CN113707335B - Method, device, electronic equipment and storage medium for determining target consultation user - Google Patents
Method, device, electronic equipment and storage medium for determining target consultation user Download PDFInfo
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Abstract
The invention discloses a method, a device, electronic equipment and a storage medium for determining a target consultation user. The method comprises the steps of determining a dynamic label and a static label corresponding to a patient user, determining a patient to be selected and connected with target search words, and determining at least one target patient from the patient to be selected and connected according to the dynamic label and the static label. The embodiment of the invention solves the problem of low system recommendation accuracy caused by the fact that the target diagnosis receiving user cannot know the current diagnosis wish of the user and give recommendation information in real time only by means of the user offline personalized tag by the traditional method, improves the recommendation accuracy and better meets the user requirements.
Description
Technical Field
Embodiments of the present invention relate to computer processing technologies, and in particular, to a method, an apparatus, an electronic device, and a storage medium for determining a target clinical user.
Background
Along with the popularization of the network, a large amount of information of the doctor users and the doctor users is accumulated in the medical field, and when the doctor users select the doctor users, the server can determine and push the doctor users resources which are most suitable for the doctor users, so that the medical doctor requirements of the netizen are better met.
At present, in the prior art, a diagnosis receiving user matched with a diagnosis receiving user is determined for the diagnosis receiving user according to the attribute of the diagnosis receiving user and the attribute of the diagnosis receiving user, and the universality determining method cannot be matched with the personalized requirements of the user, so that the information pushed by a server is often not the information required by the user, and the technical problem of poor user experience is caused.
Disclosure of Invention
The embodiment of the invention provides a method, a device, electronic equipment and a storage medium for determining a target diagnosis-receiving user, so as to realize the technical effect of recommending a more adaptive target diagnosis-receiving user for the diagnosis-receiving user and further improving the treatment efficiency.
In a first aspect, an embodiment of the present invention provides a method for determining a target consultation user, including:
determining a dynamic label and a static label corresponding to the user for treatment;
determining a to-be-selected consultation user associated with the target search word;
and determining at least one target consultation user from the to-be-selected consultation users according to the dynamic label and the static label.
In a second aspect, an embodiment of the present invention further provides an apparatus for determining a target consultation user, where the apparatus includes:
the label determining module is used for determining a dynamic label and a static label corresponding to the user for treatment;
The to-be-selected consultation user determining module is used for determining to-be-selected consultation users associated with the target search word;
and the target diagnosis receiving user determining module is used for determining at least one target diagnosis receiving user from the users to be selected according to the dynamic label and the static label.
In a third aspect, an embodiment of the present invention further provides an electronic device for determining a target consultation user, where the electronic device includes:
One or more processors;
Storage means for storing one or more programs,
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of determining a targeted screening user as described in any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for determining a target consultation user provided by any of the embodiments of the present invention.
According to the embodiment of the invention, the target tag weight of each dynamic tag is calculated by acquiring the search behavior data, the click behavior data and the next-row data of the user to be treated, the tag weight corresponding to the static tag generated by combining the offline data of the user to be treated and the user weight to be treated of the user to be treated are considered, the attribute evaluation value of each user to be treated is calculated, and then the target user to be treated is determined, so that the problem that the determined target user information is often not matched with the user to be treated and the user experience is poor due to the fact that the target user to be treated cannot be matched with the user personalized needs at present is solved, and the target user to be treated is determined to be more suitable for the user to be treated by combining the offline tag corresponding to the user information and the dynamic tag, so that the adaptation rate of the user to be treated and the target user to be treated is improved, and the effect of improving the user experience is achieved.
Drawings
In order to more clearly illustrate the technical solution of the exemplary embodiments of the present invention, a brief description is given below of the drawings required for describing the embodiments. It is obvious that the drawings presented are only drawings of some of the embodiments of the invention to be described, and not all the drawings, and that other drawings can be made according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for determining a target consultation user according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for determining a target consultation user according to a second embodiment of the present invention;
Fig. 3 is a block diagram of an apparatus for determining a target consultation user according to a fourth embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a method for determining a target patient receiving user according to an embodiment of the present invention, where the embodiment is applicable to determining a patient receiving user most suitable for a patient receiving user, and the method may be performed by an apparatus for determining a target patient receiving user according to an embodiment of the present invention, where the apparatus may be implemented by software and/or hardware, and optionally, the apparatus may be implemented by an electronic device, where the electronic device may be a mobile terminal, a PC side, a server side, or the like. The device can be configured in a computing device, and the method for determining the target consultation user provided by the embodiment specifically comprises the following steps:
S110, determining a dynamic label and a static label corresponding to the user for treatment.
