, and the initial BERT network model after the training is completed is the target BERT network model.
  • Steps 301 - 304 and 309 - 310 in this embodiment are similar to steps 101 - 104 and 105 - 106 in the first embodiment, and are not repeated here.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data; Sentence pairs contained in the data; call the preset target BERT network model to perform feature extraction on sentence pairs and main complaint information, and obtain sentence pair vectors and main complaint vectors; The distance is used to determine the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and the triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • the fourth embodiment of the triage method based on dialogue representation in the embodiment of the present application includes:
  • the Euclidean distance between the main complaint vector and each sentence pair vector in each round of dialogue is calculated.
  • the Euclidean distances between all sentence pairs in the consultation data and the main complaint vector are calculated respectively.
  • the Euclidean distance means that the Euclidean distance is an effective method for calculating the closest distance between a sample and a "centre of gravity" of a sample set, or for effectively calculating the similarity between two unknown sample sets. It takes into account the connection between various properties, can exclude the interference of the correlation between variables, and the Euclidean distance is scale-independent, that is, independent of the measurement scale. When ⁇ is the identity matrix, the Euclidean distance is the Euclidean distance. To sum up, the Euclidean distance can easily measure the distance between the observed sample and the known sample set, so it is very suitable for fault diagnosis.
  • the weight of each sentence pair in a corresponding round of dialogue is determined based on the Euclidean distance.
  • the representation of the main complaint is completed based on the pre-trained word vector (vector representation), and the correlation between the vector of each sentence pair and the main complaint vector is calculated as the basis for evaluating the importance of the sentence pair.
  • the specific calculation method is:
  • the weights corresponding to each sentence pair vector are weighted and averaged to obtain a dialogue representation vector corresponding to each round of dialogues in multiple rounds of dialogues. For example, in a round of dialogue, if the answer sentence in the sentence pair (question and answer sentence and answer sentence) is the answer to the question sentence, the answer sentence usually contains the keywords in the question sentence; the common key between the question sentence and the answer sentence The more and longer the number of words, the more effective information it contains, and the more important the corresponding round of dialogue is.
  • the answer sentence in the sentence pair is more likely to be the answer to the question sentence; since the target question-answer pair expected to be obtained is a complete question and answer, the longer the question sentence can better describe a complete question , and the longer the answer sentence can better describe a complete answer; if the answer sentence in the sentence pair is the answer to the question sentence, the themes between the answer sentence and the question sentence are usually consistent; if the answer sentence in the sentence pair is the answer to the question sentence is the answer to the question sentence, then there is usually a certain syntactic connection between the answer sentence and the question sentence.
  • a weighted importance measurement module (hereinafter referred to as the weighting module) is introduced to evaluate the importance of each round of dialogue, and weighted representation of all dialogues Then make a judgment.
  • the target disease information in the target consultation data is determined based on the dialogue representation vector corresponding to each round of dialogue.
  • the target disease information refers to text information or voice information input by the person to be triaged. If the person to be triaged inputs voice information, the system will now convert the voice information into text information.
  • the target disease information and the main complaint information corresponding to each round of dialogue are encoded to obtain the disease entity vector of the target consultation data.
  • coding is the process of converting information from one form or format to another, also known as coding in computer programming languages for short. Digitize characters, numbers or other objects with a predetermined method, or convert information and data into specified electrical pulse signals. Coding is widely used in electronic computers, television, remote control and communication. Encoding is the process of converting information from one form or format to another. Decoding is the reverse process of encoding.
  • the disease entity vector is input into the preset triage model for prediction, and the triage probability of different departments is obtained.
  • the convolution operation is performed on the splicing vector at the convolution layer to obtain the convolution correlation vector, and the convolution correlation vector is input to the output layer to obtain the prediction output result.
  • the prediction error loss is calculated, and the parameters of the neural network model are updated according to the prediction error loss.
  • the triage model is obtained.
  • the disease entity vector is input into the preset triage model for prediction, and the triage probability of different departments is obtained.
  • the triage probability is sorted to obtain triage information.
  • the triage probability of each department is sorted, and the recommended department with the highest set number and the corresponding triage probability are returned to the patient.
  • the triage model includes: multiple convolution layers, multiple pooling layers, fully connected layers and Softmax layers.
  • Steps 401-404 in this embodiment are similar to steps 101-104 in the first embodiment, and are not repeated here.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data; Sentence pairs contained in the data; call the preset target BERT network model to perform feature extraction on sentence pairs and main complaint information, and obtain sentence pair vectors and main complaint vectors; The distance is used to determine the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and the triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • the fifth embodiment of the triage method based on dialogue representation in the embodiment of the present application includes:
  • historical consultation data is obtained from a preset database.
