Intelligent inquiry method and device for medical history of patient, storage medium and equipment

Abstract

The invention relates to the technical field of intelligent medical treatment, and discloses an intelligent inquiry method and device for the medical history of a patient, a storage medium and computer equipment, and the method comprises the steps: outputting first-round doctor dialogue information of an intelligent doctor in response to an initiation request for an intelligent inquiry service, acquiring a first round of user dialogue information aiming at the first round of doctor dialogue information, and performing medical history keyword identification on the first round of user dialogue information; based on the recognized medical history keywords, multiple inquiry key points corresponding to the medical history keywords are obtained in a preset structured knowledge base to serve as inquiry key point information, and the preset structured knowledge base comprises multiple inquiry key points corresponding to multiple preset medical history keywords; and according to the first round of user dialogue information and the inquiry key point information, doctor dialogue information is generated and output until the dialogue between the target user and the intelligent doctor covers each piece of inquiry key point information. Therefore, the efficiency and accuracy of inquiry are improved.

Description

Technical Field

[0001] The present application relates to the field of smart medical technology, and in particular to an intelligent diagnosis method, device, storage medium and computer equipment for a patient's medical history. Background Art

[0002] In the modern medical system, accurate information about a patient's medical history is essential for doctors to make a correct diagnosis. The traditional consultation process relies on face-to-face communication between doctors and patients, where doctors obtain the patient's medical history by asking questions and listening. However, this method has some problems.

[0003] First, face-to-face consultations are limited by time and space. Doctors need to obtain as much medical history information as possible from patients within a limited time, which may result in the omission of some key information. Second, different doctors may ask patients questions based on their own experience and habits, resulting in differences in the content of the medical history information obtained, which is not conducive to subsequent diagnosis and treatment.

[0004] In order to solve these problems, some computer-assisted consultation systems have emerged in recent years. These systems usually guide doctors to conduct consultations through preset consultation templates, thereby improving the standardization of consultations. However, these systems still have some shortcomings. For example, they can only conduct consultations according to fixed templates and cannot be flexibly adjusted according to the actual situation of the patient. Summary of the invention

[0005] In view of this, the embodiments of the present application provide an intelligent medical consultation method, apparatus, storage medium and computer equipment for a patient's medical history, which improves the accuracy and efficiency of the medical consultation.

[0006] According to one aspect of the present application, an intelligent diagnosis method for a patient's medical history is provided, the method comprising:

[0007] In response to a request for initiating an intelligent consultation service, outputting a first round of doctor dialogue information of an intelligent doctor, acquiring a first round of user dialogue information for the first round of doctor dialogue information, and performing medical history keyword recognition on the first round of user dialogue information;

[0008] Based on the identified medical history keywords, a plurality of medical inquiry key points corresponding to the medical history keywords are obtained from a preset structured knowledge base as medical inquiry key point information, wherein the preset structured knowledge base contains a plurality of medical inquiry key points corresponding to each of the plurality of preset medical history keywords;

[0009] Based on the first round of user dialogue information and the consultation key points information, doctor dialogue information is generated and outputted until the dialogue between the target user and the intelligent doctor covers all the consultation key points information.

[0010] Optionally, based on the identified medical history keywords, obtaining a plurality of medical inquiry key points corresponding to the medical history keywords in a preset structured knowledge base as medical inquiry key point information includes:

[0011] Obtain the consultation category of the initiation request instruction;

[0012] A plurality of consultation key points corresponding to the disease keyword under the consultation category are obtained in the preset structured knowledge base as the consultation key point information.

[0013] Optionally, generating doctor dialogue information for output according to the first round of user dialogue information and the consultation key points information includes:

[0014] The first round of doctor dialogue information and the first round of user dialogue information are used as the current round of dialogue information, and the current round of dialogue information is understood based on the consultation key points information by using the consultation key points understanding big model, so as to determine whether the current round of dialogue information satisfies the key point information obtaining condition of any consultation key point in the consultation key point information, wherein the key point information obtaining condition includes that the consultation key point has been asked and an answer that complies with the rule has been obtained;

[0015] If the key point information obtaining condition of any of the key points of the medical consultation is met, the satisfied key points of the medical consultation are removed from the key points of the medical consultation information, and the next round of doctor dialogue information is continued to be generated if the key points of the medical consultation information is not cleared, otherwise the next round of doctor dialogue information is stopped from being generated;

[0016] If the key information acquisition conditions of any of the key questions are not met, the next round of doctor dialogue information is directly generated.

[0017] Optionally, before the large model for understanding the key points of the consultation is used to understand the key points of the consultation for the current round of dialogue information based on the key points of the consultation information, the method further includes:

[0018] Obtaining a sample of medical consultation dialogue information pre-labeled with medical consultation key point labels, wherein the sample of medical consultation dialogue information includes at least one round of medical consultation dialogue, each round of medical consultation dialogue is labeled with a medical consultation key point label, the medical consultation key point label includes an empty label and a plurality of medical consultation key point identification labels, the empty label indicates that the corresponding round of medical consultation dialogue does not contain medical consultation key points or does not meet the key point information acquisition condition, and the medical consultation key point identification label indicates the identification of the medical consultation key points contained in the corresponding round of medical consultation dialogue;

[0019] The initial large model is fine-tuned and trained based on the medical consultation dialogue information sample to obtain the medical consultation key points understanding large model.

