CN111814060A - Medical knowledge learning recommendation method and system - Google Patents
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Abstract
The invention relates to the field of big data, and provides a medical knowledge learning recommendation method, which comprises the following steps: constructing a medical knowledge system according to various medical knowledge in the medical field; establishing a corresponding learning path of each learning population in the medical knowledge system according to a pre-defined learning population and the preset difficulty level of each medical knowledge in the medical field knowledge system; acquiring an operation record of a user and a learning crowd to which the user belongs, and uploading the operation record and the learning crowd to which the user belongs to a block chain, wherein the operation record comprises learning duration, medical knowledge answer records and medical knowledge attention conditions; and recommending a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs. According to the embodiment of the invention, more comprehensive medical knowledge can be recommended to the user according to the operation records of the user and the affiliated learning crowd, so that the interest of learning is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to a medical knowledge learning recommendation method and system.
Background
With the development of economy and the progress of society, the health consciousness of people is obviously improved, and medical practitioners and even the common people want to have enough cognition on medical knowledge. Public numbers, APPs and websites which are relevant to medicine are not lacked in the market, and various medical knowledge can be retrieved and inquired by users. Traditional recommendations of medical knowledge tend to mine knowledge of interest to the user. By recording the user's operations, together with the knowledge that is currently popular or highly focused, relevant medical knowledge points and medical consultation are recommended.
However, this recommendation method is often only extended longitudinally to a certain medical knowledge, recommending the same and related knowledge, so as to make the knowledge point better mastered by the user. However, this recommendation approach lacks a more comprehensive system of medical knowledge, so that the user cannot learn more comprehensive medical knowledge.
Disclosure of Invention
In view of this, there is a need to provide a method, a system, a computer device and a readable storage medium for recommending medical knowledge learning, which can solve the problem in the prior art that the recommendation of the medical knowledge system is not comprehensive, so that a user cannot learn more comprehensive medical knowledge.
In order to achieve the above object, an embodiment of the present invention provides a medical knowledge learning recommendation method, where the method includes:
constructing a medical knowledge system according to various medical knowledge in the medical field;
establishing a corresponding learning path of each learning population in the medical knowledge system according to a predefined learning population and the preset difficulty level of each medical knowledge in the medical field knowledge system, wherein the learning population comprises gender, age and identity;
acquiring an operation record of a user and a learning crowd to which the user belongs, wherein the operation record comprises learning duration, medical knowledge answer records and medical knowledge attention conditions;
and recommending a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs.
Optionally, the method further comprises:
acquiring medical knowledge interested by the user according to the operation record of the user within a first preset time;
and skipping according to the medical knowledge at a target position node in the knowledge system, so that the user can continuously learn on a learning path with the target position node.
Optionally, the acquiring, according to the operation record of the user within the first preset time, medical knowledge in which the user is interested includes:
counting the times of executing each operation by the user according to the operation record of the user in a first preset time;
and when the times of one operation exceed a first preset value, judging that the user is interested in the medical knowledge corresponding to the operation, and acquiring the medical knowledge.
Optionally, the method further comprises:
acquiring the learning completion rate of the user in each learning path;
and sending prompt information to the user according to the learning completion rate of each learning path so as to prompt the user to switch the position of the learning path.
Optionally, the method further comprises:
creating a virtual community corresponding to a plurality of preset medical knowledge;
and recommending the corresponding virtual community to the user according to the learning population to which the user belongs and the operation record.
Optionally, the method further comprises:
acquiring hot knowledge within a second preset time;
creating a corresponding learning path according to the popular knowledge;
and pushing the learning path to all virtual communities so as to enable users of all virtual communities to exchange and learn.
Optionally, the method further comprises:
acquiring request information of the user for modifying the learning path;
the request information is issued to a virtual community where the user is located;
obtaining feedback information of other users in the virtual community to the request information;
calculating the support rate of the feedback information to the request information;
and when the support rate exceeds a second preset value, updating the medical knowledge system according to the modified learning path.
In order to achieve the above object, an embodiment of the present invention further provides a medical knowledge learning recommendation system, including:
the construction module is used for constructing a medical knowledge system according to each medical knowledge in the medical field;
the system comprises a creating module, a judging module and a judging module, wherein the creating module is used for creating a corresponding learning path of each learning population in a medical knowledge system according to a predefined learning population and the preset difficulty degree of each medical knowledge in the medical knowledge system, and the learning population comprises gender, age and identity;
the acquisition module is used for acquiring operation records of a user and a learning crowd to which the user belongs, wherein the operation records comprise learning duration, medical knowledge answer records and medical knowledge attention conditions;
and the recommending module is used for recommending a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs.
