CN116631574A - Prescription recommendation method and related equipment - Google Patents
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
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Abstract
The application provides a prescription recommendation method and related equipment, wherein the method comprises the following steps: acquiring first user information; comparing the first user information with at least one piece of second user information except the first user information based on the first user information to obtain a first similarity; determining third user information according to the first similarity based on the second user information; and determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information. By screening and recommending prescriptions of known cases to users, the case-based prescription recommendation method is more accurate than conventional reasoning in the case of various diseases and complications.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a prescription recommendation method and related devices.
Background
In the traditional medical service, after a doctor communicates with a patient for multiple times, knows the condition of the patient and gives out a diagnosis result, the doctor can further prescribe a corresponding prescription according to the diagnosis result. And doctors need to consider factors in several aspects when prescribing. For example, even for the same disease, doctors need to prescribe different medicines and dosages for different patients with different ages, sexes, physiological periods and physical characteristics. In addition, the prior art does not relate to the application of ontology technology to an intelligent recommendation system, and the reuse and sharing of knowledge cannot be realized.
Disclosure of Invention
In view of the above, the present application is directed to a prescription recommendation method and related devices.
Based on the above object, the present application provides a prescription recommendation method, comprising:
acquiring first user information;
comparing the first user information with at least one piece of second user information except the first user information based on the first user information to obtain a first similarity;
determining third user information according to the first similarity based on the second user information;
and determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information.
In one possible implementation, the first user information includes: disease conditions, disease types, and symptoms.
In one possible implementation manner, the comparing, based on the first user information, with at least one second user information other than the first user information, to obtain a first similarity includes:
comparing the disease condition in the first user information with the disease condition in the second user information to obtain a second similarity;
comparing the disease type in the first user information with the disease type in the second user information to obtain a third degree of similarity;
comparing the symptoms in the first user information with the symptoms in the second user information based on the symptoms in the first user information to obtain a fourth similarity;
and determining to obtain the first similarity based on the second similarity, the third similarity and the fourth similarity.
In one possible implementation manner, the determining, based on the second similarity, the third similarity, and the fourth similarity, to obtain the first similarity includes:
acquiring a first preset weight corresponding to the disease condition, a second preset weight corresponding to the disease type and a third preset weight corresponding to the symptom;
and correspondingly carrying out weighted calculation on the second similarity, the third similarity and the fourth similarity based on the first preset weight, the second preset weight and the third preset weight to obtain the first similarity.
In a possible implementation manner, the determining, based on the second user information, third user information according to the first similarity includes:
and taking the second user information corresponding to the first similarity with the largest similarity as the third user information, so as to determine the third user information from the second user information.
In one possible implementation manner, the determining a recommended prescription according to the third user information and recommending the recommended prescription to the user corresponding to the first user information includes:
acquiring the prescription corresponding to the third user information, and taking the prescription as the recommended prescription;
and recommending the recommended prescription to the user corresponding to the first user information.
In one possible implementation, the method further includes:
and responding to the situation that a first doctor name in the prescriptions corresponding to one piece of second user information is higher than a second doctor name in the prescriptions corresponding to the other pieces of second user information, and taking the prescriptions corresponding to the first doctor name as the recommended prescriptions.
Based on the same inventive concept, the embodiment of the application also provides a prescription recommendation device, which comprises:
the acquisition module is configured to acquire first user information;
a comparison module configured to compare with at least one second user information other than the first user information based on the first user information, resulting in a first similarity;
a determining module configured to determine third user information from the first similarity based on the second user information;
and the recommending module is configured to determine a recommending prescription according to the third user information and recommend the recommending prescription to a user corresponding to the first user information.
Based on the same inventive concept, the embodiment of the application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the prescription recommendation method according to any one of the above when executing the program.
Based on the same inventive concept, the embodiments of the present application also provide a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute any one of the above-described prescription recommendation methods.
From the above, it can be seen that the prescription recommendation method and the related device provided by the present application obtain the first user information; comparing the first user information with at least one piece of second user information except the first user information based on the first user information to obtain a first similarity; determining third user information according to the first similarity based on the second user information; and determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information. The ontology technology is effectively applied to the intelligent recommendation method, and knowledge reuse and sharing are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is a flowchart of a recipe recommendation method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a prescription recommendation device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the application.
Detailed Description
The present application will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As described in the background section, in the conventional medical service, a doctor communicates with a patient for multiple times, knows the condition of the patient, gives a diagnosis result, and then issues a corresponding prescription according to the diagnosis result. And doctors need to consider factors in several aspects when prescribing. For example, even for the same disease, doctors need to prescribe different medicines and dosages for different patients with different ages, sexes, physiological periods and physical characteristics. In addition, the prior art does not relate to the application of ontology technology to an intelligent recommendation system, and the reuse and sharing of knowledge cannot be realized.
In view of the above, the embodiment of the application provides a prescription recommendation method and related equipment, by acquiring first user information; comparing the first user information with at least one piece of second user information except the first user information based on the first user information to obtain a first similarity; determining third user information according to the first similarity based on the second user information; and determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information. The ontology technology is effectively applied to the intelligent recommendation method, and knowledge reuse and sharing are effectively achieved.