The label can be information marked according to a certain behavior feature of the user, the behavior feature can be a behavior feature such as searching, clicking or registering on a terminal by the user, for example, a hospital name searched by the user can be used as a label, and the registered information of the user can be used as a label, and further, the label corresponding to the user represents a degree of attention of the user to each doctor, hospital or department, and is used for representing the requirement of the user. The dynamic tag may be tag information that changes with a behavior feature of the user, for example, a tag that is most suitable for the behavior feature may be determined according to a certain behavior feature of the user, and the tag is used as the dynamic tag. The static label may be label information which is stored in the user history electronic medical record and/or the electronic prescription and does not change in real time according to the behavior characteristics of the user, or may be label information marked by the user on the previous day and before according to the behavior characteristics, for example, a hospital name searched by the user between the previous day and the previous N days may be used as the static label, and N is a positive constant.
It should be noted that, the dynamic tag corresponding to the user for diagnosis is determined, and the dynamic tag may be determined according to the operation behavior data of the user for diagnosis, for example, the tag data related to the operation behavior may be obtained through the operation behavior of the user for diagnosis, where the operation behavior may be a search behavior, a click behavior or an order behavior of the user for diagnosis, and tag data information corresponding to the operation behavior of the user for diagnosis is used as the dynamic tag.
The static label corresponding to the user can be determined according to the offline data of the user, for example, the electronic medical record and/or the data information on the electronic prescription of the user can be extracted as the offline data of the user by acquiring the historical diagnosis record of the user, and the offline data label is further used as the static label.
Specifically, the dynamic label and the static label corresponding to the user can be determined according to the operation behavior data and the offline data of the user. For example, the behavior data and the offline data in the historical behavior log of the doctor-seeing user or the historical diagnosis record of the doctor-seeing user are extracted, and accordingly, the operation behavior data label information and the offline data label information corresponding to the doctor-seeing user can be obtained.
Optionally, the determining of the dynamic label and the static label corresponding to the user comprises determining the dynamic label according to operation behavior data of the user, wherein the operation behavior data comprises search behavior data, click behavior data and order behavior data, determining an offline label corresponding to the user, and taking the offline label as the static label.
The search behavior may be understood as a behavior that a user at a doctor uses a certain input device to search certain information on a certain terminal, and further, information data searched according to the search behavior of the user at the doctor is used as search behavior data, for example, the search behavior data may be data of a doctor word, a department word, a hospital word, or the like searched by the user at the doctor. The clicking action may be understood as an action that a user at a doctor uses a certain input device to click certain information on a certain terminal, and further, information data confirmed according to the clicking action of the user at the doctor is taken as clicking action data, for example, the clicking action data may be data of a doctor, a department where the doctor is located, a hospital where the doctor is located, or the like where the doctor at the doctor uses the input device to click a page of the terminal. The order action may be understood as an action of generating an order by the doctor user through registration or ordering, and further, information data corresponding to the order action of the doctor user is taken as order action data, for example, the order action data may be information data on a registration form acquired by the doctor user performing registration operation, and the data may be a hospital name, a doctor name, a check item, or the like. The search behavior data, the click behavior data and the order behavior data of the doctor-seeing user can be extracted from the history behavior log of the doctor-seeing user, and further dynamic labels corresponding to the search behavior data, the click behavior data and the order behavior data of the doctor-seeing user are determined, so that dynamic label information is obtained.
The offline label may be understood as data label information corresponding to the history behavior of the user who has been stored in the terminal database, and may be a history diagnosis record of the user who has been diagnosed, and further, the offline label is used as a static label by extracting offline data information on the electronic medical record and/or the electronic prescription in the history diagnosis record of the user who has been diagnosed, thereby obtaining label information of offline data of the user who has been diagnosed.
By determining the dynamic label according to the operation behavior data of the user and determining the static label according to the offline label corresponding to the user, the label information of the user in offline state is considered, the intention of the user can be monitored in real time, and the recommendation accuracy is improved.
