CN115547479B - Intelligent medical control method and system based on 5G message - Google Patents
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Abstract
The invention relates to an intelligent medical control method and system based on 5G messages, wherein the method comprises the following steps of S1: the user accesses the user interface to reserve a doctor; step S2: automatically issuing a 5G confirmation message to the user terminal; and step S3: sending a data collection 5G message to collect data; and step S4: automatically sending a diagnosis 5G message after the diagnosis time is up; step S5: the online consulting room carries out a visit based on the collected data. On the basis of comprehensively and effectively protecting the safety of data in the whole data chain, the invention balances the contradiction between the storage space and the acquisition efficiency, improves the user experience of intelligent medical treatment, reduces the resource waste caused by invalid medical service, and finally improves the efficiency of on-line consultation.
Description
Technical Field
The invention belongs to the technical field of intelligent medical treatment, and particularly relates to an intelligent medical treatment control method and system based on 5G messages.
Background
With the acquisition of various health medical data becoming more stereoscopic, more and more channels for acquiring data are provided, and single entry is changed into clustered collection. Data generated in the 5G + Internet medical application digitization process is more comprehensive, complete and fine, and meanwhile, the data security, the data utilization, the data discovery and the data pushing of the whole industry are more important.
The internet medical treatment becomes a popular 'emergency choice' due to medical requirements in special periods, many consumers begin to contact the internet medical treatment, and the online inquiry and other modes are adopted to replace the offline hospital outpatient visit, so that the risk of cross infection is avoided, and the pressure of the prevention and control of the epidemic situation of the hospital is relieved. If the epidemic situation is a catalyst for internet medical care to the public, how to continuously stabilize the internet medical awareness of the public in the post-epidemic situation era, establish the public confidence in internet medical care and form a good mutual trust relationship for consumption remains one of the most concerned issues of internet medical practitioners and relevant policy makers. Through internet medical treatment, smart medical treatment modes such as cloud medical treatment and cloud report are generated; and the professional image diagnosis cloud platform supports remote image consultation on the Internet.
From the perspective of data, medical data has extremely strong privacy, and once leaked, the medical data can bring negative effects to life and work of patients. Meanwhile, a large amount of medical data is provided for third party development and testing, and personal privacy data is easily leaked. Thus, medical data is typically stored in a distributed manner at each medical facility and can only be accessed by users with the appropriate permissions. The provision of the 5G message enhances the message service between individuals and applications, realizes 'message as a service', the 5G message can output personalized services of smart medical treatment based on various disease types to users in rich media modes such as characters, voice, tabs and the like, and the users can intuitively and conveniently enjoy the functions of finding doctors, seeing science popularities, online consultation, checking cloud reports and the like through message windows. The user mobile phone terminal directly uses the wireless mobile network for access without login, and the user information of the mobile phone card number of the operator is connected, so that a plurality of complicated processes of registration of a real network station, information filling of the user and the like are omitted.
Then, how to effectively provide intelligent medical services based on rich media resources which can be provided by 5G messages, rapid development of current big data technologies and accumulation of a large amount of medical data on the basis of guaranteeing safety and independence of medical data of users is a technical problem to be solved. On the basis of comprehensively and effectively protecting the safety of data in the whole data chain, the invention balances the contradiction between the storage space and the acquisition efficiency, improves the user experience of intelligent medical treatment, reduces the resource waste caused by invalid medical service, and finally improves the efficiency of on-line consultation.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a method and a system for intelligent medical control based on 5G messages, wherein the method comprises:
step S1: the user accesses the user interface to reserve a doctor;
step S2: automatically issuing a 5G confirmation message to the user terminal; the user terminal knows about the detailed introduction of the specialized diseases of the reserved doctor and the summary introduction of the related diseases based on the doctor confirmation 5G message; the user judges whether to confirm the reservation doctor based on the detailed introduction and the summary introduction, if so, a reservation request is generated based on the user confirmation, and the next step is carried out; otherwise, returning to the step S1;
and step S3: sending a data collection 5G message to collect data; the method specifically comprises the following steps: determining a collection template based on the reservation request, wherein the data collection 5G message comprises the collection template, and performing data collection based on the collection template;
and step S4: automatically sending a visit 5G message after the visit time is up; the user enters an online consulting room by clicking the consultation 5G message;
