CN113140314A - Medical science popularization system, method and server - Google Patents
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Abstract
The invention provides a medical science popularization system, a medical science popularization method and a server. The medical information science popularization system comprises: the storage module is used for storing an association model; the association model includes a plurality of diseases and disease information; the health information acquisition module is used for acquiring the health information of the user; the health information comprises symptom sub-information, index sub-information and/or archive sub-information; the related disease searching module is connected with the storage module and the health information acquiring module and used for searching related diseases from the correlation model according to the health information of the user; and the science popularization information acquisition module is connected with the storage module and the related disease searching module and is used for acquiring the science popularization information of the user according to the related diseases and the association model. The user science popularization information acquired by the medical science popularization system fully considers the health condition of the user, so that the user science popularization information is more likely to be the content interested by the user, and the science popularization effect is good.
Description
Technical Field
The invention belongs to the field of medical care informatics, relates to a science popularization system, and particularly relates to a medical science popularization system, a medical science popularization method and a server.
Background
In recent years, with the increasing health consciousness of people, the public demand for medical science popularization is increasing. The medical science popularization refers to the way of utilizing various media to accept medical knowledge, advocate medical methods, conduct medical ideas and develop medical spirit in a superficial, popular and understandable way. The existing medical science popularization method is that medical workers select corresponding science popularization information according to own judgment and push the information to users. After receiving the pushed science popularization information, the user can select corresponding science popularization information to look up according to own interests and requirements. The inventor finds that in practical application, users are often favored to acquire science popularization information related to their health conditions, and for example, users with high blood pressure prefer to receive information on prevention, relief, treatment and the like of the high blood pressure. However, the existing medical science popularization method does not consider the health condition of the user, so that the science popularization effect is poor.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a medical science popularization system, method and server, which are used to solve the problem that the prior medical science popularization method does not consider the health condition of the user, thereby resulting in poor science popularization effect.
To achieve the foregoing and other related objects, a first aspect of the present invention provides a medical science popularization system. The medical information science popularization system comprises: the storage module is used for storing an association model; the association model includes a plurality of diseases and disease information; wherein each disease is associated with at least one disease information, the disease information including symptom signs, examination index and/or profile-related information; the health information acquisition module is used for acquiring the health information of the user; the health information comprises symptom sub-information, index sub-information and/or archive sub-information; the system comprises a user, a checking index and archive related information, wherein the symptom sub-information of the user is associated with symptom signs, the index sub-information of the user is associated with the checking index, and the archive sub-information of the user is associated with the archive related information; the related disease searching module is connected with the storage module and the health information acquiring module and used for searching related diseases from the correlation model according to the health information of the user; and the science popularization information acquisition module is connected with the storage module and the related disease searching module and is used for acquiring the science popularization information of the user according to the related diseases and the association model.
In an embodiment of the first aspect, the related disease finding module includes: the user disease acquisition unit is connected with the health information acquisition module and used for acquiring the disease information of the user according to the health information of the user; and the related disease searching unit is connected with the user disease acquiring unit and the storage module and is used for searching the related diseases from the correlation model according to the disease information of the user.
In an embodiment of the first aspect, the medical information science popularization system further includes: and the prevalence probability calculation module is connected with the related disease search module, the storage module and the science popularization information acquisition module and is used for calculating the prevalence probability of the related disease according to the association model.
In an embodiment of the first aspect, the prevalence probability calculating module includes: the weight calculation unit is connected with the related disease search module and the storage module and is used for calculating the weight value of the disease information of the user according to the association model; and the probability calculation unit is connected with the weight calculation unit and the storage module and is used for calculating the disease probability of the related diseases according to the weight value of the disease information of the user and the association model.
In an embodiment of the first aspect, the prevalence probability calculating module includes: the training data acquisition unit is connected with the storage module and used for acquiring training data; the training data is derived from the correlation model and a diagnostic case database; the neural network training unit is connected with the training data acquisition unit and used for training a neural network model by using the training data to obtain a trained probability calculation neural network model; and the neural network processing unit is connected with the neural network training unit and the related disease searching module and is used for processing the disease information of the user by utilizing the probability calculation neural network model so as to obtain the disease probability of the related disease.
In an embodiment of the first aspect, the medical information science popularization system further includes: the health portrait acquisition module is connected with the science popularization information acquisition module and is used for acquiring a health portrait of a user; the science popularization information acquisition module is further used for acquiring the science popularization information of the user according to the health portrait of the user.
In an embodiment of the first aspect, the science popularization information obtaining module includes: the disease information acquisition unit is connected with the related disease searching module and the storage module and is used for acquiring the disease information of the related disease from the correlation model; the science popularization information acquisition unit is connected with the disease information acquisition unit and used for acquiring the science popularization information of the user according to the disease information of the related diseases; and the priority acquisition unit is connected with the science popularization information acquisition unit and is used for setting the priority of the user science popularization information.
In an embodiment of the first aspect, the science popularization information obtaining unit searches the user science popularization information from a science popularization information database according to the disease information of the related disease.
In an embodiment of the first aspect, the science popularization information obtaining module further includes: and the reading condition counting unit is connected with the priority acquisition unit and is used for counting the reading condition of the science popularization information contained in the science popularization information database.
In an embodiment of the first aspect, the science popularization information obtaining module further includes: and the science popularization information editing unit is connected with the storage module, the related disease searching module and the science popularization information acquiring unit and is used for editing the user science popularization information acquired by the science popularization information acquiring unit according to the association model and the related diseases so as to generate new science popularization information.
In an embodiment of the first aspect, the medical information science popularization system further includes: the reading preference acquisition module is connected with the science popularization information acquisition module and is used for acquiring the reading preference of the user on the science popularization information; the science popularization information acquisition module generates the user science popularization information according to the reading preference of the user on the science popularization information, the disease information of the related diseases and the association model.
In an embodiment of the first aspect, the reading preference obtaining module includes: the user interest obtaining unit is used for obtaining the interest degree of the user on different popular science information; the group interest acquiring unit is used for acquiring the interest degree of one or more groups including users in different popular science information; and the reading preference acquisition unit is connected with the user interest acquisition unit and the group interest acquisition unit and is used for acquiring the reading preference of the user on the popular science information according to the interest degree of the user on different popular science information and the interest degree of the group on different popular science information.