It should be noted that, according to the operation behavior data of the patient user, the dynamic tag is determined, and according to the condition of obtaining the operation behavior, the tag information corresponding to the operation behavior data may be extracted, for example, according to the operation behavior of the patient user in the preset duration or the preset number range, the operation behavior data is obtained, and further, the tag information corresponding to the operation behavior data is determined, so as to obtain the dynamic tag information.
It should be further noted that the operation behaviors include a search behavior, a click behavior, and an order behavior.
Specifically, according to the operation behavior data of the user in the preset duration or the preset quantity range, the label information corresponding to the operation behavior data is obtained, and correspondingly, the dynamic label corresponding to the operation behavior data is obtained.
In this embodiment, determining the dynamic label according to the operation behavior data of the user includes obtaining operation behavior data of the user in a preset time period or a preset number, determining a search label of each search behavior data, a click label of each click behavior data and an order label of each order behavior data in the operation behavior data, and taking the search label, the click label and the order label as the dynamic labels.
The preset duration may be day information preset for the operation behavior of the patient user, for example, the preset duration is 1 day, the operation behavior data of the patient user in the last 1 day may be obtained, and if the operation behavior data in the last 1 day is not found, the preset duration may be increased, which may be 2 days or 3 days. The preset number is number information preset by indicating the operation behaviors of the doctor users, for example, the preset number is 20, and the operation behavior data of the doctor users of the last 20 times can be obtained. The preset duration and the preset number are not limited herein. The search tag may be understood as tag information corresponding to search behavior data determined by each search behavior of the user for diagnosis, for example, the search tag may be tag information generated by the user data for diagnosis obtained by each search operation performed by the user for diagnosis, for example, a tag corresponding to a user for diagnosis such as a doctor, a hospital, or a department may be generated. The click label may be understood as label information corresponding to click behavior data determined by each click behavior of the user, for example, the click label may be label information generated by the user data obtained by each click operation of the user, for example, a label corresponding to the user may be generated by a doctor, a hospital, a department, or the like. The order label may be understood as label information corresponding to order action data determined by each order action of the user, for example, the order label may be label information generated by taking user data of a user order acquired by each order operation of the user, for example, a label corresponding to a doctor, a hospital, a department, or the like may be generated. The label of each operation behavior data is determined by acquiring the operation behavior data of the user in the preset time length or the preset quantity, and accordingly, the search label of each search behavior data, the click label of each click behavior data and the order label of each order behavior data are obtained, so that dynamic label information is acquired, the use requirement of the user is more accurately determined, and the follow-up recommendation of the user in the doctor is facilitated.
S120, determining the to-be-selected consultation user associated with the target search word.
The target search word may be understood as search word information searched in a search edit box by a doctor-seeing user, and corresponding search contents may be obtained based on the search words, for example, the doctor-seeing user searches for "diabetes" in the search edit box, and may use "diabetes" as the target search word to obtain a series of search contents based on the diabetes search word, for example, a diabetes hospital, a diabetes doctor, or a diabetes medicine. The user for diagnosis can be doctor, but in order to improve the diagnosis efficiency and the treatment efficiency, the user for diagnosis can also correspond to different departments, different job titles and different cities, for example, the user for diagnosis in different areas, departments and job titles can be registered in the diagnosis platform, and after the authentication is passed, the corresponding on-line diagnosis task can be accessed. The user to be selected for diagnosis may be understood as information of the user to be diagnosed associated with the target search term, for example, the target search term is diabetes, and the user to be selected for diagnosis may be the user to be diagnosed that includes tag information such as diabetes, diabetes doctor or diabetes medicine in the data of searching, clicking or ordering behavior of the user to be diagnosed. Alternatively, the subscriber to be selected associated with the target search term may be determined based on the target search term and tag information generated from the search, click or order performance data of each of the subscribers.
It should be noted that, when each user to be selected for diagnosis is registered in the website, the information can be edited to perfect the information of the user for diagnosis, the perfect information of the user for diagnosis can include information of disease types, regions, departments or hospitals, etc. which are good for the user for diagnosis, further, the disease types good for the user for diagnosis can be used as labels, so that the follow-up user for diagnosis can conveniently perfect the label information according to the comment information of the user for diagnosis.
