CN113821730A - Medical information pushing method and device and electronic equipment - Google Patents
Medical information pushing method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a medical information pushing method, a medical information pushing device and electronic equipment, wherein the method comprises the following steps: according to the search keywords of the user and the basic information of the user, the interested keywords representing the intention of the user are determined, the medical knowledge base is searched based on the interested keywords, the target medical information is obtained, and the final determination process of the target medical information takes the basic information of the user into consideration, so that the medical information pushed to the user is more consistent with the real intention of the user, the effectiveness of the search result is improved, and the search efficiency of the user is improved.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a medical information pushing method and device and electronic equipment.
Background
When a user uses the medical knowledge base searching system to search, the searching system often provides intelligent recommendation for the user, and can conveniently search required information for medical staff, so that the searching efficiency and the working efficiency of the user are improved, and effective medical knowledge information can be provided for the patient according to self diagnosis problems through intelligent recommendation, and the cognition of the patient on the medical information is improved.
In the prior art, when a user searches, the system can perform full-text matching according to search words input by the user and return the most relevant medical knowledge information, but the returned medical knowledge information cannot be well matched with the real intention of the user, so that the user cannot quickly and accurately search out the required medical information.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus and an electronic device for pushing medical information, so as to improve the accuracy of pushing medical information and reduce the search time of a user.
In a first aspect, an embodiment of the present invention provides a medical information pushing method, where the method is applied to an electronic device, and the method includes: acquiring a search keyword set of a current user; the search keyword set comprises current search keywords corresponding to the current moment of a current user and/or historical search keywords corresponding to the historical moment of the current user; acquiring basic information of a current user according to the user type of the current user; the user type comprises a doctor type and a patient type, and the basic information comprises case information corresponding to the patient type and function information corresponding to the doctor type; determining an interested keyword set of the current user according to the search keyword set and the basic information; wherein the interesting keywords in the interesting keyword set are used for representing information related to diseases matched with the intention of the current user; searching in a medical knowledge base corresponding to the electronic equipment according to the interested keyword set to obtain target medical information, of which the matching degree with the interested keywords in the medical knowledge base is higher than a first threshold value; and pushing the target medical information to the current user.
Further, after the step of obtaining the search keyword set corresponding to the current user, the method further includes: determining a similar user corresponding to the current user according to the search keyword set corresponding to the current user; the similar users are one or more users similar to the search records of the current user; and updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user.
Further, the step of determining the similar user corresponding to the current user according to the search keyword set corresponding to the current user includes: calculating a first similarity between a search keyword set corresponding to a current user and a search keyword set corresponding to a target user; and determining the target user with the first similarity larger than the second threshold as a similar user corresponding to the current user.
Further, the step of updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user includes: determining a difference keyword set between a search keyword set corresponding to a current user and a search keyword set corresponding to a similar user; the keywords in the difference keyword set are words which appear in the search keyword set corresponding to the similar user and do not appear in the search keyword set corresponding to the current user; and adding the difference keyword set into the search keyword set corresponding to the current user.
Further, after the step of obtaining the search keyword set corresponding to the current user, the method further includes: determining an expanded keyword set according to a knowledge graph prestored in the electronic equipment and a search keyword set corresponding to a current user; the knowledge graph comprises a plurality of words and correlation relations among the words; the keywords in the expanded keyword set and the keywords in the search keyword set corresponding to the current user have a direct incidence relation in the knowledge graph; and adding the expanded keyword set into the search keyword set corresponding to the current user.
Further, the step of obtaining the basic information of the current user according to the user type of the current user includes: if the user type of the current user is the patient type, acquiring basic information containing case information of the current user; the case information is a set of disease-related words appearing in a case corresponding to the patient, and the disease-related words at least comprise one of the following items: disease name, symptom name, operation name, medicine name, inspection name, examination name; if the user type of the current user is the doctor type, acquiring basic information containing the function information of the current user; wherein the functional information at least comprises one of the following items: department, specialty, and excellence.
Further, the case information includes current case information and historical case information, which are corresponding to the current user and have the latest generation time before the current time; the step of acquiring basic information including case information of a current user includes: determining a first case information set corresponding to the current case information and a second case information set corresponding to the historical case information; and fusing the first case information set and the second case information set to obtain basic information containing case information of the current user.
Further, the step of obtaining the basic information including the case information of the current user by fusing the first case information set and the second case information set includes: determining a second similarity of each term in the first case information set to each term in the second case information set; judging whether the second similarity is larger than a third threshold value or not, and if so, replacing the current word in the first case information set with the current word in the second case information set; otherwise, combining the current word in the second case set with the current word in the first case set to serve as the current word in the updated first case set; and determining the updated first case set as the basic information corresponding to the current user.
Further, the step of determining the current user's interest keyword set according to the search keyword set and the basic information includes: determining the occurrence frequency of each word contained in the search keyword set and the basic information as the weight of the word; and sequencing the words according to the weight, and determining a set formed by the sequenced words with the sequence number larger than a fourth threshold value as an interested keyword set of the current user.