A second aspect of the present invention provides a medical information science popularization method, including: acquiring health information of a user; the health information comprises symptom sub-information, index sub-information and/or archive sub-information; finding out at least one related disease from a correlation model according to the health information of the user; the association model includes a plurality of diseases and disease information; wherein each disease is associated with at least one disease information, the disease information including symptom signs, examination index and/or profile-related information; the symptom sub-information of the user is associated with symptom signs, the index sub-information of the user is associated with the examination index, and the file sub-information of the user is associated with the file related information; and generating user popular science information according to the related diseases and the association model.
A third aspect of the invention provides a medical information science popularization server. The medical information science popularization server comprises the medical information science popularization system according to the first aspect of the invention.
As described above, one technical solution of the medical science popularization system, the medical science popularization method and the medical science popularization server of the invention has the following beneficial effects:
the medical science popularization system can acquire related diseases according to health information of a user, and further acquire user science popularization information according to the related diseases. Therefore, the user science popularization information acquired by the medical science popularization system fully considers the health condition of the user, so that the user science popularization information is more likely to be the content in which the user is interested, and the science popularization effect is good.
Drawings
Fig. 1A is a schematic structural diagram of a medical information science popularization system according to an embodiment of the invention.
Fig. 1B is a diagram illustrating a relationship between corresponding information in an embodiment of the medical information science popularization system according to the present invention.
Fig. 2 is a diagram illustrating an example of a correlation model of the medical information science popularization system according to an embodiment of the invention.
Fig. 3 is a schematic structural diagram of a related disease search module in an embodiment of the medical information science popularization system according to the present invention.
Fig. 4 is a schematic structural diagram of a medical information science popularization system according to an embodiment of the invention.
Fig. 5A is a schematic structural diagram of a prevalence probability calculation module of the medical information science popularization system according to an embodiment of the invention.
Fig. 5B is a schematic structural diagram of a prevalence probability calculation module of the medical information science popularization system according to an embodiment of the invention.
Fig. 5C is a flowchart illustrating the training data acquisition of the medical information science popularization system according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a science popularization information obtaining module of the medical information science popularization system according to an embodiment of the invention.
Fig. 7 is a diagram illustrating an example of a correlation model of the medical information science popularization system according to an embodiment of the invention.
Fig. 8A is a schematic structural diagram of a health information acquiring module of the medical information science popularization system according to an embodiment of the invention.
Fig. 8B is a flowchart illustrating a method for acquiring user health information in an embodiment of the medical information science popularization system according to the present invention.
Fig. 9 is a flowchart illustrating a medical information science popularization method according to an embodiment of the present invention.
Description of the element reference numerals
1 medical information science popularization system
11 memory module
12 health information acquisition module
121 self-test template generation unit
122 health information receiving unit
123 self-test template updating unit
13 related disease searching module
131 user disease acquisition unit
132 related disease finding unit
14 science popularization information acquisition module
141 disease information acquiring unit
142 science popularization information acquisition unit
143 priority acquisition unit
15 sick probability calculation module
151 weight calculation unit
152 probability calculation unit
153 training data acquisition unit
154 neural network training unit
155 neural network processing unit
16 health portrait acquisition module
17 reading preference acquisition module
2 correlation model
3 correlation model
S51-S53
S81-S87
S91-S93
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, number and proportion of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In practical applications, users tend to prefer to acquire popular science information related to their health conditions, for example, users with high blood pressure prefer to receive information on prevention, relief, treatment, and the like of hypertension. However, the existing medical science popularization method does not consider the health condition of the user, but medical workers select corresponding science popularization information to push the science popularization information to the user according to the judgment of the medical workers, and by the mode, only a small part of a large amount of science popularization information pushed to the user is the content which the user is interested in, so that the science popularization effect is poor. In order to solve the problem, the invention provides a medical information science popularization system which can acquire related diseases from a correlation model according to health information of a user and further acquire user science popularization information according to the related diseases. Therefore, the user science popularization information acquired by the medical science popularization system fully considers the health condition of the user, so that the user science popularization information is more likely to be the content in which the user is interested, and the science popularization effect is good.
Referring to fig. 1A, in an embodiment of the present invention, the medical information science popularization system 1 includes a storage module 11, a health information obtaining module 12, a related disease searching module 13, and a science popularization information obtaining module 14.
The storage module 11 stores a correlation model, which includes various diseases and disease information; wherein each disease is associated with at least one disease information; the disease information includes symptom signs, examination indices, and/or profile-related information. The symptom signs refer to subjective abnormal feelings of patients or some objective pathological conditions caused by a series of abnormal changes of functions, metabolism and morphological structures in organisms in the disease process, such as: cough, nausea, weakness, etc. The examination index refers to one or more parameters set for quantitative assessment of the health condition of the body, such as: blood pressure, body temperature, heart rate, etc. The archive-related information refers to information related to diseases formed by the body in the past activity process, such as: past history, used products, family inheritance and/or personal history, etc.
The health information acquisition module 12 is used for acquiring health information of a user; the health information of the user comprises symptom sub-information, index sub-information and/or archive sub-information. Referring to fig. 1B, the symptom sub-information of the user is associated with the symptom sign in the association model, the index sub-information of the user is associated with the examination index in the association model, and the profile sub-information of the user is associated with the profile-related information in the association model.
In particular, the symptom sub-information includes relevant symptom signs physically or psychologically exhibited by the user, such as: fever, dry cough, hypodynamia, dyspnea and the like, and the user can determine the symptom sub-information through the physical expression of the user. When the body of the user shows a certain symptom sign, the health information of the user records the symptom sign in the form of symptom sub-information. For example, when a symptom sign of cough appears in the user, the health information of the user includes a symptom sub-information of cough.