It should be further noted that, determining the to-be-selected diagnosis-receiving user associated with the target search word may determine the to-be-selected diagnosis-receiving user by determining a relationship between the target search word and each diagnosis-receiving user, for example, according to target search word information of the diagnosis-receiving user, acquiring association information of the target search word and each diagnosis-receiving user, and extracting diagnosis-receiving user information with a matching degree with the target search word higher than a certain preset threshold from the association information of each diagnosis-receiving user, where the to-be-selected diagnosis-receiving user with the matching degree with the target search word higher than the certain preset threshold is used as the to-be-selected diagnosis-receiving user associated with the target search word.
Optionally, the determining the to-be-selected consultation user associated with the target search word comprises determining the to-be-selected consultation user associated with the target search word according to the target search word and the associated information of each consultation user.
The associated information comprises the main information of the diagnosis user and comment information of the diagnosis user. The indication information is understood to be information related to a disease that the user is good at diagnosing, for example, the indication information may be information of a doctor good at performing an osteosurgery, such as various types of fracture, bone tumor, bone tuberculosis or osteomyelitis, and the indication information may also be information of various treatable diseases included in an osteosurgery department. The comment information can be understood as some analytic language information aiming at the user to be diagnosed, the language information can objectively evaluate the user to be diagnosed, and it is required to say that the user to be diagnosed can reserve each user to be diagnosed, when the user to be diagnosed is finished, the target user to be diagnosed can be commented on to generate comment information, and when the target user to be diagnosed is determined, the main information and comment information of the user to be diagnosed can be comprehensively determined, so that the accuracy of diagnosis is improved.
In a specific application, by acquiring the main information of each diagnosis-receiving user and the comment information of the diagnosis-receiving user, the diagnosis-receiving user corresponding to the information that the matching degree of the related information and the target search word is higher than a certain preset threshold value is found, the diagnosis-receiving user with the matching degree of the target search word higher than the certain preset threshold value is determined in each diagnosis-receiving user, and further, the user to be selected, which is associated with the target search word, is determined, the main information of the diagnosis-receiving user is considered, the comment information of the diagnosis-receiving user is considered, the accuracy of the diagnosis is improved, the requirement of the diagnosis-receiving user is better met, and the information of the better diagnosis-receiving user is recommended to the diagnosis-receiving user.
It should be noted that, the relevant information of each patient may be dynamically adjusted, the comment information may increase with the number of comments of the patient to the patient, the main information may be continuously perfected according to the comment information of the patient, for example, the patient reserves a certain target patient, after the patient finishes the treatment, the target patient may be commented on, and the server may update the label for the target patient according to the comment information of the patient as the target patient, i.e., the label corresponding to the target patient may be dynamically adjusted, and correspondingly, the relevant information corresponding to the target patient may also be dynamically adjusted.
Further, in order to improve the convenience of determining the target consultation user, comment information corresponding to the target consultation user may be preprocessed to dynamically adjust association information corresponding to the target consultation user. For example, the machine learning model may be used to input each comment information, and may output a keyword corresponding to each comment information, and the server may extract the keyword by using the keyword information, and further determine a label corresponding to the user for receiving the diagnosis according to the occurrence frequency of the keyword, and update the label for the target user for receiving the diagnosis, so as to dynamically adjust the associated information corresponding to the target user for receiving the diagnosis.
S130, determining at least one target diagnosis receiving user from the to-be-selected diagnosis receiving users according to the dynamic label and the static label.
The target consultation user can be understood as a consultation user matched with the target search word of the consultation user. Determining a to-be-selected diagnosis user according to a dynamic label generated by operation behavior data of the diagnosis user and a static label corresponding to the diagnosis user, combining target search words of the diagnosis user and associated information of each diagnosis user, further determining at least one target diagnosis user in the to-be-selected diagnosis users, for example, determining the to-be-selected diagnosis user according to the matching degree of the target search words and main information of each diagnosis user and comment information of the diagnosis user, correspondingly, generating the dynamic label and the static label of the diagnosis user by extracting operation behavior data and offline data of the diagnosis user, and determining at least one target diagnosis user from the to-be-selected diagnosis users according to the label information of the diagnosis user and the to-be-selected diagnosis user.
It should be noted that, after each dynamic tag in each operation behavior data is obtained, in order to better determine the matching relationship between the dynamic tag corresponding to the patient user and the user to be selected for patient receiving, a weight may be assigned to each dynamic tag, so that the tag generated by each data obtains a corresponding weight, and accordingly, the weights corresponding to each search tag, the click tag and the order tag are determined.