[0020] Optionally, after removing the satisfied medical consultation points from the medical consultation points information, the method further includes:

[0021] Record the removed key points as the key points asked;

[0022] Generate the next round of doctor dialogue information, including:

[0023] Generate multiple candidate doctor dialogue information of the intelligent doctor for the current round of user dialogue information based on the current round of dialogue information and historical dialogue context through the consultation dialogue big model;

[0024] The candidate doctor conversation information is used to identify the key points of the medical consultation, and the candidate doctor conversation information that contains the key points of the medical consultation is deleted. One item of the remaining candidate doctor conversation information is selected as the next round of doctor conversation information for output, and the next round of doctor conversation information and the user conversation information corresponding to the next round of doctor conversation information are used as new information for this round of conversation, and the step of understanding the key points of the medical consultation for the current round of conversation information based on the key points of the medical consultation information is returned to.

[0025] Optionally, the large consultation dialogue model generates multiple candidate doctor dialogue information of the intelligent doctor for the current round of user dialogue information based on the current round of dialogue information and the historical dialogue context, including:

[0026] Determine whether the current dialogue round reaches the preset dialogue round;

[0027] If the current conversation round does not reach the preset conversation round, the consultation conversation big model is used to generate multiple candidate doctor conversation information of the intelligent doctor for the current round of user conversation information based on the current round of conversation information and the historical conversation context;

[0028] If the current conversation round has reached the preset conversation round, a conversation generation prompt word is constructed based on the consultation key points information, the current round conversation information and the historical conversation context, and the conversation generation prompt word is input into the consultation conversation big model, so that the consultation conversation big model generates the next round of doctor conversation information containing any consultation key points in the consultation key points information based on the current round conversation information and the historical conversation context.

[0029] Optionally, selecting one of the remaining candidate doctor dialogue information as the next round of doctor dialogue information to output includes:

[0030] If any of the remaining candidate doctor dialogue information contains any of the consultation key points in the consultation key point information, then one of the candidate doctor dialogue information containing any of the consultation key points is selected as the next round of doctor dialogue information for output;

[0031] If the remaining candidate doctor dialogue information does not contain any of the consultation key points in the consultation key point information, then one of the remaining candidate doctor dialogue information is selected as the next round of doctor dialogue information for output.

[0032] According to another aspect of the present application, there is provided an intelligent diagnosis device for a patient's medical history, the device comprising:

[0033] A keyword recognition module, configured to respond to a request for initiating an intelligent consultation service, output a first round of doctor dialogue information of an intelligent doctor, obtain a first round of user dialogue information for the first round of doctor dialogue information, and perform medical history keyword recognition on the first round of user dialogue information;

[0034] A medical question key point acquisition module, used for acquiring, based on the identified medical history keywords, a plurality of medical question key points corresponding to the medical history keywords in a preset structured knowledge base as medical question key point information, wherein the preset structured knowledge base contains a plurality of medical question key points corresponding to each of the plurality of preset medical history keywords;

[0035] The intelligent consultation module is used to generate doctor dialogue information for output based on the first round of user dialogue information and the consultation key points information, until the dialogue between the target user and the intelligent doctor covers all the consultation key points information.

[0036] Optionally, the medical consultation key points acquisition module is further used to:

[0037] Obtain the consultation category of the initiation request instruction;

[0038] A plurality of consultation key points corresponding to the disease keyword under the consultation category are obtained in the preset structured knowledge base as the consultation key point information.

[0039] Optionally, the intelligent consultation module is further used to:

[0040] The first round of doctor dialogue information and the first round of user dialogue information are used as the current round of dialogue information, and the current round of dialogue information is understood based on the consultation key points information by using the consultation key points understanding big model, so as to determine whether the current round of dialogue information satisfies the key point information obtaining condition of any consultation key point in the consultation key point information, wherein the key point information obtaining condition includes that the consultation key point has been asked and an answer that complies with the rule has been obtained;

[0041] If the key point information obtaining condition of any of the key points of the medical consultation is met, the satisfied key points of the medical consultation are removed from the key points of the medical consultation information, and the next round of doctor dialogue information is continued to be generated if the key points of the medical consultation information is not cleared, otherwise the next round of doctor dialogue information is stopped from being generated;

[0042] If the key information acquisition conditions of any of the key questions are not met, the next round of doctor dialogue information is directly generated.

[0043] Optionally, the device further comprises: a model training module, configured to:

[0044] Obtaining a sample of medical consultation dialogue information pre-labeled with medical consultation key point labels, wherein the sample of medical consultation dialogue information includes at least one round of medical consultation dialogue, each round of medical consultation dialogue is labeled with a medical consultation key point label, the medical consultation key point label includes an empty label and a plurality of medical consultation key point identification labels, the empty label indicates that the corresponding round of medical consultation dialogue does not contain medical consultation key points or does not meet the key point information acquisition condition, and the medical consultation key point identification label indicates the identification of the medical consultation key points contained in the corresponding round of medical consultation dialogue;

[0045] The initial large model is fine-tuned and trained based on the medical consultation dialogue information sample to obtain the medical consultation key points understanding large model.

