CN115101194A - Symptom pushing method, device and equipment with label and storage medium - Google Patents
Symptom pushing method, device and equipment with label and storage medium Download PDFInfo
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Abstract
The application provides a symptom pushing method, a symptom pushing device and a symptom pushing storage medium with labels, on one hand, the pushing symptom can be determined according to the user symptom input by a user, so that the symptom prompt can be performed on the user, the user can further improve the discomfort information of the user based on the recommendation result, the comprehensive illness state information can be collected, and the accuracy of diagnosis and treatment results is improved; on the other hand, the push symptoms and the symptom category labels to which the push symptoms belong can be associated and pushed, so that the push symptoms can be classified, a user can quickly perform symptom self-check according to the symptom category labels, the efficiency of the user in performing the symptom self-check can be improved, and the user experience satisfaction is improved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a symptom pushing method, a symptom pushing device, symptom pushing equipment and a storage medium with labels.
Background
The inquiry of the patient is an important work of the doctor, and the doctor can only give a medicine according to symptoms if all symptoms of the patient are accurately acquired. However, patients sometimes have difficulty describing their symptoms accurately, or may miss some less-fitting symptoms. For example, when a patient initiates an inquiry request through an online inquiry platform, the patient may omit some symptoms with less discomfort because the patient expresses the symptoms with higher discomfort, which may affect the diagnosis and treatment of the doctor, so it is necessary to intelligently recommend the patient with the symptoms to prompt the patient with the symptoms.
Disclosure of Invention
An object of the embodiments of the present application is to provide a symptom pushing method, device, and apparatus with a tag, and a storage medium, so as to solve the problem that diagnosis accuracy is affected due to the fact that relatively complete information of a patient's condition cannot be obtained in the prior art.
The embodiment of the application provides a symptom pushing method with a label, which comprises the following steps:
acquiring user information; the user information comprises user symptoms input by a user;
determining a push symptom from the user symptom;
determining a symptom category label to which each of the push symptoms belongs;
and performing associated pushing on each pushing symptom and the symptom category label to which each pushing symptom belongs.
In the implementation process, on one hand, the pushing symptom can be determined according to the user symptom input by the user, so that the user can be prompted for the symptom, the user can further improve the discomfort information based on the recommendation result, and the comprehensive illness state information can be collected; on the other hand, the push symptoms and the symptom category labels to which the push symptoms belong can be pushed in a correlation mode, namely, the push symptoms are classified, so that a user can quickly perform symptom self-check according to the symptom category labels, the efficiency of the user in performing the symptom self-check can be improved, and the user experience satisfaction degree is further improved.
Further, the determining a push symptom according to the user symptom comprises:
determining a set of relevant symptoms related to the user symptoms; the set of relevant symptoms consists of relevant symptoms related to the user symptoms;
and filtering the relevant symptom set according to a preset filtering rule to obtain a plurality of pushing symptoms.
In the implementation process, the relevant symptoms related to the user symptoms input by the user can be determined, and the set formed by the relevant symptoms is filtered, so that the accuracy of the pushing result is improved, the obtained pushing symptoms are more in line with the current physical condition of the user, and more comprehensive symptom information can be collected.
Further, the user information further comprises user age information and user gender information; the preset filtering rule comprises at least one of the following rules:
age filtering rules: filtering symptoms from the set of relevant symptoms that do not belong to the age group of the user;
gender filtering rules: filtering symptoms that do not belong to the user's gender from the set of relevant symptoms;
mutual exclusion symptom filtering rule: filtering symptoms mutually exclusive from the user symptoms from the set of related symptoms;
positive symptom filtering rule: filtering positive symptoms corresponding to negative symptoms in the user symptoms from the set of related symptoms;
symptom dimension filtering rules: classifying each relevant symptom in the relevant symptom set according to symptom dimensions to obtain a plurality of relevant symptom subsets forming the relevant symptom set, and filtering redundant relevant symptoms when the number of relevant symptoms in the relevant symptom subsets exceeds a preset number threshold;
collected symptom filtering rules: filtering the user symptom from the set of related symptoms.
In the implementation process, the relevant symptoms in the relevant symptom set are filtered according to the preset filtering rule, so that the accuracy and reliability of the pushing result are further improved.