step S5: the on-line consulting room carries out the diagnosis based on the collected data; specifically, the method comprises the following steps: loading data in sequence and in advance based on a collection template and big data information containing historical doctor visit records; under the condition of high using probability, the data loading mode is changed to actively push the data to the user terminal and the doctor terminal; under the condition of low use probability, loading partial data or not loading in advance; under the condition of using probability, loading all or part of data in advance;
the step S5 specifically includes the following steps:
step S51: loading and collecting user identity information in the template in advance, and presenting the user identity information in an online consulting room before consulting;
step S52: acquiring required data based on a collection template and presenting the data according to the selection of a doctor and/or a user; collecting data used in the current diagnosis in real time and arranging the data according to a time sequence to form a data sequence; extracting a data characteristic combination of each data in the data sequence; the data feature combination comprises one or more data features; each data sequence corresponds to a data characteristic combination sequence;
step S53: determining the next data feature combination based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence set, and acquiring the next data set based on the next data feature combination and a collection template; loading the next data set in advance;
determining the next data feature combination based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence set: the method specifically comprises the following steps: the current doctor historical data feature combination sequence set comprises one or more sequences; comparing the sequences one by one to obtain a maximum matching sequence; the maximum matching sequence comprises a matching portion and a subsequent combining portion directly contiguous with the matching portion; using the first of the subsequent combination parts as the next data characteristic combination; taking the subsequent combination as a subsequent combination of one or more data characteristics;
step S6: sending a viewing report 5G message after the visit is finished; by clicking on the review report 5G message, the review conclusion can be seen and the electronic case can be picked up.
Further, the user accesses the user interface through the actively pushed user interface link.
Further, a presentation identifier of the data collection template is presented on one or both sides of the online consulting room user interface, and the data collection template is presented when the user or doctor clicks the identifier.
Further, the detailed introduction and the summary introduction are included in a 5G message; the user automatically learns the detailed description and the summary description by clicking on the 5G message.
A smart medical control system based on 5G messages, comprising: the system comprises a doctor terminal, a user terminal, a cloud server and a support server; the intelligent medical control system based on the 5G message is used for realizing the intelligent medical control method based on the 5G message.
A processor configured to execute a program, wherein the program executes the intelligent medical control method based on 5G messages.
An execution device comprising a processor coupled to a memory, the memory storing program instructions that when executed by the processor implement the 5G message-based smart medical control method.
A computer-readable storage medium containing a program which, when run on a computer, causes the computer to execute the intelligent medical control method based on 5G messages.
A cloud server is characterized in that the cloud server is configured to execute the intelligent medical control method based on the 5G message.
The beneficial effects of the invention include:
(1) Based on the relevance among all layers of diseases, the user is guided to make subsequent appointment by utilizing the judgment information of the user, so that the user experience of intelligent medical treatment is greatly improved, and the resource waste caused by invalid medical service is reduced;
(2) When data is acquired, a dynamic template matched with the current visit is formed in a template classification and template supplement mode, all aspects of factors are considered comprehensively, the data is acquired and quantitatively evaluated based on data characteristics and big data analysis results of the current visit doctor, such as the visit speciality, the mode and the habit, and the balance is made between the availability and the acquisition cost, so that the acquisition efficiency is improved;
(3) By appointing the acquisition time and the acquisition time limit, the data security is ensured, and meanwhile, the movable virtual online consulting room is supported without generating a large amount of data transmission and transfer, so that the dependence on a local storage space is reduced;
(4) The provided data feature combination sequence contains disease features and fuses doctor habit information at the same time, has multi-feature combinations and time factors, fully utilizes data use convenience brought by advanced authentication reservation use by simultaneously combining and loading mode change and loading quantity change, and obtains data which can be obtained only by authentication without interrupting the seeing of a user; the efficiency of seeing and examining experience and data provision is greatly improved, and the efficiency of on-line consultation is finally greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, are not intended to limit the invention, and:
fig. 1 is a schematic diagram of a 5G message-based intelligent medical control method according to the present invention.
Fig. 2 is a schematic diagram of a delivered data collection 5G message provided by the present invention.
FIG. 3 is a diagram illustrating data collection based on a collection template according to the present invention.
