A search method, device and medium

Abstract

The embodiments of the present invention provide a search method, device and medium. The method specifically includes: receiving a user's search request; determining a first-layer medical feature corresponding to the search request; determining multiple second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features; and determining corresponding search result data for multiple combinations corresponding to the search request and multiple second-layer medical features. The embodiments of the present invention can improve the efficiency of users in obtaining medical information from search result data, and can save network resources consumed in the process of obtaining information.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a search method, device and medium. Background Art

[0002] With the continuous development of science and technology and the continuous improvement of living standards, users pay more and more attention to medical health. At the same time, with the continuous development of Internet technology, search engines have become an important tool for users to obtain Internet information. Therefore, when there is a demand for information related to medical health, most users will choose to obtain the required information through search engines.

[0003] Currently, a user enters a search request in a search box of a search engine, and the search engine can obtain multiple search result items matching the search request and display the multiple search result items in a search result page. Assuming that the search request is "how to treat a rash", the search result items may include: the title and summary of a web page containing "rash" or "rash", so that the user can determine whether to open the corresponding web page based on the title, summary and other information.

[0004] The inventors found in the process of implementing the embodiments of the present invention that when users search for symptoms such as "rash", they usually hope to find medical information that matches the specific situation. The search result page usually includes a large number of search result items, so the user needs to search for medical information that matches the specific situation from a large number of search result items. In the search process, the user usually needs to spend a lot of operation costs, resulting in low efficiency in obtaining medical information and consumption of network resources. Summary of the invention

[0005] The embodiments of the present invention provide a search method, device and medium, which can improve the efficiency of users in obtaining medical information from search result data and save network resources consumed in the process of obtaining information.

[0006] In order to solve the above problem, an embodiment of the present invention discloses a search method, including:

[0007] Receive a user's search request;

[0008] Determining a first-level medical feature corresponding to the search request;

[0009] Determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features;

[0010] Corresponding search result data is determined respectively for multiple combinations corresponding to the search request and multiple second-layer medical features.

[0011] In order to solve the above problem, an embodiment of the present invention discloses a search method, including:

[0012] Receive a user's search request;

[0013] Determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0014] In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

[0015] On the other hand, an embodiment of the present invention discloses a search device, including:

[0016] A receiving module, used for receiving a user's search request;

[0017] A first determination module, used to determine the first-level medical feature corresponding to the search request;

[0018] A second determination module is used to determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features; and

[0019] The search result determination module is used to determine corresponding search result data respectively for multiple combinations corresponding to the search request and multiple second-level medical features.

[0020] On the other hand, an embodiment of the present invention discloses a search device, including:

[0021] A receiving module, used for receiving a user's search request;

[0022] A determination module, used to determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0023] The display module is used to display the search result data corresponding to the multiple combinations respectively in the search result page.

[0024] In another aspect, an embodiment of the present invention discloses a device for searching, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations:

[0025] Receive a user's search request;

[0026] Determining a first-level medical feature corresponding to the search request;

[0027] Determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features;

[0028] Corresponding search result data is determined respectively for multiple combinations corresponding to the search request and multiple second-layer medical features.

[0029] In another aspect, an embodiment of the present invention discloses a device for searching, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations:

[0030] Receive a user's search request;

[0031] Determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0032] In the search results page, the search results data corresponding to the multiple combinations are displayed separately

[0033] In yet another aspect, an embodiment of the present invention discloses a machine-readable medium having instructions stored thereon, which, when executed by one or more processors, enables a device to perform one or more of the search methods described above.

[0034] The embodiments of the present invention include the following advantages:

[0035] In an embodiment of the present invention, the first-layer medical features are used to characterize the medical features corresponding to the search request, which can reflect the search intent for medical information. The embodiment of the present invention subdivides the first-layer medical features into multiple second-layer medical features, and can subdivide the search intent into multiple sub-search intents to improve the precision of the search intent. Moreover, the embodiment of the present invention determines corresponding search result data for multiple combinations corresponding to the above-mentioned search request and multiple second-layer medical features, respectively, and can provide corresponding search result data based on the sub-search intent corresponding to the second-layer medical features, thereby improving the precision of the search result data.

[0036] In the case where search result data corresponding to the sub-search intent is provided, the user can filter and view the search result data according to the sub-search intent corresponding to the second-level medical feature. For example, the user can select a target sub-search intent that matches their specific information and view the search result data corresponding to the target sub-search intent. Since the embodiment of the present invention allows the user to filter and view the search result data based on the second-level medical feature, it can improve the efficiency of the user in obtaining medical information from the search result data and save network resources consumed in the process of obtaining information. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0038] Figure 1 It is a schematic diagram of an application environment of a search method according to an embodiment of the present invention;

[0039] Figure 2 is a flowchart of the steps of a search method embodiment 1 of the present invention;

[0040] Figure 3 is a flowchart of the steps of Embodiment 2 of a search method of the present invention;

[0041] Figure 4 is a structural block diagram of an embodiment of a search device of the present invention;

[0042] Figure 5 is a structural block diagram of an embodiment of a search device of the present invention;

[0043] Figure 6 is a block diagram of an apparatus 1100 for searching according to the present invention; and

[0044] Figure 7It is a schematic diagram of the structure of the server in some embodiments of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In actual applications, when a user searches for a symptom such as "rash", they usually hope to obtain medical information that matches the specific information. The specific information can be supplementary symptom information, such as the specific information of the first symptom corresponding to the search request, or the accompanying second symptom information. Alternatively, the specific information can be user attribute information, such as user age, user gender, user medical history, etc.

[0047] In order to improve the coverage of search results, the search results items on the search results page usually cover a variety of information, and the search results page usually interweaves these search results items, requiring users to find the medical information they really need from the interwoven search results items, which results in low efficiency in obtaining medical information and wastes network resources.

[0048] In response to the technical problems of low efficiency in obtaining medical information and consumption of network resources, an embodiment of the present invention provides a search solution, which may include: receiving a user's search request; determining a first-level medical feature corresponding to the above-mentioned search request; determining multiple second-level medical features corresponding to the above-mentioned first-level medical features; the above-mentioned second-level medical features may be subdivided features corresponding to the above-mentioned first-level medical features; and determining corresponding search result data for multiple combinations corresponding to the above-mentioned search request and multiple second-level medical features.

[0049] In an embodiment of the present invention, the first-layer medical features are used to characterize the medical features corresponding to the search request, which can reflect the search intent for medical information. The embodiment of the present invention subdivides the first-layer medical features into multiple second-layer medical features, and can subdivide the search intent into multiple sub-search intents to improve the precision of the search intent. Moreover, the embodiment of the present invention determines corresponding search result data for multiple combinations corresponding to the above-mentioned search request and multiple second-layer medical features, respectively, and can provide corresponding search result data based on the sub-search intent corresponding to the second-layer medical features, thereby improving the precision of the search result data.