Further, prior to the determining the symptom category labels to which the push symptoms each belong, the method further comprises:
generating a symptom category label set according to the user symptom and/or the push symptom;
the determining a symptom category label to which each of the push symptoms belongs comprises:
and for each pushed symptom, determining a symptom class label to which the pushed symptom belongs from the symptom class label set.
In the implementation process, the symptom category label is generated according to the user symptom input by the user and/or the pushed symptom, so that the correlation between the generated label and the user symptom input by the user is improved, and the accuracy of the pushed symptom category label can be improved.
Further, the determining the symptom category labels to which the push symptoms each belong includes:
and determining the symptom category labels to which the push symptoms belong respectively according to the preset corresponding relation between the symptoms and the symptom category labels.
In the implementation process, the corresponding relation between the symptoms and the symptom category labels is preset, and the corresponding relation can be directly matched with the pushed symptoms subsequently to obtain the corresponding symptom category labels, so that the operation efficiency is improved.
Further, after the symptom category labels to which the push symptoms respectively belong are determined according to the preset correspondence between the symptoms and the symptom category labels, the method further includes:
filtering a first label set formed by the symptom category labels to which the push symptoms belong to respectively to obtain a second label set;
for a target push symptom, re-determining a symptom category label to which the target push symptom belongs from the second label set; the target push symptom is a push symptom corresponding to the filtered symptom category label;
the pushing of each pushing symptom in association with the symptom category label to which each pushing symptom belongs includes:
and performing associated pushing on the target pushing symptom and the redetermined symptom category label to which the target pushing symptom belongs.
In the implementation process, the obtained symptom category labels can be filtered, and new symptom category labels are determined for the push symptoms again, so that the accuracy of the push result is improved.
Further, the filtering a first tag set composed of the symptom category tags to which the push symptoms respectively belong to obtain a second tag set includes:
classifying the symptom category labels in the first label set according to symptom dimensions to obtain a plurality of label subsets forming the first label set, filtering the label subsets, and taking a collection of the filtered label subsets as a second label set; the filtering treatment comprises the following steps: for each label subset, judging whether the number of the symptom category labels contained in the label subset exceeds a preset number threshold, if so, filtering redundant symptom category labels, and otherwise, not processing;
and/or the presence of a gas in the gas,
filtering the tags with target preposed symptoms in the first tag set to obtain a second tag set; the label with the target antedisposition symptom is: a label with a pre-symptom and the user symptom does not have the pre-symptom.
In the implementation process, redundant symptom category labels in the label subset are filtered, or labels with target leading symptoms are filtered, and the pushed symptom category labels are further optimized, so that the pushed symptom category labels are more reliable.
Further, the performing associated pushing on each pushed symptom and the symptom category label to which each pushed symptom belongs includes:
for each of the push symptoms, displaying it under an associated field of the symptom category label corresponding to the push symptom.
In the implementation process, the pushed symptom is displayed under the associated column of the corresponding symptom category label, so that the pushed symptom is more convenient for a user to view.
The embodiment of the present application further provides a symptom pusher with a label, including:
the acquisition module is used for acquiring user information; the user information comprises user symptoms input by a user;
a first determining module for determining a plurality of push symptoms according to the user symptom;
the second determination module is used for determining the symptom category label to which each pushed symptom belongs;
and the association pushing module is used for performing association pushing on each pushing symptom and the symptom category label to which the pushing symptom belongs.
An embodiment of the present application further provides an electronic device, which includes a processor and a memory, where the memory stores a computer program, and the processor executes the computer program to implement any one of the above methods.
Embodiments of the present application further provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by at least one processor, the computer program implements any one of the above methods.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for pushing a symptom with a tag according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a process for determining push symptoms according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a user performing symptom and tag pushing according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a symptom pushing device with a label according to a second embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the descriptions relating to "first", "second", etc. in the embodiments of the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between the embodiments may be combined with each other, but must be based on the realization of the technical solutions by a person skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
In the description of the present invention, it should be understood that the numerical references before the steps do not identify the order of performing the steps, but merely serve to facilitate the description of the present invention and to distinguish each step, and thus should not be construed as limiting the present invention.
