Method for providing medical question and answer service, related device and computer program product

Abstract

The disclosure provides a method, device, electronic equipment, computer readable storage medium and computer program product for providing a medical question and answer service, relating to the technical field of artificial intelligence such as text generation, data processing, intelligent recommendation and smart medical treatment. A specific embodiment of the method comprises: determining a candidate medical entity based on historical search data of a user; determining a target medical entity based on historical case information of the user and the candidate medical entity; generating question information based on at least the target medical entity and the language style configuration of the user; and providing feedback information corresponding to the question information selected by the user to the user. In this way, the user's consultation points can be mined based on the user's historical search data and historical cases, assisting the user to more efficiently and effectively propose medical questions. As a result, the interaction cost of the user when conducting medical consultation based on the question and answer mode can be reduced, the user can more efficiently and effectively use the medical question and answer service, and the user experience is improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to artificial intelligence technologies such as text generation, data processing, intelligent recommendation, and smart healthcare, and particularly to methods, apparatuses, electronic devices, computer-readable storage media, and computer program products for providing medical question-and-answer services. Background Technology

[0002] With the development of society and computer technology, online medical Q&A and medical intelligent agents have emerged to facilitate access to medical knowledge for people and users. Online medical Q&A assistants and medical intelligent agents are innovative applications that have rapidly emerged in recent years with the development of technologies such as "artificial intelligence," "big data," "Internet of Things," and "cloud computing."

[0003] Online medical Q&A assistants and medical AI agents, technologies that provide medical knowledge and services, are gradually gaining recognition and popularity. Based on these assistants and agents, users can conveniently access medical-related knowledge through interaction.

[0004] Correspondingly, the development of technologies such as online medical Q&A assistants and medical intelligent agents can bring more efficient, intelligent, and inclusive medical services to the healthcare industry. Therefore, how to more effectively utilize technologies such as online medical Q&A assistants and medical intelligent agents to provide users with more efficient and higher-quality services is a matter of concern and urgent need. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for providing medical question-and-answer services.

[0006] In a first aspect, embodiments of this disclosure propose a method for providing medical question-and-answer services, comprising: determining candidate medical entities based on a user's historical search data; determining a target medical entity based on the user's historical medical records and the candidate medical entities; generating question information based at least on the target medical entity and the user's language style configuration; and providing the user with feedback information corresponding to the question information selected by the user.

[0007] Secondly, embodiments of this disclosure propose an apparatus for providing medical question-and-answer services, comprising: a candidate medical entity determination unit configured to determine candidate medical entities based on a user's historical search data; a target medical entity determination unit configured to determine a target medical entity based on a user's historical medical records and candidate medical entities; a question information generation unit configured to generate question information based at least on the target medical entity and the user's language style configuration; and a feedback information providing unit configured to provide feedback information to the user corresponding to the question information selected by the user.

[0008] Thirdly, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement a method for providing medical question-and-answer services as described in any implementation of the first aspect.

[0009] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions that, when executed by a computer, enable the provision of a medical question-and-answer service as described in any implementation of the first aspect.

[0010] Fifthly, embodiments of this disclosure provide a computer program product including a computer program that, when executed by a processor, can implement the method for providing medical question-and-answer services as described in any implementation of the first aspect.

[0011] The methods, apparatus, electronic devices, computer-readable storage media, and computer program products for providing medical question-and-answer services provided in this disclosure determine candidate medical entities based on a user's historical search data. Then, based on the user's historical medical records and the candidate medical entities, a target medical entity is determined. Next, question information is generated, at least based on the target medical entity and the user's language style configuration. Finally, feedback information corresponding to the user's selected question information is provided to the user.

[0012] This disclosure can identify users' consultation points based on their historical search data and medical records, helping them to ask medical questions more efficiently and effectively. This reduces the interaction costs for users seeking medical advice through question-and-answer formats, allowing them to use medical Q&A services more efficiently and effectively, thus improving the user experience.

[0013] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0014] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0015] Figure 1 This is an exemplary system architecture to which this disclosure can be applied;

[0016] Figure 2A flowchart illustrating a process for providing a medical question-and-answer service, provided as an embodiment of this disclosure;

[0017] Figure 3 A flowchart illustrating a process for providing feedback information to a user, as provided in this embodiment of the disclosure;

[0018] Figure 4 A flowchart illustrating the process of providing medical question-and-answer services in an application scenario, as provided in an embodiment of this disclosure;

[0019] Figure 5 A structural block diagram of an apparatus for providing medical question-and-answer services provided in an embodiment of this disclosure;

[0020] Figure 6 This is a schematic diagram of the structure of an electronic device suitable for performing a method of providing medical question-and-answer services, provided as an embodiment of this disclosure. Detailed Implementation

[0021] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding; these should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0022] Furthermore, the acquisition, storage, use, processing, transportation, provision, and disclosure of user personal information (such as users' historical search data, historical medical records, etc., which will be discussed later in this disclosure) in the technical solutions disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0023] For example, users can be asked in advance for their authorization regarding actions involving their personal information, such as acquisition, storage, use, processing, transportation, provision, and disclosure, through interaction with the user. Accordingly, after user authorization, the user's personal information can be used in accordance with the specific type and scope of the authorized actions.

