CN113140272A - Medical information learning system and method - Google Patents
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- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
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- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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Abstract
The invention provides a learning system and a learning method of medical information, which comprises the following steps: the priority configuration module is used for configuring the priority of medical experts, and the priority of the medical experts is arranged from low to high in sequence; the system comprises a first communication module, a second communication module and a third communication module, wherein the first communication module is used for acquiring medical information uploaded by a user, the medical information at least comprises index information and symptom information, and the user is a doctor; the information screening module is used for screening effective medical information from the medical information; a second communication module for sending the valid medical information to the medical professional of the first priority; a feedback module for feeding back the reply content to the user through the feedback module when the medical expert of the first priority answers the valid medical information. The medical information learning system and the medical information learning method can quickly improve the medical skill level of doctors.
Description
Technical Field
The present invention relates to the field of medical data or health data, and in particular, to a system and a method for learning medical information.
Background
With the improvement of the living standard of residents in China, the medical health service requirements are continuously increased. As the population cardinality of China is huge, but the manpower and material resources of the medical service are limited, the contradiction between the medical supply and demand of China is particularly prominent.
At present, high-level medical doctors owned by China are relatively few compared with the vast population number of China, medical resources of doctors and nurses are very short, and main experts are generally concentrated in a few large hospitals in a central city, and because the main experts need to face patients from all over the country, the number of the patients is large, so that sometimes in some hospitals, common patients can be treated even for weeks or even months. In hospitals in remote areas, the owned doctor resources are more scarce, and the condition that medical treatment cannot be performed in time often occurs, so that effective medical guarantee cannot be provided for the life health of patients in time. Meanwhile, medical information such as diagnosis and treatment methods of various diseases and the like is not shared to the maximum extent due to the problems of busy daily work, untimely information circulation and the like among hospitals and doctors.
Disclosure of Invention
In view of the defects of the prior art, the invention provides a learning system and a learning method of medical information, so that a primary doctor can learn medical knowledge through a mobile terminal, the business trip learning is avoided, and the learning efficiency is improved.
In order to achieve the above and other objects, the present invention provides a medical learning system based on an intelligent mobile terminal, including:
the priority configuration module is used for configuring the priority of medical experts, and the priority of the medical experts is arranged from low to high in sequence;
the system comprises a first communication module, a second communication module and a third communication module, wherein the first communication module is used for acquiring medical information uploaded by a user, the medical information at least comprises index information and symptom information, and the user is a doctor;
the information screening module is used for screening effective medical information from the medical information;
a second communication module for sending the valid medical information to the medical professional of the first priority;
a feedback module, for feeding back the reply content to the user through the feedback module when the medical expert of the first priority answers the valid medical information;
wherein, when the medical expert of the first priority cannot answer the valid medical information, the valid medical information is fed back to the medical expert of a second priority through the second communication module, and the level of the medical expert of the second priority is greater than that of the medical expert of the first priority;
wherein the reply information comprises medical point information, diagnosis suggestion information and diagnosis thought information; the medical gist information, the diagnosis advice information, and the diagnosis idea information are stored in a storage module.
Further, the priority configuration module comprises:
a first priority configuration module for prioritizing the first time of the medical experts according to the index information and the symptom information;
and the second priority configuration module is used for carrying out second priority sequencing according to the doctor level, the doctor workload and the region where the doctor is located.
Further, the information screening module comprises:
the information screening submodule is used for screening the effective medical information from the medical information;
and the information identification submodule identifies the medical information as invalid medical information and feeds back the invalid medical information to the user when the effective medical information cannot be screened out by the information screening submodule.
The system further comprises a model building module, wherein the model building module builds a diagnosis information model according to the medical point information, the diagnosis suggestion information and the diagnosis idea information.
Further, the model building module builds a medical information model according to the index information and the symptom information.
Further, the medical information model and the diagnostic information model are stored within the storage module.