The following description will provide embodiments to specifically describe a labeled symptom pushing method, device, apparatus, and storage medium.
The first embodiment is as follows:
the embodiment of the application provides a symptom pushing method with a label, which can be applied to electronic equipment, wherein the electronic equipment includes but is not limited to a Personal Computer (PC), a mobile phone, a tablet Computer, a notebook Computer, and the like.
Referring to fig. 1, a method provided in an embodiment of the present application may include the following steps:
s11: acquiring user information; the user information includes user symptoms input by the user.
S12: push symptoms are determined from user symptoms.
S13: and determining the symptom category labels to which the push symptoms respectively belong.
S14: and carrying out related pushing on each pushing symptom and the symptom category label to which each pushing symptom belongs.
The above steps are specifically described below.
It should be noted that the user in the above steps S11-S14 is usually a patient user, for example, the patient himself or herself, or a person familiar with the patient' S condition, such as a family member of the patient. The user can input user information through an application program on the electronic equipment according to the actual situation of the user. The user information includes user symptoms, and may also include information that can assist a doctor in diagnosis, such as age and sex of the patient user.
The patient user can input user symptoms, such as cough, headache and the like, on the intelligent inquiry platform of the application program according to the actual physical condition.
Referring to fig. 2, step S12 may include the following sub-steps:
s121: determining a set of relevant symptoms related to the user's symptoms; the set of relevant symptoms consists of relevant symptoms related to the user's symptoms.
S122: and filtering the relevant symptom set according to a preset filtering rule to obtain a plurality of pushing symptoms.
For step S121, a correlation degree value between the user symptom input by the user and each preset symptom in the preset symptom database may be determined, and a preset symptom with the correlation degree value greater than or equal to a preset correlation degree threshold may be selected as the correlation symptom, or a preset number of preset symptoms may be sequentially selected from among the correlation degree values from high to low as the correlation symptom.
In order to ensure the accuracy of the pushing result, a large number of common symptoms such as cough symptoms, pharyngalgia symptoms, headache symptoms, glowing throat symptoms and the like can be included in the preset symptom database.
It should be noted that, when the user inputs a plurality of user symptoms, the correlation degree value between the plurality of user symptoms and the preset symptom may be calculated as a whole. For example, when calculating the degree of correlation between a plurality of user symptoms and a preset symptom, the first degree of correlation between each user symptom and the preset symptom may be determined, the weight coefficient of each user symptom may be determined, each first degree of correlation value and the corresponding weight coefficient may be multiplied, and the respective products may be added to obtain the degree of correlation between the plurality of user symptoms and the preset symptom. In this embodiment, the weighting factor corresponding to each user symptom may be determined according to the input order of the user symptoms, and the weighting factor corresponding to the user symptom input later may be increased.
In other embodiments, the user information may be input into the trained relevant symptom recommendation model to obtain relevant symptoms, and then the relevant symptoms are filtered to obtain push symptoms. The relevant symptom recommendation model is obtained by training based on a large amount of training sample data, each training sample data may include user information and corresponding relevant symptoms, the user information includes user symptoms, and in addition, the user information may include user age, gender and the like. And (4) taking the user information as model input and the relevant symptoms as model output to train until the model converges to obtain a relevant symptom recommendation model.
The preset filtering rule in the embodiment of the present application may include at least one of an age filtering rule, a gender filtering rule, a mutually exclusive symptom filtering rule, a positive symptom filtering rule, a symptom dimension filtering rule, and a collected symptom filtering rule.
The preset filtering rules mentioned above will be specifically described below.
Age filtering rules: and filtering the symptoms which do not belong to the age group of the user from the related symptom set.
Gender filtering rule: the symptoms not belonging to the gender of the user are filtered from the set of related symptoms.
Mutual exclusion symptom filtering rule: the symptoms that are mutually exclusive from the user symptoms entered by the user are filtered from the set of related symptoms.
Positive symptom filtering rule: positive symptoms corresponding to negative symptoms among the user symptoms are filtered from the set of related symptoms.
Collected symptom filtering rules: the user symptoms that the user has entered are filtered from the set of related symptoms.