It should be further noted that, the dynamic tag generated by each operation behavior data may be affected by factor information such as self behavior weight, behavior generation time or current access time. The dynamic tags include a search tag for each search behavior data, a click tag for each click behavior data, and an order tag for each order behavior data.
Optionally, after determining the search tag of each search behavior data, the click tag of each click behavior data and the order tag of each order behavior data in the operation behavior data, determining the tag weight to be processed of the current tag according to the action weight corresponding to the current tag, the action generation time corresponding to the current tag and the current access time.
The current tag may be understood that when the same processing is performed on the dynamic tag generated by each piece of operation behavior data, any one of the processed dynamic tags may be used as the current tag, for example, the behavior weight of the dynamic tag may be processed, and a specific processing manner is not described again, so that one of the tags is used as the current tag to perform processing description. The behavior weight refers to weight information of the current operation behavior corresponding to the current tag, the behavior generation time refers to time information generated by the operation behavior corresponding to the current tag, for example, the time generated by the current search behavior can be performed for a doctor-seeing user, the current access time refers to time information of the operation behavior corresponding to the current tag accessing a page, and the tag weight to be processed can be understood as the weight corresponding to the tag generated by each piece of operation behavior data. After determining the search tag of each piece of search behavior data, the click tag of each piece of click behavior data and the order tag of each piece of order behavior data in the operation behavior data, according to each search tag, click tag and order tag, acquiring the behavior weight, behavior generation time and current access time corresponding to the tag generated by each piece of operation behavior data, further determining the tag weight generated by each piece of operation behavior data, for example, the "diabetes" tag generated by the search behavior data can be used as the current tag, the "diabetes" tag can be processed, the difference value between the generation time and the current access page time can be calculated by acquiring the generation time or the current access page time of the search behavior, the weight of the current tag "diabetes" can be determined by combining the behavior weights of the current search behavior, and accordingly, the tag weight to be processed corresponding to each piece of dynamic tag can be obtained. The information of the user for diagnosis is acquired in real time, real-time personalized factors of the user are fully considered, and the accuracy of the user for diagnosis is improved.
It should be noted that, in order to better determine the attention of the user to be selected for the patient according to the dynamic tag and the static tag, the weights of the dynamic tag and the static tag corresponding to the patient may be processed, for example, the weights of the tags to be processed of the same tag corresponding to the patient may be processed, further, the weights of the same tag corresponding to the patient may be determined based on the search behavior, the click behavior and the order behavior of the patient, and meanwhile, the corresponding weight information may be set for the static tag, and the adapted target patient may be recommended to the patient more accurately according to the weight information of the same tag in the dynamic tag and the weights corresponding to the static tag.
Optionally, the determining at least one target diagnosis receiving user from the users to be selected according to the dynamic tag and the static tag includes combining the weights of the tags to be processed of the same tag to obtain the weights of the tags corresponding to the same tag, and determining at least one target diagnosis receiving user from the users to be selected according to the weights of the tags corresponding to the tags and the static tag and the weights of the users to be selected.
The target tag weight can be understood as a tag weight determined based on all operation behaviors of the current user, the tag weight is determined based on a static tag of the user, and the user weight can be understood as weight information of the user set based on matching information of the target search word and the user to be selected. The method comprises the steps that all tags of a user to be treated are processed, the tags to be treated of the same tag in dynamic tags generated based on different operation behavior data are added to obtain target tag weights based on the tag, for example, 20 pieces of latest search behavior data of the user to be treated can be obtained to generate corresponding tags, 20 pieces of latest click behavior data of the user to be treated are taken to generate corresponding tags, 5 pieces of latest order behavior data of the user to be treated are taken to generate corresponding tags, the tags obtained by three operation behaviors possibly show repeated tags, and for the repeated tags, for example, the repeated tags are diabetes tags, the diabetes tag weights in the search behavior data, the diabetes tag weights in the click behavior data and the diabetes tag weights generated in the order behavior data can be added to obtain target tag weights of the diabetes tags, further, in order to prevent the target tag weights corresponding to the dynamic tags from being infinitely increased, normalization processing can be carried out on the target tag weights, and the processed tag weights are used as final target tag weights. Meanwhile, the static label generated based on the offline data is provided with a corresponding weight, and further, at least one target diagnosis receiving user can be determined from the users to be selected according to the diagnosis receiving user weight and the target label weight of the users to be selected and the label weight corresponding to the static label.