In a second aspect, an embodiment of the present invention further provides a medical information pushing apparatus, where the apparatus is connected to an electronic device, and the apparatus includes: the search keyword set acquisition module is used for acquiring a search keyword set of a current user; the search keyword set comprises current search keywords corresponding to the current moment of a current user and/or historical search keywords corresponding to the historical moment of the current user; the basic information acquisition module is used for acquiring the basic information of the current user according to the user type of the current user; the user type comprises a doctor type and a patient type, and the basic information comprises case information corresponding to the patient type and function information corresponding to the doctor type; the interested keyword set determining module is used for determining an interested keyword set of the current user according to the search keyword set and the basic information; wherein the interesting keywords in the interesting keyword set are used for representing information related to diseases matched with the intention of the current user; the target medical information determining module is used for searching in a medical knowledge base corresponding to the electronic equipment according to the interested keyword set to obtain target medical information, wherein the matching degree of the target medical information and the interested keyword in the medical knowledge base is higher than a first threshold; and the pushing module is used for pushing the target medical information to the current user.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a processor and a memory, where the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the medical information pushing method according to the first aspect.
In a fourth aspect, embodiments of the present invention further provide a computer-readable storage medium, where computer-executable instructions are stored, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the medical information pushing method of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
according to the medical information pushing method, the medical information pushing device and the electronic equipment, the interested keywords representing the intention of the user are determined according to the search keywords of the user and the basic information of the user, the search is carried out in the medical knowledge base based on the interested keywords to obtain the target medical information, and the final determination process of the target medical information takes the basic information of the user into consideration, so that the medical information pushed to the user is more consistent with the real intention of the user, the effectiveness of the search result is improved, and the search efficiency of the user is improved.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic structural diagram of an electronic system according to an embodiment of the present invention;
fig. 2 is a flowchart of a medical information pushing method according to an embodiment of the present invention;
fig. 3 is a flowchart of an expanding method for a search keyword set according to an embodiment of the present invention;
FIG. 4 is a flowchart of another method for expanding a search keyword set according to an embodiment of the present invention;
fig. 5 is a flowchart of a case information determination method according to an embodiment of the present invention;
fig. 6 is a schematic mechanism diagram of a medical information pushing apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present 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.
Medical treatment is a relatively specialized field, and it is difficult for people who are not related to the specialized field to search for medical information useful for themselves. The medical knowledge base contains a plurality of medical ontologies including diseases, examinations and the like, when a user searches based on the medical knowledge base, full-text matching is carried out according to search words, and the most relevant medical knowledge information is returned according to the weight of indexes, or a long time is needed. In the prior art, the user cannot quickly search out the information wanted by the user by simply matching the search terms.
Based on this, the embodiment of the invention provides a medical information pushing method, a medical information pushing device and electronic equipment, so as to improve the accuracy of medical information pushing and reduce the search time of a user.
The terms referred to in the embodiments of the present invention are explained first below:
(1) medical electronic medical record
The medical electronic medical record is the original record of the whole process of the diagnosis and treatment of a patient in a hospital and comprises a medical record front page, a medical course record, an examination and examination result, a medical advice, an operation record, a nursing record and the like. Electronic Medical Records (EMRs) refer not only to static medical record information, but also to the related services provided. Is electronically managed information about the health status and health care activities of an individual throughout life, all process information related to the collection, storage, transmission, processing and utilization of patient information.
(2) Structured data for medical electronic medical records
Based on the electronic medical record data, the structured data corresponding to the electronic medical record can be determined, and specifically, the electronic medical record data can be stored according to a defined structure according to an actual application scene.
(3) Case information
The case information specifically includes: first history of complaints: the chief complaints, the current medical history, the past history, the personal history, the family history and the menstruation, marriage and childbirth history.
Referring to fig. 1, a schematic diagram of an electronic system 100 is shown. The electronic system can be used for realizing the medical information pushing method and device of the embodiment of the invention. Electronic system 100 includes one or more processing devices 102, one or more memory devices 104, an input device 106, an output device 108, and one or more data acquisition devices 110, which are interconnected via a bus system 112 and/or other form of connection (not shown). It should be noted that the components and structure of the electronic system 100 shown in fig. 1 are exemplary only, and not limiting, and that the electronic system may have other components and structures as desired.
The processing device 102 may be a server, a smart terminal, or a device including a Central Processing Unit (CPU) or other form of processing unit having data processing capability and/or instruction execution capability, may process data of other components in the electronic system 100, and may control other components in the electronic system 100 to perform a medical information push function.
Storage 104 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer-readable storage medium and executed by processing device 102 to implement the client functionality (implemented by the processing device) of the embodiments of the invention described below and/or other desired functionality. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer-readable storage medium.
The input device 106 may be a device used by a user to input instructions and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.
The output device 108 may output various information (e.g., images or sounds) to the outside (e.g., a user), and may include one or more of a display, a speaker, and the like.