[0024] Figure 1 An exemplary system architecture 100 is shown, in which embodiments of the methods, apparatuses, electronic devices, and computer-readable storage media for providing medical question-and-answer services can be applied.

[0025] like Figure 1As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0026] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various applications for enabling information communication between the terminal devices 101, 102, and 103 and server 105 can be installed. These applications include online medical Q&A applications, information retrieval applications, and instant messaging applications.

[0027] Terminal devices 101, 102, and 103 and server 105 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices, and can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here.

[0028] Server 105 can provide various services through its built-in applications. Taking an online medical Q&A application that provides online question answering and online access to medical knowledge as an example, when running this online medical Q&A application, server 105 can achieve the following effects: First, server 105 determines candidate medical entities based on the user's historical search data (e.g., historical search data stored locally on server 105, or historical search data obtained by network 104 from terminal devices 101, 102, and 103); then, server 105 determines the target medical entity based on the user's historical medical record information (e.g., historical medical record information stored locally on server 105, or historical medical record information obtained by network 104 from terminal devices 101, 102, and 103) and the candidate medical entities; then, server 105 generates question information based at least on the target medical entity and the user's language style configuration; finally, server 105 provides the user with feedback information corresponding to the question information selected by the user.

[0029] Because storing historical search data and case information may require substantial storage resources, and determining candidate and target medical entities may require significant computing resources and strong computational capabilities, the methods for providing medical question-and-answer services provided in the subsequent embodiments of this disclosure are generally executed by a server 105 with strong computing power and abundant computing resources. Correspondingly, the device for providing medical question-and-answer services is also generally located in the server 105. However, it should also be noted that when terminal devices 101, 102, and 103 also possess sufficient computing power and resources, they can also complete the aforementioned calculations performed by the server 105 through online medical question-and-answer applications installed on them, thereby outputting the same results as the server 105. Especially when multiple terminal devices with different computing capabilities exist simultaneously, but the online medical Q&A application determines that the terminal device it is using has strong computing power and sufficient remaining computing resources, the terminal device can perform the aforementioned calculations, thereby appropriately reducing the computing pressure on server 105. Correspondingly, the device providing medical Q&A services can also be located in terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also exclude server 105 and network 104.

[0030] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0031] Please refer to Figure 2 , Figure 2 A flowchart of a process 200 for providing a medical question-and-answer service is provided for embodiments of this disclosure, wherein process 200 includes the following steps:

[0032] Step 201: Identify candidate medical entities based on the user's historical search data;

[0033] In embodiments of this disclosure, for example... Figure 1 The server 105 shown serves as the entity executing the method for providing medical question-and-answer services. For example, server 105 could be a server used by a service platform capable of providing "medical question-and-answer services".

[0034] Accordingly, in providing "medical Q&A services," the implementing entity can first obtain the user's historical search data. For example, historical search data can be data determined based on the specific search content and search questions pointed to by the user's historical search behavior on various search engines and medical platforms, after authorization.

[0035] For example, after obtaining user authorization, the executing entity can access the aforementioned search engines and medical platforms through the authorized Application Programming Interface (API) to collect users' health-related search history (behavioral) data and interaction data on these medical platforms and search engines.

[0036] The executing entity can then use this search behavior data and interaction data to determine, for example, the following fields as historical search data:

[0037] Search keywords: Keywords that users enter into search engines, medical platforms, etc.;

[0038] Browsing history: Medical articles, disease descriptions, drug information, etc. that have been accessed;

[0039] Data that reflects user interests and concerns, such as those generated through interactions like likes, comments, and favorites.

[0040] Historical questions: Questions previously asked by users to doctors, medical AI agents, etc.

[0041] In some optional implementations of this embodiment, after obtaining historical search data, the executing entity may also choose to filter missing or incomplete data, convert data from different sources into a unified format, and decrypt the historical search data to further improve the quality of the historical search data.

[0042] Then, after obtaining the historical search data, the executing entity can identify (a group of) candidate medical entities based on the user's historical search data.

[0043] The candidate medical entity can be a "medical entity" directly included in the text of the search keywords, or it can be a "medical entity" analyzed and identified through semantic and content analysis of medical articles, disease introductions, drug information, etc. that the user has visited.

[0044] For example, candidate medical entities can be specific "disease names", "drug names", "treatment methods", "symptom names", etc.

[0045] In some alternative implementations of this embodiment, in order to more effectively identify candidate medical entities, the implementing entity can process historical search data using, for example, a Latent Dirichlet Allocation (LDA) model to uncover the "topics" that the user is interested in.

[0046] Alternatively, a term frequency-inverse document frequency algorithm can be used to extract high-frequency keywords that may better reflect the user's interests.

[0047] Alternatively, algorithms such as Random Forest, Support Vector Machine, or Convolutional Neural Network can be used to train users' interest points, form an interest model, and then use this interest model to mine "user interests" from historical search data.

[0048] Then, candidate medical entities are identified based on these "topics," "high-frequency keywords," and "user interests." The quality of candidate medical entity identification is improved by "simplifying" and "refining" the information. For example, "topics" and "user interests" are used as processing samples, and "candidate medical entities" are identified through methods such as semantic analysis and natural language processing.

[0049] Step 202: Based on the user's historical medical records and candidate medical entities, identify the target medical entity;

[0050] In the embodiments of this disclosure, after the executing entity determines the candidate medical entity based on the above step 201, the executing entity can determine the target medical entity based on the user's historical medical information and the candidate medical entities.