To achieve the above object, an embodiment of the present invention further provides a computer device, a memory of the computer device, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the medical knowledge learning recommendation method as described above.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is executable by at least one processor to cause the at least one processor to execute the steps of the medical knowledge learning recommendation method as described above.
According to the medical knowledge learning recommendation method, the system, the computer equipment and the readable storage medium, the learning path corresponding to each learning population in the medical knowledge system is established according to the pre-defined learning population and the preset difficulty level of each medical knowledge in the medical field knowledge system, and the corresponding learning path is recommended to the user according to the operation record of the user and the learning population to which the user belongs. According to the embodiment of the invention, more comprehensive medical knowledge can be recommended to the user according to the operation records of the user and the affiliated learning crowd, so that the interest of learning is improved.
Drawings
FIG. 1 is a flow chart illustrating the steps of a method for recommending medical knowledge learning according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of program modules of a medical knowledge learning recommendation system according to an embodiment of the invention;
FIG. 3 is a diagram illustrating a hardware architecture of a computer device according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Example one
Referring to fig. 1, a flow chart of steps of a medical knowledge learning recommendation method of an embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. It should be noted that the present embodiment is exemplarily described with a computer device as an execution subject, and the computer device may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palm computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and a fixed terminal such as a Digital TV, a desktop computer, and the like. The method specifically comprises the following steps:
step S100: according to each medical knowledge in the medical field, a medical knowledge system is constructed.
In an exemplary embodiment, the medical knowledge system includes knowledge systems of various departments, e.g., surgical and medical knowledge systems. Each department builds a knowledge system according to the treatment target, the disease type and the corresponding summary of each disease. In the embodiment of the present invention, a surgical knowledge system is taken as an example for explanation. The surgical system comprises general surgery, cardiac surgery and the like, wherein each department corresponds to different diseases according to different treatment targets, and provides an overview of the disease range related to each department. For each disease, knowledge of the corresponding disease definition, etiology, manifestation, diagnosis, staging, treatment, prognosis, indications of surgery, preoperative and postoperative care, operative complications, etc. is provided. The construction of the knowledge system for each disease encompasses technical medicine, pathology, physiology, pharmacology, etc. Each large knowledge hierarchy includes a plurality of small knowledge hierarchies, the smallest-grained knowledge hierarchy having interdependent knowledge. The knowledge of interdependencies may include, but is not limited to: through key vocabulary entry association, the degree of association depends on the knowledge relevance.
Step S102: according to predefined learning groups and the difficulty degree preset in the medical field knowledge system of each medical knowledge, establishing corresponding learning paths of each learning group in the medical field knowledge system, wherein the learning groups comprise genders, ages and identities.
In particular, the learned population is defined from different dimensions, including: gender, age, identity. And according to the difficulty degree of the medical knowledge, different learning paths are created for people with different dimensions. For example: creating a simple learning path for the child, the medical knowledge on the learning path being simple medical knowledge related to prevention; a deeper learning path is created for a patient, and medical knowledge on the learning path comprises deeper medical knowledge of etiology, treatment mode, side effect, prevention and the like of related diseases; professional learning paths are created for doctors (including ordinary doctors and research doctors), and professional medical knowledge including disease causes, treatment schemes and the like is provided.
In an exemplary embodiment, the learning path may be created by: acquiring knowledge points at the front edge of the current medical field, and acquiring the preset difficulty degree of each medical knowledge point; sequentially storing each medical knowledge point into a database according to the difficulty degree, and setting the weight of each medical knowledge point; and acquiring the operation information of the system management personnel on each medical knowledge point to complete the creation of the learning path. The ease of each medical knowledge point may be a common easy-to-difficult medical knowledge point that is teased out by an authoritative medical expert. The operational information may include an order adjustment of medical knowledge points in the database and a weight adjustment of each medical knowledge point. The learning path is composed of medical knowledge points, each medical knowledge point is called a position node at the position of the learning path, and a tree structure or a mesh structure is presented in the medical knowledge system. And determining the position of each medical knowledge in the learning path through the incidence relation of each medical knowledge, and further determining the connection relation of each position node.
In an exemplary embodiment, the user can freely click the medical knowledge point corresponding to the node at any position to learn the medical knowledge, and does not depend on whether the learning of the medical knowledge point corresponding to the node at the previous position is finished or not. For example, when the user learns the medical knowledge point of the next location node, the user may click the previous location node to learn the medical knowledge point of the previous location node, which is not limited herein.