Detailed Description
The invention will be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and the description are only for the purpose of illustrating the invention, and are not to be construed as limiting the invention
As shown in fig. 1, the present invention provides a method and a system for intelligent medical control based on 5G messages, wherein the method comprises the following steps:
step S1: the user accesses the user interface to order the doctor;
preferably, the following components: the user interface is provided by a support server of the intelligent medical system based on the 5G message; the method can also be conveniently used for the elderly by using a reservation mode based on 5G messages;
step S2: automatically issuing a 5G confirmation message of a doctor to a user terminal; the user terminal knows the detailed introduction of the special symptoms of the appointed doctor and the general introduction of the related symptoms based on the 5G message; the user judges whether to confirm the reservation doctor based on the detailed introduction and the summary introduction, if so, a reservation request is generated based on the user confirmation, and the next step is carried out; otherwise, returning to the step S1;
a great problem of the user in ordering the doctor is that the doctor cannot be correctly associated with the disease of the user and the department type to be ordered or the disease type, so that the ordered doctor is probably not suitable or not most suitable; that is to say, the initial diagnosis conclusion of the user is often incorrect, but the initial diagnosis conclusion of the user is often associated with a real disease, so that the method has certain reference significance, the association is fully utilized, namely, the judgment information of the user on the basis of knowing the association is utilized to guide the user to make a subsequent appointment, the user experience of intelligent medical treatment is greatly improved, and the resource waste caused by invalid medical service is reduced;
the confirmation 5G message comprises three introduction parts, and the three introduction parts are presented hierarchically based on user feedback; the first introduction part is a detailed introduction comprising a disease or a disease special for a reserved doctor; through the information of the part, the user can fully know whether the symptom of the user is matched with the current doctor; if the matching is carried out, the user can directly carry out reservation confirmation by skipping the first introduction part; otherwise, the user may continue to learn about the remaining introductory portion; the second introduction part is a summary introduction comprising covering diseases and symptoms of departments where the appointment doctor is located; because the same or similar diseases are often concentrated in the same department, the disease types covered by the department where the doctor is reserved are most probably related to the diseases of the user, and the user can quickly acquire the matched doctor through the information of the part; the user can also directly make reservation confirmation by skipping the second introduction part; the third introduction part is a summary introduction of symptoms related to symptoms which are good at the current appointment doctor; the related diseases are one or more, and the third introduction part comprises names and summary introduction thereof about the one or more disease types; extracting keywords for the adept diseases and keywords for the related diseases to be compared so as to obtain keywords hit multiple kinds as related disease types; of course, other acquisition modes may also be employed, such as: acquiring the most similar disease symptoms in an expert labeling mode; more preferably, the related symptoms are manually set, and the relevance is set according to the wrong habits of users or the adjustment condition among the consulting departments;
preferably: the detailed description and the summary description are contained in a 5G message; the user can automatically know detailed introduction and summary introduction by clicking the 5G message;
preferably: obtaining a user's attention to a medical condition adept by a reservation doctor in a process of generating a reservation request based on user confirmation
And sorting and putting the diseases and the attention degrees thereof into a reservation request after associating; then when the user is simply looking at a condition, the focus for that condition may be set to 1; each physician is often adept at more than one condition, the more subdivided, the more types of conditions it is adept at; such distinction is difficult, and when the disease itself is not clear and the user does not know the symptom, sufficient and appropriate data can be prepared by setting the attention as a weight;
and step S3: sending a data collection 5G message to collect data; the method specifically comprises the following steps: as shown in fig. 2, a collection template is determined based on the reservation request, and the data collection 5G message includes the collection template; the data collection aims at patients and doctors, and the final aim is to facilitate the smooth operation of the diagnosis;
the step S3 specifically includes the following steps:
step S31: extracting symptoms and attention degrees thereof from the reservation request;
step S32: acquiring a collecting template corresponding to a disease state with the highest attention as a main template, and acquiring a collecting template corresponding to a disease state with nonzero attention as an auxiliary template; supplementing the main template based on the auxiliary template to obtain a collection template; specifically, for each auxiliary template, determining items which are contained in the auxiliary template but not contained in the main template to be added into the main template;
preferably: setting a corresponding collecting template for each disease in advance;