Further, the feedback module feeds back the diagnostic information model and the medical information model to the user;
when the same medical information is uploaded by a new user, the feedback module sends the diagnosis information model and the medical information model to the new user in a feedback mode, and the new user is a doctor.
Further, the storage module also records the number of times the diagnostic information model and the medical information model are referenced, respectively.
Further, when the number of times the diagnostic information model and the medical information model are referenced is greater than a threshold, the priority of the medical expert who solves the valid medical information is increased.
Further, the invention also provides a method for learning medical information, which comprises the following steps:
acquiring medical information uploaded by a user, wherein the medical information at least comprises index information and symptom information, and the user is a doctor;
screening out effective medical information from the medical information;
sending the valid medical information to a first priority medical expert;
when the medical expert with the first priority answers the effective medical information, the answering content is fed back to the user through a feedback module;
when the medical expert with the first priority cannot answer the valid medical information, the valid medical information is fed back to the medical expert with a second priority through a second communication module, and the level of the medical expert with the second priority is higher than that of the medical expert with the first priority;
wherein the reply information comprises medical point information, diagnosis suggestion information and diagnosis thought information; the medical gist information, the diagnosis advice information, and the diagnosis idea information are stored in a storage module.
In summary, the present invention provides a system and a method for learning medical information, wherein a user can upload medical information through a first communication module, and then an information screening module can screen out effective medical information from the medical information. Therefore, the effective medical information can be sent to the medical experts with the first priority through the second communication module, if the medical experts with the first priority do not answer the effective medical information, the effective medical information can be sent to the medical experts with the higher priority through the second communication module, so that the effective medical information can be answered by the medical experts with the higher priority, the effective medical information is sequentially transmitted among the medical experts with the respective priorities, the effective medical information can be solved, and the sharing of medical information and medical knowledge can be realized. If the valid medical information is solved, it may be sent to the user through a feedback module. The reply content of the medical expert for answering the valid medical information can comprise medical point information, diagnosis suggestion information and diagnosis idea information, and when the number of times that the reply content is quoted reaches a threshold value, the priority of the medical expert can be improved. According to the invention, medical information is uploaded on the mobile terminal, and the reply content is obtained on the mobile terminal, so that the medical knowledge can be quickly obtained, and the learning efficiency is improved.
Drawings
FIG. 1: the invention discloses a block diagram of a system for learning medical information.
FIG. 2: the present invention is a block diagram of a query rule.
FIG. 3: the invention relates to a block diagram of a western medicine inquiry rule.
FIG. 4: a block diagram of a symptom unit and an index unit in the present invention.
FIG. 5: block diagram of a first communication module in the present invention.
FIG. 6: the invention relates to a block diagram of a priority configuration module.
FIG. 7: the invention relates to a block diagram of an information screening module.
FIG. 8: block diagram of a model building unit in the present invention.
FIG. 9: schematic representation of a medical model in the present invention.
FIG. 10: another schematic of the medical model of the present invention.
FIG. 11: the present invention matches directed acyclic graphs of medical experts.
FIG. 12: block diagram of reply message in the present invention.
FIG. 13: block diagram of diagnostic information model in the present invention.
FIG. 14: the invention relates to a block diagram of an information management submodule.
FIG. 15: the invention relates to a block diagram of an enterprise information module.
FIG. 16: the invention discloses a flow chart of a learning method of medical information.
FIG. 17: the present invention is a flowchart of step S2 of fig. 6.
FIG. 18: block diagram of the electronic device in this embodiment.
FIG. 19: a block diagram of a computer-readable storage medium in this embodiment.
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 should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 1, the present embodiment provides a learning system 100 for medical information, and the learning system 100 may be applied to a smart mobile terminal, including but not limited to a computer console, a smart phone, a tablet computer, a notebook computer, and a wearable computer such as a watch or glasses. In some embodiments, the medical learning system 100 may also be defined as a medical interactive program that may be hosted in whole or in part on a server and electronically accessible through at least one network system. The network system may include any type of network infrastructure, such as the internet, or any other wired, wireless, and/or partially wired network. The learning system 100 may include a priority configuration module 110, a first communication module 120, an information screening module 130, a model building module 140, a second communication module 150, a feedback module 160, a storage module 170, and an information management module 180.