  • the N rounds of dialogue with the historical patient according to the preset dialogue flow can be acquired from a preset database, and the dialogue sentences of the historical patient in each round of dialogue can be acquired.
  • N is a positive integer, and its specific value can be set according to the actual situation, for example, it can be set to 2, 3, 5 or other values. That is, the dialogue robot can conduct N rounds of dialogue with the historical patient.
  • the dialogue robot can ask: “Where are you uncomfortable”, and then obtain the dialogue sentences answered by the historical patient for this question, and in the second round of dialogue, the dialogue robot can Ask: “What main symptoms do you have”, and then obtain the dialogue sentences answered by the historical patient for this question, in the Nth round of dialogue, the dialogue robot can ask: “What accompanying symptoms do you have”, and then obtain all the Dialogue sentences answered by the patient in response to the question.
  • the target BERT network model is used to perform representation learning on the historical consultation data to obtain the second dialogue representation vector of the historical consultation data.
  • the target BERT network model is obtained after being trained on the pre-training task.
  • the pre-training task is a custom task, and the pre-training task is defined as inferring the symptom name and symptom attribute contained in the representation vector according to the current representation vector.
  • the pre-training task can ensure that the target BERT network model can learn the information contained in the output representation vector, that is, the relationship between the representation vector and the symptom feature data is determined through the pre-training task. It should be noted that here, the relationship is embodied in the model parameters of the target BERT network model.
  • symptom names and symptom attributes can be accurately converted into an overall vector, that is, a representation vector.
  • the number of generated representation vectors is equal to the number of symptoms in the symptom data. That is, how many symptoms there are in the historical consultation data, the same number and corresponding representation vectors are generated.
  • a preset training symptom and a department label corresponding to the training symptom are obtained, and the preset node set association vector is screened based on the training symptom, and a target vector corresponding to the training symptom is obtained.
  • the training symptom refers to the symptom used to train the BERT network model.
  • the department label is the department corresponding to the training symptom, and the department label is the training label. For example, if the training symptom is a skin problem, the corresponding department label is dermatology.
  • the target vector refers to the vector corresponding to the training symptom.
  • the target vector is filtered from the association vector of the node set according to the symptom.
  • the node set association vectors are screened according to the training symptoms to match the target vectors corresponding to the training symptoms, so as to ensure that the model training samples have a corresponding relationship and ensure the feasibility of model training.
  • a mapping operation is performed on the second dialogue representation vector and the target vector to obtain the dialogue embedding vector and the target embedding vector.
  • a mapping operation is performed on the graph symptom vector in the second dialogue representation vector to obtain the dialogue embedding vector.
  • the first embedding layer is a layer for performing dimension reduction processing on the second dialogue representation vector, so that the second dialogue representation vector is mapped to a dialogue embedding vector with a lower dimension.
  • a mapping operation is performed on the target vector in the second embedding layer to obtain the target embedding vector.
  • the second embedding layer is a layer used to reduce the dimension of the target vector, so that the target vector is mapped to a target embedding vector with a lower dimension.
  • a preset mapping table is used to process the target vector to obtain the target embedding vector, thereby reducing the dimension of the target embedding vector and reducing the difficulty of subsequent operations.
  • the dialogue embedding vector and the target embedding vector are spliced based on the training symptom to obtain the splicing vector.
  • the splicing vector is a vector obtained from the dialogue embedding vector and the target embedding vector to form a vector with deep meaning.
  • the splicing vector with more comprehensive information is fully utilized in the process of triage model training, so that the acquired triage model has strong generalization ability and high robustness.
  • the tf.concat() function of TensorFlow is used to splicing the dialogue embedding vector and the target embedding vector to quickly obtain the splicing vector.
  • the convolution operation is performed on the splicing vector at the convolution layer to obtain the convolution correlation vector, and the convolution correlation vector is input to the output layer to obtain the prediction output result.
  • the predicted output result is the predicted department result corresponding to the training symptom.
  • the output layer is used to calculate the probability of possible departments corresponding to the training symptoms, and the department with the highest probability is used as the prediction output result, so as to obtain the corresponding prediction output result according to the model training samples.
  • the prediction error loss is calculated based on the prediction output result and the department label, and the parameters of the target BERT network model are updated according to the prediction error loss until the target BERT network model converges to obtain a triage model based on dialogue representation. Specifically, the partial derivative of the prediction error loss is obtained to obtain the gradient value, and the parameters of the target BERT network model are updated according to the gradient value to realize the optimization of the target BERT network model.
  • the prediction error loss is less than the preset threshold, the target BERT network model Convergence, the target BERT network model is determined as the triage model.