[0046] Optionally, the intelligent consultation module is further used to:

[0047] Record the removed key points as the key points asked;

[0048] Through the consultation dialogue large model, based on the current round of dialogue information and historical dialogue context, the intelligent doctor generates multiple candidate doctor dialogue information for the current round of user dialogue information; the consultation key points of the candidate doctor dialogue information are identified, the candidate doctor dialogue information containing the consultation key points are deleted, and one of the remaining candidate doctor dialogue information is selected as the next round of doctor dialogue information for output, and the next round of doctor dialogue information and the user dialogue information corresponding to the next round of doctor dialogue information are used as the new current round of dialogue information, and the process returns to the step of understanding the consultation key points of the current round of dialogue information based on the consultation key points information.

[0049] Optionally, the intelligent consultation module is further used to:

[0050] Determine whether the current dialogue round reaches the preset dialogue round;

[0051] If the current conversation round does not reach the preset conversation round, the consultation conversation big model is used to generate multiple candidate doctor conversation information of the intelligent doctor for the current round of user conversation information based on the current round of conversation information and the historical conversation context;

[0052] If the current conversation round has reached the preset conversation round, a conversation generation prompt word is constructed based on the consultation key points information, the current round conversation information and the historical conversation context, and the conversation generation prompt word is input into the consultation conversation big model, so that the consultation conversation big model generates the next round of doctor conversation information containing any consultation key points in the consultation key points information based on the current round conversation information and the historical conversation context.

[0053] Optionally, the intelligent consultation module is further used to:

[0054] If any of the remaining candidate doctor dialogue information contains any of the consultation key points in the consultation key point information, then one of the candidate doctor dialogue information containing any of the consultation key points is selected as the next round of doctor dialogue information for output;

[0055] If the remaining candidate doctor dialogue information does not contain any of the consultation key points in the consultation key point information, then one of the remaining candidate doctor dialogue information is selected as the next round of doctor dialogue information for output.

[0056] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned intelligent diagnosis method for the patient's medical history is implemented.

[0057] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned intelligent diagnosis method for the patient's medical history when executing the program.

[0058] By means of the above technical solution, the embodiments of the present application provide an intelligent consultation method, device, storage medium and computer equipment for the patient's medical history. Through the intelligent consultation service, patients can initiate consultation requests at any time and any place without waiting for face-to-face consultation with doctors, which saves patients' time and energy, and also improves the utilization efficiency of medical resources. By identifying the medical history keywords in the user's dialogue and searching the knowledge base for relevant consultation key points based on these keywords to guide the consultation process, the standardization of the consultation is ensured, so that the intelligent consultation process can more deeply mine and analyze the user's medical history information, thereby providing more accurate and comprehensive diagnostic suggestions.

[0059] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0061] Figure 1 A schematic diagram of a process of an intelligent diagnosis method for a patient's medical history provided in an embodiment of the present application is shown;

[0062] Figure 2 A schematic diagram of a process of another intelligent diagnosis method for a patient's medical history provided in an embodiment of the present application is shown;

[0063] Figure 3 A schematic diagram of the structure of an intelligent medical history inquiry device for a patient provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0064] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0065] In this embodiment, an intelligent diagnosis method for a patient's medical history is provided. Figure 1 As shown, the method includes:

[0066] Step 101: In response to a request for initiating an intelligent medical consultation service, output the first round of doctor dialogue information of the intelligent doctor, obtain the first round of user dialogue information for the first round of doctor dialogue information, and perform medical history keyword recognition on the first round of user dialogue information.

[0067] Step 102: Based on the identified medical history keywords, multiple medical inquiry points corresponding to the medical history keywords are obtained from a preset structured knowledge base as medical inquiry point information, wherein the preset structured knowledge base contains multiple medical inquiry points corresponding to each of the preset medical history keywords.

[0068] Step 103: Generate doctor dialogue information based on the first round of user dialogue information and the consultation key points information for output until the dialogue between the target user and the intelligent doctor covers all the consultation key points information.

[0069] In the embodiment of the present application, when a user (a patient or a patient's family member, etc.) initiates a request for an intelligent consultation service in some way (such as clicking a webpage button, using a mobile application, etc.), the request is first responded to and the first round of doctor dialogue information of the intelligent doctor is output. This is usually a guiding speech to guide the user to start describing their medical history. Next, the user's first round of dialogue information for this guiding speech is obtained, and the medical history keywords are identified for this information. Medical history keywords refer to key words that can reflect the user's medical history, symptoms, disease history, etc., and the name of the disease can be determined based on the medical history keywords. After identifying the medical history keywords, the medical history keywords are used to query the preset structured knowledge base. This knowledge base is constructed in advance and contains multiple consultation points corresponding to multiple preset medical history keywords. Specifically, the preset structured knowledge base can contain multiple preset medical history keywords corresponding to multiple disease names, and multiple consultation points corresponding to multiple disease names. The medical history keywords are matched with the preset medical history keywords, and the multiple consultation points of the disease name corresponding to the matched preset medical history keywords are used as the consultation points information. The key points of the medical consultation refer to questions or guiding statements that are asked to the user to obtain more detailed medical history information based on a certain medical history keyword. After obtaining the key points of the medical consultation, the doctor generates the next round of dialogue information based on this information and the user's first round of dialogue information. Then, this information is output to the user and the doctor waits for the user's next round of dialogue. This process is repeated until all the key points of the medical consultation related to the current medical history keyword have been covered through dialogue.

[0070] In addition, it should be noted that if the medical history keywords are not identified from the first round of user dialogue information, the guiding discourse can be regenerated and output based on the first round of user dialogue information until the disease keywords can be obtained from the user dialogue information and the subsequent steps are executed.