In this embodiment, the user may upload medical information through the first communication module 120, and the user may select the chinese medical query rule 101, the pharmacy query rule 102, the western medical query rule 103, the nursing query rule 104, the medical query rule 105, the health management query rule 106, and the public health query rule 107. The user may select at least one query rule to upload medical information, for example, the user may select both the TCM query rule 101 and the Western query rule 103. In this embodiment, the user is a doctor, such as a primary doctor or a family doctor.
As shown in fig. 3, when the user selects the western medicine inquiry rule 103, the effective medical information screened by the information screening module 130 can be embodied in the basic information unit 1031, the symptom unit 1032 and the index unit 1033, and the user directly further changes the information in the basic information unit 1031, the symptom information 1032 and the index unit 1033.
As shown in fig. 3, in the present embodiment, the basic information unit 1031 is used to fill in basic information of the patient, such as sex, age, place of residence, and work of the patient. Of course, the basic information unit 1031 may also fill in patient's disease history, family history, medication history, allergy history, etc. For example, if a patient has a history of hypertension and a genetic history of heart disease, the basic information unit 1031 may add prompt information related to hypertension and heart disease to the board information module 1031 based on the genetic history and the disease history, thereby completing the basic information unit 1031 and also contributing to the determination of the preparation by the medical specialist. In the present embodiment, if the environment of the residence of the patient is poor, the environment is also likely to cause diseases to the patient, for example, the lungs of the patient are likely to be infected, assuming that the residence of the patient is dusty. If the patient is in the dangerous industry for a long time, the work of the patient can easily cause diseases for the patient, for example, if the patient works in the heavy metal industry for a long time, the heavy metal in the blood of the patient is easy to exceed the standard.
As shown in fig. 3 to 4, in the present embodiment, the symptom unit 1032 and the index unit 1033 are associated, a large number of symptom tags are set in the symptom unit 1032, and a large number of abnormal index tags are set in the index unit 1033. In this embodiment, the symptom tags of the symptom unit 1032 include relevant symptom signs exhibited by the patient's body or mind, such as: fever, dry cough, asthenia, dyspnea, etc. The abnormal index label in the index unit 1033 includes physical indexes describing the physical condition of the patient in a quantitative manner, such as: blood pressure, body temperature, white blood cell count, hemoglobin, etc. In this embodiment, during the diagnosis of the patient by the doctor, the symptom label and the abnormality index label of the patient can be obtained.
As shown in fig. 5, in the present embodiment, the user can upload the medically related information through the voice recognition unit 106, the picture recognition unit 107, and the text editing unit 108. The speech recognition unit 106 is configured to collect speech information (e.g., a description of medically related information) of the user, convert the speech information into preset speech feature data, and send the speech feature data to the medical knowledge base 1082. In this embodiment, the speech recognition unit 106 may be any language module capable of converting the language information of the user into preset speech feature data (e.g. a language file in MP3 or WAV format), for example, an audio module connected with a microphone. Therefore, the voice recognition unit 106 can receive the voice signal transmitted from the mobile terminal or the PC terminal, convert the voice signal of the user into voice feature data required by the system by performing semantic analysis on the voice signal, and then directly transmit the voice feature data to the medical knowledge base 1082, and the voice feature data is recognized and compared by the medical knowledge base 1082.