step S33: collecting data based on the collecting template; the collection template comprises a user filling part, historical diagnosis information, a user preparation part, an authentication acquisition part and/or a network data part; wherein: each part of information comprises one or more records, and different types of user data in each part of information are organized according to the records; the steps specifically comprise the following steps; for example: the user preparation part comprises a first position image information record, a second position image information record and the like;
step S331: the user fills in user identity information and historical diagnosis records; wherein: the user identity information comprises: personal identity information (identity cards, social security cards, mobile phones and the like), network identity information (system account numbers, mailbox addresses and the like), biological identification information (auricles, irises and the like); the historical visit information includes: the medical record comprises a visit record, a hospitalization record, a nursing record, a medication record, a past medical history and the like;
step S332: the user preparation part comprises information which needs to be prepared for the visit by the user; the user can make information completion preparation (such as reservation inspection and the like) based on the prompt of the user preparation part of information, and after the information preparation by the user is completed, corresponding data or a data acquisition address is uploaded to a collection template or is presented on the current day of the inspection and the diagnosis; as shown in fig. 3;
step S333: determining the association degree between each historical visit record and the current visit; when the correlation degree is larger than a first preset value, acquiring an access address according to the historical diagnosis record, and acquiring data related to the record based on the access address and the user identity information;
determining the degree of association between each historical visit record and the current visit
The method specifically comprises the following steps: the degree of correlation is calculated using the following formula
；
Wherein: t is the historical visit record number;
time weight corresponding to the diagnosis time and the historical diagnosis record time difference is obtained, and the weight is larger when the time difference is smaller, and vice versa;
the grade weight of the observation place is obtained, the higher the grade of the observation place corresponding to the historical observation record is, the higher the weight is, and vice versa;
the attention degree of the user to the disease c is shown in the current diagnosis;
is the concern of the historical visit record on the disease condition c; when the historical visit record is made for a single disease, the attention is only numerical on the disease, and the attention on other diseases is 0;
is a medical appointmentLiving under specific seeing condition
Utilization of data of the same characteristics;
preferably, the following components: the specific diagnosis condition is that a doctor is reserved to see a specific disease; the specific disorder is a disorder type with a degree of attention greater than a second preset value; acquisition of appointment physician inclusion in targeting one or several conditions by historical data analysis
Utilization of data of the same characteristics; wherein:
is the pth data feature;
(ii) a The data characteristics comprise data carrying modes (images, audios and images), acquisition modes (CT, PET, X-ray and the like), action parts (lung, heart, kidney and the like) and the like; automatically screening the seeing and examining records from the perspective of a doctor according to the utilization rate;
according to the invention, a dynamic template matched with the current diagnosis is formed through template classification and template supplement modes during data acquisition, various factors influencing data utilization are fully considered, quantitative measurement is carried out on data acquisition based on data characteristics and the large data analysis result of the characteristic, mode and habit of the current doctor during diagnosis, and measurement is carried out between availability and acquisition overhead, so that the acquisition efficiency is improved; a high-efficiency proceeding foundation is provided for online diagnosis;
for example: the user can obtain image data or medical advice information in a mode of accessing a remote server of a relevant clinic hospital when the facial authentication is passed by seeing the patient with the disease at the last time and the association degree is higher than a preset value;
preferably: the user can complete the acquisition of partial data in a filling mode;
step S334: acquiring a network material part irrelevant to the user from a local place or a network and filling the network material part in a template; the filling mode comprises two modes, namely directly filling data into corresponding records and adding acquisition addresses of data in the records; when the data is large, the second mode can be adopted; the acquisition address is a local address or a remote server address; the related data acquired by the network is convenient for both parties to understand and see the doctor smoothly;
preferably: when the acquisition address is a remote address, performing access authentication on the acquisition data in a remote server through the user identity information; after the authentication is passed, the access party and the remote server appoint acquisition time, and the appointed acquisition time is slightly advanced with the appointed visit time; after the appointed acquisition time is up, the data can be acquired by the access identifier without re-authentication; wherein: the slightly advanced time refers to the time difference between the appointed acquisition time and the appointed seeing and diagnosing time is within a preset time range; the access identifier is an access party identifier or encrypted identifier information which is generated at the time of appointment and is commonly recognized by both parties; the access party is a support server;