[0051] As discussed above, historical case information can also be obtained from sources such as medical institutions after user authorization, or it can be uploaded by the user to the implementing entity.

[0052] Then, the implementing entity can identify the target medical entity from the candidate medical entities by analyzing historical case information, or by using historical case information as a reference.

[0053] Typically, the executing entity can process historical medical record information in a similar manner to identify potential medical entities. For example, the executing entity can similarly identify medical entities recorded in the medical record information, such as disease names or drug names. Then, if the same medical entity is matched from both the historical search data and the historical medical record information, that medical entity can be identified as the target medical entity.

[0054] In some optional implementations of this embodiment, the executing entity may use candidate medical entities as a "base" and historical case information as a reference to determine whether the corresponding candidate medical entity can be identified and matched through the records in the "historical case information". If a match is found, the candidate medical entity is determined to be the target medical entity.

[0055] For example, for a candidate medical entity with the disease name "X", the implementing entity can determine whether it can verify that the user has the disease "X" or has the risk of having "X" based on the information recorded in the historical medical records (usually, the risk of having "X" is considered as "having the risk of having X" if it is greater than or equal to a preset threshold).

[0056] For example, the implementing entity can determine the risk of having "X" based on the disease name in the "diagnosis information" recorded in historical case information.

[0057] Alternatively, in some optional implementations of this embodiment, the executing entity may also "predict" the aforementioned "risk level" by analyzing and processing historical case information.

[0058] For example, for medical entities in the form of "disease name", the implementing entity can determine whether a user has the "disease" or whether the "disease" poses a "high risk" to the user (i.e., the risk level is greater than or equal to a preset threshold) by matching the past medical history, medication, biochemical indicators, and blood test results recorded in historical case information with a pre-determined disease model (e.g., a disease model corresponding to the "disease name").

[0059] Accordingly, if it exists or is "high-risk", the implementing entity can identify the "candidate medical entity" as the "target medical entity".

[0060] Similarly, the implementing entity can also use algorithms such as random forest, support vector machine or convolutional neural network to determine the "risk level", which will not be repeated here.

[0061] Step 203: Generate problem information based at least on the language style configuration of the target medical entity and the user;

[0062] In the embodiments of this disclosure, after the target medical entity is determined based on the above step 202, the executing entity can at least generate question information pointing to and associated with the target medical entity based on the target medical entity and the user's language style configuration.

[0063] For language style configuration, the executing agent can typically obtain it from a language style reference package provided by the user. For example, the user can provide the executing agent with reference text in a natural language style, allowing the executing agent to learn its language style.

[0064] For example, for a reference text, the implementing agent can use sentiment analysis (e.g., Bi-LSTM) to analyze the "language style" included in it.

[0065] In some optional implementations of this embodiment, the executing entity can also learn the user's language style by collecting the expressed text in the user's historical search data, so as to determine the corresponding language style configuration.

[0066] Accordingly, based on this language style configuration, when generating corresponding text and voice information, the executing entity can ensure that the generated and output content is "close" to the user's expression habits. For example, the executing entity can call BERT (Bidirectional Encoder Representations from Transformers) to generate question information for the target medical entity based on the user's language style configuration.

[0067] In some embodiments, Top-k sampling or nucleus sampling may be introduced during the generation of problem information to increase the diversity of generated problem information and avoid duplication.

[0068] In some embodiments, a pre-defined question template may be provided corresponding to the question information. For example, a question template with pre-defined conjunctions may be derived from the grammatical results. Subsequently, question information can be generated by adding grammatical components such as subjects (e.g., the target medical entity of the disease name) and predicates to the template.

[0069] Accordingly, in some embodiments, the executing entity may also utilize a syntax correction model (e.g., Transformer-based) to “correct the syntax” of the problem information in order to avoid syntax errors.

[0070] In some optional implementations of this embodiment, in order to more specifically clarify the inquiry requirements, the executing entity can also introduce an "intent" during the process of generating the question information, making the question information more specific and targeted.

[0071] In some optional implementations of this embodiment, the executing entity may choose to detect whether the user's search intent for the target medical entity can be determined based on historical search data.

[0072] Subsequently, if the executing entity can determine the user's search intent for the target medical entity based on historical search data, then when generating question information, the executing entity can choose to generate question information based on the target medical entity, search intent, and the user's language style configuration.

[0073] Specifically, the executing entity can identify portions of historical search data that can be used to pinpoint and target the target medical entity. Then, based on the records in that portion, it attempts to read the user's search intent for the target medical entity to determine whether the user's search intent for the target medical entity can be determined based on the historical search data.

[0074] Taking "disease name" as an example, if the record used to determine the "disease name" in the historical search data is "how to prevent Y", then the executing entity can determine that the "search intent" is "how to prevent (target medical entity)" based on this content.

[0075] Therefore, when generating problem information, the implementing entity can directly incorporate the intention of "how to prevent" into it, so as to adjust the direction of the inquiry of "problem information" and improve the quality of "problem information".

[0076] In some optional implementations of this embodiment, if the executing entity cannot determine the user's search intent for the target medical entity based on historical search data, the executing entity can respond by selecting to generate question information based on the target medical entity, the default search intent, and the user's language style configuration during the process of generating question information.