[0071] By applying the technical solution of this embodiment, through the intelligent consultation service, patients can initiate consultation requests at any time and any place without waiting for face-to-face consultation with doctors, which saves patients' time and energy and improves the utilization efficiency of medical resources. By identifying the medical history keywords in the user's dialogue and searching the knowledge base for relevant consultation key points based on these keywords to guide the consultation process, the standardization of the consultation is ensured, so that the intelligent consultation process can more deeply mine and analyze the user's medical history information, thereby providing more accurate and comprehensive diagnostic suggestions.

[0072] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another intelligent diagnosis method for the patient's medical history is provided, such as Figure 2 As shown, the method includes:

[0073] Step 201: In response to a request for initiating an intelligent medical consultation service, output the first round of doctor dialogue information of the intelligent doctor, obtain the first round of user dialogue information for the first round of doctor dialogue information, and perform medical history keyword recognition on the first round of user dialogue information.

[0074] In an embodiment of the present application, when a user initiates a request for an intelligent consultation service, the first round of dialogue information of the intelligent doctor is output first, which is usually an opening remark or a guiding question to start the conversation. Subsequently, the user's response, that is, the first round of user dialogue information, is obtained, and medical history keywords are identified. Medical history keywords are important disease or symptom information that may be involved in the user's answer. By quickly responding and guiding users to provide medical history information, the foundation is laid for the subsequent consultation process and the consultation efficiency is improved.

[0075] Step 202, obtaining the consultation category indicated by the initiation request; obtaining a plurality of consultation key points corresponding to the disease keyword under the consultation category in the preset structured knowledge base as the consultation key point information.

[0076] In an embodiment of the present application, according to the consultation category (such as depression) specified by the user when initiating the request, the key points of the consultation corresponding to the consultation category and the identified disease keywords (such as epilepsy) are searched in the preset structured knowledge base. These key points of the consultation are the key information points that the doctor needs to obtain during the consultation process. By accurately matching the consultation category and the disease keywords, the system can customize the extraction of the key points of the consultation for the user's specific situation to ensure the pertinence and comprehensiveness of the consultation.

[0077] Step 203: Use the first round of doctor dialogue information and the first round of user dialogue information as this round of dialogue information, and use the large model for understanding the key points of consultation to understand the key points of consultation for the current round of dialogue information based on the key points of consultation information, so as to determine whether the current round of dialogue information satisfies the key point information acquisition condition for any key point in the key point information, wherein the key point information acquisition condition includes that the key point of consultation has been asked and an answer that complies with the rules has been obtained; if the key point information acquisition condition for any key point of consultation is not met, execute step 205.

[0078] In the embodiment of the present application, the first round of doctor dialogue information and the first round of user dialogue information are used as the current round of dialogue information, and the information is processed by the big model for understanding the key points of the consultation to determine whether the key point information acquisition conditions of any consultation point are met, including that the consultation point has been asked and the user has given an answer that complies with the rules. The answer that complies with the rules usually means that the user has given a clear answer to the consultation point. Through the intelligent understanding of the big model, the progress of the consultation can be evaluated in real time to ensure that each consultation point has received appropriate attention and processing, avoiding omissions.

[0079] Step 204: If the conditions for obtaining the key information of any of the key points of the medical consultation are met, the satisfied key points of the medical consultation will be removed from the key points of the medical consultation information, and the removed key points of the medical consultation will be recorded as the key points of the medical consultation that have been asked. If the key points of the medical consultation information has not been cleared, step 205 will be executed, otherwise the generation of the next round of doctor dialogue information will be stopped.

[0080] In an embodiment of the present application, if it is found that a certain inquiry point has met the conditions for obtaining the key point information, it will be removed from the inquiry point information and marked as the inquiry point has been asked. If all the inquiry points have been met, the generation of the next round of doctor dialogue information will be stopped, because the necessary inquiry process has been completed at this time, and the subsequent process can be continued, such as sending the inquiry record to the doctor terminal for evaluation by the doctor terminal. By dynamically updating the inquiry point list, the system can efficiently manage the inquiry process, ensure the completeness and accuracy of the inquiry, and avoid unnecessary repeated inquiries. In addition, when it is determined to stop generating the next round of doctor dialogue information, a dialogue record can also be generated based on the dialogue information of each round, and the dialogue record marks the inquiry points contained in each round of dialogue and the answers given by the user to the inquiry points that comply with the rules, so that subsequent doctors can quickly and clearly understand the information related to the inquiry points, which is convenient for doctors to accurately and quickly give judgments and suggestions.

[0081] Step 205: Generate multiple candidate doctor dialogue information of the intelligent doctor for the current round of user dialogue information based on the current round of dialogue information and historical dialogue context through the consultation dialogue big model.

[0082] In the embodiment of the present application, if the information of the key points of the consultation is not cleared, that is, if a dialogue that satisfies the conditions for obtaining the key points of all the key points of the consultation is not obtained, a plurality of candidate doctor dialogue information will be generated through the consultation dialogue large model based on the current round of dialogue information and the historical dialogue context. These candidate dialogue information is intended to guide the user to provide further information to meet the unmet key points of the consultation. By generating a variety of candidate dialogue information, the system can flexibly respond to various answers and reactions of users, and improve the adaptability and interactivity of the consultation.