As shown in fig. 5, in the present embodiment, the picture recognition unit 107 is configured to receive an image of an affected part of a patient acquired by an external image acquisition device (e.g., a mobile phone or a computer), and then, after performing a preprocessing operation, send the preprocessed image of the affected part of the patient to the picture recognition detection unit 109. The picture recognition detecting unit 109 is connected to the picture recognizing unit 107, and the picture recognition detecting unit 109 is configured to receive the diseased part image of the patient sent from the picture recognizing unit 107, extract and recognize diseased part image information of the patient in which diagnosis is needed, and then send the information to the medical knowledge base 1082. In this embodiment, the external image capturing device may be any device having an image capturing and transmitting function, such as a mobile phone, a tablet computer, or a computer. In the picture recognition unit 107, the preprocessing operation is preferably an illumination compensation operation, so that the quality of the image of the affected part of the patient is improved through illumination compensation, and finally the preprocessed image is transmitted to the picture recognition detection unit 109, and recognition and feature extraction are performed in the picture recognition detection unit 109.
As shown in fig. 5, in this embodiment, the text editing unit 108 is configured to upload medical related information through the text editing unit 108, the text editing unit 108 is further connected to an auxiliary information input unit 1081, a health information standard word is provided in the auxiliary information input unit 1081, and the auxiliary information input unit sends the health information standard word to the user to prompt the user to input the health information standard word. For example, when the user inputs the word "hair" through the text editing unit 108, the information auxiliary input unit 1081 displays the prompt words such as "heat", "inflammation", or "swelling" for the user to select. When the user uploads the medically relevant information through the word programming unit 108, the medically relevant information is then sent to the medical knowledge base 1082.
As shown in fig. 5, in the present embodiment, the medical knowledge base 1082 is connected to the voice recognition unit 106, the picture recognition detection unit 109, and the text editing unit 108. The medical knowledge base 1082 stores preset medical knowledge, and after the medical knowledge base 1082 receives the voice feature data sent by the voice recognition unit 106 or the image information of the affected part of the patient sent by the image recognition detection unit 109 or the text information sent by the text editing unit 108, the medical knowledge base 1082 screens out important medical information from the medical knowledge and fills the important medical information in the first communication module 120.
As shown in fig. 6, in the present embodiment, the priority configuration module 110 includes a first priority configuration module 111 and a second priority configuration module 112, and the first priority configuration module 111 and the second priority configuration module 112 may rank the priorities of the medical experts, for example, rank the priorities of the medical experts from low to high. After the user uploads the medical information through the first communication module 120, the first priority configuration module 111 may perform a first priority ranking on the medical experts according to symptom information and index information in the medical information. For example, if the symptom information is related to heart disease, the first priority configuration module 111 may screen all medical experts for the medical experts whose medical disciplines are related to heart disease, and then may perform first priority ranking on the medical experts according to a preset rule, for example, the medical experts may be prioritized by age and working time. Then, after the second priority ranking is performed on the medical experts through the second priority configuration module 112, the second priority configuration module 112 may perform the second priority ranking according to the doctor level, the doctor workload, the region where the doctor is located, the doctor activity, and the like. The higher the doctor level of the medical expert, the higher the priority of the medical expert is indicated; the more the workload of the medical expert, the lower the priority of the medical expert, i.e., the less time the medical expert has to solve the medical information online. After the second priority ranking by the second priority configuration module 112, the priority of the medical experts can be obtained, and the priority of the medical experts can be ranked from low to high. It should be noted that the embodiment may solve the medical information by a lower priority medical expert by default.
As shown in fig. 7, in the present embodiment, after the user uploads the medical information, valid medical information can be screened out from the medical information by the information screening module 130. The information filtering module 130 may further include a first filtering submodule 131, a second filtering submodule 132, and an identification submodule 133. After the user uploads the medical information, the first screening submodule 131 is configured to directly screen out valid medical information from the medical information, when the first screening submodule 131 cannot directly screen out valid medical information, the medical information is split into a plurality of pieces of medical sub-information by the second screening submodule 132, then the valid medical information is screened out from the plurality of pieces of medical sub-information, and meanwhile, if the valid medical information cannot be screened out by both the first screening submodule 131 and the second screening submodule 132, the medical information can be identified as invalid medical information by the identifying submodule 133, and then the invalid medical information is sent to the user by the first communication module 120. After the valid medical information is screened out by the first screening submodule 131 or the second screening submodule 132.