preferably: the access party and the remote server also appoint the valid period of the data, and the data is converted into invalid data and cannot be used continuously after the period is up; by appointing the acquisition time and the acquisition time limit, the on-line diagnosis room is supported by the non-fixed physical device while the data security is ensured, and a large amount of data transmission and transfer are not required, so that the dependence on a local storage space is reduced;
and step S4: automatically sending a visit 5G message after the visit time is up; the user enters an online consulting room by clicking the consultation 5G message;
preferably: an online consulting room is established on a cloud server before the visit 5G message is issued; loading a data collection template after a user enters an online consulting room; authenticating the entered user based on the user identity information in the data collection template, continuing to see the doctor after the authentication is passed, and stopping the user's seeing the doctor when the authentication is not passed;
step S5: the on-line consulting room carries out the diagnosis based on the collected data; specifically, the method comprises the following steps: loading data in sequence and in advance based on a collection template and big data information containing historical doctor visit records; under the condition of high probability of being used, the data loading mode is changed to actively push the data to the user terminal and the doctor terminal; under the condition of low use probability, loading partial data or not loading in advance; and under the condition of using probability, loading all or part of data in advance;
the step S5 specifically includes the following steps:
step S51: loading and collecting user identity information in the template in advance, and presenting the user identity information in an online consulting room before consulting;
preferably: displaying the acquisition identification of the data collection template on one side or two sides of an online consulting room interface, and presenting the data collection template when a user or a doctor clicks the identification;
step S52: acquiring required data based on a collection template and presenting the data according to doctor and/or user selection; collecting data used in the current diagnosis in real time and arranging the data according to a time sequence to form a data sequence; extracting the data characteristic combination of each data in the data sequence; the data feature combination comprises one or more data features; each data sequence corresponds to a data characteristic combination sequence; for example:
(ii) a The collection is therefore done in real time, and therefore the sequence is also dynamically changing in real time; thus, both step S52 and step S53 are performed dynamically in real time; the matching and loading of step S53 can be performed each time a change of material occurs;
step S53: determining the next data feature combination based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence set, and acquiring the next data set based on the next data feature combination and a collection template; loading the next data set in advance;
and determining the next data feature combination based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence set: the method specifically comprises the following steps: current historical data of doctorThe material characteristic combination sequence set comprises one or more sequences; comparing the sequences one by one to obtain a maximum matching sequence; the maximum matching sequence comprises a matching portion and a subsequent combining portion directly contiguous with the matching portion; using the first of the subsequent combination parts as the next data characteristic combination; using the subsequent combination as a subsequent combination of one or more data characteristics; for example: the data feature combination sequence is
The maximum matching sequence is
Wherein the matching part is:
the subsequent part then comprising
Then, it is
Is the next data feature combination;
the maximum matching sequence refers to the sequence with the largest number of matched feature combinations in the sequences of the set; the matching modes comprise completely consistent matching modes with strict requirements on feature combination and sequence, or slightly redundant feature combinations and/or matching modes with basically consistent sequence; substantially uniform is to allow some feature combinations. Or the order between features in a diagnostic combination, or a subset of feature combinations is not completely consistent;
acquiring a next data set based on the next data feature combination and the collection template, and loading the next data set in advance; the method specifically comprises the following steps: acquiring data matching all the data characteristics in the next data characteristic combination from the collecting template to form a next data set; acquiring corresponding data from a local or remote server based on the acquisition mode in the collection template; taking the number of the feature combinations in the matching sequence as a matching length, pushing the next data set to a doctor terminal and a user terminal when the matching length is larger than a first preset length, and otherwise, loading the next data set to an online diagnosis room in advance; for example: the matching length is 3; because the visit time interval belongs to the appointed time range formed by the remote server, the data which can be obtained only by authentication can be obtained without interrupting the visit of the user, and the loading or pushing of the data which is performed in advance can be supported; the efficiency of seeing and examining experience and providing data is greatly improved;