[0050] When search result data corresponding to the sub-search intent is provided, the user can filter and view the search result data according to the sub-search intent corresponding to the second-level medical features. For example, the user can select a target sub-search intent that matches their specific information and view the search result data corresponding to the target sub-search intent. Since the embodiment of the present invention allows the user to filter and view the search result data based on the second-level medical features, it can improve the efficiency of the user in obtaining medical information from the search result data and save network resources consumed in the process of obtaining information.

[0051] For example, if search request A is “how to treat a rash”, the embodiment of the present invention can determine the first-layer medical feature “features of the site of onset” corresponding to search request A, and subdivide the “features of the site of onset” into second-layer medical features such as “arm”, “face”, “back”, and “thigh”, and provide corresponding search result data for each of the second-layer medical features. In this way, users can be helped to select second-layer medical features that match their specific information. Assuming that the “site of onset” of the user or the user’s family is “back”, the user can select “back” to obtain search result data with “how to treat a rash on the back” as a new search request, so as to obtain medical information that better matches the specific information. Since the embodiment of the present invention helps users filter search result data based on the second-layer medical features, the efficiency of obtaining medical information can be improved.

[0052] The search method provided by the embodiment of the present invention may be applied to application scenarios such as websites and / or APPs (application programs). For example, the application scenarios of the embodiment of the present invention may include: search engine websites, search engine APPs, and the like.

[0053] The search method provided by the embodiment of the present invention can be applied to Figure 1 In the application environment shown, Figure 1 As shown, the client 100 and the server 200 are located in a wired or wireless network, and the client 100 and the server 200 perform data exchange through the wired or wireless network.

[0054] Optionally, the client 100 can run on a terminal, and the above-mentioned terminal specifically includes but is not limited to: smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, car computers, desktop computers, set-top boxes, smart TVs, wearable devices, etc.

[0055] Method Example 1

[0056] Reference Figure 2 , shows a flowchart of a search method embodiment 1 of the present invention, which may specifically include the following steps:

[0057] Step 201: receiving a user's search request;

[0058] Step 202: determining the first-level medical features corresponding to the search request;

[0059] Step 203: determining a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features may be subdivided features corresponding to the first-layer medical features;

[0060] Step 204: Determine corresponding search result data for each of the multiple combinations corresponding to the search request and the multiple second-layer medical features.

[0061] Figure 2 At least one step of the illustrated method embodiment may be executed by the client and / or the server. Of course, the embodiment of the present invention does not limit the specific execution subject of each step.

[0062] In step 201, the client of the search APP or search website may provide a UI (User Interface) to allow the user to submit a search request to the client through a search box on the UI, a voice interface, etc. It is understood that the embodiment of the present invention does not limit the specific submission method of the search request.

[0063] In step 202, the first layer of medical features is used to characterize the medical features corresponding to the search request, which can reflect the search intention of medical information.

[0064] In an optional embodiment of the present invention, the above-mentioned determination of the first-level medical features corresponding to the search request specifically includes: obtaining the target medical entity word corresponding to the above-mentioned search request from a medical entity library; and determining the first-level medical features corresponding to the medical entity word.

[0065] The embodiment of the present invention can store medical entity words in a medical entity database. The medical entity words can represent the vocabulary corresponding to the medical entity.

[0066] In the embodiment of the present invention, entity refers to an objectively existing and distinguishable matter, including specific people, things, objects, abstract concepts or connections, etc. The entity in the medical field may be a medical entity. Optionally, natural language processing (NLP) technology may be used to extract medical entity words from medical corpus. Medical corpus may include: at least one of literature data, medical encyclopedia data, medical question and answer data, case data, and popular science data in the medical field. It can be understood that the embodiment of the present invention does not limit the specific determination process of medical entity words.

[0067] The medical entity type may refer to the type corresponding to the medical entity, and the medical entity type may include but is not limited to: disease type, symptom type, examination type, surgery type, drug type, treatment plan type, disease influencing factor type, population type, and department type, etc.

[0068] For example, the medical entity words corresponding to the disease type may include: "hypertension", "leukemia", etc.; the medical entity words corresponding to the symptom type may include: "headache", "fever", etc.; the medical entity words corresponding to the examination type may include: "laboratory test", "imaging examination", etc.; the surgery type and drug type can both represent the treatment method adopted for the disease; the treatment plan type may include: specific surgery information, or specific drug dosage, course of treatment and other information; the disease influencing factor type may refer to the factors that affect the disease, such as the cause of the disease; the population type can represent the population corresponding to the patient; the department type may refer to the corresponding department in the hospital.

[0069] It is understood that those skilled in the art can further divide the above medical entity types according to actual application requirements, for example, the symptom types can be divided into the following medical entity types: onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and accompanying symptom characteristics, etc. It is understood that the embodiments of the present invention are not limited to specific medical entity types.

[0070] In an optional embodiment of the present invention, entity recognition can be performed on medical corpus to obtain medical entity words; medical entity words with the same semantics but different descriptions can be uniformly processed, and the uniformly processed medical entity words can be saved in a medical entity library.

[0071] The embodiment of the present invention may use a NER (Named Entity Recognition) method to perform entity recognition on medical corpus. According to one embodiment, the NER method may include: a vocabulary-based method, a rule-based method, or a statistical learning-based method. It is understood that the embodiment of the present invention does not limit the specific NER method.

[0072] In order to solve the problem that the same medical entity word corresponds to different descriptions in medical corpus, the embodiment of the present invention uniformly processes medical entity words with the same semantics but different descriptions, thereby achieving standardization of medical entity words and further reducing the amount of computation required for medical entity word processing.

[0073] For example, the embodiments of the present invention can preset standard descriptions of medical entity words according to age ranges, such as "infants", "children", "children", "teenagers", "adults", "elderly people", etc., for population types. In addition, medical entity words with the same semantics but different descriptions in the medical corpus can be unified into standard descriptions of medical entity words. For example, "baby" can be unified into "infant", "uncle" can be unified into "adult" or "elderly people", etc. Of course, the above-mentioned unified processing can be performed based on the age information contained in the medical corpus.

[0074] For another example, the embodiment of the present invention can preset standard descriptions of medical entity words for symptom types, such as "headache", "tongue pain", etc. Moreover, medical entity words with the same semantics but different descriptions in the medical corpus can be unified into standard descriptions of medical entity words. In this way, personalized or colloquial symptom descriptions can be unified into standard descriptions of medical entity words. For example, the personalized or colloquial symptom descriptions corresponding to "headache" specifically include: "pain like a needle prick", "throbbing pain", "pain when touched", etc. For another example, the personalized or colloquial symptom descriptions corresponding to "tongue pain" specifically include: "pain on the left side of the tongue", "pain at the tip of the tongue", "pain at the root of the tongue", "pain on the edge of the tongue", etc.

[0075] Optionally, the above-mentioned obtaining the target medical entity word corresponding to the above-mentioned search request from the medical entity library may specifically include: identifying the first entity word in the search request, matching the first entity word with the medical entity words in the medical entity library, and if the match is successful, then the successfully matched medical entity word may be used as the target medical entity word corresponding to the search request.