The data acquisition device 110 may acquire the data to be processed and store the data to be processed in the storage 104 for use by other components.
For example, the devices used for implementing the medical information pushing method, apparatus and electronic device according to the embodiments of the present invention may be integrally disposed, or may be disposed in a decentralized manner, such as integrally disposing the processing device 102, the storage device 104, the input device 106 and the output device 108, and disposing the data acquisition device 110 at a specific location where data can be acquired. When the above-described devices in the electronic system are integrally provided, the electronic system may be implemented as an intelligent terminal such as a camera, a smart phone, a tablet computer, a vehicle-mounted terminal, and the like.
Fig. 2 is a flowchart of a medical information pushing method provided in an embodiment of the present invention, where the method is applied to an electronic device, and referring to fig. 2, the method includes the following steps:
s202: acquiring a search keyword set of a current user; the search keyword set comprises current search keywords corresponding to the current moment of a current user and/or historical search keywords corresponding to the historical moment of the current user;
various applications relating to medical information, such as APP in a hospital or a diagnosis and treatment system in a hospital, currently provide various supplementary services for a doctor or a patient to perform medical information search. After logging in a diagnosis and treatment system of a hospital, a user can search for interesting medical information in a relevant interface, for example, search for information related to the condition of the user or search for medical information related to symptoms of the user. Through the search keywords of the user, the system can search in a medical knowledge base associated with the system, find information with high correlation degree with the search keywords and push the information to the user. According to the medical information pushing method provided by the embodiment of the invention, when retrieval is carried out, not only the search keywords input by the user at this time are considered, but also the historical search keywords input by the user at the website can be included, and the medical information which is more in line with the intention of the user can be found in the medical knowledge base by searching the current search keywords and/or the historical search keywords.
S204: acquiring basic information of a current user according to the user type of the current user; the user type comprises a doctor type and a patient type, and the basic information comprises case information corresponding to the patient type and function information corresponding to the doctor type;
the person using the system can be a patient or a doctor, the determination mode of the basic information is different for different users, and the basic information is the case information of the patient for the type of the patient. For the doctor type, the basic information is the corresponding function information of the doctor.
S206: determining an interested keyword set of the current user according to the search keyword set and the basic information; wherein the interesting keywords in the interesting keyword set are used for representing information related to diseases matched with the intention of the current user;
after the search keyword set and the basic information are obtained, the interested keyword set of the current user is determined according to the search keyword set and the basic information, wherein the interested keyword set is a keyword of the information which the current user really wants to retrieve.
S208: searching in a medical knowledge base corresponding to the electronic equipment according to the interested keyword set to obtain target medical information, of which the matching degree with the interested keywords in the medical knowledge base is higher than a first threshold value;
the medical knowledge base is a database containing medical information that is accessible via an electronic device. Relevant medical information can be retrieved by retrieving the interested keyword set in the medical knowledge base, and finally, which medical information is used as target medical information can be determined according to the matching degree of the relevant information and the interested keyword. The matching degree may be how many keywords of interest are included in a certain piece of medical information.
S210: and pushing the target medical information to the current user.
After the target medical information is determined, the target medical information may be pushed to the current user, for example, after the current user clicks the search box, a title of the target medical information is displayed in a drop-down list of the search box, or the target medical information is displayed to the user in a list form in a certain area of the interface according to a mode that the degree of correlation is from high to low.
According to the medical information pushing method provided by the embodiment of the invention, the interested keywords representing the intention of the user are determined according to the search keywords of the user and the basic information of the user, the search is carried out in the medical knowledge base based on the interested keywords to obtain the target medical information, and the final determination process of the target medical information is realized by considering the basic information of the user, so that the medical information pushed to the user is more consistent with the real intention of the user, the effectiveness of the search result is further improved, and the search efficiency of the user is improved.
In order to more accurately obtain the real intention of the user, the method provided by the embodiment of the present invention further expands the search keyword set after the search keyword set is obtained, and based on this, the embodiment of the present invention also provides an expansion method of the search keyword, which focuses on how to effectively expand the search keyword so that the search keyword can more accurately reflect the search intention of the user, as shown in fig. 3, the method includes:
s302: determining a similar user corresponding to the current user according to the search keyword set corresponding to the current user; the similar users are one or more users similar to the search records of the current user;
specifically, similar users can be determined as follows:
(1) calculating a first similarity between a search keyword set corresponding to a current user and a search keyword set corresponding to a target user;
the first similarity may be calculated using a Jaccard formula or a cosine similarity formula. For example, by representing the current user's search keyword set by N (U) and the other user's search keyword set by N (v), the first similarity between N and U can be determined by the following Jaccard formula:
alternatively, the first similarity between N and U may also be determined by the following cosine similarity formula:
it should be noted that, through any one of the above formulas, the obtained similar users may be one or a similar user set composed of multiple users.
(2) And determining the target user with the first similarity larger than the second threshold as a similar user corresponding to the current user.
S304: and updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user.