the historical data feature combination sequence is similar to the data feature combination sequence, but is obtained based on the historical visit record of the current doctor; for example: acquiring the use sequence of the data of the current doctor in the historical diagnosis process corresponding to the historical diagnosis record in advance to form one or more historical data use sequences; corresponding to the historical data use sequence is one or more historical data feature combination sequences; because of the different number of data used during the review process, the length of each sequence is the same or different;
alternatively: determining one or more subsequent data feature combinations based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence; obtaining a subsequent one or more data sets based on the subsequent one or more data feature combinations and the collection template; determining to load all data sets or part of the data sets in advance based on the matching length of the data feature combination sequence; the method comprises the following specific steps: the current doctor historical data feature combination sequence set comprises one or more sequences; comparing the sequences one by one to obtain a maximum matching sequence; the maximum matching sequence comprises a matching portion and a subsequent combining portion directly contiguous with the matching portion; using the subsequent combination as a subsequent combination of one or more data characteristics; obtaining data matching each combination of the subsequent one or more data feature combinations from the collection template to respectively form a next one or more data sets corresponding to each combination; when the matching length is larger than a second preset length, loading all data sets; when the matching length is at the first preset length and the second preset lengthLoading part of data set when the length is between the preset lengths; the partial data set is pushed to a doctor terminal and a user terminal; when the matching length is smaller than the first preset length, the partial data set is loaded to an online diagnosis room in advance; wherein: the first preset length is smaller than the second preset length; for example: the subsequent part comprises
The number of the corresponding data sets is 3, and the number of the loading sets and the quality of the loading mode can be reduced according to the matching condition;
preferably: the partial data set is a data set corresponding to the next data characteristic combination;
preferably, the following components: determining whether to record the data set in advance and the loading mode of the data set based on the hit rate of the data characteristic combination sequence; the loading mode is loaded to a temporary storage space used by an on-line doctor room and is loaded to a doctor terminal and a user terminal in a pushing mode;
step S6: sending a viewing report 5G message after the visit is finished; the 5G message is clicked to see the diagnosis conclusion and draw the electronic case;
the method further comprises the following steps: step S7: judging whether the follow-up time is reached, if so, sending a reminding message to remind a user of making an appointment; after the user finishes the reservation, the flow from the step S1 is started;
based on the same inventive concept, the invention provides an intelligent medical control system based on 5G messages, which comprises: the system comprises a doctor terminal, a user terminal, a cloud server and a support server; the intelligent medical control system based on the 5G message is used for realizing the intelligent medical control method based on the 5G message;
more specifically:
the cloud server provides hardware resources required by an online consulting room;
the doctor terminal is used for a doctor to visit an online consulting room; the user terminal is used for accessing the online consulting room by the user;
the support server is used for providing a user interface and is used for performing data interaction with the cloud server to create an online consulting room; the system is also used for sending 5G messages, storing and constructing a collection template, and supporting data supply and diagnosis in an online consulting room based on the collection template;
preferably: the system also comprises a big data server, a data analysis module and a data analysis module, wherein the big data server is used for storing the use condition of the data in the historical diagnosis process of the doctor, including the used data and the use sequence thereof, analyzing the use condition and acquiring a data characteristic combination sequence;
preferably, the following components: classifying and sorting the use condition of the data according to the disease types, and correspondingly classifying and sorting the data feature combination sequence; correspondingly, in the matching process in step S53, a history data feature combination sequence of a corresponding type may also be selected based on the disease type to narrow the matching range;
preferably: the doctor terminal and the user terminal are multiple;
the data feature combination sequence of the invention simultaneously contains information of disease features and doctor habits, has multi-feature combination and time factors, fully utilizes data use convenience brought by advanced authentication by simultaneously combining loading mode change and loading quantity change, balances contradiction between storage space and acquisition efficiency, and finally greatly improves the efficiency of online consultation;
the terms "big data server", "cloud server", "user terminal" or "doctor terminal" encompass all kinds of devices, apparatuses and machines for processing data, including for example programmable processors, computers, systems on chip, or a plurality or combination of the above. The apparatus can comprise special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform execution environment, a virtual machine, or a combination of one or more of the foregoing. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.