As shown in fig. 4, in the present embodiment, the valid medical information screened by the first screening submodule 121 or the second screening submodule 122 can be displayed in the symptom unit 1032 and the index unit 1033. Since the symptom unit 1032 and the index unit 1033 are linked, when one symptom label is selected, a plurality of abnormal index labels are displayed. For example, the symptom label 1 is fever, the abnormality index label 1 is blood pressure, the abnormality index label 2 is body temperature, the abnormality index label 3 is white blood cell count, and the abnormality index label 4 is hemoglobin. It should be noted that the user may also select another symptom tab in the symptom unit 1032, and may also select another abnormal index tab in the index unit 1033.
After the valid medical information is screened out, the model update sub-module 142 may then build the medical model, as shown in fig. 8-9. The medical model takes the symptom label 1 as a core, and establishes association between the symptom label 1 and the abnormal index labels 1 to 4 according to the association relationship. Specifically, for any symptom tag and any abnormal index tag, if the symptom tag causes the index tag to be abnormal, the symptom tag and the abnormal index tag are considered to have an association relationship, otherwise, the symptom tag and the abnormal index tag are considered to have no association relationship. For example, when the symptom label selected by the user is hypertension, the abnormal index label 1 selected by the user is hypertension, and the abnormal index label 2 is bleeding, it indicates that the symptom label 1 and the abnormal index label 1 are in a correlation relationship, that is, a medical correlation model of the symptom label 1, the abnormal index label 1, and the abnormal index label 2 can be established.
As shown in fig. 9-10, in the present embodiment, when the medical information is updated by the user, that is, the symptom label selected by the user changes, the model update sub-module 142 updates the medical model. Specifically, when the user selects the symptom tab 1 and the symptom tab 2, the number of abnormality index tabs also changes, and thus the medical model also changes. As can be seen from fig. 8, the medical model also takes the symptom label as a core, an association relationship exists between the symptom label 1 and the symptom label 2, and the association relationship is established between the symptom label 1 and the symptom label 2 through the abnormal index label 4. For example, the symptom label 1 is fever, the symptom label 2 is cough, and the abnormality index label 4 is blood-related abnormality. When a patient is febrile, a blood routine abnormality occurs, and when the patient is coughed, a blood routine abnormality occurs, so that fever and coughs can be associated by the blood routine abnormality. It should be noted that the medical model or the medical information model may be stored in the storage module.
As shown in fig. 1, in this embodiment, after the user uploads the medical information, the information filtering module 130 may filter out valid medical information, the priority configuring module 110 may configure the priority of the medical expert, and then the valid medical information may be sent to the medical expert with the first priority through the second communication module 150, and the medical expert with the first priority replies the valid medical information, and the medical expert with the first priority may also modify the medical model. For example, the valid medical information is about a respiratory disease, to which the medical professional of the first priority answers, and the answer information is fed back to the user through the feedback module 160. If the user approves the reply message, a confirmation may be clicked in the feedback module; if the user does not approve the reply information, the suspicious point about the reply information may be uploaded through the first communication module 120 and then solved by the medical specialist or a medical specialist of higher priority. Of course, if the medical expert of the first priority fails to resolve the valid medical information, the medical expert of the first priority may also transmit the valid medical information to the medical expert of the second priority through the second communication module 160 and then be answered by the medical expert of the second priority, which may also answer the doubt. Similarly, if the second priority medical specialist can solve the valid medical specialist, the reply content is fed back to the user through the feedback module 160, and if the second priority medical specialist fails to solve the valid medical specialist, the valid medical information may be transmitted to the third priority medical specialist through the second communication module 150. The doctor level of the medical expert of the first priority is less than the doctor level of the medical expert of the second priority. The higher the doctor level of a medical expert, the better the ability of the medical expert to solve medical information is expressed. The medical expert may also make modifications to the medical model and save the modified medical model in the storage module 170.