Specifically, the search keyword set corresponding to the current user may be updated by the following method:
(1) determining a difference keyword set between a search keyword set corresponding to a current user and a search keyword set corresponding to a similar user; the keywords in the difference keyword set are words which appear in the search keyword set corresponding to the similar user and do not appear in the search keyword set corresponding to the current user;
(2) and adding the difference keyword set into the search keyword set corresponding to the current user.
In an actual application scenario, for each target user, a search keyword set of the target user may be acquired, and the keyword set of the target user may be acquired in the same manner as the keyword set of the current user or the keyword set acquired at a certain historical time and stored in the electronic device. The above-mentioned difference keyword set may be obtained by first determining an intersection between the search keyword set of the current user and the search keyword set of the similar user, and excluding the intersection in the search keyword set of the current user, and the remaining set of contents is the difference keyword set.
It should be noted that, if there are multiple similar users, the search keyword set corresponding to each similar user may be updated by the above method.
For example: the historical search word set of the current user is as follows: coronary heart disease, hypertension, chest pain, hypertension grade 1; combining search keywords of a similar user corresponding to the current user into: coronary heart disease, hypertension grade 1, and hypertension grade 2. By comparison, it can be seen that the difference keywords are: and 2, updating the keywords to a set of the current user to obtain an updated search keyword set of the current user, wherein the updated search keyword set of the current user is as follows: coronary heart disease, hypertension, chest pain, hypertension grade 1, and hypertension grade 2.
Based on the above embodiment, the embodiment of the present invention further provides another keyword expansion method, as shown in fig. 4, where the method includes:
s402: determining an expanded keyword set according to a knowledge graph prestored in the electronic equipment and a search keyword set corresponding to a current user;
the knowledge graph comprises a plurality of words and correlation relations among the words; the keywords in the expanded keyword set and the keywords in the search keyword set corresponding to the current user have a direct incidence relation in the knowledge graph;
specifically, the knowledge graph is a graph-based data structure, and is composed of nodes (points) and edges (edges), each node represents an "entity", each Edge is a relationship between the entities, and the knowledge graph is essentially a semantic network. An entity refers to things in the real world, such as people, place names, diseases, inspection, and the like; relationships are used to express some kind of linkage between different entities.
S404: and adding the expanded keyword set into the search keyword set corresponding to the current user.
For example, for the medical field, the knowledge-graph includes the following two branches:
branch 1: nodal (hypertension), relationship (symptoms), nodal (dizziness);
and branch 2: node (level 1 hypertension), relationship (symptom), node (dizziness).
Based on the two nodes, hypertension and hypertension level 1 can be inferred to be similar diseases, and the hypertension level 1 is added into the search keyword set of the current user.
In some possible implementations, step S204 in the embodiment shown in fig. 2 may specifically be:
(1) if the user type of the current user is the patient type, acquiring basic information containing case information of the current user; the case information is a set of disease-related words appearing in a case corresponding to the patient, and the disease-related words at least comprise one of the following items: disease name, symptom name, operation name, medicine name, inspection name, examination name;
based on the five history information of the first complaint in the electronic medical record of the patient, the case information of the current user is subjected to structured processing, and the set formed by the structured words is the basic information of the current user:
for example, the following is the current user's medical history: sudden pulsation headache occurs in the sleep of a patient before 5 days, the degree is severe, the patient is accompanied by dizziness, profuse sweat, palpitation and hypodynamia, the headache is not relieved after lasting for about 3 hours, chest pain, chest distress, vomiting, disturbance of consciousness and the like do not exist, and the coronary heart disease hypertension is diagnosed. The set obtained after structuring is: time: 5 days before; symptom name: throbbing headache; symptom name: dizziness; symptom name: sweating; symptom name: palpitation, etc.
(2) If the user type of the current user is the doctor type, acquiring basic information containing the function information of the current user; wherein the functional information at least comprises one of the following items: department, specialty, and excellence.
Specifically, the function information of the doctor can be decomposed to obtain the basic information corresponding to the current user: the functional information may specifically be a physician profile. The decomposition may be a decomposition process of the natural language, for example, a word segmentation tool is used to perform word segmentation, or a preset structured algorithm is used to perform decomposition. For example, the current user's physician profile is: it is good at treating dizziness, headache, protrusion of lumbar intervertebral disc, scapulohumeral periarthritis, etc. After decomposition, the obtained basic information is as follows: dizziness, headache, prolapse of lumbar intervertebral disc, and scapulohumeral periarthritis.
For the case when the current user type is a patient, the case information includes current case information and historical case information corresponding to the current user and having the latest generation time before the current time, and the determination method of the case information is specifically described below with reference to fig. 5:
s502: determining a first case information set corresponding to the current case information and a second case information set corresponding to the historical case information;
s504: and fusing the first case information set and the second case information set to obtain basic information containing case information of the current user.