A computer program (also known as a program, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. The computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subroutines, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An intelligent medical control method based on 5G messages, which is characterized by comprising the following steps:
step S1: the user accesses the user interface to order the doctor;
step S2: automatically issuing a 5G confirmation message of a doctor to a user terminal; the user terminal knows the detailed introduction of the special diseases of the appointed doctor and the general introduction of the related diseases based on the confirmation 5G message of the doctor; the user judges whether to confirm the reservation doctor based on the detailed introduction and the summary introduction, if so, a reservation request is generated based on the user confirmation, and the next step is carried out; otherwise, returning to the step S1;
and step S3: sending a data collection 5G message to collect data; the method comprises the following specific steps: determining a collection template based on the reservation request, wherein the data collection 5G message comprises the collection template, and performing data collection based on the collection template;
the step S3 specifically includes the following steps:
step S31: extracting symptoms and attention degrees thereof from the reservation request;
step S32: acquiring a collecting template corresponding to a disease state with the highest attention degree as a main template, and acquiring a collecting template corresponding to a disease state with non-zero attention degree as an auxiliary template; supplementing the main template based on the auxiliary template to obtain a collecting template;
step S33: collecting data based on the collecting template; the collection template comprises a user filling part, historical diagnosis information, a user preparation part, an authentication acquisition part and/or a network data part; wherein: each part of information comprises one or more records, and different types of user data in each part of information are organized according to the records; the step S33 specifically includes the following steps;
step S331: the user fills in user identity information and historical diagnosis records; wherein: the user identity information comprises: personal identity information, network identity information, and/or biometric information; the historical visit information includes: visit records, hospitalization records, care records, medication records, and/or past medical history;
step S332: the user preparation part comprises information which needs to be prepared by the user for the visit;
step S333: determining the association degree between each historical visit record and the current visit; when the correlation degree is larger than a first preset value, acquiring an access address according to the historical diagnosis record, and acquiring data related to the record based on the access address and the user identity information;
step S334: acquiring a network material part irrelevant to the user from the local or network and filling the network material part in a collection template;
and step S4: automatically sending a visit 5G message after the visit time is up; the user enters the on-line consulting room by clicking the consulting 5G message;
step S5: the on-line consulting room carries out the diagnosis based on the collected data; specifically, the method comprises the following steps: loading data in sequence and in advance based on a collection template and big data information containing historical doctor visit records; under the condition of high using probability, the data loading mode is changed to actively push the data to the user terminal and the doctor terminal; under the condition of low use probability, loading partial data or not loading in advance; under the condition of using probability, loading all or part of data in advance;
the step S5 specifically includes the following steps:
step S51: loading and collecting user identity information in the template in advance, and presenting the user identity information in an online consulting room before consulting;
step S52: acquiring required data based on a collection template and presenting the data according to doctor and/or user selection; collecting data used in the current diagnosis in real time and arranging the data according to a time sequence to form a data sequence; extracting a data characteristic combination of each data in the data sequence; the data feature combination comprises one or more data features; each data sequence corresponds to a data characteristic combination sequence;
step S53: determining the next data feature combination based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence set, and acquiring the next data set based on the next data feature combination and a collection template; loading the next data set in advance;
and determining the next data feature combination based on the matching condition of the data feature combination sequence and the current doctor historical data feature combination sequence set: the method comprises the following specific steps: the current doctor historical data feature combination sequence set comprises one or more sequences; comparing the sequences one by one to obtain a maximum matching sequence; the maximum matching sequence comprises a matching portion and a subsequent combining portion directly contiguous with the matching portion; using the first of the subsequent combination parts as the next data characteristic combination; taking the subsequent combination as a subsequent combination of one or more data characteristics;
step S6: sending a viewing report 5G message after the visit is finished; by clicking on the review report 5G message, the review conclusion can be seen and the electronic case can be picked up.
2. The intelligent medical control method based on 5G messages, according to claim 1, characterized in that the user accesses the user interface through an actively pushed user interface link.
3. The intelligent medical control method based on 5G messages, according to claim 2, is characterized in that the presented identification of the collection template is displayed on one side or two sides of the online consulting room user interface, and when the user or doctor clicks the identification, the collection template is presented.
4. The intelligent medical control method based on 5G messages, according to claim 3, characterized in that the data used in the current visit are collected in real time and arranged in time sequence to form a data sequence, and the data sequence is dynamically changed in real time.
5. The intelligent medical control method based on 5G message according to claim 4, wherein the detailed introduction and the summary introduction are included in the 5G message; the user automatically learns the detailed description and the summary description by clicking on the 5G message.
6. An intelligent medical control system based on 5G messages, comprising: the system comprises a doctor terminal, a user terminal, a cloud server and a support server; the intelligent medical control system based on 5G messages is used for realizing the intelligent medical control method based on 5G messages in any one of claims 1-5.
7. A processor, wherein the processor is configured to run a program, wherein the program is executed to execute the intelligent medical control method based on 5G message according to any one of claims 1-5.
8. An execution device comprising a processor coupled to a memory, the memory storing program instructions that, when executed by the processor, implement the 5G message-based intelligent medical control method of any one of claims 1-5.
9. A computer-readable storage medium, characterized by comprising a program which, when run on a computer, causes the computer to execute the 5G message-based smart medical control method according to any one of claims 1 to 5.
10. A cloud server, characterized in that the cloud server is configured to execute the intelligent medical control method based on 5G message according to any one of claims 1-5.
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