In an exemplary embodiment, each location node may provide some quizzes of medical knowledge according to the difficulty level of the corresponding medical knowledge point for the user to verify the learning effect. Since the learning completion condition of each location node is determined according to the actual learning condition of the user, the determination of the learning completion of each location node is not limited in the embodiment of the present invention. In an exemplary embodiment, when the learning duration of the medical knowledge of the location node by the user reaches a preset duration and the test score of the medical knowledge by the user reaches a preset score, it may be determined that the user completes learning of the medical knowledge point of the location node.
Of course, new medical knowledge may be added to the medical knowledge system to continuously refine the medical knowledge system according to the medical development conditions. For example: the condition, the cause of the condition and the corresponding treatment regimen are added to the medical knowledge system based on the current new condition medical study.
Step S104: the method comprises the steps of obtaining operation records of a user and learning crowds to which the user belongs, wherein the operation records comprise learning duration, medical knowledge answer records and medical knowledge attention conditions.
Specifically, according to the acquired identity information (that is, professional information) input by the user, the crowd to which the user belongs is judged. For example, the study population to which the user belongs is judged to be a doctor according to the doctor identity information. Certainly, the method can also collect the knowledge points learned by the user periodically, and the crowd to which the user possibly belongs can be calculated according to the accumulated learning time length and the medical knowledge answer records of each knowledge point learned by the user.
It should be noted that the learning duration may be an accumulated learning duration for the user to learn all medical knowledge, an accumulated learning duration for a certain disease, and/or an accumulated learning duration for the user in a preset stage. Of course, in the embodiment of the present invention, the accumulated learning duration of the user in a predetermined period (for example, half a year or 1 year) is used as the judgment basis of the learning crowd. In other embodiments, the short-term learning duration of the user in the last week (or last month) is used for making the recommendation of the medical knowledge learning, and relevant medical knowledge is pushed for the knowledge points which are interested by the user in the recent period.
In an exemplary embodiment, the method further comprises: and uploading the operation records and the learning population to which the user belongs to a block chain. The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.
Step S106: and recommending a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs.
In an exemplary embodiment, if the disease a exists in the operation record of the user, a learning path related to the disease a may be recommended for the user. In another exemplary embodiment, if the time length for the user to learn the disease B in the operation record of the user exceeds a preset value (for example, 15 minutes), a learning path corresponding to the disease B is recommended to the user. In another exemplary embodiment, if the user focuses on a disease C, a learning path corresponding to the disease C is recommended to the user. In another exemplary embodiment, if the error rate of the disease D in the medical knowledge answer record of the user reaches a preset value (e.g., 50%), the learning path corresponding to the disease D is recommended to the user. In another exemplary embodiment, the same operations in the operation record may be counted to calculate an execution frequency of the same operations, and a corresponding learning path may be recommended to the user according to the execution frequency. For example: and counting the frequency of clicking the disease E by the user, and if the frequency is higher than a preset frequency (for example, 3 times), recommending a learning path related to the disease E to the user.
Of course, a learning path of a disease of the same category as the disease in the operation record may also be recommended to the user for the user to comprehensively learn medical knowledge related to the disease. For example: the disease A and the disease B belong to the same disease category (for example, belong to the same organ), and the learning path related to the disease A is recommended to the user at the same time. In practical application, a corresponding learning path may also be provided for the user according to the selection operation of the user.
In an exemplary embodiment, the medical knowledge learning recommendation method may further obtain medical knowledge interested by the user according to an operation record of the user within a first preset time, and jump to a target position node in the knowledge system according to the medical knowledge, so that the user continues to learn on a learning path having the target position node.
Specifically, according to the operation record of the user within a first preset time, counting the times of the same operation in the operation record of the user within a preset time (for example, half a month), and when the times are checked by a preset value, judging that the user is interested in medical knowledge corresponding to the times of the operation, and acquiring the medical knowledge. Illustratively, if the number of times that a user searches or learns the disease a medical knowledge in the last month exceeds a preset value of 5 times, skipping is performed according to a target position node of the disease a medical knowledge in the knowledge system, so that the user can continue learning on a learning path with the target position node. For example: and if the target position node of the disease A medical knowledge in the knowledge system is the position node 1, skipping to the position node 1 to allow the user to carry out extended learning on the disease A medical knowledge at the position node 1, so as to avoid always recommending the same or uninteresting medical knowledge to the user.
In an exemplary embodiment, the medical knowledge learning recommendation method may further include: and acquiring the learning completion rate of the user on each learning path, and sending prompt information to the user according to the learning completion rate of each learning path so as to prompt the user to switch the position of the learning path.