The technical scheme of the embodiment of the application is described in detail by specific embodiments.
Referring to fig. 1, the prescription recommendation method according to the embodiment of the present application includes the following steps:
step S101, acquiring first user information;
step S102, comparing the first user information with at least one piece of second user information except the first user information to obtain a first similarity;
step S103, determining third user information according to the first similarity based on the second user information;
step S104, determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information.
For step S101, in the embodiment of the present application, knowledge summarized in the clinical knowledge base needs to be converted into rules, and then the rules generated based on the knowledge are applied to the recommendation system. The ontology technology is applied to the intelligent recommendation system, so that the knowledge can be reused and shared. Domain ontologies (Domain ontologies) are specialized ontologies describing concepts and relationships between concepts in a particular Domain, providing a vocabulary of concepts and relationships between concepts in a Domain of specialized disciplines, or a theory of dominance in that Domain. By constructing the domain ontology, the explanation and expression of the concepts by the computer can be closer to the understanding of the concepts by people, and the expression and sharing of domain knowledge are realized.
First, first user information needs to be acquired, and in the embodiment of the present application, the first user information may include disease conditions, disease types, and symptoms of the user. Of course, in addition to the above three types of user information shown in the embodiments of the present application, a person skilled in the art may add other types of user information by himself, so that the final result is more accurate.
Further, based on the obtained first user information, the first similarity is obtained by comparing the obtained first user information with at least one piece of second user information except the first user information.
Specifically, the method comprises the following steps:
comparing the disease condition in the first user information with the disease condition in the second user information to obtain a second similarity;
comparing the disease type in the first user information with the disease type in the second user information to obtain a third degree of similarity;
comparing the symptoms in the first user information with the symptoms in the second user information based on the symptoms in the first user information to obtain a fourth similarity;
and determining to obtain the first similarity based on the second similarity, the third similarity and the fourth similarity.
In the embodiment of the present application, based on the obtained three kinds of first user information, the first user information is compared with corresponding kinds of data in the second user information. In the comparison process, the similarity corresponding to each type needs to be obtained by comparing the two types respectively.
Further, according to the similarity corresponding to each category obtained in the previous step, the overall first similarity of the final user is determined.
Specifically, the method comprises the following steps:
acquiring a first preset weight corresponding to the disease condition, a second preset weight corresponding to the disease type and a third preset weight corresponding to the symptom;
and correspondingly carrying out weighted calculation on the second similarity, the third similarity and the fourth similarity based on the first preset weight, the second preset weight and the third preset weight to obtain the first similarity.
In the embodiment of the present application, each user information type is preset with a corresponding preset weight, and the sum of weights of all types is 1, however, if a person skilled in the art chooses to add a user information type, the weight of each type should be adjusted correspondingly. In addition, the weight corresponding to each type is not a constant value, and a person skilled in the art can customize the corresponding weight according to own needs so as to adapt to different application scenes.
Further, after obtaining the first similarities corresponding to all the second user information, taking the second user information corresponding to the first similarities corresponding to the maximum value, taking the second user information as third user information, taking the prescription corresponding to the third user information as a recommended prescription, and recommending the recommended prescription to the user corresponding to the first user information.
In addition, the method further comprises the steps of:
and responding to the situation that a first doctor name in the prescriptions corresponding to one piece of second user information is higher than a second doctor name in the prescriptions corresponding to the other pieces of second user information, and taking the prescriptions corresponding to the first doctor name as the recommended prescriptions.
Specifically, if the prescription set included in the second user information includes the prescription of the historical patient of the doctor corresponding to the first user information, and the user is found to have good medication effect after tracing, or the doctor responsible for the prescription has high job title and abundant medication experience, the relevant prescription is preferentially used as the recommended prescription. Specifically, the above mentioned features may be set to independent priorities, after the first similarity is calculated, the second user information is comprehensively ordered by combining the conditions, the second user information corresponding to the highest value is taken as the third user information, and then the prescription corresponding to the third user information is recommended to the user as the recommended prescription.
According to the embodiment, the prescription recommendation method provided by the embodiment of the application obtains the first user information; comparing the first user information with at least one piece of second user information except the first user information based on the first user information to obtain a first similarity; determining third user information according to the first similarity based on the second user information; and determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information. The ontology technology is effectively applied to the intelligent recommendation method, and knowledge reuse and sharing are achieved.
In another possible embodiment, the application may also be applied to a WeChat public platform.
Specifically, the public platform of WeChat relies on hundreds of millions of users of WeChat, so that the WeChat has more advantages in popularization, gradually breaks through the limitation of traditional medical treatment, and touches and influences the related behaviors in the medical field. With the advent of the big data age, hospital informatization construction is also becoming increasingly common. The development of the WeChat public platform becomes an important development direction of hospital information construction. The public platform of WeChat relies on hundreds of millions of users of WeChat, so that the WeChat has more advantages in popularization, gradually breaks through the limitation of traditional medical treatment, and touches and influences the related behaviors in the medical field. The WeChat public platform is introduced into a medical system, and by virtue of the advantage of WeChat informatization popularization, the informatization construction of the hospital can be effectively promoted, the service level and the quality of the hospital can be improved, and the comprehensive benefit of the informatization construction of the hospital can be realized.