Specifically, the fusion method of the first case information and the second case information comprises the following steps:
(1) determining a second similarity of each term in the first case information set to each term in the second case information set;
(2) judging whether the second similarity is larger than a third threshold value or not, and if so, replacing the current word in the first case information set with the current word in the second case information set;
(3) otherwise, combining the current word in the second case set with the current word in the first case set to serve as the current word in the updated first case set;
(4) and determining the updated first case set as the basic information corresponding to the current user.
When the medical information is specifically applied, each case information comprises the current medical history, the past history, the personal history, the family history and the menstruation, marriage and childbirth history. When a current case and a certain historical case are fused, the current history in the current case and the current history in the historical case have definite time nodes, so that for the current history in the same time period, a structured set of the current history can be obtained firstly, a second similarity is determined for each structured word, for the word with the second similarity larger than a third threshold value, the structured word in the current case is replaced by the structured word in the historical case, otherwise, the two structured words are combined, and the next pair of structured words is judged continuously.
In some examples, the current case or the current medical history in the historical case cannot acquire the structured set, the text similarity of the two current medical histories can be directly judged, and if the text similarity is greater than a preset fifth threshold, the current medical history with a longer text length is determined as the target current medical history; otherwise, combining the two sections of texts, and taking the combined current medical history as a target current medical history.
The text similarity calculation process comprises the following steps: firstly, performing word segmentation on a text, calculating the text similarity of two current medical histories by using a TF-IDF algorithm, wherein the similarity is greater than a fifth threshold value, and taking the current medical history with longer text length as a target current medical history.
The TF-IDF algorithm calculates the TF-IDF value of each word, and then calculates the text similarity of the two text contents by combining the cosine similarity.
TF: the word frequency, the occurrence frequency of a certain word in a document and the length of the document are divided, so that the word frequency can be standardized to facilitate the comparison of different documents. The word frequency (TF) is the number of occurrences of a word in a document/the total number of words in a document, or the word frequency (TF) is the number of occurrences of a word in a document/the number of words with the highest word frequency.
IDF: a word appears in N documents, the greater N, the less weight the word, and vice versa. When the word is a common word, the weight of the word is extremely small, so that the defect of word frequency statistics is overcome. Inverse Document Frequency (IDF) = log (total number of documents of corpus/total number of documents containing the word + 1).
TF-IDF =TF * IDF。
For example, the set of structured words extracted from the present history in the current case is: profuse sweating, palpitation, hypertension, the collection of structured words extracted in historical cases is: palpitation, hypertension. By the method, the target current medical history is obtained as follows: profuse sweating, palpitation, hypertension.
In some examples, if the current case has a time node and the historical case does not have a time node, the structured terms in each case may be compared, and if the structured terms are the same, or if the structured terms are containment relationships, other medical histories in the current case with longer text lengths may be targeted to be present; and if the structured words are in a cross relationship, performing text similarity calculation of the current medical history, taking the current medical history with longer text length as the target current medical history under the condition that the obtained similarity is greater than a fifth threshold, and otherwise, combining the two current medical histories to serve as the target current medical history.
In other examples, we may refer to the current case and the past history, personal history, family history, menstruation history, and other medical histories in the historical case, which have no definite time node, and then directly compare the text length, and determine that the text length is longer is the target other medical history.
Further, after the target current medical history and the target other medical histories are obtained, the target current medical history and the target other medical histories are combined to become expanded basic information.
In some possible implementations, the step S206 (determining the interest keyword set of the current user according to the search keyword set and the basic information) in the embodiment shown in fig. 2 may specifically be:
(1) determining the occurrence frequency of each word contained in the search keyword set and the basic information as the weight of the word;
(2) sorting the words according to the weight, and determining a set formed by the words with the sorted serial numbers larger than a fourth threshold value as an interested keyword set of the current user;
the following explains how to determine the interested keyword set by taking an actual application scenario as an example:
step 1: by the method provided by the embodiment of the invention, the following information is determined:
refined basic information in case information of the current user: dizziness, headache, chest pain;
current user's search keyword set: coronary heart disease, unstable angina, hypertension, diabetes;
search keyword set of similar users: grade 2 hypertension;
and (3) keyword set obtained by expanding the knowledge graph: grade 1 hypertension;
keyword set obtained by the user's history case expansion: dizziness, headache, profuse perspiration, chest distress.
Step 2: analyzing the set, and obtaining the occurrence frequency of the keywords as follows:
dizziness, 2, headache, 2, chest pain, coronary heart disease, unstable angina, hypertension, diabetes, hypertension grade 2, hypertension grade 1, headache, hyperhidrosis, chest distress.
And step 3: finally, words appearing 2 times or more can be preset and determined as interesting keywords.
And 4, step 4: and searching in a search engine (medical knowledge base) according to the interested keywords, and pushing a search result to the current user.