The indicator sub-information includes physical indicators describing the physical condition of the user in a quantitative manner, such as: blood pressure, body temperature, white blood cell count, hemoglobin, etc.; the user can obtain the index sub-information through corresponding medical equipment, and can also obtain the index sub-information through modes such as hospital physical examination and the like. For example, when a user measures a blood pressure value by a blood pressure meter, the health information of the user records the blood pressure value in the form of index sub-information.
The profile sub-information includes relevant information in the user's health profile, such as: sex, age, place of residence, history of disease, genetic history, history of allergy, history of medication, etc.
The related disease searching module 13 is connected to the storage module 11 and the health information obtaining module 12, and is configured to search for a related disease from the association model according to the health information of the user. Specifically, the related disease searching module 13 searches the related disease in the correlation model according to the correlation between the health information and the disease information of the user and the correlation between the disease information and the disease.
The science popularization information obtaining module 14 is connected to the storage module 11 and the related disease searching module 13, and is configured to obtain the science popularization information of the user according to the related disease and the association model. Specifically, the science popularization information obtaining module 14 obtains part of or all of the disease information associated with the related disease from the association model, and further obtains the science popularization information associated with the related disease as the user science popularization information. Wherein the science popularization information related to the related diseases comprises disease information related to the partial disease information or disease information related to the whole disease information. The science popularization information obtaining module 14 may obtain, through a web crawler, science popularization information related to the related disease in a network as the user science popularization information, may also search, from a science popularization information database, science popularization information related to the related disease as the user science popularization information, and may also search, from a corresponding professional literature, content related to the related disease as the user science popularization information, which is not limited herein.
As can be seen from the above description, the medical information science popularization system according to the embodiment can acquire the health information of the user, acquire the related diseases according to the health information of the user, and further acquire the science popularization information related to the related diseases. Therefore, the user science popularization information acquired by the medical science popularization system fully considers the health condition of the user and is more easily accepted by the user, and therefore the science popularization effect is better.
Referring to FIG. 2, a correlation model according to an embodiment of the invention is shown. The association model 2 includes disease 1, disease 2, and disease 3. Furthermore, the association model 2 further includes: symptom signs such as symptom 1 to symptom 6, examination indexes such as index 1 to index 4, and file-related information such as file information 1 and file information 2. And, the association model 2 also defines the association relationship between the disease and the disease information.
Specifically, for any disease and any symptom sign, if the disease causes the user to have the symptom sign, an association relationship between the disease and the symptom sign is considered to exist, and the association relationship is represented by a straight line between the disease and the symptom sign in the figure, for example: disease 1 and symptom 2 in the association model 2; if the disease does not cause the symptom sign to appear in the user, the disease and the symptom sign are considered to have no association relationship, for example: disease 1 and symptom 3 in the association model 2. Therefore, in the diagnosis process, if the user has the symptom sign, the user can be considered to have the disease according to the association relationship. For example, a user generally has fever symptoms when having a cold, and thus there is an association between cold diseases and fever symptoms; in the diagnosis process, when the user has fever symptoms, the user can be considered to possibly suffer from cold.
For any disease and any examination index, if the disease causes the examination index to be abnormal, the disease and the examination index are considered to have a correlation, and the correlation is represented by a straight line between the disease and the examination index in the figure, for example: disease 1 and index 1 in the association model 2; otherwise, the disease and the examination index are not considered to have a correlation, for example: disease 1 and index 4 in the association model 2. Therefore, in the diagnosis process, if the examination index of the user is abnormal, the user can be considered to be possibly suffered from the disease according to the association relation. For example, hypertension may cause the blood pressure value of the user to exceed the normal value range, so there is an association relationship between hypertension disease and the blood pressure value index; in the diagnosis process, when the blood pressure value index of the user is too high, the user can be considered to be possibly suffered from hypertension.
Regarding any disease and any profile-related information which may cause the user to present the disease, the disease and the profile-related information are considered to have an association relationship, and the association relationship is represented by a straight line between the disease and the profile-related information in the figure, for example, disease 1 and profile information 3 in the association model 2; otherwise, the disease and the profile related information are considered to have no association relationship, for example, the disease 1 and the profile information 1 in the association model 2. Therefore, in the diagnosis process, if the user is found to have the profile-related information, the user can be considered to have the disease according to the association relationship. For example, the genetic history of heart disease increases the probability that the user has heart disease, and thus it can be considered that there is a correlation between the information related to the file of the genetic history of heart disease and heart disease. In the diagnostic process, when the user has information about a file of the genetic history of a heart disease, the user is considered to be likely to have the heart disease.
Referring to fig. 3, in an embodiment of the present invention, the related disease search module 13 includes a user disease obtaining unit 131 and a related disease search unit 132.
The user disease obtaining unit 131 is connected to the health information obtaining module 12, and is configured to obtain disease information of the user according to the health information of the user. Specifically, the user disease acquisition unit 131 acquires abnormality information in the health information of the user, and acquires disease information associated with the abnormality information as the disease information of the user.
When the health information of the user comprises symptom sub-information, all the symptom sub-information is the abnormal information; for example, symptoms such as fever, dry cough, weakness, and dyspnea are all abnormal information. When the health information of the user comprises the index sub-information, the index sub-information with the index value out of the normal value range is the abnormal information; for example, when the systolic blood pressure is more than 139mmHg, the systolic blood pressure is abnormal information; when the body temperature is higher than 37.3 ℃, the body temperature is abnormal information; the normal value range may be defined by an authoritative medical professional. When the health information of the user comprises the file sub-information, the file sub-information different from the health crowd is the abnormal information; for example, healthy people generally do not have a pneumonia disease history, and the sub-file information of the user includes the pneumonia disease history, so that the sub-file information of the pneumonia disease history is abnormal information; for example, a healthy person generally does not smoke, and if the health information of the user includes health sub-information, which is smoking, smoking is abnormal information. Wherein, the healthy population refers to the population completely in a healthy state and can be defined by an authoritative medical expert.
The related disease searching unit 132 is connected to the user disease obtaining unit 131 and the storage module 11, and is configured to search the related disease from the association model according to the disease information of the user.