[0083] Step 206: identify the key points of the candidate doctor dialogue information, delete the candidate doctor dialogue information that contains the key points of the already asked dialogue, select one of the remaining candidate doctor dialogue information as the next round of doctor dialogue information for output, and use the next round of doctor dialogue information and the user dialogue information corresponding to the next round of doctor dialogue information as new current round dialogue information, and return to the step of understanding the key points of the current round of dialogue information based on the key points of the dialogue information.

[0084] In an embodiment of the present application, the candidate doctor dialogue information is identified for the key points of the consultation, thereby deleting the candidate dialogue information containing the key points of the consultation that have been asked, and then the most suitable one is selected from the remaining candidate dialogue information as the next round of doctor dialogue information for output. At the same time, this new doctor dialogue information and its corresponding user dialogue information are used as the new dialogue information for this round, and the process of understanding the key points of the consultation based on the key points of the consultation is returned to continue the consultation process. By continuously iterating and optimizing the content of the dialogue, the user's medical history information can be gradually and deeply obtained to ensure the comprehensiveness and depth of the consultation. At the same time, by deleting the dialogue information containing the key points of the consultation that have been asked, redundancy and repetition are avoided, and the efficiency of the consultation is improved.

[0085] In summary, this intelligent medical history consultation method achieves intelligent, efficient and customized acquisition of patient medical history information by combining a structured knowledge base, a large model for understanding medical history key points and a large model for medical history dialogue. This method can not only improve the accuracy and completeness of medical consultation, but also enhance the interactivity and communication efficiency between doctors and patients, providing strong support for the intelligent upgrade of medical services.

[0086] In the embodiment of the present application, optionally, step 205 includes:

[0087] Determine whether the current dialogue round reaches the preset dialogue round;

[0088] If the current conversation round does not reach the preset conversation round, the consultation conversation big model is used to generate multiple candidate doctor conversation information of the intelligent doctor for the current round of user conversation information based on the current round of conversation information and the historical conversation context;

[0089] If the current conversation round has reached the preset conversation round, a conversation generation prompt word is constructed based on the consultation key points information, the current round conversation information and the historical conversation context, and the conversation generation prompt word is input into the consultation conversation big model, so that the consultation conversation big model generates the next round of doctor conversation information containing any consultation key points in the consultation key points information based on the current round conversation information and the historical conversation context.

[0090] In the above embodiment, the conversation round between the current intelligent doctor and the user is recorded. Before generating the next round of doctor conversation information, it is first determined whether the current conversation round has reached the preset conversation round. If the current conversation round has not reached the preset conversation round, multiple candidate doctor conversation information is directly generated based on the current round of conversation information and the historical conversation context through the consultation conversation big model. If the current conversation round has reached or exceeded the preset conversation round, a more refined generation strategy can be adopted. Specifically, a conversation generation prompt is first constructed based on the consultation key points information, the current round of conversation information and the historical conversation context. This prompt contains the current unmet consultation key points, the current round of conversation information and the conversation context information, aiming to guide the consultation conversation big model to generate more targeted and more accurate next round of doctor conversation information. Then, this prompt is input into the consultation conversation big model, and the model generates the next round of doctor conversation information containing the unmet consultation key points based on this information. Ensure that the next round of doctor conversation information output by the big model can not only contain the uncovered consultation key points, but also keep it as coherent and unobtrusive as possible with the above text. By setting preset dialogue rounds, the embodiment of the present application can effectively control the rounds and duration of dialogues while ensuring the quality of consultation, avoid lengthy and repetitive consultation processes, and improve consultation efficiency. After reaching the preset dialogue rounds, by constructing dialogue generation prompts and inputting them into the consultation dialogue model, the system can generate more targeted and accurate information for the next round of doctor dialogues, ensuring the targetedness and depth of the consultation. And through refined dialogue generation strategies, the computing power of the consultation dialogue model can be more effectively utilized, unnecessary computing overhead can be reduced, and resource utilization efficiency can be optimized.

[0091] In an embodiment of the present application, optionally, the step 206 of selecting one item from the remaining candidate doctor dialogue information as the next round of doctor dialogue information for output includes: if any remaining candidate doctor dialogue information contains any consultation point in the consultation point information, then selecting one item from the candidate doctor dialogue information containing any consultation point as the next round of doctor dialogue information for output; if none of the remaining candidate doctor dialogue information contains any consultation point in the consultation point information, then selecting one item from the remaining candidate doctor dialogue information as the next round of doctor dialogue information for output.

[0092] In the above embodiment, when selecting the next round of doctor dialogue information from the remaining candidate doctor dialogue information, specifically, first check the remaining candidate doctor dialogue information to determine whether they contain any of the inquiry points in the inquiry points information. This step is to ensure that the next round of doctor dialogue information can continue to advance the consultation process and guide the user to answer the unsatisfied inquiry points. If there is at least one dialogue information containing the inquiry points in the remaining candidate doctor dialogue information, then one of the candidate dialogue information containing the inquiry points is selected as the next round of doctor dialogue information for output, wherein the selection criteria can be based on a variety of factors, such as the clarity, pertinence, and guidance of the dialogue information, to ensure that the next round of dialogue can effectively guide the user to answer the unsatisfied inquiry points. If none of the remaining candidate doctor dialogue information contains any of the inquiry points in the inquiry points information, then one of the candidate dialogue information that does not contain the inquiry points is selected as the next round of doctor dialogue information for output. The embodiment of the present application can ensure the continuity of the consultation process and avoid missing important inquiry points by preferentially selecting candidate dialogue information containing inquiry points as the next round of doctor dialogue information. Selecting one of the candidate dialogue messages containing the key points of the consultation to output can more effectively guide the user to answer the unmet key points of the consultation, reduce unnecessary dialogue turns, and improve the efficiency of the consultation.