[0076] For example, if search request A is "how to treat a rash", the target medical entity word "rash" corresponding to search request A can be determined from the medical entity library. For another example, if search request B is "symptoms of diabetes", the target medical entity word "diabetes" corresponding to search request B can be determined from the medical entity library.

[0077] In an embodiment of the present invention, the first layer of medical features can be used to characterize the dimensions of the search intent corresponding to the target medical entity word, which can be used to describe the medical entity according to the thinking angle corresponding to the dimension to improve the precision of the medical entity.

[0078] Optionally, the first-level medical features can characterize the features of the disease dimension. In the medical field, there is disease analysis data for the disease (hereinafter referred to as the first disease) on the Internet, and the search result items may also include the above disease analysis data. Users usually need to match the second disease (the user's own or the user's family's disease) with the first disease to determine whether the above analysis data is applicable. The embodiment of the present invention determines the first-level medical features of the disease dimension, and can classify the disease analysis data in the search results according to the disease dimension, thereby improving the precision of the search result data, and further improving the efficiency of users in obtaining medical information from the search result data.

[0079] In an optional embodiment of the present invention, a first-level medical feature set can be preset, which may include characteristics of the disease dimension, specifically including but not limited to: population characteristics, onset site characteristics, symptom frequency characteristics, symptom persistence characteristics, symptom subtype characteristics, symptom level characteristics, and accompanying symptom characteristics.

[0080] Among them, the population characteristics can characterize the characteristics of the population corresponding to the patient; the onset site characteristics can characterize the body part corresponding to the symptom; the symptom frequency characteristics can characterize the frequency of occurrence of the symptom; the symptom duration characteristics can characterize the time when the patient has symptoms; the symptom subtype characteristics can characterize the subdivision type of the symptom; the symptom level characteristics can characterize the severity of the disease and / or the difficulty of treatment. The accompanying symptom characteristics can characterize other symptoms that occur at the same time as a symptom. It can be understood that those skilled in the art can determine the first-level medical characteristics according to actual application requirements, and the embodiments of the present invention do not limit the specific first-level medical characteristics.

[0081] In the embodiment of the present invention, optionally, the determining the first-level medical features corresponding to the medical entity word specifically includes: determining the first-level medical features corresponding to the target medical entity word from a first-level medical feature set.

[0082] In practical applications, all or part of the first-layer medical features in the first-layer medical feature set can be used as the first-layer medical features corresponding to the target medical entity word. In other words, the first-layer medical features corresponding to the target medical entity word can be one or more.

[0083] In an optional embodiment of the present invention, the first-layer medical features in the first-layer medical feature set may be screened, and the screened first-layer medical features may be used as the first-layer medical features corresponding to the target medical entity words.

[0084] Correspondingly, the above-mentioned determining the first-layer medical features corresponding to the medical entity words from the first-layer medical feature set may specifically include: determining the first-layer medical features corresponding to the target medical entity words from the first-layer medical feature set based on the relevant information between the target medical entity words and the first-layer medical features.

[0085] The relevant information between the target medical entity word and the first-level medical features can improve the relevance between the first-level medical features corresponding to the target medical entity word and the search request, and then the search result data containing medical information can be classified according to the first-level medical features that are more relevant to the search request.

[0086] Optionally, the first-layer medical features may correspond to preset medical entity words, and the relevant information between the target medical entity words and the first-layer medical features may be determined based on the relevant information between the target medical entity words and the preset medical features.

[0087] It should be noted that a first-layer medical feature may correspond to multiple preset medical entity words. For example, the preset medical entity words corresponding to the first-layer medical feature may include: multiple second-layer medical features obtained by classifying the above-mentioned first-layer medical features, and / or, identification entity words corresponding to the first-layer medical features, etc.

[0088] In practical applications, corresponding sub-related information can be determined for multiple preset medical entity words, and then the multiple sub-related information can be fused to obtain the relevant information between the target medical entity word and the first-level medical features. The fusion method includes but is not limited to: summation, average, or weighted average.

[0089] The embodiment of the present invention can provide the following determination method of the score-related information:

[0090] Determination method 1: According to the semantic similarity between the target medical entity word and the preset medical entity word, the relevant information between the two can be determined.

[0091] Determination method 2: The sub-related information between the target medical entity word and the preset medical entity word can be determined based on the co-occurrence probability between the two in the medical corpus. For example, the sub-related information may include: The above-mentioned co-occurrence probability, the co-occurrence probability of the target medical entity word and the preset medical entity word in the medical corpus exceeds the probability threshold, so that the matching degree between the search intent corresponding to the search request and the first-level medical features can be improved.

[0092] In an embodiment of the present invention, optionally, the co-occurrence probability of multiple medical entity words in a medical corpus can be determined in the following manner: based on a mapping relationship between standard descriptions and non-standard descriptions of the medical entity words, the occurrence information of the medical entity words in the medical corpus is determined; based on the occurrence information, the co-occurrence probability of multiple medical entity words in the medical corpus is determined.

[0093] Since medical corpus usually includes personalized or colloquial symptom descriptions, if the appearance information of medical entity words in medical corpus is determined directly based on the standard description of medical entity words, it will affect the accuracy of the appearance information. For example, "headache" does not appear in medical corpus X, resulting in the number of appearances of "headache" in medical corpus X being 0; however, the non-standard description of "headache" "painful as a needle prick" appears in medical corpus X.

[0094] The embodiment of the present invention can convert the standard description of the medical entity word into a non-standard description, and determine the occurrence information of the medical entity word in the medical corpus based on the non-standard description, thereby improving the accuracy of the occurrence information.

[0095] The co-occurrence probability may be obtained based on a first quantity of medical corpora in which the plurality of medical entity words co-occur and a second quantity of medical corpora. For example, the co-occurrence probability may be a ratio of the first quantity to the second quantity.

[0096] In an embodiment of the present invention, optionally, the co-occurrence probability of the target medical entity word and the preset medical entity word in the medical corpus exceeds a probability threshold, which indicates that the target medical entity word corresponding to the search request has a high correlation with the preset medical entity word, thereby improving the matching degree between the search intent corresponding to the search request and the first-level medical features, thereby improving the accuracy of the search result data.

[0097] Determination method 3: The relevant information between any two medical entity words can be determined based on the search behavior data.

[0098] Search behavior data may include: search terms. The relevant information between any two medical entity terms can be determined based on the co-occurrence information of the two medical entity terms in the search terms. For example, if multiple search terms include: the first medical entity term and age characteristics, it can be considered that the first medical entity term has a high correlation with the population characteristics.

[0099] Search behavior data may include: the first medical entity word in the search term, and the second medical entity word in the search result data clicked by the user, and the related information between the two may be determined based on the first medical entity word and the second medical entity word. For example, after most users search for medical entity words for skin types such as "rash" and "pimples", the search result data they click on usually contains "baby", "adolescence", "old mother", etc., so it can be considered that the medical entity words for skin types are highly correlated with population characteristics.