It should be noted that, according to the target tag weight, the tag weight corresponding to the static tag, and the weight of the user to be diagnosed, the attribute of each user to be diagnosed may be evaluated, for example, the evaluation value of each user to be diagnosed relative to the user to be diagnosed may be calculated according to the attribute of each user to be diagnosed, for convenience of subsequent users to be diagnosed with high evaluation value, the evaluation values of each user to be diagnosed may be ordered, convenience of the user to be diagnosed may find information of each user to be diagnosed according to the evaluation value, or the user to be diagnosed with evaluation value higher than a certain set threshold may be recommended to the user to be diagnosed, so as to determine the target user to be diagnosed.
Optionally, the determining at least one target diagnosis receiving user from the target diagnosis receiving users according to the target tag weight, the tag weight corresponding to the static tag and the diagnosis receiving user weight of the to-be-selected diagnosis receiving user includes determining attribute evaluation values of the to-be-selected diagnosis receiving users according to the target tag weight, the tag weight of the static tag and the diagnosis receiving user weight, and determining the target diagnosis receiving user according to the attribute evaluation values of the to-be-selected diagnosis receiving users.
The attribute evaluation value can be understood as an evaluation score value of each to-be-selected consultation user relative to the consultation user, and the attribute evaluation value is used for representing preference degree of each to-be-selected consultation user for the target consultation user. The evaluation values of the attributes of the to-be-selected patients relative to the patients are calculated by combining the target label weights corresponding to the dynamic labels of the patients and the label weights of the static labels of the to-be-selected patients, so that the evaluation values of the to-be-selected patients can be ranked for the convenience of the patients to find the information of the to-be-selected patients according to the evaluation values, the to-be-selected patients corresponding to the evaluation values of the previous k to-be-selected patients are recommended to the patients, k is a normal number, and the to-be-selected patients with the evaluation values higher than a certain set threshold value can be recommended to the patients, so that the target patients are determined, the optimal target patients for the patients are determined, and the user experience requirements are better met.
According to the technical scheme, the search behavior data, the click behavior data and the next-row data of the user to be treated are obtained to generate dynamic labels, the target label weight of each dynamic label is calculated, the real-time personalized label attribute of the user is considered, the label weight corresponding to the static label generated by the offline data of the user to be treated and the user weight to be treated of the user to be treated are combined, the attribute evaluation value of each user to be treated is calculated, and then the target user to be treated is determined, so that the problem that the target user to be treated which is determined to be matched by the user to be treated by the aid of the attribute of the user to be treated and the attribute of the user to be treated cannot be matched with the personalized requirement of the user, and therefore the determined target user information to be treated is often not matched with the user to be treated, and the problem that the user experience is poor is caused is solved.
Example two
As an alternative embodiment of the foregoing embodiment, fig. 2 is a flowchart of a method for determining a target patient according to the second embodiment of the present invention, and the following details may be referred to specifically.
As shown in fig. 2, determining the target diagnosis-receiving user can be accomplished by determining three parts, namely, the off-line personalized tag of the diagnosis-receiving user matches with the diagnosis-receiving user, the search term matches with the diagnosis-receiving user, and the real-time personalized tag of the diagnosis-receiving user matches with the diagnosis-receiving user.
Determining the matching score of the off-line personalized tag of the doctor user and the doctor user, referring to the box 1, generating the off-line personalized tag of the doctor user by acquiring the off-line data of the doctor user, determining the weight information of the corresponding tag, and acquiring the matching score of the off-line personalized tag of the doctor user and the doctor user. And determining matching scores of the search words and the consultation users, referring to a box 2, determining weight information of the search words and the consultation users according to the matching of the search words and the consultation user documents, and obtaining the matching scores of the search words and the consultation users.
Determining the matching score of the real-time personalized tag of the doctor user and the doctor-taking user, referring to a box 3, generating a corresponding tag by acquiring the latest 20 times of search behavior data of the doctor user, determining the weight information of the corresponding tag, acquiring the latest 20 times of click behavior data of the doctor user, generating a corresponding tag by each piece of click behavior data, determining the weight information of the corresponding tag, acquiring the latest 20 times of order behavior data of the doctor user, generating a corresponding tag by each piece of order behavior data, determining the weight information of the corresponding tag, obtaining the real-time personalized tag information and the tag weight information of the doctor user, and obtaining the matching score of the real-time personalized tag of the doctor user and the doctor-taking user.