[0093] In an embodiment of the present application, optionally, before the consultation key points understanding big model understands the consultation key points of the current round of dialogue information based on the consultation key points information, the method further includes: obtaining consultation dialogue information samples pre-labeled with consultation key points labels, wherein the consultation dialogue information samples include at least one round of consultation dialogue, each round of consultation dialogue is labeled with consultation key points labels, the consultation key points labels include empty labels and multiple consultation key points identification labels, the empty labels indicate that the corresponding round of consultation dialogue does not contain consultation key points or does not meet the key points information acquisition conditions, and the consultation key points identification labels indicate the identification of the consultation key points contained in the corresponding round of consultation dialogue; fine-tuning the initial big model based on the consultation dialogue information samples to obtain the consultation key points understanding big model.

[0094] In the above embodiment, the specific training steps of the large model for understanding the key points of the consultation are as follows: first, a series of consultation dialogue information samples pre-labeled with the key points of the consultation are obtained, and these samples include at least one round of consultation dialogues, and each round of consultation dialogues is labeled with the key points of the consultation. The key points of the consultation labels are divided into empty labels and multiple key points of the consultation identification labels. The empty label indicates that the consultation dialogue of the corresponding round does not contain the key points of the consultation or does not meet the conditions for obtaining the key points information, that is, the round of dialogue does not provide any key points of the consultation that are helpful for diagnosis or the user's answer to the key points of the consultation does not comply with the rules. The key points of the consultation identification label indicates the identification of the specific key points of the consultation contained in the corresponding round of the consultation dialogue, and these identifications correspond to the various key points in the preset key points of the consultation information library. Then, these labeled consultation dialogue information samples are used to fine-tune the initial large model. Fine-tuning training is a method for further training on a specific task or data set based on a large-scale pre-trained model, aiming to improve the performance of the model on a specific task. During the fine-tuning training process, the model will learn to identify and understand the key information in the consultation dialogue, especially the information related to the key points of the consultation. Through continuous iterative training, the model can gradually improve its recognition ability and accuracy of the key points of the consultation information. After fine-tuning training, a large model specifically used for understanding the key points of the consultation is obtained, that is, the large model for understanding the key points of the consultation. This model can deeply understand and analyze the consultation dialogue information based on the key points of the consultation information, determine whether the dialogue contains specific key points of the consultation, and evaluate whether these key points meet the conditions for obtaining the key points information. In the embodiment of the present application, the large model for understanding the key points of the consultation can more accurately identify and understand the key information in the consultation dialogue, especially the information related to the key points of the consultation, by using the labeled consultation dialogue information samples for fine-tuning training. This helps to reduce the risk of misdiagnosis and missed diagnosis, and improve the accuracy and reliability of diagnosis. Fine-tuning training not only improves the performance of the model on specific tasks, but also enhances its generalization ability, so that the large model can better adapt to the consultation dialogues in different scenarios and situations, and improve its performance in complex situations. The application of a large model for understanding the key points of medical consultation can optimize the consultation process and enable doctors to obtain patient medical history information more efficiently. By automatically identifying and understanding the key points of medical consultation, the model can reduce unnecessary rounds of dialogue, improve consultation efficiency, and more accurately understand the patient's medical history and needs, thereby providing more accurate diagnosis and treatment recommendations, and improving patient satisfaction and trust.

[0095] By applying the technical solution of this embodiment, by introducing a large model for understanding the key points of the consultation and a large model for the consultation dialogue, and combining the intelligent judgment of the dialogue rounds with the fine selection of candidate dialogue information, the intelligent consultation method provided by the embodiment of the present application significantly improves the accuracy and efficiency of the consultation. The large model for understanding the key points of the consultation can deeply understand and analyze the consultation dialogue information, accurately identify and evaluate the key points of the consultation, and reduce the risk of misdiagnosis and missed diagnosis. At the same time, the large model for the consultation dialogue can intelligently generate the next round of doctor dialogue information based on the historical dialogue context and the current round of dialogue information, guide the user to continue to provide information, and meet the unmet consultation points. In addition, by judging the dialogue rounds, the system can flexibly adjust the dialogue generation strategy to ensure the continuity and efficiency of the consultation process. Finally, by finely selecting the candidate dialogue information, the system can provide a more personalized and intimate consultation service, and enhance the user's satisfaction and trust. In summary, the embodiment of the present application provides a more complete, efficient and reliable solution for the intelligent upgrade of medical services.

[0096] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides an intelligent diagnosis device for the patient's medical history, such as Figure 3 As shown, the device comprises:

[0097] A keyword recognition module, configured to respond to a request for initiating an intelligent consultation service, output a first round of doctor dialogue information of an intelligent doctor, obtain a first round of user dialogue information for the first round of doctor dialogue information, and perform medical history keyword recognition on the first round of user dialogue information;

[0098] A medical question key point acquisition module, used for acquiring, based on the identified medical history keywords, a plurality of medical question key points corresponding to the medical history keywords in a preset structured knowledge base as medical question key point information, wherein the preset structured knowledge base contains a plurality of medical question key points corresponding to each of the plurality of preset medical history keywords;

[0099] The intelligent consultation module is used to generate doctor dialogue information for output based on the first round of user dialogue information and the consultation key points information, until the dialogue between the target user and the intelligent doctor covers all the consultation key points information.