[0100] The relevant information obtained by determining methods 1 to 3 can be used to obtain the first-level medical features that match the search request, that is, the first-level medical features that the user is more concerned about, so as to provide search result data that matches the search request.

[0101] In step 203, the first-layer medical features are subdivided into multiple second-layer medical features, and the search intent can be subdivided into multiple sub-search intents to improve the precision of the search intent.

[0102] In the embodiment of the present invention, optionally, a plurality of second-layer medical features corresponding to the first-layer medical features may be determined based on the correspondence between the first-layer medical features and the second-layer medical features.

[0103] The process of establishing the above correspondence may include: determining the first-layer medical features based on the medical corpus; classifying the above-mentioned first-layer medical features to obtain the second-layer medical features; and saving the correspondence between the first-layer medical features and the second-layer medical features.

[0104] Referring to Table 1, an example of the correspondence between the first-layer medical features and the second-layer medical features in an embodiment of the present invention is shown. It can be understood that those skilled in the art can determine the second-layer medical features corresponding to the first-layer medical features according to actual application requirements, and the embodiment of the present invention does not limit the specific correspondence between the two.

[0105] Table 1

[0106]

[0107]

[0108] In step 204, the search request and each of the plurality of second-layer medical features may be combined to obtain a plurality of corresponding combinations. The combination may correspond to a new search request, so that corresponding search result data may be retrieved according to the new search request.

[0109] For example, if the search request A is "how to treat a rash", the embodiment of the present invention can determine the first-layer medical feature "onset site feature" corresponding to the search request A, and subdivide the "onset site feature" into second-layer medical features such as "arm", "face", "back", and "thigh", and combine the search request A and each second-layer medical feature respectively. The resulting multiple combinations may include: "how to treat a rash + arm", "how to treat a rash + face", "how to treat a rash + back", "how to treat a rash + thigh", etc.

[0110] The embodiment of the present invention can determine corresponding search result data for multiple combinations respectively. Among them, according to a single combination, a search can be performed in the search database to obtain the search result data corresponding to the combination. For example, the search database may include: medical question and answer pair data, and the medical question and answer pair data may include: analysis data or answer data for the condition; then the search result data may include: the target medical question and answer pair data hit by the above combination. It can be understood that the embodiment of the present invention does not limit the search result data of the screenshot.

[0111] In the embodiment of the present invention, the search result data corresponding to the multiple combinations can be output. For example, the server can send the search result data corresponding to the multiple combinations to the client; or the client can display the search result data corresponding to the multiple combinations in the search result page, etc.

[0112] Optionally, the above-mentioned search result data corresponding to the multiple combinations are displayed separately, including: using tabs (Tab) in the search result page to display the search result data corresponding to the multiple combinations respectively; the label of the tab may include: the second-level medical characteristics corresponding to the combination.

[0113] Different combinations (second-layer medical features) may correspond to different tabs. For example, multiple combinations may include: "How to treat rash + arm", "How to treat rash + face", "How to treat rash + back", "How to treat rash + thigh", and the search result page may include: 4 tabs, and the labels of the 4 tabs are: "arm", "face", "back", "thigh", etc.

[0114] At a certain moment, one of the multiple tags is in a selected state, and the selected tag can be switched according to a trigger operation of the user.

[0115] Optionally, the method may further include: in response to a user triggering operation on any tag, displaying search result data of the target combination corresponding to the tag. For example, in the initial case, the tag in the selected state is "arm", and the user can switch the selected tag to other tags according to actual application requirements.

[0116] It should be noted that there may be multiple first-layer medical features in the embodiment of the present invention. In this case, the combination may include: a search request and second-layer medical features corresponding to multiple first-layer medical features. For example, multiple first-layer medical features include: first-layer medical feature A, first-layer medical feature B, first-layer medical feature C, etc., then the combination may include: search request + second-layer medical feature Ai corresponding to first-layer medical feature A + second-layer medical feature Bj corresponding to first-layer medical feature B + second-layer medical feature Ck corresponding to first-layer medical feature C, where i, j, and k may represent the numbers of the corresponding second-layer medical features, and i, j, and k may be natural numbers.

[0117] The label of the embodiment of the present invention can be multi-layered, and the multi-layer label can include: labels corresponding to multiple first-layer medical features respectively, for example, the first-layer label includes: a label corresponding to the first-layer medical feature A, a label corresponding to the first-layer medical feature B, a label corresponding to the first-layer medical feature C...etc.

[0118] For example, if search request A is "how to treat a rash", the embodiment of the present invention can determine that the first-level medical characteristics corresponding to search request A include: "onset site characteristics" and "population characteristics", and set corresponding levels of labels for "onset site characteristics" and "population characteristics". Specifically, "onset site characteristics" include: "arm", "face", "back", "thigh" and other second-level medical characteristics, and "population characteristics" include: "infants", "teenagers", "adults", "elderly", etc. In this way, the user can switch between multiple layers of labels to filter and view the search result data based on the multiple layers of labels.

[0119] It can be understood that in addition to the search result data corresponding to multiple combinations, the search result data corresponding to the search request can also include: search result data retrieved according to the search request, which can be retrieved in the search database according to the search request to obtain the search result data corresponding to the search request.

[0120] In summary, in the search method of the embodiment of the present invention, the first-layer medical features are used to characterize the medical features corresponding to the search request, which can reflect the search intent of medical information. The embodiment of the present invention subdivides the first-layer medical features into multiple second-layer medical features, and can subdivide the search intent into multiple sub-search intents to improve the precision of the search intent. Moreover, the embodiment of the present invention determines corresponding search result data for multiple combinations corresponding to the above-mentioned search request and multiple second-layer medical features, respectively, and can provide corresponding search result data based on the sub-search intent corresponding to the second-layer medical features, thereby improving the precision of the search result data.

[0121] In the case where search result data corresponding to the sub-search intent is provided, the user can filter and view the search result data according to the sub-search intent corresponding to the second-level medical feature. For example, the user can select a target sub-search intent that matches their specific information and view the search result data corresponding to the target sub-search intent. Since the embodiment of the present invention allows the user to filter and view the search result data based on the second-level medical feature, it can improve the efficiency of the user in obtaining medical information from the search result data and save network resources consumed in the process of obtaining information.

[0122] Method Example 2

[0123] Reference Figure 3 , shows a flowchart of the steps of Embodiment 2 of a search method of the present invention, which may specifically include the following steps:

[0124] Step 301: receiving a user's search request;

[0125] Step 302: determine the search result data corresponding to the search request; the search result data may include: search result data corresponding to a plurality of combinations; the plurality of combinations specifically include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features may be subdivided features corresponding to the first-layer medical features of the search request;

[0126] Step 303: In the search result page, the search result data corresponding to the above multiple combinations are displayed respectively.