Based on the above method embodiment, an embodiment of the present invention further provides a medical information pushing apparatus, where the apparatus is connected to an electronic device, and as shown in fig. 6, the apparatus includes:
a search keyword set obtaining module 602, configured to obtain a search keyword set of a current user; the search keyword set comprises current search keywords corresponding to the current moment of the current user and/or historical search keywords corresponding to the historical moment of the current user;
a basic information obtaining module 604, configured to obtain basic information of the current user according to a user type of the current user; the user type comprises a doctor type and a patient type, and the basic information comprises case information corresponding to the patient type and function information corresponding to the doctor type;
an interested keyword set determining module 606, configured to determine an interested keyword set of the current user according to the search keyword set and the basic information; wherein the keywords of interest in the set of keywords of interest are used to characterize information related to a disease that matches the current user's intent;
a target medical information determining module 608, configured to search in a medical knowledge base corresponding to the electronic device according to the interest keyword set, so as to obtain target medical information in the medical knowledge base, where a matching degree of the target medical information in the medical knowledge base and the interest keyword is higher than a first threshold;
a pushing module 610, configured to push the target medical information to the current user.
According to the medical information pushing device provided by the embodiment of the invention, the interested keywords representing the intention of the user are determined according to the search keywords of the user and the basic information of the user, the search is carried out in the medical knowledge base based on the interested keywords to obtain the target medical information, and the final determination process of the target medical information is realized by considering the basic information of the user, so that the medical information pushed to the user is more consistent with the real intention of the user, the effectiveness of the search result is further improved, and the search efficiency of the user is improved.
The above-mentioned device still includes: the similar user determining module is used for determining similar users corresponding to the current user according to the search keyword set corresponding to the current user; the similar users are one or more users similar to the search records of the current user; and the updating module is used for updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user.
The process of determining the similar user corresponding to the current user according to the search keyword set corresponding to the current user includes: calculating a first similarity between a search keyword set corresponding to a current user and a search keyword set corresponding to a target user; and determining the target user with the first similarity larger than the second threshold as a similar user corresponding to the current user.
The process of updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user includes: determining a difference keyword set between a search keyword set corresponding to a current user and a search keyword set corresponding to a similar user; the keywords in the difference keyword set are words which appear in the search keyword set corresponding to the similar user and do not appear in the search keyword set corresponding to the current user; and adding the difference keyword set into the search keyword set corresponding to the current user.
The above-mentioned device still includes: the extended keyword combination determining module is used for determining an extended keyword set according to a knowledge graph prestored in the electronic equipment and a search keyword set corresponding to the current user; the knowledge graph comprises a plurality of words and correlation relations among the words; the keywords in the expanded keyword set and the keywords in the search keyword set corresponding to the current user have a direct incidence relation in the knowledge graph; and the adding module is used for adding the expanded keyword set into the search keyword set corresponding to the current user.
The process of obtaining the basic information of the current user according to the user type of the current user includes: if the user type of the current user is the patient type, acquiring basic information containing case information of the current user; the case information is a set of disease-related words appearing in a case corresponding to the patient, and the disease-related words at least comprise one of the following items: disease name, symptom name, operation name, medicine name, inspection name, examination name; if the user type of the current user is the doctor type, acquiring basic information containing the function information of the current user; wherein the functional information at least comprises one of the following items: department, specialty, and excellence.
The case information comprises current case information and historical case information which are corresponding to the current user and have the latest generation time before the current time; the above process of acquiring the basic information including the case information of the current user includes: determining a first case information set corresponding to the current case information and a second case information set corresponding to the historical case information; and fusing the first case information set and the second case information set to obtain basic information containing case information of the current user.
The process of obtaining the basic information including the case information of the current user by fusing the first case information set and the second case information set includes: determining a second similarity of each term in the first case information set to each term in the second case information set; judging whether the second similarity is larger than a third threshold value or not, and if so, replacing the current word in the first case information set with the current word in the second case information set; otherwise, combining the current word in the second case set with the current word in the first case set to serve as the current word in the updated first case set; and determining the updated first case set as the basic information corresponding to the current user.
The process of determining the current user's interest keyword set according to the search keyword set and the basic information includes: determining the occurrence frequency of each word contained in the search keyword set and the basic information as the weight of the word; and sequencing the words according to the weight, and determining a set formed by the sequenced words with the sequence number larger than a fourth threshold value as an interested keyword set of the current user.
The implementation principle and the generated technical effect of the medical information pushing device provided by the embodiment of the invention are the same as those of the embodiment of the method, and for brief description, reference may be made to corresponding contents in the embodiment of the medical information pushing method where no mention is made in the embodiment of the device.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, which is a schematic structural diagram of the electronic device, where the electronic device includes a processor 701 and a memory 702, the memory 702 stores computer-executable instructions that can be executed by the processor 701, and the processor 701 executes the computer-executable instructions to implement the medical information pushing method.
In the embodiment shown in fig. 7, the electronic device further comprises a bus 703 and a communication interface 704, wherein the processor 701, the communication interface 704 and the memory 702 are connected by the bus 703.
The Memory 702 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 704 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used. The bus 703 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 703 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 7, but this does not indicate only one bus or one type of bus.