Determining an attribute evaluation value of the diagnosis receiving user corresponding to the target diagnosis receiving user according to the offline personalized tag and diagnosis receiving user matching score of the diagnosis receiving user, the search word and diagnosis receiving user matching score and the real-time personalized tag and diagnosis receiving user matching score of the diagnosis receiving user, sorting the attribute evaluation values of the diagnosis receiving user, recommending the diagnosis receiving user information with the evaluation value higher than a certain preset threshold value to the diagnosis receiving user, and further obtaining the target diagnosis receiving user information.
Illustratively, the last 20 search behavior data of the visiting user are taken. The search data comprises doctor words, hospital words, department words, disease words and symptom words, and data in a basic database corresponding to the search words in each piece of search behavior data are found from the basic database of a certain hospital to generate corresponding labels. The label and the corresponding weight generated by each piece of data are calculated, and the calculation formula of the weight is as follows:
Wherein alpha is a time attenuation coefficient, T now is the visit time of the user, T action is the behavior generation time of the user, the difference value of T now-Taction is converted into an hour unit to be calculated, w is the weight of the behavior, and S is the label weight generated by each piece of behavior data.
The latest 20 pieces of click behavior data of the user for medical treatment are taken. And acquiring doctor clicked by the doctor-seeing user, department where the doctor is located and hospital data where the doctor is located, and generating corresponding doctor, hospital and department labels. And acquiring hospital data clicked by the user in the doctor-seeing, and generating a corresponding hospital label. And (3) calculating the label and the corresponding weight of each piece of data, wherein the weight formula is the same as the formula (1).
The latest 5 pieces of ordering behavior data of the user for medical treatment are taken. Acquiring doctor ordered by the user, department where the doctor ordered, hospital where the doctor ordered, order self-filling disease label information according to registration and expert consultation order information of the user to generate corresponding doctor, hospital, department and disease label. And (3) calculating the label and the corresponding weight of each piece of data, wherein the weight formula is the same as the formula (1).
And merging and summarizing the search behavior, the clicking behavior and the labels generated by the next row data to obtain the real-time personalized labels and the weights of the user. And (3) aggregating according to each label, such as a 'diabetes label', and respectively adding weights corresponding to the diabetes labels obtained by searching the labels, clicking the labels and placing the labels, namely the weights corresponding to the diabetes labels of the patients.
In this embodiment, optionally, in order to prevent the weight corresponding to the real-time personalized tag of the patient user from infinitely increasing, the weight corresponding to the real-time personalized tag is normalized, and the conversion formula is as follows:
S=lnS+300 (2)
in order to reject abnormal data, S is treated as 0 when S is smaller than 0 after conversion, and is treated as 350 when S is larger than 350.
And matching the real-time personalized tag of the patient user with the document of the patient user, wherein the weight sum of the matched real-time personalized tag is the matching score of the real-time personalized tag of the patient user and the patient user, and the matching score is marked as S 1.
And adding the real-time personalized tag of the diagnosis user into the sequencing, thereby influencing the sequencing of the diagnosis user.
Snew＝S1*a+S2*b (3)
Wherein, S 2 is the matching score of the off-line personalized tag of the patient user and the patient user, a and b are distribution coefficients, S new is the attribute evaluation value of the patient user, and the patient user is ranked according to the evaluation value of S new.
According to the technical scheme, the search behavior data, the click behavior data and the next-row data of the user to be treated are obtained to generate dynamic labels, the target label weight of each dynamic label is calculated, the real-time personalized label attribute of the user is considered, the label weight corresponding to the static label generated by the offline data of the user to be treated and the user weight to be treated of the user to be treated are combined, the attribute evaluation value of each user to be treated is calculated, and then the target user to be treated is determined, so that the problem that the target user to be treated which is determined to be matched by the user to be treated by the aid of the attribute of the user to be treated and the attribute of the user to be treated cannot be matched with the personalized requirement of the user, and therefore the determined target user information to be treated is often not matched with the user to be treated, and the problem that the user experience is poor is caused is solved.
Example III
Fig. 3 is a block diagram of an apparatus for determining a target consultation user according to a third embodiment of the present invention. The device comprises a label determining module 310, a to-be-selected-consultation user determining module 320 and a target consultation user determining module 330.