  • Steps 501 - 505 and 513 in this embodiment are similar to steps 101 - 105 and 106 in the first embodiment, and are not repeated here.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data to obtain the target consultation data;
  • the sentence pairs contained in the diagnosis data call the preset target BERT network model to extract the features of the sentence pairs and the main complaint information, and obtain the sentence pair vector and the main complaint vector; respectively calculate the Euclidean distance between the main complaint vector and the sentence pair vector, and based on the The Euclidean distance determines the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • the triage method based on the dialogue representation in the embodiment of the present application is described above, and the triage device based on the dialogue representation in the embodiment of the present application is described below. Please refer to FIG. 6 , the triage device based on the dialogue representation in the embodiment of the present application is described
  • the first embodiment includes:
  • the data extraction module 601 is used for acquiring multiple rounds of dialogues generated by the subject to be triaged when visiting a doctor, and extracting the consultation data in the multiple rounds of dialogues;
  • a data cleaning module 602, configured to perform data cleaning on the consultation data to obtain target consultation data, wherein the target consultation data includes the main complaint information of the user;
  • Intention recognition module 603, configured to perform intention recognition on the target consultation data to obtain sentence pairs included in the target consultation data, wherein the target consultation data includes at least one sentence pair;
  • the feature extraction module 604 is used to call the preset target BERT network model to perform feature extraction on the sentence pair and the main complaint information, and obtain the sentence pair vector of the sentence pair and the main complaint vector of the main complaint information;
  • the first calculation module 605 is used to calculate the Euclidean distance between the main complaint vector and the sentence pair vector, and determine the dialogue representation vector corresponding to each round of dialogue based on the Euclidean distance;
  • the identification module 606 is configured to input the dialogue representation vector of each round of dialogue into a preset triage model for identification, and obtain triage information.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data; Sentence pairs contained in the data; call the preset target BERT network model to perform feature extraction on sentence pairs and main complaint information, and obtain sentence pair vectors and main complaint vectors; The distance is used to determine the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and the triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • the second embodiment of the triage device based on dialogue representation specifically includes:
  • the data extraction module 601 is used for acquiring multiple rounds of dialogues generated by the subject to be triaged when visiting a doctor, and extracting the consultation data in the multiple rounds of dialogues;
  • a data cleaning module 602, configured to perform data cleaning on the consultation data to obtain target consultation data, wherein the target consultation data includes the main complaint information of the user;
  • Intention recognition module 603, configured to perform intention recognition on the target consultation data to obtain sentence pairs included in the target consultation data, wherein the target consultation data includes at least one sentence pair;
  • the feature extraction module 604 is used to call the preset target BERT network model to perform feature extraction on the sentence pair and the main complaint information, and obtain the sentence pair vector of the sentence pair and the main complaint vector of the main complaint information;
  • the first calculation module 605 is used to calculate the Euclidean distance between the main complaint vector and the sentence pair vector, and determine the dialogue representation vector corresponding to each round of dialogue based on the Euclidean distance;
  • the identification module 606 is configured to input the dialogue representation vector of each round of dialogue into a preset triage model for identification, and obtain triage information.
  • the intent recognition module 603 includes:
  • Obtaining unit 6031 configured to obtain all question and answer sentences in the target consultation data and object identifiers of objects corresponding to the question and answer sentences according to preset characterization rules;
  • an intent identification unit 6032 configured to perform intent identification on each question and answer sentence in the target medical consultation data based on each of the object identifiers, to obtain an intent identification sequence corresponding to the medical consultation data;
  • the determining unit 6033 is configured to determine, according to the intent identifier sequence, a target answer sentence intent identifier corresponding to the question; and based on the target answer sentence intent identifier, obtain sentence pairs included in the target questioning data.
  • the triage device based on dialogue representation further includes:
  • the vectorization module 607 is used for establishing a pre-training task, calling a preset vectorization model to process historical user consultation data into several word vectors, wherein the word vectors include word vectors based on the historical user consultation data;
  • the first acquisition module 608 is used to input the several word vectors into the initial BERT network model, and obtain the training representation vector output by the initial BERT network model;
  • the second calculation module 609 is configured to calculate the loss value of the initial BERT network model according to the training characterization vector; according to the loss value of the initial BERT network model, adjust the model parameters of the initial BERT network model to obtain the target BERT network model.
  • the first calculation module 605 is specifically used for:
  • the weights corresponding to each sentence pair vector are weighted and averaged to obtain a dialogue representation vector corresponding to each round of dialogues in multiple rounds of dialogues.
  • the identifying module 606 is specifically used for:
  • Sorting the triage probabilities to obtain triage information wherein the triage information includes a preset number of recommended departments with the highest ranking and triage probabilities corresponding to the recommended departments.