Specifically, the learning completion rate of the user on each learning path is obtained every preset time, the learning completion rate on each learning path is compared with the preset learning completion rate, and when a target learning path with the learning completion rate lower than the preset learning completion rate exists, prompt information is sent to a terminal corresponding to the user to prompt the user to switch to the target learning path. The prompt information may be a current learning position node of the target learning path. For example: and sending prompt information to a terminal corresponding to the user according to the comparison result at regular intervals every month so as to remind the user to switch to the current learning position node of the target learning path with the learning completion rate lower than 30%.
In an exemplary embodiment, the learning completion rate calculation method in each learning path may calculate a basic learning completion rate according to a ratio of the completed knowledge points in the learning path and an answer accuracy rate of the medical knowledge. Of course, the learning completion rate calculation method on each learning path may also perform weighting calculation according to the answer accuracy of the medical knowledge, the learning duration, and the proportion of each knowledge point in the whole knowledge system, so as to calculate the real learning completion rate. The calculation method of the real learning completion rate integrates the proportion of knowledge points in the whole knowledge system, so that the real learning completion rate is more real than the basic learning completion rate. For example, if the simpler knowledge points on one learning path are not learned, the more complex knowledge points are all learned, and the answer accuracy is higher, the real learning completion rate of the user on the learning path is higher than the basic learning completion rate. Of course, in the embodiment of the present invention, any one of the learning completion rates of the user on each learning path may be selected according to a requirement, or two calculation methods may be simultaneously performed, and the corresponding learning completion rate is selected as a final learning completion rate according to the requirement, for example, a low learning completion rate is selected as a final learning completion rate.
Of course, in another exemplary embodiment, the user may freely select whether to switch to the current learning location node of the target learning path according to the requirement, which is not limited herein.
In an exemplary embodiment, the medical knowledge learning recommendation method may further include: and creating virtual communities corresponding to a plurality of preset medical knowledge, and recommending the corresponding virtual communities to the user according to the group of the user and the operation record.
In particular, depending on the human condition or organ, a corresponding virtual community is created, for example: a "heart" virtual community is created. The "heart" virtual community is then recommended to the physician, research specialist and interviewer, or to the presence of the heart in the operating record. By recommending the virtual community to different crowds, the crowd types of the virtual community can be enriched so as to promote the learning and communication of users in the virtual community. For example: the interested population forms a virtual community with doctors, research experts or interviewers in the form of questions and answers. By means of the virtual community, a good learning communication environment can be provided for the user on the premise of guaranteeing the personal privacy of the user.
Certainly, in the virtual community, all users in the virtual community are ranked according to the speaking times of each user, so as to count the activity degree of each user in the virtual community.
In another exemplary embodiment, the user can also freely select a virtual community in which the user is interested according to the requirement. And after receiving request information for the user to select to join the virtual community, sending the request information to a terminal corresponding to an administrator to audit the user, and joining the user to the virtual community after the audit is passed. For example: and obtaining the speech record of the user, wherein if the user issues illegal speech or non-speech, the administrator can refuse the user to join the virtual community. Through the mode of examining and verifying by an administrator, the communication quality of the community can be greatly improved.
In an exemplary embodiment, the medical knowledge learning recommendation method may further include: acquiring hot knowledge within a second preset time; creating a corresponding learning path according to the popular knowledge; and pushing the learning path to all virtual communities so as to enable users in all virtual communities to exchange and learn.
In an exemplary embodiment, the medical knowledge learning recommendation method may further include: acquiring request information of the user for modifying the learning path; the request information is issued to a virtual community where the user is located; obtaining feedback information of other users in the virtual community to the request information, and calculating a support rate in the feedback information; and when the support rate exceeds a preset value, updating the medical knowledge system according to the modified learning path.
Specifically, when request information of a user for modifying the learning path 1 is received, the request information for modifying the learning path 1 is sent to a virtual community where the user is located, and if more than 70% of users in the virtual community support modification of the learning path 1, the modified learning path 1 is issued to the medical knowledge system. And then, updating the learning path 1 display after the administrator of the medical knowledge system passes the examination. By means of modifying the learning path by the user, the learning path in the medical knowledge system can be continuously improved, the participation sense of the user is increased, the learning experience of the user is greatly improved, and the user is retained.