As shown in fig. 11, fig. 11 is shown as a directed acyclic graph matching a medical expert. After the user uploads the symptom information through the first communication module 120, the symptom information may include other information such as the disease of the case, the subject of the case, the hospital of the case, the location of the case, and the like. The priority configuration module 110 matches the symptom information with the priority of the medical expert, and then matches the symptom information with the corresponding medical expert through the second communication module 150, and according to the pre-configured priority, the learning system can form a plurality of execution paths, so as to match the most suitable medical expert for the user. For example, taking the node a1 as a root node, the execution paths formed by the learning system may include a1-a2-A3-a4, a1-a2-a5-a4, a1-A6-a4, and a1-A6-a7, and the execution paths are configured with different search rules. For example, after the user uploads the symptom information (i.e., the symptom information is input at the root node a 1), the learning system starts to search the node a2 and the node A6, and if a medical expert matching the symptom information is searched at the node a2, the node A3 and the node A5 are continuously searched, for example, an online medical expert is searched at the node A3, the node A4 is continuously searched, a medical expert matching the symptom information is searched at the node A4, if an online medical expert is not searched at the node A3, the node a2 is returned to and the node A5 is searched, and if an online medical expert is searched at the node A5, the node A4 is searched, so that a medical expert matching the symptom information is searched at the node A4. Similarly, if no medical specialist is matched in the node a4, return to the node A3 or a5, if the nodes A3 and a5 do not search for related information, return to the node a2, then return to the node a1, and search for the nodes a6 and a7 until a medical specialist matching the symptom information is searched.
As shown in FIG. 1, the storage module 170 may be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. The storage module 170 may also be on a computer readable medium, which may be a magnetically and optically readable and removable computer diskette, a fixed magnetic disk, a floppy disk drive, an optical disk drive, a magneto-optical disk drive, a magnetic tape, a hard disk drive, a solid state drive, compact flash, or non-volatile memory, and electronically distributed over a network including a cloud.
As shown in fig. 12 to 13, in the present embodiment, the reply information 400 of the medical specialist may include diagnosis idea information 401, diagnosis advice information 402, and medical gist information 403. The model building unit 140 may then build a diagnostic information model based on the medical gist information 401, the diagnostic advice information 402, the medical gist information 403, and the medical condition 404. The diagnosis information model takes the disease 404 as a core, and associates the diagnosis suggestion information 401, the diagnosis thought information 402 and the medical gist information 403 according to the association relationship. Meanwhile, when the diagnosis suggestion information 402 or the diagnosis thought information 401 or the medical gist information 403 changes, the diagnosis information model is updated as well. When the new user uploads the same medical information, the feedback module 160 may push the medical model and the diagnostic information model to the new user, so that a medical expert is not required to perform a secondary response to the same medical information. Of course, the diagnostic information model is also maintained in the storage module 170.
As shown in fig. 1, in this embodiment, the storage module 170 continuously stores medical information, medical models and diagnosis information models, that is, the storage module 170 stores more and more data, and when a new user uploads the same medically-related information, the storage module 170 may display the medical model of the medically-related information. Meanwhile, when the new user uploads similar medical related information, the storage terminal 500 may also display a medical model for the new user to select and provide for the new user to refer.
As shown in fig. 1, in this embodiment, when the medical model and the diagnostic information model are continuously referred to by other users, the storage module 170 may further record the number of times that the medical models are referred to, and when the number of times that the medical models are referred to is greater than a threshold, for example, the number of times that the medical models are referred to is greater than 300, the priority of the medical expert may be increased, that is, the medical expert has a strong ability to solve the medical information.