Specifically, the disease searching unit 132 may search for the related disease in a sum manner, that is: the disease search unit 132 selects a disease associated with the disease information of all users from the association model as the related disease. For example, if the disease information of the user includes symptom 1 and symptom 2, the related disease found by the disease finding unit 132 from the association model 2 in a sum manner is disease 1.
The disease finding unit 132 may also find the related disease in an or manner, that is: the disease finding unit 132 selects a disease associated with the disease information of at least one user from the association model as the related disease. For example, if the disease information of the user includes symptom 1 and symptom 2, the related diseases searched by the disease search unit 132 from the association model 2 in an or manner are disease 1, disease 2 and disease 3.
The disease finding unit 132 may further select a disease associated with the disease information of at least N users from the association model as the related disease. The value of N is set according to actual requirements, and the larger the value of N is, the more relevant diseases are found; the smaller the value of N, the less relevant diseases are found.
Referring to fig. 4, in an embodiment of the present invention, the medical information science popularization system 1 further includes a prevalence probability calculating module 15. The prevalence probability calculating module 15 is connected to the related disease searching module 13, the storage module 11, and the science popularization information obtaining module 14, and is configured to calculate the prevalence probability of the related disease according to the association model. The science popularization information obtaining module 14 may determine the priority of the user science popularization information according to the prevalence probability of the related disease: the higher the prevalence probability of the related diseases is, the higher the priority of the related science popularization information is; the smaller the prevalence probability of the related disease, the lower the priority of the science popularization information related to the related disease. For example, if the prevalence probability of the disease 1 is 90% and the prevalence probability of the disease 2 is 10%, the priority of the science popularization information related to the disease 1 is higher than the priority of the science popularization information related to the disease 2, and at this time, the science popularization information acquisition module 14 preferentially generates, pushes and/or displays the science popularization information related to the disease 1.
In this embodiment, by calculating the prevalence probability of the related disease and determining the priority of the science popularization information of the user according to the prevalence probability of the related disease, the science popularization information preferentially generated, pushed and/or displayed by the science popularization information acquisition module 14 is high-priority science popularization information, and the high-priority science popularization information is related to the related disease with a higher prevalence probability, so that the science popularization information is more likely to be popular science information of which the user is interested. Therefore, according to the embodiment, not only are related diseases possibly suffered by the user considered, but also the probability of the user suffering from different related diseases is considered, so that the science popularization information obtained by the medical information science popularization system is stronger in pertinence and better in science popularization effect.
Referring to fig. 5A, in an embodiment of the present invention, the prevalence probability calculating module 15 includes a weight calculating unit 151 and a probability calculating unit 152. The weight calculating unit 151 is connected to the related disease searching module 13 and the storage module 11, and is configured to calculate a weight value of the disease information of the user according to the association model. The disease information of the user refers to disease information associated with abnormal information in the health information of the user, and weight values of the disease information of the same user in different related diseases may be different.
Specifically, for any user's disease information m and any related disease n, the weight calculation unit 151 obtains the diagnosis criterion C of the related disease nn(ii) a Wherein, CnConsists of all disease information associated with the relevant disease n. If the diagnostic criterion CnIf the user does not contain the disease information m of the user, the weight value of the disease information m of the user in the related diseases n is 0; otherwise, the weight value W of the disease information m of the user in the related disease nm,nIs composed of
Wherein N ism,nThe number of all diseases associated with the disease information m of the user; for example, in the correlation model 2, the number of all diseases associated with index 3 is 2, and the number of all diseases associated with symptom 1 is 3. N is a radical ofi,nNumber of all diseases associated with disease information i, MnAs the diagnostic criteria CnThe number of disease information contained, disease information 1, disease information 2, … …, and disease information MnAll belong to the diagnostic criteria Cn. The above parameter Nm,n、Ni,nAnd MnMay be obtained from the correlation model.
All disease information associated with the relevant disease n is determined by the association model, namely: the diagnostic criteria CnDetermined by the correlation model. Wherein, if the disease information of the user includes all the disease information related to the related disease n, the probability that the user has the related disease n is considered to be 100%.
The probability calculating unit 152 is connected to the weight calculating unit 151 and the storage module 11, and is configured to calculate the prevalence probability of the related disease according to the weight of the disease information of the user and the association model. Specifically, for the related disease n, it is assumed that in the disease information of all users, the related disease n is associated withThe set of the disease information is Q, the probability P that the user has the related disease nnComprises the following steps:
the sum of the weight values representing the disease information of all users in Q. Wherein, Wj,nIs the weight value of the disease information j of the user in the related disease n.
The method for calculating the probability of illness will be described in detail below with a specific example based on the association model 2. For disease 1, criteria for diagnosis C1The method comprises the following steps: symptom 1 (named disease information 1), number N of all diseases associated therewith1，13; symptom 2 (named disease information 2), number N of all diseases associated therewith2，11 is ═ 1; symptom 5 (named disease information 3), number N of all diseases associated therewith3，11 is ═ 1; index 1 (named disease information 4), number N of all diseases associated therewith4，12; index 2 (named disease information 5), number N of all diseases associated therewith5，11 is ═ 1; index 3 (named disease information 6), number N of all diseases associated therewith6，12; profile information 3 (named disease information 7), number N of all diseases associated therewith7，1＝2。
If the disease information of the user includes symptom 1, symptom 2, symptom 3, index 3, and index 4, the set Q of the disease information associated with the disease 1 among the disease information of all users includes: symptom 1 (disease information 1), symptom 2 (disease information 2), and index 3 (disease information 6), and:
the weight value of the disease information 1 is
The weight of the disease information 2 is:
the weight of the disease information 6 is:
based on this, the probability calculation unit 152 obtains the probability P that the user has the disease 11＝W1+W2+W634.5 percent. According to the embodiment, the disease probability of the related diseases is calculated by calculating the weight value of the disease information of the user, so that the method does not depend on a real diagnosis case in the process, and the workload required for collecting the diagnosis case is reduced.
Referring to fig. 5B, in an embodiment of the invention, the prevalence calculation module includes a training data acquisition unit 153, a neural network training unit 154, and a neural network processing unit 155.