Symptom dimension filtering rules: and classifying all relevant symptoms in the relevant symptom set according to symptom dimensions to obtain a plurality of relevant symptom subsets forming the relevant symptom set, and filtering redundant relevant symptoms when the number of relevant symptoms in a certain relevant symptom subset exceeds a preset number threshold.
In the embodiment of the present application, a plurality of symptom dimensions may be preset for a symptom, for example, the setting may be performed based on the dimensions of a body part or a body organ. For example, the preset symptom dimension may include a laryngo-nasal symptom dimension, an extremity symptom dimension, a spleen-stomach symptom dimension, and the like.
When the relevant symptoms in the relevant symptom set are classified according to the symptom dimension, a plurality of relevant symptom subsets can be obtained through classification based on a clustering algorithm, the corresponding symptom dimension of each relevant symptom subset is determined, then the quantity threshold corresponding to the relevant symptom subset is determined according to the corresponding relation between the preset symptom dimension and the quantity threshold, the quantity threshold represents the upper limit of the quantity of the relevant symptoms contained in the subset, if the quantity of the relevant symptoms in a certain relevant symptom subset reaches the corresponding quantity threshold, the information of the symptom dimension corresponding to the subset is completely collected, and the relevant symptoms exceeding the quantity threshold can not be deduced. That is, if the number of related symptoms in a subset of related symptoms exceeds a corresponding number threshold, then the excess related symptoms are filtered, avoiding recommending too many symptoms for the same symptom dimension.
In a first alternative embodiment, before step S13, the method may include the steps of: and generating a symptom category label set according to the user symptom and/or the push symptom. At this time, in step S13, for each push symptom, the symptom category label to which the push symptom belongs is determined from the symptom category label set.
In the first optional embodiment, the user symptom and/or the push symptom may be input into the trained tag set recommendation model to obtain a symptom category tag set. The label set recommendation model is obtained by training based on a large amount of training sample data, and each training sample data can contain a plurality of symptoms and corresponding symptom class label sets. And (4) taking the symptoms as model input and the symptom category label set as model output to train until the model converges, so as to obtain a label set recommendation model.
The physician prescriptions and the dosages are different, and the directions of the prescriptions are completely opposite due to different attributes, so that in the embodiment of the application, besides the relevance pushing of each pushed symptom and the corresponding symptom category label, the general attribute of the symptom can be recommended according to the user symptom input by the user. Generic attributes refer to common features that exist for multiple symptoms.
When the label set recommendation model is trained, each training sample data may further include a general attribute of a symptom and an attribute label corresponding to the attribute, and then the general attribute of the symptom and the attribute label corresponding to the attribute are used as model outputs to be trained. In this way, when a user symptom and/or a pushed symptom is input into the tag set recommendation model, in addition to outputting a symptom category tag set, a general attribute of the symptom and an attribute tag corresponding to the attribute may be output, where the attribute tag represents the symptom associated with the general attribute, and for example, the attribute tag and the general attribute that are pushed out are as follows:
degree of cough: generally; mild degree; severe.
Time to onset of cough: morning rising; high incidence at night.
The cough exacerbation factor: aggravation of fatigue; the onset is aggravated by cold.
In a second alternative embodiment, the symptom category labels to which the push symptoms belong may be determined according to a preset correspondence between the symptoms and the symptom category labels.
In the second alternative embodiment, the correspondence relationship between the symptoms and the symptom type labels may be flexibly set by the developer, for example, the symptom type labels of the symptoms such as "nausea", "retching", "vomiting" and the like are set as "spleen and stomach discomfort", and the symptom type labels of the symptoms such as "dry mouth", "chest distress" and "short breath" are set as "related symptoms".
In the embodiment of the present application, after step S13 and before step S14, the following steps may be further included:
filtering a first label set formed by the symptom category labels to which the push symptoms belong to obtain a second label set; for the target push symptom, re-determining the symptom category label to which the target push symptom belongs from the second label set; the target push symptom is a push symptom corresponding to the filtered symptom category label.
At this time, for step S14, the target push symptom may be pushed in association with the newly determined symptom category label to which it belongs.
The manner in which the second set of tags is obtained is described in detail below.