[0127] Figure 3 At least one step of the illustrated embodiment may be executed by a client, which may receive a user's search request, determine search result data corresponding to the search request, and display search result data corresponding to multiple combinations in a search result page.

[0128] According to one embodiment, the client can use Figure 2The method embodiment shown determines the search result data corresponding to the above search request. According to another embodiment, the client can send a search request to the server, and receive the search result data corresponding to the search request returned by the server.

[0129] In an embodiment of the present invention, in the search results page, the search result data corresponding to the above-mentioned multiple combinations are displayed separately, which can help users filter and view the search result data according to the sub-search intentions corresponding to the second-level medical characteristics. For example, the user can select the target sub-search intention that matches his or her specific information, and view the search result data corresponding to the target sub-search intention.

[0130] In an embodiment of the present invention, multiple display areas corresponding to multiple combinations can be set in the search result page, and the search result data corresponding to each combination can be displayed in the multiple display areas.

[0131] In another embodiment of the present invention, the search result data corresponding to the plurality of combinations are displayed separately, including: displaying the search result data corresponding to the plurality of combinations in the form of tabs in the search result page; the labels of the tabs may include: the second-layer medical features corresponding to the combination. The plurality of combinations may share a display area, but a plurality of labels corresponding to the plurality of combinations are set in the display area to switch the search result data according to the labels.

[0132] In summary, the search method of the embodiment of the present invention, in the search results page, respectively displays the search result data corresponding to the above-mentioned multiple combinations, which can help users filter and view the search result data according to the sub-search intentions corresponding to the second-level medical characteristics. For example, the user can select the target sub-search intention that matches his or her specific information, and view the search result data corresponding to the target sub-search intention.

[0133] Since the embodiment of the present invention allows users to filter and view search result data based on the second-level medical features, it can improve the efficiency of users in obtaining medical information from search result data and save network resources consumed in the process of obtaining information.

[0134] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0135] Device Embodiment

[0136] Reference Figure 4 , shows a structural block diagram of an embodiment of a search device of the present invention, which may specifically include: a receiving module 401, a first determination module 402, a second determination module 403, and a search result determination module 404.

[0137] The receiving module 401 is used to receive a user's search request;

[0138] A first determination module 402, configured to determine a first-level medical feature corresponding to the search request;

[0139] The second determination module 403 is used to determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features; and

[0140] The search result determination module 404 is used to determine corresponding search result data respectively for multiple combinations corresponding to the above search request and multiple second-level medical features.

[0141] Optionally, the first determining module 402 may include:

[0142] An entity word acquisition module is used to acquire the target medical entity word corresponding to the above search request from the medical entity library;

[0143] The first-layer medical feature determination module is used to determine the first-layer medical features corresponding to the above-mentioned target medical entity words.

[0144] Optionally, the first-layer medical feature determination module may include:

[0145] The set determination module is used to determine the first-level medical features corresponding to the above-mentioned target medical entity words from the first-level medical feature set.

[0146] Optionally, the above-mentioned first-level medical feature set may include: population characteristics, onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and concomitant symptom characteristics.

[0147] Optionally, the above-mentioned set determination module may include:

[0148] The set selection module is used to determine the first-level medical features corresponding to the above-mentioned target medical entity words from the first-level medical feature set based on the relevant information between the above-mentioned target medical entity words and the first-level medical features.

[0149] Optionally, the co-occurrence probability of the preset medical entity word corresponding to the above-mentioned first-layer medical features and the above-mentioned target medical entity word in the medical corpus exceeds a probability threshold.

[0150] Optionally, the above device may further include:

[0151] A third determination module is used to determine the occurrence information of the medical entity word in the medical corpus according to the mapping relationship between the standard description and the non-standard description of the medical entity word;

[0152] The fourth determination module is used to determine the co-occurrence probability of multiple medical entity words in the medical corpus based on the above occurrence information.

[0153] Optionally, the above device may further include:

[0154] An entity recognition module is used to perform entity recognition on medical corpus to obtain medical entity words;

[0155] The unified processing module is used to uniformly process medical entity words with the same semantics but different descriptions, and save the uniformly processed medical entity words into the medical entity library.

[0156] Optionally, the search result determination module 404 may include:

[0157] The retrieval module is used to search in the medical corpus according to the above combination to obtain the search results corresponding to the above combination.

[0158] Optionally, the above device may further include:

[0159] The display module is used to display the search result data corresponding to the above multiple combinations respectively in the search result page.

[0160] Optionally, the above-mentioned display module may include:

[0161] The tab display module is used to display the search result data corresponding to the above-mentioned multiple combinations in the form of tabs in the search result page; the labels of the above-mentioned tabs may include: the second-level medical characteristics corresponding to the above-mentioned combinations.

[0162] Optionally, the above device may further include:

[0163] A fifth determination module is used to determine the first-level medical features based on the medical corpus;

[0164] A classification module, used for classifying the above-mentioned first-layer medical features to obtain second-layer medical features;

[0165] The saving module is used to save the corresponding relationship between the first layer medical features and the second layer medical features.

[0166] Reference Figure 5 , shows a structural block diagram of an embodiment of a search device of the present invention, which may specifically include: a receiving module 501, a determining module 502 and a display module 503.

[0167] The receiving module 501 is used to receive a user's search request;

[0168] Determination module 502, used to determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0169] The display module 503 is used to display the search result data corresponding to the multiple combinations in the search result page.

[0170] Optionally, the display module 503 may specifically include:

[0171] The tab display module is used to display the search result data corresponding to the multiple combinations in the form of tabs in the search result page; the label of the tab includes: the second-level medical characteristics corresponding to the combination.

[0172] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0173] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0174] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0175] An embodiment of the present invention provides a device for searching, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, wherein the one or more programs include instructions for performing the following operations: receiving a user's search request; determining a first-level medical feature corresponding to the search request; determining multiple second-level medical features corresponding to the first-level medical features; the second-level medical features are subdivided features corresponding to the first-level medical features; and determining corresponding search result data for multiple combinations corresponding to the search request and the multiple second-level medical features.

[0176] Figure 6 1 is a block diagram of a device 1100 for searching according to an exemplary embodiment. For example, the device 1100 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0177] Reference Figure 6 The device 1100 may include one or more of the following components: a processing component 1102 , a memory 1104 , a power component 1106 , a multimedia component 1108 , an audio component 1110 , an input / output (I / O) interface 1112 , a sensor component 1114 , and a communication component 1116 .

[0178] The processing component 1102 generally controls the overall operation of the device 1100, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 1102 may include one or more processors 1120 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 1102 may include one or more modules to facilitate the interaction between the processing component 1102 and other components. For example, the processing component 1102 may include a multimedia module to facilitate the interaction between the multimedia component 1108 and the processing component 1102.

[0179] The memory 1104 is configured to store various types of data to support the operation of the device 1100. Examples of such data include instructions for any application or method operating on the device 1100, contact data, phone book data, messages, pictures, videos, etc. The memory 1104 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0180] The power supply component 1106 provides power to the various components of the device 1100. The power supply component 1106 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 1100.