  • the triage device based on dialogue representation further includes:
  • the second obtaining module 610 is configured to obtain historical consultation data from a preset database
  • a representation learning module 611 configured to perform representation learning on the historical consultation data through the target BERT network model, and obtain a second dialogue representation vector of the historical consultation data;
  • a screening module 612 configured to obtain a preset training symptom and a department label corresponding to the training symptom, screen a preset node set association vector based on the training symptom, and obtain a target vector corresponding to the training symptom;
  • a mapping module 613 configured to perform a mapping operation on the second dialogue representation vector and the target vector to obtain a dialogue embedding vector and a target embedding vector;
  • a splicing module 614 configured to splicing the dialogue embedding vector and the target embedding vector based on the training symptom to obtain a splicing vector
  • the convolution processing module 615 is configured to perform a convolution operation on the splicing vector at the convolution layer, obtain a convolution association vector, input the convolution association vector into the output layer, and obtain a prediction output result;
  • the updating module 616 is configured to calculate the prediction error loss based on the prediction output result and the department label, and update the parameters of the target BERT network model according to the prediction error loss, until the target BERT network model converges, obtain Diagnosis model based on dialogue representation.
  • the data cleaning module 602 is specifically used for:
  • Data cleaning is performed on the consultation data according to the target cleaning rule to obtain target consultation data.
  • the target consultation data is obtained by acquiring the consultation data entered by the object to be triaged in each round of dialogues and cleaning the consultation data; Sentence pairs contained in the data; call the preset target BERT network model to perform feature extraction on sentence pairs and main complaint information, and obtain sentence pair vectors and main complaint vectors; The distance is used to determine the dialogue representation vector corresponding to each round of dialogue in multiple rounds of dialogue; the dialogue representation vector of each round of dialogue is input into the preset triage model for identification, and the triage information is obtained.
  • This solution solves the technical problem of low triage accuracy by characterizing the chief complaint information and multiple rounds of dialogue.
  • FIG. 8 is a schematic structural diagram of a dialogue representation-based triage device provided by an embodiment of the present application.
  • the dialogue representation-based triage device 800 may vary greatly due to different configurations or performances, and may include one or more than one Central processing units (CPU) 810 (eg, one or more processors) and memory 820, one or more storage media 830 (eg, one or more mass storage devices) that store application programs 833 or data 832.
  • the memory 820 and the storage medium 830 may be short-term storage or persistent storage.
  • the program stored in the storage medium 830 may include one or more modules (not shown), and each module may include a series of instructions to operate on the dialogue characterization-based triage device 800 .
  • the processor 810 may be configured to communicate with the storage medium 830, and execute a series of instruction operations in the storage medium 830 on the dialogue-characterization-based triage device 800, so as to realize the dialogue-characterization-based diagnosis provided by the above method embodiments. Steps of the triage method.
  • Dialog-based triage device 800 may also include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input-output interfaces 860, and/or, one or more operating systems 831, For example Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.
  • operating systems 831 For example Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.
  • FIG. 8 does not constitute a limitation on the triage device based on dialogue representation provided by the present application, and may include more or less components than those shown in the figure. Either some components are combined, or different component arrangements.
  • the present application also provides a computer-readable storage medium.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • the computer-readable storage medium may also be a volatile computer-readable storage medium.
  • the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the above-mentioned method for triage based on dialogue representation.
  • 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.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium.
  • the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

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Abstract

Procédé, appareil et dispositif de triage basés sur une représentation de dialogue, et support de stockage, qui se rapportent au domaine des mégadonnées. Le procédé consiste : à acquérir une pluralité de cycles de dialogue générés lorsqu'un objet devant être soumis au triage est soumis à un traitement médical, et à extraire des données d'interrogation de diagnostic dans la pluralité de cycles de dialogue (101) ; à effectuer un nettoyage de données sur les données d'interrogation de diagnostic, de façon à obtenir des données d'interrogation de diagnostic cibles (102) ; à effectuer une reconnaissance d'intention sur les données d'interrogation de diagnostic cibles, de façon à obtenir une paire de phrases comprise dans les données d'interrogation de diagnostic cibles (103) ; à appeler un modèle de réseau BERT cible prédéfini pour effectuer une extraction de caractéristiques sur la paire de phrases et des informations de motif de consultation, de manière à obtenir un vecteur de paire de phrases de la paire de phrases et un vecteur de motif de consultation des informations de motif de consultation (104) ; à calculer une distance Euclidienne entre le vecteur de motif de consultation et le vecteur de paire de phrases, et sur la base de la distance Euclidienne, à déterminer un vecteur de représentation de dialogue correspondant à chaque cycle de dialogue parmi la pluralité de cycles de dialogue (105) ; et à entrer le vecteur de représentation de dialogue de chaque cycle de dialogue dans un modèle de triage prédéfini en vue d'une reconnaissance, de façon à obtenir des informations de triage (106). Au moyen de la représentation d'informations de motif de consultation et d'une pluralité de cycles de dialogue, le problème technique de faible précision de triage est résolu.
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