[0077] Default search intents can usually be preset, such as "What is (target medical entity)" or "What is the meaning of (target medical entity)".

[0078] In some optional implementations of this embodiment, the executing entity may also pre-set or default the search intent based on the historical search and inquiry intent of users for the target medical entity.

[0079] For example, the implementing entity can use big data analytics to analyze the popularity of search and query intentions of multiple historical users for the target medical entity, and select those "intents" that meet the requirements (i.e., greater than or equal to the popularity threshold) as the default search intent, or select the "intent" with the highest popularity as the "default".

[0080] Therefore, the implementing entity can use "big data" to determine the "intents" that are more likely to be of interest to users, thereby generating problem information and increasing its value.

[0081] Similarly, for the "default search intent," the executing entity can also set a default based on the user's configuration provided to the executing entity, or the "search intent" that appears most frequently in the user's historical search data. This satisfies the user's personalized habits and needs, thereby increasing the value of the question information.

[0082] In some embodiments, the problem information generated by the executing entity may include at least the target information from historical case information used to identify the target medical entity.

[0083] For example, the "problem information" generated by the implementing entity could take the form of "based on (target information used to identify the target medical entity in historical case information), it is necessary to understand the control methods for (the target medical entity)".

[0084] Therefore, this form of question information allows users to directly understand the basis for determining the "target medical entity" and the basis for asking questions based on the textual description of the question information (or, allows users to use this as a reference) so as to better select the "question information" to use when asking questions to the implementing entity.

[0085] Next, the implementing entity can provide users with (these) question information so that users can "ask questions" to the implementing entity by directly selecting or referring to the question information.

[0086] For example, the implementing entity can be presented directly to the user, or the problem information can be presented to the user using the terminal device they are using, so that the user can choose and refer to it.

[0087] Next, if the user selects a problem message, the executing entity can continue to step 204.

[0088] Step 204: Provide the user with feedback information corresponding to the problem information selected by the user.

[0089] In the embodiments of this disclosure, if a user selects one of the question messages generated in step 203 above, the executing entity can obtain the feedback information corresponding to that question message and provide it to the user. This achieves the provision of a "medical Q&A service" to the user.

[0090] For feedback information, you can choose to use existing knowledge such as expert knowledge for matching and selection, or you can choose to provide it to a pre-approved and certified third party with (medical) question-and-answer capabilities for processing (e.g., a doctor).

[0091] Accordingly, in order to improve the quality and efficiency of feedback information determination, the implementing entity can choose to classify the "problem information" to determine a more "suitable" "feedback information determination strategy," or in other words, choose whether to use expert knowledge to respond and provide feedback, or to provide it to a third party for processing.

[0092] Accordingly, the implementing entity can classify questions based on the "search intent" of the question information. For example, for question information under the first question type, the implementing entity can choose to use a pre-maintained medical knowledge base to match feedback information as "answers".

[0093] For example, such "search intent" can typically correspond to, for instance, providing a "definition" of the name of a specific disease or offering symptom descriptions for that disease.

[0094] Accordingly, for these questions that rely more on “inherent, known, and explicit” expert knowledge, the implementing entity can choose to use a pre-maintained medical knowledge base (as discussed above, which may include various types of expert knowledge and prior knowledge related to medical entities) to match the feedback information corresponding to the question information selected by the user.

[0095] Accordingly, in the process of providing feedback information to the user corresponding to the question information selected by the user, if the question information selected by the user is the first question type, the executing entity can respond to this and further, as an alternative, use a pre-maintained medical knowledge base to match the feedback information corresponding to the question information selected by the user.

[0096] Then, provide this feedback to the user.

[0097] The method for providing medical question-and-answer services provided in this disclosure can identify users' consultation points based on their historical search data and medical records, helping users to ask medical questions more efficiently and effectively. This reduces the interaction cost for users when conducting medical consultations via question-and-answer, allowing them to use medical question-and-answer services more efficiently and effectively, thus improving the user experience.

[0098] In some embodiments, when utilizing a pre-maintained medical knowledge base, the content provided by expert knowledge may be highly specialized (e.g., including technical terms that are not easily understood directly), making it inconvenient for users to read it completely in one go.

[0099] Therefore, the implementing entity can also detect the feedback information to determine whether it has hit other candidate medical entities besides the target medical entity.

[0100] Accordingly, if the executing entity determines that the feedback information contains other candidate medical entities besides the target medical entity, the executing entity can respond to this and similarly generate follow-up questions based at least on the other candidate medical entities that were hit and the user's language style configuration.

[0101] Then, the implementing entity can similarly provide these follow-up questions for users to choose from and refer to. Correspondingly, users can similarly use these follow-up questions to ask follow-up questions about the medical entities in the initial feedback information in order to obtain more comprehensive and complete information.

[0102] Correspondingly, if a user selects a follow-up question, the executing entity can similarly provide the user with follow-up feedback information corresponding to the selected question. This allows for providing the user with more comprehensive and in-depth information.

[0103] In some embodiments, as discussed above, different "feedback information determination strategies" can be selected based on the classification of problem information.

[0104] For example, compared to the first type of question mentioned above, for question information classified as the second type of question, the executing entity can choose to communicate with the target device used by an audited and certified third party with question-and-answer capabilities based on a pre-configured communication path in order to request the third party to provide feedback information.