Example two
Referring to fig. 2, a schematic diagram of program modules of a medical knowledge learning recommendation system 20 according to an embodiment of the present invention is shown. The medical knowledge learning recommendation system 20 may be applied in an electronic device. In this embodiment, the medical knowledge learning recommendation system 20 may include or be divided into one or more program modules, which are stored in a storage medium and executed by one or more processors to implement the present invention and implement the medical knowledge learning recommendation method. The program modules referred to in the embodiments of the present invention refer to a series of computer program instruction segments capable of performing specific functions, and are more suitable than the program itself for describing the execution process of the medical knowledge learning recommendation system 20 in the storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:
the construction module 201 is configured to construct a medical knowledge system according to each medical knowledge in the medical field.
In an exemplary embodiment, the medical knowledge system includes knowledge systems of various departments, e.g., surgical and medical knowledge systems. Each department builds a knowledge system according to the treatment target, the disease type and the corresponding summary of each disease. In the embodiment of the present invention, a surgical knowledge system is taken as an example for explanation. The surgical system comprises general surgery, cardiac surgery and the like, wherein each department corresponds to different diseases according to different treatment targets, and provides an overview of the disease range related to each department. For each disease, knowledge of the corresponding disease definition, etiology, manifestation, diagnosis, staging, treatment, prognosis, indications of surgery, preoperative and postoperative care, operative complications, etc. is provided. The construction of the knowledge system for each disease encompasses technical medicine, pathology, physiology, pharmacology, etc. Each large knowledge hierarchy includes a plurality of small knowledge hierarchies, the smallest-grained knowledge hierarchy having interdependent knowledge. The knowledge of interdependencies may include, but is not limited to: through key vocabulary entry association, the degree of association depends on the knowledge relevance.
The creating module 202 is configured to create a learning path corresponding to each learning population in the medical knowledge system according to predefined learning populations and the difficulty level preset in the medical knowledge system, where the learning populations include gender, age, and identity.
In particular, the learned population is defined from different dimensions, including: gender, age, identity. And according to the difficulty degree of the medical knowledge, different learning paths are created for people with different dimensions. For example: creating a simple learning path for the child, the medical knowledge on the learning path being simple medical knowledge related to prevention; a deeper learning path is created for a patient, and medical knowledge on the learning path comprises deeper medical knowledge of etiology, treatment mode, side effect, prevention and the like of related diseases; professional learning paths are created for doctors (including ordinary doctors and research doctors), and professional medical knowledge including disease causes, treatment schemes and the like is provided.
In an exemplary embodiment, the creation module 202 may be configured to: acquiring knowledge points at the front edge of the current medical field, and acquiring the preset difficulty degree of each medical knowledge point; sequentially storing each medical knowledge point into a database according to the difficulty degree, and setting the weight of each medical knowledge point; and acquiring the operation information of the system management personnel on each medical knowledge point to complete the creation of the learning path. The ease of each medical knowledge point may be a common easy-to-difficult medical knowledge point that is teased out by an authoritative medical expert. The operational information may include an order adjustment of medical knowledge points in the database and a weight adjustment of each medical knowledge point. The learning path is composed of medical knowledge points, each medical knowledge point is called a position node at the position of the learning path, and a tree structure or a mesh structure is presented in the medical knowledge system. And determining the position of each medical knowledge in the learning path through the incidence relation of each medical knowledge, and further determining the connection relation of each position node.
In an exemplary embodiment, the user can freely click the medical knowledge point corresponding to the node at any position to learn the medical knowledge, and does not depend on whether the learning of the medical knowledge point corresponding to the node at the previous position is finished or not. For example, when the user learns the medical knowledge point of the next location node, the user may click the previous location node to learn the medical knowledge point of the previous location node, which is not limited herein.
In an exemplary embodiment, each location node may provide some quizzes of medical knowledge according to the difficulty level of the corresponding medical knowledge point for the user to verify the learning effect. Since the learning completion condition of each location node is determined according to the actual learning condition of the user, the determination of the learning completion of each location node is not limited in the embodiment of the present invention. In an exemplary embodiment, when the learning duration of the medical knowledge of the location node by the user reaches a preset duration and the test score of the medical knowledge by the user reaches a preset score, it may be determined that the user completes learning of the medical knowledge point of the location node.
Of course, new medical knowledge may be added to the medical knowledge system to continuously refine the medical knowledge system according to the medical development conditions. For example: the condition, the cause of the condition and the corresponding treatment regimen are added to the medical knowledge system based on the current new condition medical study.
The obtaining module 203 is configured to obtain an operation record of a user and a learning crowd to which the user belongs, where the operation record includes a learning duration, a medical knowledge answer record, and a medical knowledge attention situation.