[0100] Optionally, the medical consultation key points acquisition module is further used to:

[0101] Obtain the consultation category of the initiation request instruction;

[0102] A plurality of consultation key points corresponding to the disease keyword under the consultation category are obtained in the preset structured knowledge base as the consultation key point information.

[0103] Optionally, the intelligent consultation module is further used to:

[0104] The first round of doctor dialogue information and the first round of user dialogue information are used as the current round of dialogue information, and the current round of dialogue information is understood based on the consultation key points information by using the consultation key points understanding big model, so as to determine whether the current round of dialogue information satisfies the key point information obtaining condition of any consultation key point in the consultation key point information, wherein the key point information obtaining condition includes that the consultation key point has been asked and an answer that complies with the rule has been obtained;

[0105] If the key point information obtaining condition of any of the key points of the medical consultation is met, the satisfied key points of the medical consultation are removed from the key points of the medical consultation information, and the next round of doctor dialogue information is continued to be generated if the key points of the medical consultation information is not cleared, otherwise the next round of doctor dialogue information is stopped from being generated;

[0106] If the key information acquisition conditions of any of the key questions are not met, the next round of doctor dialogue information is directly generated.

[0107] Optionally, the device further comprises: a model training module, configured to:

[0108] Obtaining a sample of medical consultation dialogue information pre-labeled with medical consultation key point labels, wherein the sample of medical consultation dialogue information includes at least one round of medical consultation dialogue, each round of medical consultation dialogue is labeled with a medical consultation key point label, the medical consultation key point label includes an empty label and a plurality of medical consultation key point identification labels, the empty label indicates that the corresponding round of medical consultation dialogue does not contain medical consultation key points or does not meet the key point information acquisition condition, and the medical consultation key point identification label indicates the identification of the medical consultation key points contained in the corresponding round of medical consultation dialogue;

[0109] The initial large model is fine-tuned and trained based on the medical consultation dialogue information sample to obtain the medical consultation key points understanding large model.

[0110] Optionally, the intelligent consultation module is further used to:

[0111] Record the removed key points as the key points asked;

[0112] Through the consultation dialogue large model, based on the current round of dialogue information and historical dialogue context, the intelligent doctor generates multiple candidate doctor dialogue information for the current round of user dialogue information; the consultation key points of the candidate doctor dialogue information are identified, the candidate doctor dialogue information containing the consultation key points are deleted, and one of the remaining candidate doctor dialogue information is selected as the next round of doctor dialogue information for output, and the next round of doctor dialogue information and the user dialogue information corresponding to the next round of doctor dialogue information are used as the new current round of dialogue information, and the process returns to the step of understanding the consultation key points of the current round of dialogue information based on the consultation key points information.

[0113] Optionally, the intelligent consultation module is further used to:

[0114] Determine whether the current dialogue round reaches the preset dialogue round;

[0115] If the current conversation round does not reach the preset conversation round, the consultation conversation big model is used to generate multiple candidate doctor conversation information of the intelligent doctor for the current round of user conversation information based on the current round of conversation information and the historical conversation context;

[0116] If the current conversation round has reached the preset conversation round, a conversation generation prompt word is constructed based on the consultation key points information, the current round conversation information and the historical conversation context, and the conversation generation prompt word is input into the consultation conversation big model, so that the consultation conversation big model generates the next round of doctor conversation information containing any consultation key points in the consultation key points information based on the current round conversation information and the historical conversation context.

[0117] Optionally, the intelligent consultation module is further used to:

[0118] If any of the remaining candidate doctor dialogue information contains any of the consultation key points in the consultation key point information, then one of the candidate doctor dialogue information containing any of the consultation key points is selected as the next round of doctor dialogue information for output;

[0119] If the remaining candidate doctor dialogue information does not contain any of the consultation key points in the consultation key point information, then one of the remaining candidate doctor dialogue information is selected as the next round of doctor dialogue information for output.

[0120] It should be noted that for other corresponding descriptions of the functional units involved in the intelligent medical history inquiry device provided in the embodiment of the present application, reference can be made to Figure 1 to Figure 2 The corresponding description in the method will not be repeated here.

[0121] The embodiment of the present application also provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a bus, a processor, a memory and a communication interface, and can also include an input and output interface and a display device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in each method embodiment are implemented.

[0122] Those skilled in the art will appreciate that the structure of the above-mentioned computer device is only a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components, or combine certain components, or have a different arrangement of components.

[0123] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program thereon. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0124] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0125] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An intelligent diagnosis method for a patient's medical history, characterized in that: The method comprises: In response to a request for initiating an intelligent consultation service, outputting a first round of doctor dialogue information of an intelligent doctor, acquiring a first round of user dialogue information for the first round of doctor dialogue information, and performing medical history keyword recognition on the first round of user dialogue information; Based on the identified medical history keywords, a plurality of medical inquiry key points corresponding to the medical history keywords are obtained from a preset structured knowledge base as medical inquiry key point information, wherein the preset structured knowledge base contains a plurality of medical inquiry key points corresponding to each of the plurality of preset medical history keywords; Based on the first round of user dialogue information and the consultation key points information, doctor dialogue information is generated and outputted until the dialogue between the target user and the intelligent doctor covers all the consultation key points information.