The training data obtaining unit 153 is connected to the storage module 11, and is configured to obtain training data; wherein the training data is derived from the correlation model and a database of diagnostic cases, namely: the training data includes two types, one from the correlation model and the other from the diagnostic case database. The diagnosis case database contains a large number of real diagnosis cases including, but not limited to, on-line inquiry cases and off-line diagnosis cases. The training data acquisition unit 153 mixes the two types of training data according to different mixing ratios, and can combine theoretical data with actual data to ensure the accuracy and the practicability of the probabilistic calculation neural network model.
The training data derived from the correlation model refers to training data generated from the correlation model. Referring to fig. 5C, for any related disease, the implementation method for generating training data according to the association model includes:
and S51, acquiring all disease information related to the related diseases according to the correlation model. For example, based on the correlation model 2, all disease information associated with disease 1 includes symptom 1, symptom 2, symptom 5, index 1, index 2, index 3, and profile information 3.
S52, for the one obtained in step S51And performing combined selection on the disease information to obtain different disease information combinations. Specifically, a plurality of disease information combinations are selected from all the disease information according to the concept of permutation and combination in mathematics, wherein the disease information contained in each disease information combination is different from one another. For example, if all the disease information acquired in step S51 includes disease information 1, disease information 2, and disease information 3, one disease information combination acquired in step S52 may be disease information 1, disease information 1 and disease information 2, disease information 2 and disease information 3, and so on. For the related disease n, the number of disease information combinations that can be acquired in step S52 is at most
Wherein M isnThe number of all disease information associated with the relevant disease n.
S53, calculating the disease probability corresponding to each disease information combination; the prevalence probability can be implemented by the weight calculation unit 151 and the probability calculation unit 152, or can be implemented by other methods, which is not limited herein. And each disease information combination and the corresponding disease probability are the training data.
The neural network training unit 154 is connected to the training data obtaining unit 153, and is configured to train a neural network model by using the training data to obtain a trained probabilistic computational neural network model; the training of the neural network model by using the training data can be realized by using the existing training method, and details are not repeated here.
The neural network processing unit 155 is connected to the neural network training unit 154 and the related disease searching module 13, and is configured to process the disease information of the user by using the probability computation neural network model to obtain the prevalence probability of the related disease. Specifically, the neural network processing unit 155 uses the disease information of the user as an input of the probability calculation neural network model, and an output of the probability calculation neural network model is the prevalence probability of the related disease.
When training data is obtained by using the association model, the training data obtaining unit 153 may obtain the training data only according to disease information and diagnosis cases associated with one related disease; the neural network processing unit can obtain the prevalence probability of the related disease by using the probability calculation neural network model.
When the associated model is used to obtain training data, the training data obtaining unit 153 may also obtain the training data according to disease information and diagnosis cases associated with a plurality of related diseases, and at this time, the neural network processing unit may calculate the probability that the neural network model can simultaneously obtain the probability of the plurality of related diseases by using the probability.
In this embodiment, the training data obtaining unit 153 selects training data of the diagnosis case database as a first type of training data, and generates a second type of training data according to the association model. The first type of training data is data collected in actual diagnosis, and the second type of training data is data derived theoretically. On this basis, the training data obtaining unit 153 further combines the first type of training data and the second type of training data according to a mixing ratio, so that the probability calculation neural network model has higher accuracy and practicability, and further, it is ensured that the science popularization information priority obtained according to the disease probability can be applied to a real case.
In an embodiment of the present invention, the medical information science popularization system 1 further includes a health representation obtaining module 16. The health portrait acquisition module 16 is connected to the science popularization information acquisition module 14, and is configured to acquire a health portrait of a user. The science popularization information obtaining module 14 is further configured to obtain health information and related diseases of the user according to the health portrait of the user, and further obtain the science popularization information of the user. Specifically, the health representation includes a function (Functionality) portion, a Status (Status) portion, and a Risk (Risk) portion.
The functional part of the health portrait is the basis of the health portrait and mainly comprises basic information of a user. The basic information comprises the name, age, height, weight and other basic information of the user. The science popularization information obtaining module 14 generates corresponding basic science popularization information as the user science popularization information according to the functional part. The basic science popularization information comprises medical general knowledge, eating habits, health care modes and the like related to the basic information.
The state part of the health image comprises abnormal information of the user, wherein the abnormal information is information which can possibly damage the health of the user and comprises driving without a safety belt, no smoke alarm in a residence, smoking, heavy drinking and the like. The science popularization information acquisition module 14 generates corresponding abnormal science popularization information as the user science popularization information according to the state part. The abnormal popular science information comprises the harm of the abnormal information to the health of the user, an improvement method, correct demonstration and the like.
The risk part of the health representation comprises the degree of the risk of the user, such as: the risk of hypertension is high, and the risk of diabetes is low; the degree of the risk of the disease can be described by the probability of the related disease. The science popularization information obtaining module 14 generates corresponding risk science popularization information as the user science popularization information according to the risk part, wherein the risk science popularization information includes related knowledge, preventive measures, treatment schemes and the like of high-risk diseases.
In this embodiment, the health portrait acquisition module is convenient for more directly perceived, comprehensive show through the health portrait of acquireing the user the health status of user, the health status of user can be more closely attached to according to the user science popularization information that the health portrait of user acquireed, and then promote the user to the reading interest of science popularization information, promote the science popularization effect.
Referring to fig. 6, in an embodiment of the present invention, the science popularization information acquiring module 14 further includes a disease information acquiring unit 141, a science popularization information acquiring unit 142, and a priority acquiring unit 143.
The disease information obtaining unit 141 is connected to the related disease searching module 13 and the storage module 11, and is configured to obtain the disease information of the related disease from the association model. Wherein the disease information of the related diseases refers to all or part of the disease information associated with the related diseases in the association model. Preferably, the disease information acquiring unit 141 acquires all the disease information associated with the related diseases from the association model as the disease information of the related diseases. For example, if the related disease is a disease 2 based on the correlation model 2, the disease information of the related disease acquired by the disease information acquiring unit 141 includes a symptom 1, a symptom 3, a symptom 4, an index 1, profile information 1, and profile information 3.