[0181] The multimedia component 1108 includes a screen that provides an output interface between the device 1100 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1108 includes a front camera and / or a rear camera. When the device 1100 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0182] The audio component 1110 is configured to output and / or input audio signals. For example, the audio component 1110 includes a microphone (MIC), and when the device 1100 is in an operation mode, such as a call mode, a recording mode, and a voice data processing mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 1104 or sent via the communication component 1116. In some embodiments, the audio component 1110 also includes a speaker for outputting audio signals.

[0183] I / O interface 1112 provides an interface between processing component 1102 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0184] The sensor assembly 1114 includes one or more sensors for providing various aspects of status assessment for the device 1100. For example, the sensor assembly 1114 can detect the open / closed state of the device 1100, the relative positioning of components, such as the display and keypad of the device 1100, the sensor assembly 1114 can also detect the position change of the device 1100 or a component of the device 1100, the presence or absence of user contact with the device 1100, the orientation or acceleration / deceleration of the device 1100, and the temperature change of the device 1100. The sensor assembly 1114 can include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1114 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1114 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0185] The communication component 1116 is configured to facilitate wired or wireless communication between the device 1100 and other devices. The device 1100 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1116 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1116 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency data processing (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0186] In an exemplary embodiment, the device 1100 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above methods.

[0187] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1104 including instructions, and the instructions can be executed by a processor 1120 of the device 1100 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0188] Figure 71 is a schematic diagram of the structure of the server in some embodiments of the present invention. The server 1900 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 1922 (for example, one or more processors) and memory 1932, one or more storage media 1930 (for example, one or more mass storage devices) storing application programs 1942 or data 1944. Among them, the memory 1932 and the storage medium 1930 can be short-term storage or permanent storage. The program stored in the storage medium 1930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the server. Furthermore, the central processing unit 1922 can be configured to communicate with the storage medium 1930, and execute a series of instruction operations in the storage medium 1930 on the server 1900.

[0189] The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input and output interfaces 1958, one or more keyboards 1956, and / or, one or more operating systems 1941, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0190] A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of a device (server or terminal), enables the device to perform Figure 2 or Figure 3 or Figure 4 or Figure 5 The search method shown.

[0191] A non-temporary computer-readable storage medium, when the instructions in the storage medium are executed by the processor of a device (server or terminal), the device is able to execute a search method, the method comprising: receiving a user's search request; determining a first-level medical feature corresponding to the search request; determining multiple second-level medical features corresponding to the first-level medical features; the second-level medical features are subdivided features corresponding to the first-level medical features; and determining corresponding search result data for multiple combinations corresponding to the search request and multiple second-level medical features.

[0192] The embodiment of the present invention discloses A1, a search method, the method comprising:

[0193] Receive a user's search request;

[0194] Determining a first-level medical feature corresponding to the search request;

[0195] Determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features;

[0196] Corresponding search result data is determined respectively for multiple combinations corresponding to the search request and multiple second-layer medical features.

[0197] A2. According to the method described in A1, determining the first-level medical features corresponding to the search request comprises:

[0198] Acquire the target medical entity word corresponding to the search request from the medical entity library;

[0199] Determine the first-level medical features corresponding to the target medical entity word.

[0200] A3. According to the method described in A2, determining the first-level medical features corresponding to the target medical entity word comprises:

[0201] The first-level medical features corresponding to the target medical entity word are determined from the first-level medical feature set.

[0202] A4. According to the method described in A3, the first-layer medical feature set includes: population characteristics, onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and accompanying symptom characteristics.

[0203] A5. According to the method described in A3, determining the first-level medical features corresponding to the target medical entity word from the first-level medical feature set includes:

[0204] According to the relevant information between the target medical entity word and the first-layer medical features, the first-layer medical features corresponding to the target medical entity word are determined from the first-layer medical feature set.

[0205] A6. According to the method described in A2 or A5, the co-occurrence probability of the preset medical entity word corresponding to the first layer of medical features and the target medical entity word in the medical corpus exceeds a probability threshold.

[0206] A7. The method according to A6, further comprising:

[0207] According to the mapping relationship between the standard description and the non-standard description of the medical entity word, the occurrence information of the medical entity word in the medical corpus is determined;

[0208] Based on the occurrence information, the co-occurrence probability of multiple medical entity words in the medical corpus is determined.

[0209] A8. The method according to A2, further comprising:

[0210] Perform entity recognition on medical corpus to obtain medical entity words;

[0211] Medical entity words with the same semantics but different descriptions are processed uniformly, and the processed medical entity words are saved in the medical entity database.

[0212] A9. According to any one of the methods described in A1 to A5, respectively determining corresponding search results includes:

[0213] Based on the combination, a search is performed in the medical corpus to obtain search results corresponding to the combination.

[0214] A10. The method according to any one of A1 to A5, further comprising:

[0215] In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

[0216] A11. According to the method described in A10, the displaying of the search result data corresponding to the multiple combinations respectively includes:

[0217] The search result page uses a tab page to display the search result data corresponding to the multiple combinations respectively; the label of the tab page includes: the second-level medical characteristics corresponding to the combination.

[0218] A12. The method according to any one of A1 to A5, further comprising:

[0219] Based on the medical corpus, determine the first-level medical characteristics;

[0220] Classifying the first-layer medical features to obtain second-layer medical features;

[0221] The correspondence between the first-layer medical features and the second-layer medical features is saved.

[0222] The embodiment of the present invention discloses B13, a search method, comprising:

[0223] Receive a user's search request;

[0224] Determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0225] In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

[0226] B14. The method according to B13, wherein the search result data corresponding to the multiple combinations are displayed separately, including:

[0227] The search result page uses a tab page to display the search result data corresponding to the multiple combinations respectively; the label of the tab page includes: the second-level medical characteristics corresponding to the combination.

[0228] The embodiment of the present invention discloses C15, a search device, comprising:

[0229] A receiving module, used for receiving a user's search request;

[0230] A first determination module, used to determine the first-level medical feature corresponding to the search request;

[0231] A second determination module is used to determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features; and

[0232] The search result determination module is used to determine corresponding search result data respectively for multiple combinations corresponding to the search request and multiple second-level medical features.

[0233] C16. According to the apparatus of C15, the first determining module comprises:

[0234] An entity word acquisition module, used to acquire the target medical entity word corresponding to the search request from a medical entity library;

[0235] The first-layer medical feature determination module is used to determine the first-layer medical features corresponding to the target medical entity word.

[0236] C17. According to the device of C16, the first-layer medical feature determination module comprises:

[0237] The set determination module is used to determine the first-level medical features corresponding to the target medical entity word from the first-level medical feature set.

[0238] C18. According to the device described in C17, the first-layer medical feature set includes: population characteristics, onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and accompanying symptom characteristics.