The processor 701 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be implemented by integrated logic circuits of hardware or instructions in the form of software in the processor 701. The Processor 701 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory, and the processor 701 reads information in the memory, and completes the steps of the medical information pushing method of the foregoing embodiment in combination with hardware thereof.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the medical information pushing method, and specific implementation may refer to the foregoing method embodiment, and is not described herein again.
The medical information pushing method, the medical information pushing device and the computer program product of the electronic device provided by the embodiment of the invention comprise a computer readable storage medium storing program codes, instructions included in the program codes can be used for executing the method described in the foregoing method embodiment, and specific implementation can refer to the method embodiment, and is not described herein again.
Unless specifically stated otherwise, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (12)
1. A medical information pushing method is applied to an electronic device, and comprises the following steps:
acquiring a search keyword set of a current user; the search keyword set comprises current search keywords corresponding to the current moment of the current user and/or historical search keywords corresponding to the historical moment of the current user;
acquiring basic information of the current user according to the user type of the current user; the user type comprises a doctor type and a patient type, and the basic information comprises case information corresponding to the patient type and function information corresponding to the doctor type;
determining an interested keyword set of the current user according to the search keyword set and the basic information; wherein the keywords of interest in the set of keywords of interest are used to characterize information related to a disease that matches the current user's intent;
searching in a medical knowledge base corresponding to the electronic equipment according to the interested keyword set to obtain target medical information, of which the matching degree with the interested keyword in the medical knowledge base is higher than a first threshold value;
and pushing the target medical information to the current user.
2. The method of claim 1, wherein after the step of obtaining the set of search keywords corresponding to the current user, the method further comprises:
determining a similar user corresponding to the current user according to the search keyword set corresponding to the current user; wherein the similar users are one or more users similar to the search record of the current user;
and updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user.
3. The method according to claim 2, wherein the step of determining the similar user corresponding to the current user according to the search keyword set corresponding to the current user comprises:
calculating a first similarity between the search keyword set corresponding to the current user and the search keyword set corresponding to the target user;
and determining the target user with the first similarity larger than a second threshold as a similar user corresponding to the current user.
4. The method according to claim 2, wherein the step of updating the search keyword set corresponding to the current user according to the search keyword set corresponding to the similar user comprises:
determining a difference keyword set between the search keyword set corresponding to the current user and the search keyword set corresponding to the similar user; the keywords in the difference keyword set are words which appear in the search keyword set corresponding to the similar user and do not appear in the search keyword set corresponding to the current user;
and adding the difference keyword set into the search keyword set corresponding to the current user.
5. The method of claim 1, wherein after the step of obtaining the set of search keywords corresponding to the current user, the method further comprises:
determining an expanded keyword set according to a knowledge graph prestored in the electronic equipment and a search keyword set corresponding to the current user; the knowledge graph comprises a plurality of words and correlation relations among the words; the keywords in the expanded keyword set and the keywords in the search keyword set corresponding to the current user have a direct incidence relation in the knowledge graph;
and adding the expanded keyword set into the search keyword set corresponding to the current user.
6. The method according to claim 1, wherein the step of obtaining the basic information of the current user according to the user type of the current user comprises:
if the user type of the current user is the patient type, acquiring basic information containing case information of the current user; wherein the case information is a set of disease-related words appearing in a case corresponding to the patient, the disease-related words including at least one of: disease name, symptom name, operation name, medicine name, inspection name, examination name;
if the user type of the current user is a doctor type, acquiring basic information containing the function information of the current user; wherein the functional information includes at least one of: department, specialty, and excellence.
7. The method of claim 6, wherein the case information comprises current case information and historical case information corresponding to a generation time of the current user that is most recent before a current time;
the step of acquiring basic information including case information of the current user includes:
determining a first case information set corresponding to the current case information and a second case information set corresponding to the historical case information;
and fusing the first case information set and the second case information set to obtain basic information containing the case information of the current user.
8. The method of claim 7, wherein the step of fusing the first case information set with the second case information set to obtain basic information including case information of the current user comprises:
determining a second similarity of each word in the first set of case information to each word in the second set of case information;
judging whether the second similarity is larger than a third threshold value or not, and if so, replacing the current word in the first case information set with the current word in the second case information set;
otherwise, merging the current word in the second case set with the current word in the first case set to serve as the updated current word in the first case set;
and determining the updated first case set as the basic information corresponding to the current user.
9. The method according to claim 1, wherein the step of determining the interest keyword set of the current user according to the search keyword set and the basic information comprises:
determining the occurrence frequency of each word contained in the search keyword set and the basic information as the weight of the word;
and sequencing the words according to the weight, and determining a set formed by the sequenced words with the sequence number larger than a fourth threshold value as the interested keyword set of the current user.