As shown in fig. 14-15, in this embodiment, the information management module 180 may further effectively manage medical information, the information management module 180 may include a first information management sub-module 181 and a second information management sub-module 182, the first information management sub-module 181 may establish an interlocking relationship with the first communication module 120, for example, when a primary doctor uploads the medical information through the first communication module 120, the first information management sub-module 181 may acquire the medical information, thereby determining a disease condition of the medical information in a certain area. For example, by the first information management sub-module 181 to understand the incidence of the disease at different age stages. Meanwhile, health records can be established for each patient, and health management of the patients is facilitated. In this embodiment, the health profile may include sex, age, place of residence, work, family history, medication history, disease history (past health condition and disease that has been affected in the past), symptom label, abnormality index label, medical-related model, diagnosis idea information, diagnosis advice information, and medical gist information of the patient. The first information management sub-module 181 is, for example, a local government. The second information management sub-module 182 may be connected to the enterprise information module 190, and the second information management sub-module 182 may be configured to obtain the pharmaceutical production information. Meanwhile, the curative effect of the medicine can be sent to the enterprise information module 190, so that the enterprise can conveniently obtain the medicine effect in real time.
As shown in fig. 16, the present embodiment further proposes a method for learning medical information, including:
s1: acquiring medical information for uploading, wherein the medical information at least comprises index information and symptom information, and the user is a doctor;
s2: screening out effective medical information from the medical information;
s3: sending the valid medical information to a first priority medical expert;
s4: judging whether the medical expert with the first priority answers the effective medical information;
s5: if yes, the reply content is fed back to the user through a feedback module;
s6: and if not, feeding back the effective medical information to a medical expert with a second priority through a second communication module, wherein the level of the medical expert with the second priority is higher than that of the medical expert with the first priority.
As shown in fig. 1 to 3, in step S1, the user may upload medical information through the first communication module 120, and the user selects, for example, the western medicine inquiry rule 103, and then uploads the medical information to the first communication module 120.
As shown in fig. 7 and 17, in step S2, after the user uploads the medical information, the information filtering module 130 then filters out valid medical information from the medical information. The step of the information screening module 130 screening out the valid medical information includes:
s21: judging whether effective medical information is directly screened from the medical information or not through the first screening submodule 131;
s22: judging whether medical information can be directly screened out or not;
s23: if yes, screening out effective medical information;
s24: if not, the medical information is grouped into medical sub-information by the second screening sub-module 132, and effective medical information is screened from the medical sub-information.
If valid medical information still cannot be screened out from the medical sub-information, the medical information may be identified as invalid medical information by the identification sub-module 133 and fed back to the user.
As shown in fig. 18, the present embodiment further proposes an electronic device, which includes a processor 710 and a memory 720, where the memory 720 stores program instructions, and the processor 710 executes the program instructions to implement the above-mentioned medical information learning method. The Processor 710 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; or a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component; the Memory 720 may include a Random Access Memory (RAM), and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory. The Memory 720 may also be an internal Memory of Random Access Memory (RAM) type, and the processor 710 and the Memory 720 may be integrated into one or more independent circuits or hardware, such as: application Specific Integrated Circuit (ASIC). It should be noted that the computer program stored in the memory 720 can be implemented in the form of software functional units and stored in a computer readable storage medium when the computer program is sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention.
As shown in fig. 19, the present embodiment also proposes a computer-readable storage medium 800, where the computer-readable storage medium 800 stores computer instructions 810, and the computer instructions 810 are used for causing the computer to execute the above-mentioned medical information learning method. The computer readable storage medium 800 may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or propagation medium. The computer-readable storage medium 800 may also include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Optical disks may include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-RW), and DVD.
In summary, the present invention provides a system and a method for learning medical information, wherein a user can upload medical information through a first communication module, and then an information screening module can screen out effective medical information from the medical information. Therefore, the effective medical information can be sent to the medical experts with the first priority through the second communication module, if the medical experts with the first priority do not answer the effective medical information, the effective medical information can be sent to the medical experts with the higher priority through the second communication module, so that the effective medical information can be answered by the medical experts with the higher priority, the effective medical information is sequentially transmitted among the medical experts with the respective priorities, the effective medical information can be solved, and the sharing of medical information and medical knowledge can be realized. If the valid medical information is solved, it may be sent to the user through a feedback module. The reply content of the medical expert for answering the valid medical information can comprise medical point information, diagnosis suggestion information and diagnosis idea information, and when the number of times that the reply content is quoted reaches a threshold value, the priority of the medical expert can be improved. According to the invention, medical information is uploaded on the mobile terminal, and the reply content is obtained on the mobile terminal, so that the medical knowledge can be quickly obtained, and the learning efficiency is improved.