The science popularization information acquiring unit 142 is connected to the disease information acquiring unit 141, and is configured to acquire the user science popularization information according to the disease information of the related disease. Wherein the user science popularization information is science popularization information related to the disease information of the related diseases. For example, if the disease information of the related disease is fever, the science popularization information acquiring unit 142 acquires science popularization information related to fever as the user science popularization information.
The priority acquiring unit 143 is connected to the science popularization information acquiring unit 142, and is configured to set a priority of the user science popularization information. The science popularization information with high priority corresponds to the science popularization information which is preferred by the user; the priority of the science popularization information comprises the acquisition priority, the display priority and/or the pushing priority of the science popularization information. The priority acquiring unit 143 may set the priority of the science popularization information of the user according to the health portrait of the user, where the higher the number of user tags related to a certain science popularization information in the health portrait of the user, the higher the priority of the science popularization information; the priority of the science popularization information of the user can also be set according to the disease probability of the related diseases, and the related science popularization information has higher priority for the related diseases with higher disease probability.
In this embodiment, the science popularization information acquisition module may generate personalized science popularization information according to the health information of each user, and set different priorities for different science popularization information. The science popularization information with high priority is more closely related to the health condition of the user, and is more likely to be the science popularization information in which the user is interested.
In an embodiment of the present invention, the science popularization information acquiring unit 14 is further configured to sort the user science popularization information to acquire a push sequence of the user science popularization information. Specifically, the method for implementing the science popularization information obtaining unit 14 to rank the user science popularization information includes:
step 1, acquiring a health file of a user, and formulating rules aiming at various health file information of the user to generate a user label; wherein, the user label library is a manually predefined database.
And 2, acquiring the popular label of each user popular information, wherein the popular label is mainly extracted by a TF-IDF (Fterm frequency-inverse document frequency) method. Specifically, word segmentation and stop word (stopwords) removal operations are performed on each corpus in the user science popularization information, and TF-IDF is calculated to screen out keywords of the user science popularization information. Wherein, Term Frequency (TF) refers to the frequency of a given word appearing in the user's science popularization information, and the frequency is the normalization of the number of words to prevent the thesaurus from being biased to longer science popularization information, specifically, for the ith keyword in the jth user's science popularization information, the normalized term frequency is
In the formula, the numerator is the number of times that the ith keyword appears in the jth piece of user science popularization information, and the denominator is the sum of the number of times that all keywords appear in the jth piece of user science popularization information; the Inverse Document Frequency (IDF) is a measure of the general importance of a keyword, and for the ith keyword, the inverse document frequency is
In the formula, | D | represents the total amount of the user popular science information, | j: ti∈djI represents the number of documents containing the ith keyword. Finally, the popular science information can be obtained through TF multiplied by IDFA keyword.
And 3, acquiring the similarity between users and the similarity between science popularization corpora and science popularization corpora according to the user tags and the science popularization tags. Preferably, the similarity is obtained using a pearson correlation coefficient, in particular, for the random variables X and Y, the pearson correlation coefficient is
Wherein N represents the number of values of the random variable X or Y.
And 4, acquiring a recommended corpus according to the similarity between the users, and sequencing the recommended corpus according to actual requirements (such as regions, hotspots, typesetting and the like) to obtain a pushing sequence of the user popular science information. And in the actual pushing process, pushing the science popularization information of the user is realized according to the pushing sequence.
In an embodiment of the present invention, the science popularization information obtaining unit searches the user science popularization information from a science popularization information database according to the disease information of the related disease. The science popularization information database is a database containing a large amount of science popularization information, and comprises a hierarchical database, a network database or a relational database and the like. The science popularization information database can be automatically created in an AI mode, and can also be created and maintained in a manual mode, which is not limited herein.
In an embodiment of the present invention, the popular science information obtaining module further includes a reading condition statistics unit connected to the priority obtaining unit, and configured to count reading conditions of the popular science information in the popular science information database. The reading condition comprises reading times, average reading time, total reading time and the like of the science popularization information. The priority acquiring unit may set the priority of the science popularization information according to the reading condition; the science popularization information with higher reading times and longer reading time has higher priority.
In an embodiment of the invention, the science popularization information obtaining module further includes an association model updating unit. The association model updating unit is connected with the science popularization information acquiring unit and the storage module and is used for updating the association model according to the user science popularization information acquired by the science popularization information acquiring unit. Specifically, the association model updating unit adds the user science popularization information acquired by the science popularization information acquiring module to the association model, and associates the user science popularization information with the disease and/or the disease information in the association model; or, the associated model updating unit replaces the existing similar science popularization information in the associated model with the user science popularization information acquired by the science popularization information acquiring module. Thereafter, the science popularization information acquiring unit 142 may directly acquire the science popularization information associated with the related disease from the association model as the user science popularization information, and/or acquire the science popularization information associated with the disease information of the related disease from the association model as the user science popularization information.
Referring to fig. 7, a correlation model 3 obtained after the correlation model updating unit updates the correlation model 2 is shown. The association model 3 includes science popularization 1, science popularization 3, and science popularization 5 directly associated with a disease, and science popularization 2 and science popularization 5 directly associated with disease information. The science popularization information 1 to the science popularization 5 are user science popularization information which is searched by the science popularization information acquisition unit from the science popularization information database. At this time, when the related disease searching module 13 determines that the related disease of the user is disease 1, the science popularization information obtaining unit 142 may directly obtain science popularization 1 and science popularization 2 associated with disease 1 from the association model 3 as the science popularization information of the user.