Specifically, according to the acquired identity information (that is, professional information) input by the user, the crowd to which the user belongs is judged. For example, the study population to which the user belongs is judged to be a doctor according to the doctor identity information. Certainly, the method can also collect the knowledge points learned by the user periodically, and the crowd to which the user possibly belongs can be calculated according to the accumulated learning time length and the medical knowledge answer records of each knowledge point learned by the user.
It should be noted that the learning duration may be an accumulated learning duration for the user to learn all medical knowledge, an accumulated learning duration for a certain disease, and/or an accumulated learning duration for the user in a preset stage. Of course, in the embodiment of the present invention, the accumulated learning duration of the user in a predetermined period (for example, half a year or 1 year) is used as the judgment basis of the learning crowd. In other embodiments, the short-term learning duration of the user in the last week (or last month) is used for making the recommendation of the medical knowledge learning, and relevant medical knowledge is pushed for the knowledge points which are interested by the user in the recent period.
In an exemplary embodiment, the medical knowledge learning recommendation system 20 further includes an uploading module, configured to upload the operation records and the learning population to which the user belongs to the blockchain. The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.
And the recommending module 204 is configured to recommend a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs.
In an exemplary embodiment, if the disease a exists in the operation record of the user, a learning path related to the disease a may be recommended for the user. In another exemplary embodiment, if the time length for the user to learn the disease B in the operation record of the user exceeds a preset value (for example, 15 minutes), a learning path corresponding to the disease B is recommended to the user. In another exemplary embodiment, if the user focuses on a disease C, a learning path corresponding to the disease C is recommended to the user. In another exemplary embodiment, if the error rate of the disease D in the medical knowledge answer record of the user reaches a preset value (e.g., 50%), the learning path corresponding to the disease D is recommended to the user. In another exemplary embodiment, the same operations in the operation record may be counted to calculate an execution frequency of the same operations, and a corresponding learning path may be recommended to the user according to the execution frequency. For example: and counting the frequency of clicking the disease E by the user, and if the frequency is higher than a preset frequency (for example, 3 times), recommending a learning path related to the disease E to the user.
Of course, a learning path of a disease of the same category as the disease in the operation record may also be recommended to the user for the user to comprehensively learn medical knowledge related to the disease. For example: the disease A and the disease B belong to the same disease category (for example, belong to the same organ), and the learning path related to the disease A is recommended to the user at the same time. In practical application, a corresponding learning path may also be provided for the user according to the selection operation of the user.
In an exemplary embodiment, the obtaining module 203 may be further configured to obtain medical knowledge of interest to the user according to an operation record of the user within a first preset time.
The medical knowledge learning recommendation system 20 may further include a skipping module for skipping a target location node in the knowledge system according to the medical knowledge, so that the user continues to learn on a learning path having the target location node.
Specifically, according to the operation record of the user within a first preset time, counting the times of the same operation in the operation record of the user within a preset time (for example, half a month), and when the times are checked by a preset value, judging that the user is interested in medical knowledge corresponding to the times of the operation, and acquiring the medical knowledge. Illustratively, if the number of times that the user searches or learns the disease a medical knowledge in the last month exceeds a preset value of 5 times, skipping is performed according to the target position node of the disease a medical knowledge in the knowledge system. For example: and if the target position node of the disease A medical knowledge in the knowledge system is the position node 1, skipping to the position node 1 to allow the user to carry out extended learning on the disease A medical knowledge at the position node 1, so as to avoid always recommending the same or uninteresting medical knowledge to the user.
In an exemplary embodiment, the obtaining module 203 may be further configured to obtain a learning completion rate of the user in each learning path; the medical knowledge learning recommendation system 20 may further include a sending module, configured to send prompt information to the user according to the learning completion rate of each learning path, so as to prompt the user to switch the position of the learning path.
Specifically, the learning completion rate of the user on each learning path is obtained every preset time, the learning completion rate on each learning path is compared with the preset learning completion rate, and when a target learning path with the learning completion rate lower than the preset learning completion rate exists, prompt information is sent to a terminal corresponding to the user to prompt the user to switch to the target learning path. The prompt information may be a current learning position node of the target learning path. For example: and sending prompt information to a terminal corresponding to the user according to the comparison result at regular intervals every month so as to remind the user to switch to the current learning position node of the target learning path with the learning completion rate lower than 30%.