10. A medical information pushing apparatus, wherein the apparatus is connected to an electronic device, the apparatus comprising:
the search keyword set acquisition module is used for acquiring a search keyword set of a current user; the search keyword set comprises current search keywords corresponding to the current moment of the current user and/or historical search keywords corresponding to the historical moment of the current user;
a basic information obtaining module, configured to obtain basic information of the current user according to a user type of the current user; the user type comprises a doctor type and a patient type, and the basic information comprises case information corresponding to the patient type and function information corresponding to the doctor type;
an interested keyword set determining module, configured to determine an interested keyword set of the current user according to the search keyword set and the basic information; wherein the keywords of interest in the set of keywords of interest are used to characterize information related to a disease that matches the current user's intent;
the target medical information determining module is used for searching in a medical knowledge base corresponding to the electronic equipment according to the interested keyword set to obtain target medical information, the matching degree of which with the interested keyword in the medical knowledge base is higher than a first threshold value;
and the pushing module is used for pushing the target medical information to the current user.
11. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1-9.
12. A computer-readable storage medium having computer-executable instructions stored thereon that, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1-9.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114512241A (en) * | 2021-12-27 | 2022-05-17 | 中国人民解放军总医院第一医学中心 | Esophageal phlebangioma information intelligent searching method and system based on frequency analysis |
| CN115314550A (en) * | 2022-08-17 | 2022-11-08 | 常州市儿童医院(常州市第六人民医院) | Intelligent medical information pushing method and system based on digitization |
| CN115376643A (en) * | 2022-10-26 | 2022-11-22 | 神州医疗科技股份有限公司 | Case custom retrieval method and device, electronic equipment and computer readable medium |
| CN117743376A (en) * | 2024-02-19 | 2024-03-22 | 蓝色火焰科技成都有限公司 | Big data mining method, device and storage medium for digital financial service |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832478A (en) * | 2017-12-15 | 2018-03-23 | 上海京颐科技股份有限公司 | Method and device, storage medium are recommended in medical mobile terminal and its application |
| CN108804450A (en) * | 2017-04-28 | 2018-11-13 | 百度在线网络技术（北京）有限公司 | The method and apparatus of information push |
| CN110188186A (en) * | 2019-04-24 | 2019-08-30 | 平安科技（深圳）有限公司 | Content recommendation method, electronic device, equipment and the storage medium of medical field |
| CN110580278A (en) * | 2019-07-30 | 2019-12-17 | 平安科技（深圳）有限公司 | personalized search method, system, equipment and storage medium according to user portrait |
| CN110874530A (en) * | 2019-10-30 | 2020-03-10 | 深圳价值在线信息科技股份有限公司 | Keyword extraction method and device, terminal equipment and storage medium |
| CN112328775A (en) * | 2020-12-30 | 2021-02-05 | 北京伯仲叔季科技有限公司 | Case text information retrieval system |
-
2021
- 2021-11-23 CN CN202111389742.4A patent/CN113821730A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108804450A (en) * | 2017-04-28 | 2018-11-13 | 百度在线网络技术（北京）有限公司 | The method and apparatus of information push |
| CN107832478A (en) * | 2017-12-15 | 2018-03-23 | 上海京颐科技股份有限公司 | Method and device, storage medium are recommended in medical mobile terminal and its application |
| CN110188186A (en) * | 2019-04-24 | 2019-08-30 | 平安科技（深圳）有限公司 | Content recommendation method, electronic device, equipment and the storage medium of medical field |
| CN110580278A (en) * | 2019-07-30 | 2019-12-17 | 平安科技（深圳）有限公司 | personalized search method, system, equipment and storage medium according to user portrait |
| CN110874530A (en) * | 2019-10-30 | 2020-03-10 | 深圳价值在线信息科技股份有限公司 | Keyword extraction method and device, terminal equipment and storage medium |
| CN112328775A (en) * | 2020-12-30 | 2021-02-05 | 北京伯仲叔季科技有限公司 | Case text information retrieval system |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114512241A (en) * | 2021-12-27 | 2022-05-17 | 中国人民解放军总医院第一医学中心 | Esophageal phlebangioma information intelligent searching method and system based on frequency analysis |
| CN114512241B (en) * | 2021-12-27 | 2024-05-03 | 中国人民解放军总医院第一医学中心 | Frequency analysis-based intelligent searching method and system for esophageal vein tumor information |
| CN115314550A (en) * | 2022-08-17 | 2022-11-08 | 常州市儿童医院(常州市第六人民医院) | Intelligent medical information pushing method and system based on digitization |
| CN115314550B (en) * | 2022-08-17 | 2023-08-25 | 常州市儿童医院(常州市第六人民医院) | Intelligent medical information pushing method and system based on digitization |
| CN115376643A (en) * | 2022-10-26 | 2022-11-22 | 神州医疗科技股份有限公司 | Case custom retrieval method and device, electronic equipment and computer readable medium |
| CN117743376A (en) * | 2024-02-19 | 2024-03-22 | 蓝色火焰科技成都有限公司 | Big data mining method, device and storage medium for digital financial service |
| CN117743376B (en) * | 2024-02-19 | 2024-05-03 | 蓝色火焰科技成都有限公司 | Big data mining method, device and storage medium for digital financial service |
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