In an embodiment of the invention, the science popularization information acquiring module further includes a science popularization information editing unit. The science popularization information editing unit is connected with the storage module, the related disease searching module and the science popularization information acquiring unit and is used for editing the user science popularization information acquired by the science popularization information acquiring unit according to the association model and the related diseases so as to generate new science popularization information; editing the popular science information, wherein the editing of the popular science information comprises content extraction, content deletion, combination, title editing, keyword replacement and the like; the new science popularization information can also be used as the user science popularization information to be displayed or pushed to the user. For example, the science popularization 4 acquired by the science popularization information acquiring unit 142 includes contents related to symptom 4 and symptom 6; and the disease information of the user only includes symptom 4, at this time, the science popularization information editing unit may delete the content related to symptom 6 in the science popularization 4 to generate the new science popularization information. For another example, the science popularization information acquiring unit 142 may acquire the science popularization 1 and the science popularization 2, and the science popularization information editing unit may combine the science popularization 1 and the science popularization 2 to generate the new science popularization information.
Referring to fig. 8A, in an embodiment of the present invention, the health information obtaining module 12 includes a self-test template generating unit 121, a health information receiving unit 122, and a self-test template updating unit 123.
The self-test template generating unit 121 is configured to generate a self-test template, which is an initial self-test template and is used to prompt a user to input health information. Preferably, the self-test template generating unit 121 generates the initial self-test template according to information such as a current season, a disease prevalence state, a public hot spot, and the like.
The health information receiving unit 122 is configured to receive health information input by a user.
The self-test template updating unit 123 is connected to the self-test template generating unit 121, the health information receiving unit 122 and the storage module, and is configured to update the self-test template according to the health information of the user and the association model; and the updated self-testing template is used for prompting the user to continuously input the health information.
Specifically, referring to fig. 8B, the workflow of the health information acquiring module 12 acquiring the health information of the user includes:
s81, the self-test template generating unit 121 generates a self-test template, and the user inputs the health information according to the self-test template; wherein the health information comprises symptom sub-information, index sub-information and/or archive sub-information.
S82, the health information receiving unit 122 receives the health information input by the user.
S83, the self-test template updating unit 123 obtains the health information input by the user from the health information receiving unit 122, and obtains related disease information according to an association relationship. Wherein, the related disease information refers to all disease information related to the health information input by the user, and comprises symptom signs, examination indexes and/or profile related information. The association relationship comprises: the symptom sign is associated with the symptom sub-information, the examination index is associated with the index sub-information, and the file related information is associated with the file sub-information. For example, if the health information input by the user is body temperature 39 ℃, the disease information related to the health information is a fever symptom and a body temperature 39 ℃ which are examination indexes.
S84, the self-test template updating unit 123 searches for possible diseases in the correlation model according to the related disease information. The possible disease is a disease associated with all or part of the disease information included in the related disease information.
S85, the self-test template updating unit 123 obtains all disease information of the possible diseases according to the association model, and obtains health information associated with the possible diseases according to all disease information of the possible diseases.
S86, updating the current self-testing template to obtain an updated self-testing template; the updated self-test template includes the health information acquired in step S85, and prompts the user to select health information corresponding to the self-test template for input.
S87, the steps S82 to S86 are repeated until the health information acquiring module 12 acquires enough health information.
In an embodiment of the present invention, the medical information science popularization system 1 further includes a reading preference obtaining module 17. The reading preference obtaining module is connected with the science popularization information obtaining module 14 and is used for obtaining the reading preference of the user on the science popularization information; the science popularization information obtaining module 14 generates the science popularization information of the user according to the reading preference of the user on the science popularization information, the disease information of the related diseases and the association model. The reading preference of the user on the science popularization information comprises preference on type of the science popularization information, preference on content, preference on word number, preference on pictures and the like. In this embodiment, the science popularization information obtaining module 14 obtains the science popularization information according to the disease information of the related disease and the association model, and edits information such as a type, content, word number, and picture of the science popularization information according to reading preference of the user on the science popularization information, so as to generate user science popularization information which is more easily liked by the user. Therefore, the science popularization information of the user acquired by the embodiment is more easily accepted and read by the user.
In an embodiment of the invention, the reading preference obtaining module includes a user interest obtaining unit, a group interest obtaining unit and a reading preference obtaining unit. The user interest obtaining unit is used for obtaining the interest degree of the user in different popular science information. The interest degree of the user in different popular science information can be obtained through the reading time, the reading times, the collection condition and the like of the user in different popular science information. The group interest acquiring unit is used for acquiring the interest degree of one or more groups including users in different popular science information; the group may be divided according to the health representation of the user, for example, users in the same city may be a group, and users in the same age group may be a group. The reading preference obtaining unit is connected with the user interest obtaining unit and the group interest obtaining unit and is used for obtaining the reading preference of the user for the science popularization information according to the interest degree of the user for different science popularization information and the interest degree of the group for different science popularization information.
Based on the description of the medical information science popularization system, the invention also provides a medical information science popularization method. Referring to fig. 9, the medical information science popularization method includes:
s91, acquiring the health information of the user; the health information includes symptom sub-information, index sub-information and/or profile sub-information.
S92, finding out at least one relevant disease from a correlation model according to the health information of the user; the association model includes a plurality of diseases and disease information; wherein each disease is associated with at least one disease information, the disease information including symptom signs, examination index and/or profile-related information; and the symptom sub-information of the user is associated with the symptom sign in the association model, the index sub-information of the user is associated with the check index in the association model, and the profile sub-information of the user is associated with the profile related information in the association model.
And S93, generating user science popularization information according to the related diseases and the association model.
Step S91 is the same as the method for the health information obtaining module 12 to obtain the health information of the user, and step S92 is the same as the method for the related disease searching module 13 to search the related disease from the correlation model according to the health information of the user. Step S93 is the same as the method for the science popularization information obtaining module 14 to obtain the science popularization information of the user according to the related diseases and the association model. For saving the description space, it is not repeated herein.
Based on the above description of the medical information science popularization system, the invention also provides a medical information science popularization server, and the medical information science popularization server comprises the medical information science popularization system.
The protection scope of the medical information science popularization method according to the present invention is not limited to the execution sequence of the steps listed in the embodiment, and all the solutions of the prior art including the steps addition, subtraction and step replacement according to the principle of the present invention are included in the protection scope of the present invention.
The invention also provides a medical information science popularization system, which can realize the medical information science popularization method, but the realization device of the medical information science popularization method comprises but is not limited to the structure of the medical information science popularization system listed in the embodiment, and all structural modifications and substitutions of the prior art made according to the principle of the invention are included in the protection scope of the invention.