The first method is as follows: classifying all symptom category labels in the first label set according to symptom dimensions to obtain a plurality of label subsets forming the first label set, filtering all the label subsets, and taking a collection of all the filtered label subsets as a second label set; the filtering treatment comprises the following steps: and judging whether the number of the symptom category labels contained in each label subset exceeds a preset number threshold or not, if so, filtering redundant symptom category labels, and otherwise, not processing.
And/or the presence of a gas in the gas,
the second method comprises the following steps: filtering the tags with the target preposed symptoms in the first tag set to obtain a second tag set; the labels with the target leading symptom are: a label with a pre-symptom, and no pre-symptom in the user's symptoms. For example, the first label set has a label of "expectoration color", the leading symptom of the label is expectoration, and if the user input the user symptom without the "expectoration" symptom, the label of "expectoration color" is filtered.
For the first mode, a plurality of symptom dimensions may be preset for the symptom category label, and it is understood that the symptom dimension set for the symptom may be the same as or different from the symptom dimension set for the symptom category label.
When all symptom category labels in the first label set are classified according to symptom dimension degrees, a plurality of label subsets can be obtained through classification based on a clustering algorithm, then a corresponding symptom dimension is determined for each label subset, then a quantity threshold corresponding to the label subset is determined according to a corresponding relation between a preset symptom dimension and the quantity threshold, the quantity threshold represents an upper limit of the symptom category labels contained in the subset, if the quantity of the symptom category labels in a certain label subset reaches the corresponding quantity threshold, the information of the symptom dimension corresponding to the subset is completely collected, and the symptom category labels of the quantity threshold are not deduced. That is, if the number of symptom category labels in a certain label subset exceeds a corresponding number threshold, redundant symptom category labels are filtered, and the condition category labels are not recommended too much for the same symptom dimension.
For a target push symptom corresponding to a filtered symptom category label, the symptom category label needs to be re-determined from the second set of labels since its corresponding symptom category label has been filtered. Specifically, according to the correlation between the label and the symptom, the symptom category label with the highest correlation degree with the target push symptom may be used as the new symptom category label to which the target push symptom belongs.
It should be noted that, in some other embodiments, the same symptom category label may be uniformly reset for each target push symptom corresponding to the filtered symptom category label, and new symptom category labels of the target push symptoms are all set as preset target symptom category labels, for example, may be uniformly set as "related symptoms".
In step S14, each pushed symptom may be pushed to the patient user in association with the symptom category label to which the pushed symptom belongs, that is, may be pushed to the user who sent the symptom of the user. It should be noted that some doctors with less skilled business skills, such as practice stage doctors, may not be skilled in guiding patients to describe all their symptoms during the course of making a diagnosis. Therefore, in step S14, each pushed symptom and the corresponding symptom category label may also be pushed to the doctor user in association, and the doctor queries the patient according to the pushed symptom, so as to improve the diagnosis and treatment efficiency of the doctor.
To facilitate a patient user to quickly self-examine whether there are relevant symptoms, or to facilitate a physician user to ask, each push symptom may be displayed under an associated field of a symptom category label corresponding to the push symptom. Specifically, as shown in fig. 3, the first column on the left side in fig. 3 is a symptom category label, and the right side is a corresponding push symptom.
The push symptoms corresponding to each symptom category label can be arranged in sequence from front to back according to the recommendation sequence. The recommendation order may be determined according to a correlation degree value between the recommendation symptom and the user symptom input by the user, and the higher the correlation degree value is, the earlier the recommendation order is. The symptom category labels may also be ordered by relevance to the user's symptoms entered by the user.
It should be noted that, when performing association pushing on each pushed symptom and the symptom category label to which each pushed symptom belongs, the association between the symptom category labels may be determined first, and the symptom category labels may be classified according to the association between the symptom category labels, and each symptom category label in the same label classification set has a high association, and when performing association pushing, the symptom category labels belonging to the same label classification set may be arranged together. For example, when a plurality of symptom category labels are associated with the same symptom dimension, which indicates that the several symptom category labels are relatively high in association, the plurality of symptom category labels may be classified in the same label classification set.
Similarly, for each push symptom corresponding to each symptom category label, the push symptoms may be classified according to the relevance between the push symptoms, the push symptoms in the same symptom classification set have high relevance, and when performing relevant push, the push symptoms belonging to the same symptom classification set may be arranged together. For example, when multiple push symptoms are associated with the same symptom dimension, which indicates that the relevance of the push symptoms is high, the multiple push symptoms may be classified in the same symptom classification set.