[0105] Therefore, by integrating with third parties, we can better meet the medical Q&A needs of different users in a more high-quality and personalized way.

[0106] Please refer to the following for details. Figure 3 , Figure 3 This is a flowchart illustrating a process 300 for providing feedback information to a user, as provided in an embodiment of this disclosure. Process 300 can serve as an alternative or supplementary implementation of step 204 described above for providing feedback information to the user. Process 300 specifically includes the following steps:

[0107] Step 301: In response to the user's selection of the second question type, request the user to provide user reference information associated with the target medical entity;

[0108] Specifically, if the user selects the second type of question information, the executing entity can respond by requesting the user to provide user reference information associated with the target medical entity. For example, the executing entity can provide "request information" to the user's terminal device to request user reference information.

[0109] User reference information associated with a target medical entity typically corresponds to the specific form of that target medical entity. For example, if the target medical entity is a disease name, and the question is "Do you have this (disease name) disease?", then the user reference information could be the various (physiological indicators) needed to determine and diagnose that (disease name) disease.

[0110] For example, if the question is “How to treat (disease name) in the current state”, then the user reference information can be related to the disease (disease name) and can reflect the development stage and state of the disease (physiological indicators).

[0111] In some optional implementations of this embodiment, for the convenience of users, users can provide "user reference information" to the executing entity by specifying a portion of the historical case information. For example, users can provide "user reference information" by indicating the case number and a portion of the content recorded in the case.

[0112] If the user returns user reference information, the executing entity can respond to this and continue to step 302.

[0113] Step 302: Determine the completeness of the user reference information based on the comparison between the user reference information and the standard reference information;

[0114] Specifically, if a user provides user reference information, the implementing entity can respond by determining the completeness of the user reference information based on a comparison between the user reference information and the standard reference information.

[0115] Standard reference information is a template set up to correspond to the target medical entity and the problem information, providing a standard framework for the reference information needed to answer the problem. For example, standard reference information can record specific physiological indicators that need to be obtained.

[0116] Therefore, the implementing entity can determine the completeness of the user reference information by comparing it with the standard reference information, and determine whether the "completeness" meets the standard and can satisfy the usage needs of third parties.

[0117] If the completeness of the user reference information is greater than or equal to the completeness threshold (or in other words, meets the usage requirements of the third party), the executing entity can further execute step 303 to send the problem information and user reference information selected by the user to the target device to request the third party to process it.

[0118] Step 303: Send the user-selected problem information and user reference information to the target device;

[0119] If the target device returns feedback information, the executing entity can respond to it and continue to execute step 304.

[0120] Step 304: Provide feedback to the user.

[0121] In some optional implementations of this embodiment, the completeness of the user reference information provided by the user may not meet the usage requirements in the current state, i.e., the completeness is less than the aforementioned completeness threshold. In such cases, the executing entity can respond by requesting the user to provide further user reference information to enrich and improve the quality of the user reference information, so as to fully and effectively utilize the question-and-answer service provided by the third party.

[0122] Specifically, the above process 300 may further include step 305, which can be executed if the executing entity determines that the completeness of the user reference information is less than a completeness threshold. That is, if the executing entity determines that the completeness of the user reference information is less than a completeness threshold, the executing entity can respond by executing step 305.

[0123] Step 305: Request the user to provide updated reference information to supplement the missing parts of the user's reference information compared to the standard reference information;

[0124] Specifically, the executing entity can determine the missing parts of the user reference information compared to the standard reference information based on a comparison between the user reference information and the standard reference information. Then, the executing entity can generate a prompt message based on the missing parts and request the user to provide updated reference information to supplement the missing parts of the user reference information compared to the standard reference information.

[0125] If the user returns updated user reference information, the executing entity can respond to this and continue to step 306.

[0126] Step 306: Update the completeness of the user reference information based on the updated user reference information returned.

[0127] Specifically, after step 305 above, if the user returns updated user reference information, the executing entity can respond to this and update the completeness of the user reference information based on the updated user reference information returned.

[0128] Then, the executing entity can further determine, based on the comparison between the updated integrity and the integrity threshold, whether to continue requesting the user to "update" or to provide the target device with the user-selected problem information and (updated) user reference information.

[0129] It should be understood that the method for determining feedback information can also be pre-configured by the user to meet the user's personalized needs. For example, if the user prefers to use a third party to provide a response, the user can instruct the execution entity to use a method for determining and providing feedback information, such as the one shown in process 300 above, through user settings.

[0130] In some embodiments, users can also determine the method of determining and providing feedback information by directly instructing the executing entity. For example, a text instruction based on "machine answer" can instruct the executing entity to provide feedback information using a medical knowledge base (e.g., for question information that includes this instruction, the executing entity can directly classify the question information as a first question type to answer it using "expert knowledge").

[0131] For example, text instructions such as "doctor's answer" or "human answer" can be used to instruct the executing entity to provide feedback information by connecting to a third party. (For example, for question information that includes this instruction, the executing entity can directly classify the question information as a second question type so that a third party can answer it.)

[0132] In some optional implementations of this embodiment, if feedback information is provided by a third party, the executing entity may further choose to generate problem information based on the language style configuration of the target medical entity, the user, and the language style configuration corresponding to the target device during the process of generating problem information (at least) based on the language style configuration of the target medical entity and the user.