The medical science popularization system can acquire related diseases according to the health information of the user, and further acquire the science popularization information of the user according to the related diseases. Therefore, the user science popularization information acquired by the medical science popularization system fully considers the health condition of the user, so that the user science popularization information is more likely to be the content in which the user is interested, and the science popularization effect is good.
The medical science popularization system can also set the priority of the science popularization information of the user according to the disease probability of the related diseases, the health portrait of the user and the reading condition of the science popularization information, and adjusts the science popularization information of the user according to the reading preference of the user on the science popularization information, so that the science popularization information acquired by the user is more likely to be the content interested by the user, and the science popularization effect is further improved.
In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (14)
1. A medical information science popularization system, characterized by comprising:
the storage module is used for storing an association model; the association model includes a plurality of diseases and disease information; wherein each disease is associated with at least one disease information, the disease information including symptom signs, examination index and/or profile-related information;
the health information acquisition module is used for acquiring the health information of the user; the health information comprises symptom sub-information, index sub-information and/or archive sub-information; the system comprises a user, a checking index and archive related information, wherein the symptom sub-information of the user is associated with symptom signs, the index sub-information of the user is associated with the checking index, and the archive sub-information of the user is associated with the archive related information;
the related disease searching module is connected with the storage module and the health information acquiring module and used for searching related diseases from the correlation model according to the health information of the user;
and the science popularization information acquisition module is connected with the storage module and the related disease searching module and is used for acquiring the science popularization information of the user according to the related diseases and the association model.
2. The medical information science popularization system of claim 1, wherein the related disease finding module comprises:
the user disease acquisition unit is connected with the health information acquisition module and used for acquiring the disease information of the user according to the health information of the user;
and the related disease searching unit is connected with the user disease acquiring unit and the storage module and is used for searching the related diseases from the correlation model according to the disease information of the user.
3. The medical information science popularization system according to claim 1, wherein the medical information science popularization system further comprises:
and the prevalence probability calculation module is connected with the related disease search module, the storage module and the science popularization information acquisition module and is used for calculating the prevalence probability of the related disease according to the association model.
4. The medical information science popularization system according to claim 3, wherein the prevalence probability calculation module comprises:
the weight calculation unit is connected with the related disease search module and the storage module and is used for calculating the weight value of the disease information of the user according to the association model;
and the probability calculation unit is connected with the weight calculation unit and the storage module and is used for calculating the disease probability of the related diseases according to the weight value of the disease information of the user and the association model.
5. The medical information science popularization system according to claim 3, wherein the prevalence probability calculation module comprises:
the training data acquisition unit is connected with the storage module and used for acquiring training data; the training data is derived from the correlation model and a diagnostic case database;
the neural network training unit is connected with the training data acquisition unit and used for training a neural network model by using the training data to obtain a trained probability calculation neural network model;
and the neural network processing unit is connected with the neural network training unit and the related disease searching module and is used for processing the disease information of the user by utilizing the probability calculation neural network model so as to obtain the disease probability of the related disease.
6. The medical information science popularization system according to claim 1, wherein the medical information science popularization system further comprises:
the health portrait acquisition module is connected with the science popularization information acquisition module and is used for acquiring a health portrait of a user;
the science popularization information acquisition module is further used for acquiring the user science popularization information according to the health portrait of the user.
7. The medical information science popularization system according to claim 1, wherein the science popularization information acquiring module comprises:
the disease information acquisition unit is connected with the related disease searching module and the storage module and is used for acquiring the disease information of the related disease from the correlation model;
the science popularization information acquisition unit is connected with the disease information acquisition unit and used for acquiring the science popularization information of the user according to the disease information of the related diseases;
and the priority acquisition unit is connected with the science popularization information acquisition unit and is used for setting the priority of the user science popularization information.
8. The medical information science popularization system according to claim 7, wherein: the science popularization information acquisition unit searches the user science popularization information from a science popularization information database according to the disease information of the related diseases.
9. The medical information science popularization system of claim 8, wherein the science popularization information acquisition module further comprises:
and the reading condition counting unit is connected with the priority acquisition unit and is used for counting the reading condition of the science popularization information contained in the science popularization information database.
10. The medical information science popularization system of claim 7, wherein the science popularization information acquisition module further comprises:
and the science popularization information editing unit is connected with the storage module, the related disease searching module and the science popularization information acquiring unit and is used for editing the user science popularization information acquired by the science popularization information acquiring unit according to the association model and the related diseases so as to generate new science popularization information.
11. The medical information science popularization system according to claim 1, wherein the medical information science popularization system further comprises:
the reading preference acquisition module is connected with the science popularization information acquisition module and is used for acquiring the reading preference of the user on the science popularization information;
the science popularization information acquisition module generates the user science popularization information according to the reading preference of the user on the science popularization information, the disease information of the related diseases and the association model.
12. The medical information science popularization system according to claim 11, wherein the reading preference acquisition module comprises:
the user interest obtaining unit is used for obtaining the interest degree of the user on different popular science information;
the group interest acquiring unit is used for acquiring the interest degree of one or more groups including users in different popular science information;
and the reading preference acquisition unit is connected with the user interest acquisition unit and the group interest acquisition unit and is used for acquiring the reading preference of the user on the popular science information according to the interest degree of the user on different popular science information and the interest degree of the group on different popular science information.
13. A medical information science popularization method is characterized by comprising the following steps:
acquiring health information of a user; the health information comprises symptom sub-information, index sub-information and/or archive sub-information;
finding out at least one related disease from a correlation model according to the health information of the user; the association model includes a plurality of diseases and disease information; wherein each disease is associated with at least one disease information, the disease information including symptom signs, examination index and/or profile-related information; the symptom sub-information of the user is associated with symptom signs, the index sub-information of the user is associated with the examination index, and the file sub-information of the user is associated with the file related information;
and generating user popular science information according to the related diseases and the association model.
14. A medical information science popularization server, characterized by: the medical information science popularization server includes the medical information science popularization system according to any one of claims 1 to 12.
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