In an exemplary embodiment, the learning completion rate on each learning path is calculated, and the basic learning completion rate may be calculated according to the proportion of the completed knowledge points on the learning path and the answer accuracy of the medical knowledge. Of course, the learning completion rate on each learning path may be calculated, or the real learning completion rate may be calculated by performing weighted calculation according to the answer accuracy of the medical knowledge, the learning duration, and the proportion of each knowledge point in the whole knowledge system. The calculation method of the real learning completion rate integrates the proportion of knowledge points in the whole knowledge system, so that the real learning completion rate is more real than the basic learning completion rate. For example, if the simpler knowledge points on one learning path are not learned, the more complex knowledge points are all learned, and the answer accuracy is higher, the real learning completion rate of the user on the learning path is higher than the basic learning completion rate. Of course, in the embodiment of the present invention, any one of the learning completion rates of the user on each learning path may be selected according to a requirement, or two calculation methods may be simultaneously performed, and the corresponding learning completion rate is selected as a final learning completion rate according to the requirement, for example, a low learning completion rate is selected as a final learning completion rate.
Of course, in another exemplary embodiment, the user may freely select whether to switch to the current learning location node of the target learning path according to the requirement, which is not limited herein.
In an exemplary embodiment, the creation module 202 may be further configured to create a virtual community corresponding to a plurality of preset medical knowledge; the recommending module 204 may also be configured to recommend a corresponding virtual community to the user according to the group of the user and the operation record.
In particular, depending on the human condition or organ, a corresponding virtual community is created, for example: a "heart" virtual community is created. The "heart" virtual community is then recommended to the physician, research specialist and interviewer, or to the presence of the heart in the operating record. By recommending the virtual community to different crowds, the crowd types of the virtual community can be enriched so as to promote the learning and communication of users in the virtual community. For example: the interested population forms a virtual community with doctors, research experts or interviewers in the form of questions and answers. By means of the virtual community, a good learning communication environment can be provided for the user on the premise of guaranteeing the personal privacy of the user.
Certainly, in the virtual community, all users in the virtual community are ranked according to the speaking times of each user, so as to count the activity degree of each user in the virtual community.
In another exemplary embodiment, the user can also freely select a virtual community in which the user is interested according to the requirement. And after receiving request information for the user to select to join the virtual community, sending the request information to a terminal corresponding to an administrator to audit the user, and joining the user to the virtual community after the audit is passed. For example: and obtaining the speech record of the user, wherein if the user issues illegal speech or non-speech, the administrator can refuse the user to join the virtual community. Through the mode of examining and verifying by an administrator, the communication quality of the community can be greatly improved.
In an exemplary embodiment, the obtaining module 203 may be further configured to obtain hot knowledge within a second preset time; the creating module 202 may be further configured to create a corresponding learning path according to the trending knowledge; the medical knowledge learning recommendation system 20 may further include a pushing module for pushing the learning path to all virtual communities for users in all virtual communities to exchange learning.
In an exemplary embodiment, the obtaining module 203 may be further configured to obtain request information for the user to modify the learned route; the medical knowledge learning recommendation system 20 may further include a publishing module, configured to publish the request information to a virtual community where the user is located; the obtaining module 203 may be further configured to obtain feedback information of other users in the virtual community to the request information; the medical knowledge learning recommendation system 20 may further include a calculation module and an update module, the calculation module is used for calculating a support rate in the feedback information; and the updating module is used for updating the medical knowledge system according to the modified learning path when the support rate exceeds a preset value.
Specifically, when request information of a user for modifying the learning path 1 is received, the request information for modifying the learning path 1 is sent to a virtual community where the user is located, and if more than 70% of users in the virtual community support modification of the learning path 1, the modified learning path 1 is issued to the medical knowledge system. And then, updating the learning path 1 display after the administrator of the medical knowledge system passes the examination. By means of modifying the learning path by the user, the learning path in the medical knowledge system can be continuously improved, the participation sense of the user is increased, the learning experience of the user is greatly improved, and the user is retained.
EXAMPLE III
Based on the medical knowledge learning recommendation method provided in the above embodiment, a computer device is provided in this embodiment. Specifically, please refer to fig. 3, which illustrates a hardware architecture diagram of a computer device according to an embodiment of the present invention. The computer device 2 includes, but is not limited to, a memory 21, a processor 22, and a network interface 23 communicatively coupled to each other via a system bus, and FIG. 3 illustrates only the computer device 2 having components 21-23, but it is to be understood that not all of the illustrated components are required and that more or fewer components may alternatively be implemented.