[0133] Specifically, the executing entity can determine a combined language style configuration by fusing the user's language style configuration with the target device's language style configuration (e.g., the language style of the target device user can be determined by similarly referencing the user's language style configuration). This can be done by averaging the parameters recorded for both language styles in each channel. The executing entity then generates problem information based on the target medical entity and the combined language style configuration.

[0134] Therefore, by incorporating the target device's corresponding language style configuration into the process of generating problem information, the problem information becomes more readable for third parties, such as "doctors," enabling them to understand the problem more accurately and effectively and provide responses and feedback.

[0135] Based on any of the above embodiments, since historical case information may include multiple "cases" or "cases" may include "invalid" or "low-value information" due to reasons such as exceeding the time limit, users can request adjustments to historical case information by sending a historical case information adjustment request to the executing entity to indicate the "valuable information" that can be used by the executing entity for reference.

[0136] Accordingly, if the implementing entity receives a request from a user to adjust historical medical records, it can respond by providing the user with the historical medical records information.

[0137] Then, (if the user returns the range selection result for the historical case information), the executing entity can determine the target historical case information that can be used from the historical case information based on the user's range selection result for the historical case information.

[0138] Accordingly, in the process of performing step 202 above, the executing entity may further select and determine the target medical entity based on the target historical case information and candidate medical entities.

[0139] Therefore, the implementing entity can determine the "valuable information" based on the user's selection and instructions regarding the content of historical medical records, in order to more accurately identify the target medical entity.

[0140] Similarly, the implementing entity can proactively provide the historical case information to the user before actually using it, so that the user can make adjustments and improve the quality of the historical case information.

[0141] To enhance understanding, this disclosure also provides a specific implementation scheme in conjunction with a particular application scenario.

[0142] For this, please see Figure 4 . Figure 4 This is a flowchart illustrating the process 400 of providing medical question-and-answer services in an application scenario, as provided in an embodiment of this disclosure.

[0143] For ease of understanding, an example can be provided. Figure 1 The system architecture 100 shown will be used for illustration. For example, server 100 shown in the system architecture is used as the execution entity that provides medical question-and-answer services in process 400.

[0144] In process 400, user 401 can interact with server 105 through S401 to initiate a "medical question and answer service". For example, user 401 can execute S401 through their terminal device (not shown in the figure) to request server 105 to provide medical question and answer services.

[0145] Accordingly, after receiving a medical question-and-answer service request from user 401, server 105 can execute S402, determining candidate medical entities based on user 401's historical search data 403. For example, server 105 can determine the candidate medical entities 411-41N that are hit or included in user 401's historical search data 403, where N is a positive integer.

[0146] Then, server 105 can execute S403 to determine the target medical entity (e.g., determine the target medical entity 421) based on user 401's historical medical records 405 and candidate medical entities (e.g., candidate medical entities 411-41N).

[0147] It should be understood that the target medical entity 421 is actually one of the candidate medical entities 411-41N. For example, the exemplary textual form of the target medical entity 421 may be "XX" which represents the name of a disease.

[0148] Then, server 105 can execute S404 to generate problem information (e.g., generate problem information 431 and 432) based on the language style configuration of target medical entity 421 and user 401.

[0149] Then, server 105 can execute S405 to provide user 401 with problem information 431 and 432 for user 401 to refer to and select.

[0150] For example, user 401 selected question information 431, that is, user 401 actually expects to obtain "medical Q&A service" with question information 431.

[0151] Accordingly, user 401 can interact with server 105 through S406, select problem information 431, and expect to obtain the corresponding feedback information.

[0152] Next, server 105 can execute S407 to determine the feedback information 441 corresponding to problem information 431.

[0153] Then, server 105 can continue to execute S408 to return feedback information 441 to user 401 in order to provide and implement "medical question and answer service".

[0154] Further reference Figure 5 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a device for providing medical question-and-answer services, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0155] like Figure 5As shown, the device 500 for providing medical question-and-answer services in this embodiment may include: a candidate medical entity determination unit 501, a target medical entity determination unit 502, a question information generation unit 503, and a feedback information providing unit 504. The candidate medical entity determination unit 501 is configured to determine candidate medical entities based on the user's historical search data; the target medical entity determination unit 502 is configured to determine the target medical entity based on the user's historical medical records and the candidate medical entities; the question information generation unit 503 is configured to generate question information based at least on the target medical entity and the user's language style configuration; and the feedback information providing unit 504 is configured to provide the user with feedback information corresponding to the question information selected by the user.

[0156] In this embodiment, the specific processing and technical effects of the candidate medical entity determination unit 501, the target medical entity determination unit 502, the question information generation unit 503, and the feedback information providing unit 504 in the device 500 for providing medical question-and-answer services can be referred to respectively. Figure 2 The relevant descriptions of steps 201-204 in the corresponding embodiments will not be repeated here.

[0157] In some optional implementations of this embodiment, the device 500 further includes: a search intent detection unit configured to detect whether the user's search intent for the target medical entity can be determined based on historical search data; and a question information generation unit 503, including: a first question information generation subunit configured to generate question information based on the target medical entity, the search intent, and the user's language style configuration in response to the ability to determine the user's search intent for the target medical entity based on historical search data.