The above description is only a preferred embodiment of the present application and a description of the applied technical principle, and it should be understood by those skilled in the art that the scope of the present invention related to the present application is not limited to the technical solution of the specific combination of the above technical features, and also covers other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept, for example, the technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the present application.
Other technical features than those described in the specification are known to those skilled in the art, and are not described herein in detail in order to highlight the innovative features of the present invention.
Claims (10)
1. A system for learning medical information, comprising:
the priority configuration module is used for configuring the priority of medical experts, and the priority of the medical experts is arranged from low to high in sequence;
the system comprises a first communication module, a second communication module and a third communication module, wherein the first communication module is used for acquiring medical information uploaded by a user, the medical information at least comprises index information and symptom information, and the user is a doctor;
the information screening module is used for screening effective medical information from the medical information;
a second communication module for sending the valid medical information to the medical professional of the first priority;
a feedback module, for feeding back the reply content to the user through the feedback module when the medical expert of the first priority answers the valid medical information;
wherein, when the medical expert of the first priority cannot answer the valid medical information, the valid medical information is fed back to the medical expert of a second priority through the second communication module, and the level of the medical expert of the second priority is greater than that of the medical expert of the first priority;
wherein the reply information comprises medical point information, diagnosis suggestion information and diagnosis thought information; the medical gist information, the diagnosis advice information, and the diagnosis idea information are stored in a storage module.
2. The system for learning medical information according to claim 1, wherein the priority configuration module comprises:
a first priority configuration module for prioritizing the first time of the medical experts according to the index information and the symptom information;
and the second priority configuration module is used for carrying out second priority sequencing according to the doctor level, the doctor workload and the region where the doctor is located.
3. The system for learning medical information according to claim 1, wherein the information filtering module comprises:
the information screening submodule is used for screening the effective medical information from the medical information;
and the information identification submodule identifies the medical information as invalid medical information and feeds back the invalid medical information to the user when the effective medical information cannot be screened out by the information screening submodule.
4. The system for learning medical information according to claim 1, further comprising a model building module that builds a diagnostic information model based on the medical gist information, the diagnostic advice information, and the diagnostic idea information.
5. The system for learning medical information according to claim 4, wherein the model building module further builds a medical information model based on the index information and the symptom information.
6. The system for learning medical information according to claim 5, wherein the medical information model and the diagnostic information model are stored in the storage module.
7. The system for learning medical information according to claim 5, wherein the feedback module feeds back the diagnostic information model and the medical information model to the user;
when the same medical information is uploaded by a new user, the feedback module sends the diagnosis information model and the medical information model to the new user in a feedback mode, and the new user is a doctor.
8. The system of learning medical information of claim 1, wherein the storage module further records a number of times the diagnostic information model and the medical information model are referenced, respectively.
9. The system for learning medical information according to claim 8, wherein when the number of times the diagnosis information model and the medical information model are referred is greater than a threshold value, the priority of the medical expert who solves the valid medical information is increased.
10. A method of learning medical information, comprising:
acquiring medical information uploaded by a user, wherein the medical information at least comprises index information and symptom information, and the user is a doctor;
screening out effective medical information from the medical information;
sending the valid medical information to a first priority medical expert;
when the medical expert with the first priority answers the effective medical information, the answering content is fed back to the user through a feedback module;
when the medical expert with the first priority cannot answer the valid medical information, the valid medical information is fed back to the medical expert with a second priority through a second communication module, and the level of the medical expert with the second priority is higher than that of the medical expert with the first priority;
wherein the reply information comprises medical point information, diagnosis suggestion information and diagnosis thought information; the medical gist information, the diagnosis advice information, and the diagnosis idea information are stored in a storage module.
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