The memory 21 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), and the like, provided on the computer device 2. Of course, the memory 21 may also comprise both an internal storage unit of the computer device 2 and an external storage device thereof. In this embodiment, the memory 21 is generally used for storing an operating system installed on the computer device 2 and various types of application software, such as program codes of the medical knowledge learning recommendation system 20. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 2. In this embodiment, the processor 22 is configured to execute the program code stored in the memory 21 or process data, for example, execute the medical knowledge learning recommendation system 20, so as to implement the medical knowledge learning recommendation method according to the first embodiment.
The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the computer device 2 and other electronic devices. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
Example four
The present invention also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers) capable of executing programs, and the like. The computer device of the embodiment at least includes but is not limited to: memory, processor, etc. communicatively coupled to each other via a system bus.
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the present embodiment is used for storing the medical knowledge learning recommendation system 20, and when executed by a processor, implements the above-described embodiments of the medical knowledge learning recommendation method.
According to the medical knowledge learning recommendation method, the system, the computer equipment and the readable storage medium, the learning path corresponding to each learning crowd in the medical knowledge system is created according to the pre-defined learning crowd, and the corresponding learning path is recommended to the user according to the operation record of the user and the learning crowd to which the user belongs. According to the embodiment of the invention, more comprehensive medical knowledge can be recommended to the user according to the operation records of the user and the affiliated learning crowd, so that the interest of learning is improved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A medical knowledge learning recommendation method, the method comprising:
constructing a medical knowledge system according to various medical knowledge in the medical field;
establishing a corresponding learning path of each learning population in the medical knowledge system according to a predefined learning population and the preset difficulty level of each medical knowledge in the medical field knowledge system, wherein the learning population comprises gender, age and identity;
acquiring an operation record of a user and a learning crowd to which the user belongs, wherein the operation record comprises learning duration, medical knowledge answer records and medical knowledge attention conditions;
and recommending a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs.
2. The medical knowledge learning recommendation method of claim 1, the method further comprising:
acquiring medical knowledge interested by the user according to the operation record of the user within a first preset time;
and skipping according to the medical knowledge at a target position node in the knowledge system, so that the user can continuously learn on a learning path with the target position node.
3. The medical knowledge learning recommendation method according to claim 2, wherein the obtaining of the medical knowledge of interest to the user according to the operation record of the user within a first preset time comprises:
counting the times of executing each operation by the user according to the operation record of the user in a first preset time;
and when the times of one operation exceed a first preset value, judging that the user is interested in the medical knowledge corresponding to the operation, and acquiring the medical knowledge.
4. The medical knowledge learning recommendation method of claim 1, the method further comprising:
acquiring the learning completion rate of the user in each learning path;
and sending prompt information to the user according to the learning completion rate of each learning path so as to prompt the user to switch the position of the learning path.
5. The medical knowledge learning recommendation method of claim 1, the method further comprising:
creating a virtual community corresponding to a plurality of preset medical knowledge;
and recommending the corresponding virtual community to the user according to the learning population to which the user belongs and the operation record.
6. The medical knowledge learning recommendation method of claim 5, the method further comprising:
acquiring hot knowledge within a second preset time;
creating a corresponding learning path according to the popular knowledge;
and pushing the learning path to all virtual communities so as to enable users of all virtual communities to exchange and learn.
7. The medical knowledge learning recommendation method of claim 5, the method further comprising:
acquiring request information of the user for modifying the learning path;
the request information is issued to a virtual community where the user is located;
obtaining feedback information of other users in the virtual community to the request information;
calculating the support rate of the feedback information to the request information;
and when the support rate exceeds a second preset value, updating the medical knowledge system according to the modified learning path.
8. A medical knowledge learning recommendation system, comprising:
the construction module is used for constructing a medical knowledge system according to each medical knowledge in the medical field;
the system comprises a creating module, a judging module and a judging module, wherein the creating module is used for creating a corresponding learning path of each learning population in a medical knowledge system according to a predefined learning population and the preset difficulty degree of each medical knowledge in the medical knowledge system, and the learning population comprises gender, age and identity;
the acquisition module is used for acquiring operation records of a user and a learning crowd to which the user belongs, wherein the operation records comprise learning duration, medical knowledge answer records and medical knowledge attention conditions;
and the recommending module is used for recommending a corresponding learning path to the user according to the operation record and the learning crowd to which the user belongs.
9. A computer device, characterized by a computer device memory, a processor and a computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the medical knowledge learning recommendation method according to any one of claims 1-7.
10. A computer-readable storage medium, having stored therein a computer program, the computer program being executable by at least one processor to cause the at least one processor to perform the steps of the medical knowledge learning recommendation method as claimed in any one of claims 1-7.
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