Wherein, the tag determining module 310 is configured to determine a dynamic tag and a static tag corresponding to the user for treatment;
a to-be-selected-consultation user determining module 320, configured to determine to-be-selected-consultation users associated with the target search term;
And the target consultation user determining module 330 is configured to determine at least one target consultation user from the to-be-selected consultation users according to the dynamic tag and the static tag.
According to the technical scheme, the search behavior data, the click behavior data and the next-row data of the user to be treated are obtained to generate dynamic labels, the target label weight of each dynamic label is calculated, the real-time personalized label attribute of the user is considered, the label weight corresponding to the static label generated by the offline data of the user to be treated and the user weight to be treated of the user to be treated are combined, the attribute evaluation value of each user to be treated is calculated, and then the target user to be treated is determined, so that the problem that the target user to be treated which is determined to be matched by the user to be treated by the aid of the attribute of the user to be treated and the attribute of the user to be treated cannot be matched with the personalized requirement of the user, and therefore the determined target user information to be treated is often not matched with the user to be treated, and the problem that the user experience is poor is caused is solved.
In the above apparatus, optionally, the tag determining module 310 includes:
The dynamic tag determining unit is used for determining a dynamic tag according to the operation behavior data of the user for treatment, wherein the operation behavior data comprises search behavior data, click behavior data and order behavior data;
And the static label determining unit is used for determining an offline label corresponding to the user for medical treatment and taking the offline label as the static label.
In the above apparatus, optionally, the dynamic tag determining unit includes:
an operation behavior data acquisition subunit, configured to acquire operation behavior data of the user in a preset duration or a preset number;
the dynamic label determining subunit is configured to determine a search label of each search behavior data, a click label of each click behavior data, and an order label of each order behavior data in the operation behavior data, and take the search label, the click label, and the order label as dynamic labels.
In the above apparatus, optionally, the dynamic tag determining unit further includes:
The to-be-processed tag weight determining subunit is configured to determine, for each search tag, click tag, and order tag, a to-be-processed tag weight of the current tag according to the action weight corresponding to the current tag, the action generation time corresponding to the current tag, and the current access time.
In the above apparatus, optionally, the to-be-selected-consultation user determining module 320 is specifically configured to determine, according to the target search word and association information of each consultation user, to-be-selected-consultation users associated with the target search word, where the association information includes indication information of the consultation user and comment information of the consultation user.
In the above apparatus, optionally, the target diagnosis user determining module 330 includes:
the target tag weight determining unit is used for obtaining target tag weights corresponding to the same tag by combining the tag weights to be processed of the same tag;
The target diagnosis receiving user determining unit is used for determining at least one target diagnosis receiving user from the to-be-selected diagnosis receiving users according to the target tag weight, the tag weight corresponding to the static tag and the diagnosis receiving user weight of the to-be-selected diagnosis receiving user.
In the above apparatus, optionally, the target consultation user determining unit includes:
The attribute evaluation value determining subunit is used for determining the attribute evaluation value of each to-be-selected consultation user according to the target label weight, the label weight of the static label and the consultation user weight;
And the target diagnosis user determination subunit is used for determining the target diagnosis user according to the attribute evaluation value of each to-be-selected diagnosis user.
Example IV
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. Fig. 4 shows a block diagram of an exemplary electronic device 40 suitable for use in implementing the embodiments of the present invention. The electronic device 40 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, the electronic device 40 is in the form of a general purpose computing device. The components of electronic device 40 may include, but are not limited to, one or more processors or processing units 401, a system memory 402, and a bus 403 that connects the various system components, including system memory 402 and processing units 401.
Bus 403 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 40 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 40 and includes both volatile and non-volatile media, removable and non-removable media.
The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 404 and/or cache memory 405. Electronic device 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 406 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 403 through one or more data medium interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 408 having a set (at least one) of program modules 407 may be stored in, for example, memory 402, such program modules 407 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 407 generally perform the functions and/or methods of the described embodiments of the invention.
The electronic device 40 may also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), one or more devices that enable a user to interact with the electronic device 40, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device 40 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 411. Also, electronic device 40 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 412. As shown, network adapter 412 communicates with other modules of electronic device 40 over bus 403. It should be appreciated that although not shown in FIG. 4, other hardware and/or software modules may be used in connection with electronic device 40, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 401 executes various functional applications and data processing by running a program stored in the system memory 402, for example, implements the method for determining a target consultation user provided by the embodiment of the present invention.
Example five
A fifth embodiment of the present invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a method of determining a targeted clinical user.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
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