Example two:
an embodiment of the present application provides a symptom pushing device with a label, please refer to fig. 4, including:
an obtaining module 401, configured to obtain user information; the user information includes user symptoms entered by the user.
A first determining module 402, configured to determine a plurality of push symptoms according to a user symptom.
A second determining module 403, configured to determine a symptom category label to which each push symptom belongs.
And the association pushing module 404 is configured to perform association pushing on each pushing symptom and the symptom category label to which each pushing symptom belongs.
In an exemplary embodiment, the first determination module 402 is configured to determine a set of related symptoms related to a symptom of a user; the set of relevant symptoms consists of relevant symptoms related to the user's symptoms; and filtering the relevant symptom set according to a preset filtering rule to obtain a plurality of pushing symptoms.
In an exemplary embodiment, the tagged symptom pushing device may further include a generation module configured to generate a set of symptom category tags according to the user symptom and/or the pushing symptom; the second determining module 403 is configured to determine, for each pushed symptom, a symptom category label to which the pushed symptom belongs from the set of symptom category labels.
In an exemplary embodiment, the second determining module 403 is configured to determine, according to a preset correspondence between symptoms and symptom category labels, a symptom category label to which each of the push symptoms belongs.
In an exemplary embodiment, the tagged symptom pushing device may further include a tag filtering module, configured to filter a first tag set composed of the symptom category tags to which the pushed symptoms respectively belong, to obtain a second tag set. The second determining module 403 is configured to, for the target push symptom, re-determine a symptom category label to which the target push symptom belongs from the second label set; the target push symptom is a push symptom corresponding to the filtered symptom category label. The association pushing module 404 is configured to perform association pushing on the target pushing symptom and the redetermined symptom category label to which the target pushing symptom belongs.
In an exemplary embodiment, the label filtering module is configured to classify each symptom category label in the first label set according to a symptom dimension to obtain a plurality of label subsets forming the first label set, perform filtering processing on each label subset, and use a collection of each filtered label subset as a second label set; the filtering treatment comprises the following steps: and judging whether the number of the symptom category labels contained in the label subsets exceeds a preset number threshold value or not according to each label subset, if so, filtering redundant symptom category labels, and otherwise, not processing.
The label filtering module can be further used for filtering the labels with the target pre-symptom in the first label set to obtain a second label set; the labels with the target leading symptom are: the label with the pre-symptom is not included in the user symptoms input by the user.
It should be understood that, for the sake of brevity, the contents described in some embodiments are not repeated in this embodiment.
Example three:
based on the same inventive concept, an electronic device provided in an embodiment of the present application is shown in fig. 5, and includes a processor 501 and a memory 502, where the memory 502 stores a computer program, and the processor 501 executes the computer program to implement the steps of the method in the first embodiment, the second embodiment, or the third embodiment, which are not described herein again.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative and that the apparatus may also include more or fewer components than shown in fig. 5 or have a different configuration than shown in fig. 5.
The processor 501 may be an integrated circuit chip having signal processing capabilities. The processor 501 may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. Which may implement or perform the various methods, steps, and logic blocks disclosed in embodiments of the present application.
The memory 502 may include, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read Only Memory (PROM), erasable read only memory (EPROM), electrically erasable read only memory (EEPROM), and the like.
The present embodiment further provides a computer-readable storage medium, such as a floppy disk, an optical disk, a hard disk, a flash memory, a U-disk, a Secure Digital (SD) card, a multimedia data (MMC) card, etc., where one or more programs for implementing the above steps are stored, and the one or more programs can be executed by one or more processors to implement the steps of the method in the above embodiments, which are not described herein again.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. A method for pushing a symptom with a label, comprising:
acquiring user information; the user information comprises user symptoms input by a user;
determining a push symptom from the user symptom;
determining a symptom category label to which each of the push symptoms belongs;
and carrying out associated pushing on each pushing symptom and the symptom category label to which each pushing symptom belongs.