[0158] In some optional implementations of this embodiment, the apparatus 500 further includes: a second question information generation subunit, configured to, in response to the inability to determine the user's search intent for the target medical entity based on historical search data, generate question information based on the target medical entity, the default search intent, and the user's language style configuration.

[0159] In some optional implementations of this embodiment, the feedback information providing unit 504 includes: a first feedback information determining subunit, configured to match the feedback information corresponding to the user's selected question information with a pre-maintained medical knowledge base in response to the user's selected question information being a first question type; and a first feedback information providing subunit, configured to provide feedback information to the user.

[0160] In some optional implementations of this embodiment, the apparatus 500 further includes: a follow-up question information generation unit, configured to generate follow-up question information in response to other candidate medical entities other than the target medical entity in the feedback information, based at least on the other candidate medical entities and the user's language style configuration; and a follow-up question feedback information providing unit, configured to provide the user with follow-up question feedback information corresponding to the selected follow-up question information.

[0161] In some optional implementations of this embodiment, the feedback information providing unit 504 includes: a reference information request subunit, configured to request the user to provide user reference information associated with the target medical entity in response to the user selecting the problem information as a second problem type; an information completeness determination subunit, configured to determine the completeness of the user reference information based on a comparison between the user reference information and standard reference information in response to the user returning the user reference information; a problem information and reference information forwarding subunit, configured to send the user-selected problem information and user reference information to the target device in response to the user's completeness being greater than or equal to a completeness threshold; and a third feedback information providing subunit, configured to provide feedback information to the user in response to the target device returning feedback information.

[0162] In some optional implementations of this embodiment, the apparatus 500 further includes: an update reference information request unit, configured to request the user to provide updated reference information in response to the user reference information having a completeness less than a completeness threshold, so as to supplement the part of the user reference information missing compared with the standard reference information; and an information completeness update unit, configured to update the completeness of the user reference information based on the updated user reference information returned by the user.

[0163] In some optional implementations of this embodiment, the problem information generation unit 503 is further configured to generate problem information based on the target medical entity, the user's language style configuration, and the language style configuration corresponding to the target device.

[0164] In some optional implementations of this embodiment, the problem information includes at least the target information from historical case information used to identify the target medical entity.

[0165] In some optional implementations of this embodiment, the apparatus 500 further includes: a historical case information providing unit, configured to provide historical case information to a user in response to receiving a historical case information adjustment request sent by a user; a case usage scope determination unit, configured to determine target historical case information that can be used from the historical case information based on the user's selection result of the scope of historical case information; and a target medical entity determination unit 502, further configured to determine a target medical entity based on the target historical case information and candidate medical entities.

[0166] This embodiment is a device embodiment corresponding to the method embodiment described above. The device for providing medical question-and-answer services provided in this embodiment can identify users' consultation points based on their historical search data and medical records, assisting users in asking medical questions more efficiently and with higher quality. This reduces the interaction cost for users when conducting medical consultations based on question-and-answer methods, making it easier for users to use medical question-and-answer services more efficiently and with higher quality, thus improving the user experience.

[0167] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0168] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0169] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0170] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0171] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the method of providing a medical question-and-answer service. For example, in some embodiments, the method of providing a medical question-and-answer service may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method of providing a medical question-and-answer service described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method of providing a medical question-and-answer service by any other suitable means (e.g., by means of firmware).

[0172] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0173] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0174] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0175] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0176] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0177] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are hosting products within the cloud computing service ecosystem to address the management difficulties and weak business scalability inherent in traditional physical hosts and Virtual Private Servers (VPS) services. Servers can also be categorized as distributed system servers or servers incorporating blockchain technology.

[0178] According to the technical solution of this disclosure, the user's consultation points can be identified based on the user's historical search data and medical records, assisting the user in raising medical questions more efficiently and with higher quality. This reduces the interaction cost for users conducting medical consultations based on a question-and-answer format, allowing users to use medical question-and-answer services more efficiently and with higher quality, thus improving the user experience.

[0179] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution provided in this disclosure can be achieved, and this is not limited herein.

[0180] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for providing medical question-and-answer services, comprising: Based on users' historical search data, candidate medical entities are identified, among which... The historical search data includes search behavior data and / or interaction data; Based on the user's historical medical records and candidate medical entities, a target medical entity is determined, wherein the target medical entity is the candidate medical entity matched by the historical medical records. Based at least on the language style configuration of the target medical entity and the user, problem information is generated, wherein the problem information includes at least the target information used to identify the target medical entity from the historical case information; Provide the user with feedback information corresponding to the question information selected by the user; Also includes: The test determines whether the user's search intent for the target medical entity can be determined based on the historical search data; and The generation of problem information, based at least on the language style configuration of the target medical entity and the user, includes: In response to the ability to determine the user's search intent for the target medical entity based on the historical search data, question information is generated based on the target medical entity, the search intent, and the user's language style configuration.

2. The method according to claim 1, further comprising: In response to the inability to determine the user's search intent for the target medical entity based on the historical search data, question information is generated based on the target medical entity, the default search intent, and the user's language style configuration.

3. The method according to claim 1, wherein, Providing the user with feedback information corresponding to the question information selected by the user includes: In response to the user selecting a question as the first question type, the system uses a pre-maintained medical knowledge base to match the feedback information corresponding to the user's selected question. The feedback information is provided to the user.