2. The method according to claim 1, characterized in that Based on the identified medical history keywords, a plurality of medical inquiry key points corresponding to the medical history keywords are obtained from a preset structured knowledge base as medical inquiry key point information, including: Obtain the consultation category of the initiation request instruction; A plurality of consultation key points corresponding to the disease keyword under the consultation category are obtained in the preset structured knowledge base as the consultation key point information.

3. The method according to claim 1, characterized in that Generating doctor dialogue information for output according to the first round of user dialogue information and the consultation key points information includes: The first round of doctor dialogue information and the first round of user dialogue information are used as the current round of dialogue information, and the current round of dialogue information is understood based on the consultation key points information by using the consultation key points understanding big model, so as to determine whether the current round of dialogue information satisfies the key point information obtaining condition of any consultation key point in the consultation key point information, wherein the key point information obtaining condition includes that the consultation key point has been asked and an answer that complies with the rule has been obtained; If the key point information obtaining condition of any of the key points of the medical consultation is met, the satisfied key points of the medical consultation are removed from the key points of the medical consultation information, and the next round of doctor dialogue information is continued to be generated if the key points of the medical consultation information is not cleared, otherwise the next round of doctor dialogue information is stopped from being generated; If the key information acquisition conditions of any of the key questions are not met, the next round of doctor dialogue information is directly generated.

4. The method according to claim 3, characterized in that Before the large model for understanding the key points of the medical consultation is used to understand the key points of the medical consultation information of the current round of dialogue information based on the key points of the medical consultation information, the method further includes: Obtaining a sample of medical consultation dialogue information pre-labeled with medical consultation key point labels, wherein the sample of medical consultation dialogue information includes at least one round of medical consultation dialogue, each round of medical consultation dialogue is labeled with a medical consultation key point label, the medical consultation key point label includes an empty label and a plurality of medical consultation key point identification labels, the empty label indicates that the corresponding round of medical consultation dialogue does not contain medical consultation key points or does not meet the key point information acquisition condition, and the medical consultation key point identification label indicates the identification of the medical consultation key points contained in the corresponding round of medical consultation dialogue; The initial large model is fine-tuned and trained based on the medical consultation dialogue information sample to obtain the medical consultation key points understanding large model.

5. The method according to claim 3, characterized in that: After removing the satisfied medical consultation points from the medical consultation points information, the method further includes: Record the removed key points as the key points asked; Generate the next round of doctor dialogue information, including: Generate multiple candidate doctor dialogue information of the intelligent doctor for the current round of user dialogue information based on the current round of dialogue information and historical dialogue context through the consultation dialogue big model; The candidate doctor conversation information is used to identify the key points of the medical consultation, and the candidate doctor conversation information that contains the key points of the medical consultation is deleted. One item of the remaining candidate doctor conversation information is selected as the next round of doctor conversation information for output, and the next round of doctor conversation information and the user conversation information corresponding to the next round of doctor conversation information are used as new information for this round of conversation, and the step of understanding the key points of the medical consultation for the current round of conversation information based on the key points of the medical consultation information is returned to.

6. The method according to claim 5, characterized in that The large model of the medical consultation dialogue generates multiple candidate doctor dialogue information of the intelligent doctor for the current round of user dialogue information based on the current round of dialogue information and the historical dialogue context, including: Determine whether the current dialogue round reaches the preset dialogue round; If the current conversation round does not reach the preset conversation round, then the consultation conversation big model is used to generate multiple candidate doctor conversation information of the intelligent doctor for the current round of user conversation information based on the current round of conversation information and the historical conversation context; If the current conversation round has reached the preset conversation round, a conversation generation prompt word is constructed based on the consultation key points information, the current round conversation information and the historical conversation context, and the conversation generation prompt word is input into the consultation conversation big model, so that the consultation conversation big model generates the next round of doctor conversation information containing any consultation key points in the consultation key points information based on the current round conversation information and the historical conversation context.

7. The method according to claim 5, characterized in that The selecting one of the remaining candidate doctor dialogue information as the next round of doctor dialogue information to be outputted includes: If any of the remaining candidate doctor dialogue information contains any of the consultation key points in the consultation key point information, then one of the candidate doctor dialogue information containing any of the consultation key points is selected as the next round of doctor dialogue information for output; If the remaining candidate doctor dialogue information does not contain any of the consultation key points in the consultation key point information, then one of the remaining candidate doctor dialogue information is selected as the next round of doctor dialogue information for output.

8. An intelligent medical consultation device for a patient's medical history, characterized in that: The device comprises: A keyword recognition module, configured to respond to a request for initiating an intelligent consultation service, output a first round of doctor dialogue information of an intelligent doctor, obtain a first round of user dialogue information for the first round of doctor dialogue information, and perform medical history keyword recognition on the first round of user dialogue information; A medical question key point acquisition module, used for acquiring, based on the identified medical history keywords, a plurality of medical question key points corresponding to the medical history keywords in a preset structured knowledge base as medical question key point information, wherein the preset structured knowledge base contains a plurality of medical question key points corresponding to each of the plurality of preset medical history keywords; The intelligent consultation module is used to generate doctor dialogue information for output based on the first round of user dialogue information and the consultation key points information, until the dialogue between the target user and the intelligent doctor covers all the consultation key points information.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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