2. The tagged symptom pushing method of claim 1, wherein said determining a pushing symptom from said user symptom comprises:
determining a set of relevant symptoms related to the user symptoms; the set of relevant symptoms consists of relevant symptoms related to the user symptoms;
and filtering the relevant symptom set according to a preset filtering rule to obtain a plurality of pushing symptoms.
3. The tagged symptom pushing method of claim 2, wherein the user information further comprises user age information and user gender information; the preset filtering rule comprises at least one of the following rules:
age filtering rules: filtering symptoms from the set of relevant symptoms that do not belong to the age group of the user;
gender filtering rules: filtering symptoms that do not belong to the user's gender from the set of relevant symptoms;
mutual exclusion symptom filtering rule: filtering symptoms mutually exclusive from the user symptoms from the set of related symptoms;
positive symptom filtering rule: filtering positive symptoms corresponding to negative symptoms in the user symptoms from the set of related symptoms;
symptom dimension filtering rules: classifying each relevant symptom in the relevant symptom set according to symptom dimensions to obtain a plurality of relevant symptom subsets forming the relevant symptom set, and filtering redundant relevant symptoms when the number of the relevant symptoms in the relevant symptom subsets exceeds a preset number threshold;
collected symptom filtering rules: filtering the user symptom from the set of related symptoms.
4. The tagged symptom pushing method of claim 1, wherein prior to said determining the symptom category tag to which each of said pushed symptoms belongs, said method further comprises:
generating a symptom category label set according to the user symptom and/or the push symptom;
the determining of the symptom category labels to which the push symptoms each belong comprises:
and determining the symptom class label to which the pushing symptom belongs from the symptom class label set aiming at each pushing symptom.
5. The tagged symptom pushing method of claim 1, wherein said determining the symptom category tag to which each of said pushed symptoms belongs comprises:
and determining the symptom category labels to which the push symptoms belong respectively according to the preset corresponding relation between the symptoms and the symptom category labels.
6. The labeled symptom pushing method according to claim 5, wherein after determining the symptom category labels to which the pushing symptoms belong according to the preset correspondence between the symptoms and the symptom category labels, the method further comprises:
filtering a first label set formed by the symptom category labels to which the push symptoms belong to respectively to obtain a second label set;
for a target push symptom, re-determining a symptom category label to which the target push symptom belongs from the second label set; the target push symptom is a push symptom corresponding to the filtered symptom category label;
the pushing of each pushing symptom in association with the symptom category label to which each pushing symptom belongs includes:
and performing associated pushing on the target pushing symptom and the redetermined symptom category label to which the target pushing symptom belongs.
7. The tagged symptom pushing method of claim 6, wherein said filtering a first set of tags comprising symptom category tags to which each of said pushed symptoms belongs to obtain a second set of tags comprises:
classifying the symptom category labels in the first label set according to symptom dimensions to obtain a plurality of label subsets forming the first label set, filtering the label subsets, and taking a collection of the filtered label subsets as a second label set; the filtering treatment comprises the following steps: for each label subset, judging whether the number of the symptom category labels contained in the label subset exceeds a preset number threshold, if so, filtering redundant symptom category labels, and otherwise, not processing;
and/or the presence of a gas in the gas,
filtering the tags with target preposed symptoms in the first tag set to obtain a second tag set; the label with the target antedisposition symptom is: a label with a pre-symptom and the user symptom does not have the pre-symptom.
8. The tagged symptom push method of any one of claims 1-7, wherein the pushing of each of the pushed symptoms in association with the symptom category tag to which each of the pushed symptoms belongs comprises:
for each of the push symptoms, displaying it under an associated field of the symptom category label corresponding to the push symptom.
9. A labeled symptom pushing device, comprising:
the acquisition module is used for acquiring user information; the user information comprises user symptoms input by a user;
a first determining module for determining a plurality of push symptoms according to the user symptom;
the second determination module is used for determining the symptom category label to which each pushed symptom belongs;
and the association pushing module is used for performing association pushing on each pushing symptom and the symptom category label to which the pushing symptom belongs.
10. An electronic device, comprising a processor and a memory, the memory having stored therein a computer program, the processor executing the computer program to implement the method of any one of claims 1-8.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by at least one processor, implements the method according to any one of claims 1-8.
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