4. The method according to claim 3, further comprising: In response to other candidate medical entities other than the target medical entity in the feedback information, follow-up question information is generated based at least on the other candidate medical entities that are hit and the user's language style configuration; Provide the user with follow-up feedback information corresponding to the selected follow-up question information.

5. The method according to claim 1, wherein, Providing the user with feedback information corresponding to the question information selected by the user includes: In response to the user selecting a second question type, the system requests the user to provide user reference information associated with the target medical entity. In response to the user returning the user reference information, the completeness of the user reference information is determined based on a comparison between the user reference information and standard reference information; In response to the user reference information having a completeness level greater than or equal to a completeness threshold, the user-selected question information and the user reference information are sent to the target device; In response to feedback information returned by the target device, the feedback information is provided to the user.

6. The method according to claim 5, further comprising: In response to the fact that the completeness of the user reference information is less than the completeness threshold, the user is requested to provide updated reference information to supplement the missing parts of the user reference information compared to the standard reference information; In response to the user returning updated user reference information, the completeness of the user reference information is updated based on the updated user reference information.

7. The method according to claim 5, wherein, The generation of problem information, based at least on the language style configuration of the target medical entity and the user, includes: Problem information is generated based on the target medical entity, the user's language style configuration, and the language style configuration corresponding to the target device.

8. The method according to any one of claims 1-7, further comprising: In response to receiving a request from the user to adjust historical medical records, the historical medical records information is provided to the user. Based on the user's selection of the range of the historical case information, the target historical case information that can be used is determined from the historical case information; as well as The process of identifying the target medical entity based on the user's historical medical records and candidate medical entities includes: Based on the target historical case information and candidate medical entities, the target medical entity is determined.

9. An apparatus for providing medical question-and-answer services, comprising: The candidate medical entity determination unit is configured to determine candidate medical entities based on the user's historical search data, wherein the historical search data includes search behavior data and / or interaction data; The target medical entity determination unit is configured to determine the target medical entity based on the user's historical medical information and candidate medical entities, wherein the target medical entity is the candidate medical entity matched by the historical medical information; The problem information generation unit is configured to generate problem information based at least on the language style configuration of the target medical entity and the user, wherein the problem information includes at least the target information used to identify the target medical entity from the historical case information; The feedback information providing unit is configured to provide the user with feedback information corresponding to the question information selected by the user; Also includes: The search intent detection unit is configured to detect whether the user's search intent for the target medical entity can be determined based on the historical search data; and The question information generation unit includes: a first question information generation subunit, configured to generate question information based on the target medical entity, the target medical entity, the search intent, and the user's language style configuration, in response to the ability to determine the user's search intent for the target medical entity based on the historical search data.

10. The apparatus according to claim 9, further comprising: The second question information generation subunit is configured to, in response to the inability to determine the user's search intent for the target medical entity based on the historical search data, generate question information based on the target medical entity, the default search intent, and the user's language style configuration.

11. The apparatus according to claim 9, wherein, The feedback information providing unit includes: The first feedback information determination subunit is configured to respond to the user's selected question information as the first question type by matching the feedback information corresponding to the user's selected question information using a pre-maintained medical knowledge base; The first feedback information providing subunit is configured to provide the feedback information to the user.

12. The apparatus of claim 11, further comprising: The follow-up question information generation unit is configured to generate follow-up question information in response to other candidate medical entities other than the target medical entity hit in the feedback information, based at least on the other candidate medical entities hit and the user's language style configuration. The follow-up feedback information providing unit is configured to provide the user with follow-up feedback information corresponding to the selected follow-up question information.

13. The apparatus according to claim 9, wherein, The feedback information providing unit includes: The reference information request subunit is configured to request the user to provide user reference information associated with the target medical entity in response to the user selecting the question information as the second question type. The information completeness determination subunit is configured to determine the completeness of the user reference information based on a comparison between the user reference information and standard reference information in response to the user returning the user reference information; The problem information and reference information forwarding subunit is configured to send the problem information selected by the user and the user reference information to the target device in response to the completeness of the user reference information being greater than or equal to a completeness threshold. The third feedback information providing subunit is configured to provide the feedback information to the user in response to the feedback information returned by the target device.

14. The apparatus of claim 13, further comprising: The update reference information request unit is configured to request the user to provide updated reference information in response to the user reference information having a completeness less than the completeness threshold, so as to supplement the missing parts of the user reference information compared to the standard reference information; The information completeness update unit is configured to update the completeness of the user reference information based on the updated user reference information in response to the user returning updated user reference information.

15. The apparatus according to claim 13, wherein, The problem information generation unit is further configured to generate problem information based on the target medical entity, the user's language style configuration, and the language style configuration corresponding to the target device.

16. The apparatus according to any one of claims 9-15, further comprising: The historical case information providing unit is configured to provide the historical case information to the user in response to receiving a historical case information adjustment request sent by the user. The case usage scope determination unit is configured to determine the target historical case information that can be used from the historical case information based on the user's selection result of the scope of the historical case information; as well as The target medical entity determination unit is further configured to determine the target medical entity based on the target historical case information and candidate medical entities.

17. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method of providing medical question-and-answer services as described in any one of claims 1-8.

18. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of providing a medical question-and-answer service as claimed in any one of claims 1-8.

19. A computer program product comprising a computer program that, when executed by a processor, implements the method of providing a medical question-and-answer service according to any one of claims 1-8.

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