[0239] C19. According to the apparatus of C17, the set determination module comprises:

[0240] The set selection module is used to determine the first-level medical features corresponding to the target medical entity word from the first-level medical feature set based on the relevant information between the target medical entity word and the first-level medical features.

[0241] C20. According to the device described in C16 or C19, the co-occurrence probability of the preset medical entity word corresponding to the first layer of medical features and the target medical entity word in the medical corpus exceeds a probability threshold.

[0242] C21. The device according to C20, further comprising:

[0243] A third determination module is used to determine the occurrence information of the medical entity word in the medical corpus according to the mapping relationship between the standard description and the non-standard description of the medical entity word;

[0244] The fourth determination module is used to determine the co-occurrence probability of multiple medical entity words in the medical corpus based on the occurrence information.

[0245] C22. The device according to C16, further comprising:

[0246] An entity recognition module is used to perform entity recognition on medical corpus to obtain medical entity words;

[0247] The unified processing module is used to uniformly process medical entity words with the same semantics but different descriptions, and save the uniformly processed medical entity words into the medical entity library.

[0248] C23. According to any one of C15 to C19, the search result determination module comprises:

[0249] The retrieval module is used to search in the medical corpus according to the combination to obtain search results corresponding to the combination.

[0250] C24. The device according to any one of C15 to C19, further comprising:

[0251] The display module is used to display the search result data corresponding to the multiple combinations respectively in the search result page.

[0252] C25. According to the device described in C24, the display module comprises:

[0253] The tab display module is used to display the search result data corresponding to the multiple combinations in the form of tabs in the search result page; the label of the tab includes: the second-level medical characteristics corresponding to the combination.

[0254] C26. The device according to any one of C15 to C19, further comprising:

[0255] A fifth determination module is used to determine the first-level medical features based on the medical corpus;

[0256] A classification module, used for classifying the first layer of medical features to obtain a second layer of medical features;

[0257] The saving module is used to save the corresponding relationship between the first layer medical features and the second layer medical features.

[0258] The embodiment of the present invention discloses D27, a search device, comprising:

[0259] A receiving module, used for receiving a user's search request;

[0260] A determination module, used to determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0261] The display module is used to display the search result data corresponding to the multiple combinations respectively in the search result page.

[0262] D28. According to the device described in D27, the display module includes:

[0263] The tab display module is used to display the search result data corresponding to the multiple combinations in the form of tabs in the search result page; the label of the tab includes: the second-level medical characteristics corresponding to the combination.

[0264] The embodiment of the present invention discloses E29, a device for searching, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, wherein the one or more programs include instructions for performing the following operations:

[0265] Receive a user's search request;

[0266] Determining a first-level medical feature corresponding to the search request;

[0267] Determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features;

[0268] Corresponding search result data is determined respectively for multiple combinations corresponding to the search request and multiple second-layer medical features.

[0269] E30. According to the apparatus of E29, the determining the first-level medical feature corresponding to the search request comprises:

[0270] Acquire the target medical entity word corresponding to the search request from the medical entity library;

[0271] Determine the first-level medical features corresponding to the target medical entity word.

[0272] E31. According to the apparatus described in E30, determining the first-level medical features corresponding to the target medical entity word includes:

[0273] The first-level medical features corresponding to the target medical entity word are determined from the first-level medical feature set.

[0274] E32. According to the device described in E31, the first-level medical feature set includes: population characteristics, onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and accompanying symptom characteristics.

[0275] E33. According to the apparatus of E31, determining the first-level medical features corresponding to the target medical entity word from the first-level medical feature set comprises:

[0276] According to the relevant information between the target medical entity word and the first-layer medical features, the first-layer medical features corresponding to the target medical entity word are determined from the first-layer medical feature set.

[0277] E34. According to the device described in E30 or E33, the co-occurrence probability of the preset medical entity word corresponding to the first layer of medical features and the target medical entity word in the medical corpus exceeds a probability threshold.

[0278] E35. The device according to E34, wherein the device is further configured to execute, by one or more processors, the one or more programs including instructions for performing the following operations:

[0279] According to the mapping relationship between the standard description and the non-standard description of the medical entity word, the occurrence information of the medical entity word in the medical corpus is determined;

[0280] Based on the occurrence information, the co-occurrence probability of multiple medical entity words in the medical corpus is determined.

[0281] E36. The device according to E30, wherein the device is further configured to execute, by one or more processors, the one or more programs including instructions for performing the following operations:

[0282] Perform entity recognition on medical corpus to obtain medical entity words;

[0283] Medical entity words with the same semantics but different descriptions are processed uniformly, and the processed medical entity words are saved in the medical entity database.

[0284] E37. According to any one of E29 to E33, the step of respectively determining corresponding search results comprises:

[0285] Based on the combination, a search is performed in the medical corpus to obtain search results corresponding to the combination.

[0286] E38. The device according to any one of E29 to E33, wherein the device is further configured to execute, by one or more processors, the one or more programs including instructions for performing the following operations:

[0287] In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

[0288] E39. According to the apparatus of E38, the displaying of the search result data corresponding to the multiple combinations respectively comprises:

[0289] The search result page uses a tab page to display the search result data corresponding to the multiple combinations respectively; the label of the tab page includes: the second-level medical characteristics corresponding to the combination.

[0290] E40. The device according to any one of E29 to E33, wherein the device is further configured to execute, by one or more processors, the one or more programs including instructions for performing the following operations:

[0291] Based on the medical corpus, determine the first-level medical characteristics;

[0292] Classifying the first-layer medical features to obtain second-layer medical features;

[0293] The correspondence between the first-layer medical features and the second-layer medical features is saved.

[0294] The embodiment of the present invention discloses F41, a device for searching, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, wherein the one or more programs include instructions for performing the following operations:

[0295] Receive a user's search request;

[0296] Determine search result data corresponding to the search request; the search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request;

[0297] In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

[0298] F42. According to the apparatus described in F41, the displaying of the search result data corresponding to the multiple combinations respectively comprises:

[0299] The search result page uses a tab page to display the search result data corresponding to the multiple combinations respectively; the label of the tab page includes: the second-level medical characteristics corresponding to the combination.

[0300] An embodiment of the present invention discloses G43, a machine-readable medium having instructions stored thereon, which, when executed by one or more processors, enables the device to perform the search method as described in one or more of A1 to A12.

[0301] An embodiment of the present invention discloses H44, a machine-readable medium having instructions stored thereon, which, when executed by one or more processors, enables the device to execute the search method as described in one or more of B13 to B14.

[0302] Those skilled in the art will readily appreciate other embodiments of the invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0303] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0304] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0305] The above is a detailed introduction to a search method, a search device and a device for searching provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A search method, characterized in that: The method comprises: Determine a first-layer medical feature according to the medical corpus; classify the first-layer medical features to obtain a second-layer medical feature; and save the corresponding relationship between the first-layer medical features and the second-layer medical features; Receive a user's search request; Determine the first-layer medical features corresponding to the search request, including: obtaining the target medical entity word corresponding to the search request from a medical entity library; determining the first-layer medical features corresponding to the target medical entity word; the determining the first-layer medical features corresponding to the target medical entity word includes: determining the first-layer medical features corresponding to the target medical entity word from a first-layer medical feature set; wherein the first-layer medical feature set includes: population characteristics, onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and accompanying symptom characteristics; Determining a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features, including: determining a plurality of second-layer medical features corresponding to the first-layer medical features according to the corresponding relationship; Corresponding search result data is determined respectively for multiple combinations corresponding to the search request and multiple second-layer medical features.

2. The method according to claim 1, characterized in that The step of determining the first-layer medical features corresponding to the target medical entity word from the first-layer medical feature set includes: According to the relevant information between the target medical entity word and the first-layer medical features, the first-layer medical features corresponding to the target medical entity word are determined from the first-layer medical feature set.

3. The method according to claim 1 or 2, characterized in that: The co-occurrence probability of the preset medical entity word corresponding to the first-layer medical feature and the target medical entity word in the medical corpus exceeds a probability threshold.

4. The method according to claim 3, characterized in that The method further comprises: According to the mapping relationship between the standard description and the non-standard description of the medical entity word, the occurrence information of the medical entity word in the medical corpus is determined; Based on the occurrence information, the co-occurrence probability of multiple medical entity words in the medical corpus is determined.

5. The method according to claim 1, characterized in that The method further comprises: Perform entity recognition on medical corpus to obtain medical entity words; Medical entity words with the same semantics but different descriptions are processed uniformly, and the processed medical entity words are saved in the medical entity database.

6. The method according to any one of claims 1 to 2, characterized in that: The respectively determining corresponding search results includes: Based on the combination, a search is performed in the medical corpus to obtain search results corresponding to the combination.

7. The method according to any one of claims 1 to 2, characterized in that: The method further comprises: In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

8. The method according to claim 7, characterized in that The displaying of the search result data corresponding to the multiple combinations respectively includes: The search result page uses a tab page to display the search result data corresponding to the multiple combinations respectively; the label of the tab page includes: the second-level medical characteristics corresponding to the combination.

9. A search method, characterized in that: include: Receive a user's search request; Determining search result data corresponding to the search request; The search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request; In the search result page, the search result data corresponding to the multiple combinations are displayed respectively.

10. The method according to claim 9, characterized in that The displaying of the search result data corresponding to the multiple combinations respectively includes: The search result page uses a tab page to display the search result data corresponding to the multiple combinations respectively; the label of the tab page includes: the second-level medical characteristics corresponding to the combination.

11. A search device, characterized in that: include: A fifth determination module is used to determine the first-level medical features based on the medical corpus; A classification module, used for classifying the first layer of medical features to obtain a second layer of medical features; A saving module, used for saving the corresponding relationship between the first layer medical features and the second layer medical features; A receiving module, used for receiving a user's search request; A first determination module, used to determine the first-level medical feature corresponding to the search request; The first determination module includes: an entity word acquisition module, which is used to obtain the target medical entity word corresponding to the search request from the medical entity library; a first-layer medical feature determination module, which is used to determine the first-layer medical features corresponding to the target medical entity word; the first-layer medical feature determination module includes: a set determination module, which is used to determine the first-layer medical features corresponding to the target medical entity word from the first-layer medical feature set; the first-layer medical feature set includes: population characteristics, onset site characteristics, symptom frequency characteristics, symptom duration characteristics, symptom subtype characteristics, symptom level characteristics, and concomitant symptom characteristics; A second determination module is used to determine a plurality of second-layer medical features corresponding to the first-layer medical features; the second-layer medical features are subdivided features corresponding to the first-layer medical features, including: determining a plurality of second-layer medical features corresponding to the first-layer medical features according to the corresponding relationship; and The search result determination module is used to determine corresponding search result data respectively for multiple combinations corresponding to the search request and multiple second-level medical features.

12. The device according to claim 11, characterized in that The set determination module comprises: The set selection module is used to determine the first-level medical features corresponding to the target medical entity word from the first-level medical feature set based on the relevant information between the target medical entity word and the first-level medical features.

13. The device according to claim 11 or 12, characterized in that The co-occurrence probability of the preset medical entity word corresponding to the first-layer medical feature and the target medical entity word in the medical corpus exceeds a probability threshold.

14. The device according to claim 13, characterized in that The device also includes: A third determination module is used to determine the occurrence information of the medical entity word in the medical corpus according to the mapping relationship between the standard description and the non-standard description of the medical entity word; The fourth determination module is used to determine the co-occurrence probability of multiple medical entity words in the medical corpus based on the occurrence information.

15. The device according to claim 11, characterized in that The device also includes: An entity recognition module is used to perform entity recognition on medical corpus to obtain medical entity words; The unified processing module is used to uniformly process medical entity words with the same semantics but different descriptions, and save the uniformly processed medical entity words into the medical entity library.

16. The device according to any one of claims 11 to 12, characterized in that The search result determination module comprises: The retrieval module is used to search in the medical corpus according to the combination to obtain search results corresponding to the combination.

17. The device according to any one of claims 11 to 12, characterized in that The device also includes: The display module is used to display the search result data corresponding to the multiple combinations respectively in the search result page.

18. The device according to claim 17, characterized in that The display module comprises: The tab display module is used to display the search result data corresponding to the multiple combinations in the form of tabs in the search result page; the label of the tab includes: the second-level medical characteristics corresponding to the combination.

19. A search device, characterized in that: include: A receiving module, used for receiving a user's search request; A determination module, used to determine search result data corresponding to the search request; The search result data includes: search result data corresponding to a plurality of combinations; the plurality of combinations include: combinations corresponding to the search request and a plurality of second-layer medical features; the plurality of second-layer medical features are subdivided features corresponding to the first-layer medical features of the search request; The display module is used to display the search result data corresponding to the multiple combinations respectively in the search result page.

20. The device according to claim 19, characterized in that The display module comprises: The tab display module is used to display the search result data corresponding to the multiple combinations in the form of tabs in the search result page; the label of the tab includes: the second-level medical characteristics corresponding to the combination.

21. A device for searching, characterized in that: The invention comprises a memory and one or more programs, wherein the one or more programs are stored in the memory, and when the programs are executed by one or more processors, the steps of the method described in any one of claims 1 to 8 are implemented.

22. A device for searching, characterized in that: The invention comprises a memory and one or more programs, wherein the one or more programs are stored in the memory, and when the programs are executed by one or more processors, the steps of the method described in any one of claims 9 to 10 are implemented.

23. A machine-readable medium having instructions stored thereon, which, when executed by one or more processors, cause an apparatus to perform a search method as claimed in one or more of claims 1 to 8.

24. A machine-readable medium having instructions stored thereon, which, when executed by one or more processors, causes an apparatus to perform the search method according to claim 9 or 10.

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