Medical informatization refers to the utilization of information technologies such as computer technology, communication technology, automation technology and the like, breaks through the limitation of the traditional medical mode, and displays high-quality, high-efficiency and personalized medical services, thereby improving the efficiency of medical innovation and medical management. The WeChat public platform is used as a new medium of information service and has good performance in all industries with information requirements. With the gradual opening of the public platform interface, enterprises and individuals can create public numbers through the platform, and realize omnibearing communication, interaction and convenient service with the attention person. The developed public number is light, flexible and low in cost.
The public platform of WeChat is a network platform for providing business services and user management services for enterprises, organizations and individuals, and comprises three micro-end propagation platforms used by the public at present. The use of the software not only creates a good environment for the publicity of the hospital in society to a certain extent, but also creates favorable conditions for the future development of the hospital. The development of a WeChat public platform has become an important development direction of informatization construction of hospitals.
The operation of the WeChat public platform requires hospitals to realize interconnection and intercommunication between an internal network and an external network, thereby promoting doctors and patients to realize good communication. At present, the working contents of the WeChat public platform in the hospital informatization construction are mainly divided into three modules, namely outpatient service, inpatient service and extension service. In order to fully play the role of the WeChat public platform in the hospital informatization platform, hospitals can promote informatization construction of the hospitals through effective propaganda popularization, and help more people to know the basic condition of the hospitals through the modes of establishing propaganda brochures, using official networks, weChat public numbers and the like.
The appointment registering function of the WeChat public platform can acquire relevant scheduling information and number source information of appointment registering in the hospital information system through an interface, and sends the operated appointment information back to the hospital information system to be stored in a system database. The design is combined with the traditional reservation registration form perfectly, and the reservation registration form is not interfered with each other. The incoming call information and the waiting information can be displayed in real time through the butt joint of the WeChat public platform and the hospital queuing call system. For the patient you can clearly know the current date of the visit, and several people are in line before your number. If the number is needed to wait for a period of time, the user can rest nearby the hospital and check the incoming call state by logging in the WeChat public platform through the mobile terminal equipment. Such a waiting environment is more comfortable.
Diagnosis and treatment with patients can be realized through a public platform of WeChat. The hospital can push information to users through the WeChat public platform, including health care information, recent epidemic notification and prevention, medication knowledge and the like, and can also provide point-to-point personalized service for patients, so as to solve the consultation problem of the patients. By taking each of
And a measure is adopted to enable patients and users to pay attention to the medical WeChat public platform. In a hospital environment, a hospital may provide 5 wireless network coverage services, which provides a basis for the application of intelligent terminals. To a certain extent improve
The treatment efficiency and the medical service quality of the hospital improve the satisfaction degree of patients and play a positive role in setting up good images of the hospital.
The existing hospital registration process is complicated and requires a great deal of manual intervention. During patient treatment
In the process, a large amount of time is spent in links such as queuing and card handling, registration and payment, queuing and medical treatment, and the like, which is time-consuming and labor-consuming, so that 0 hospitals are fully ill, and cross infection of patients is easy to occur. Medical registration system based on WeChat public number
The system can provide more convenient, quick and personalized service for patients. The user only needs to click the page menu, does not need to input any text, and is very convenient and quick.
The medical registration system adopts a WeChat client/server architecture, and is actually a browsing system
A server/server architecture. Unlike traditional registration systems, it has its own unique request response flow: firstly, 5 users must pay attention to the WeChat public number, and send user requests through the WeChat custom menu of the click system
And (5) solving. WeChat will analyze the type of menu clicked by the user and different types of menus will send different types of requests.
As can be seen from the architectural diagram of the system, the system mainly adopts an object-oriented design concept
Designs and implementations are contemplated. The system mainly comprises the following five parts: the system comprises a WeChat user, a WeChat front end 0 display layer, a WeChat server, a hospital HIS system and a data persistence layer.
The registration system is developed based on a database interface provided by Beijing electric power hospitals, so that a server of the system also needs to interact and synchronize with a hospital HIS system, and the registration system comprises inquiry of number source information, number locking, registration, payment and the like so as to ensure the consistency of a local database and hospital data.
The patient can scan the two-dimensional code through various mobile devices such as a smart phone, a tablet personal computer and the like, or pay attention to the doctor 5 study WeChat public number to obtain hospital services at any time and any place. Simple and convenient operation, is popular, so that the utility model is transported in the process
It is feasible to develop this system in a row. The MyEclipse, JDK, tomcat and MySQL databases used are both open source and free. The client uses the test number of the WeChat public platform, and most of interfaces can be used for free. The client operation can run as long as the request is sent to the WeChat public platform and is therefore economically viable.
0 furthermore, medical big data refers to big data generated in the medical field, and clinical medicine is one aspect of data sources thereof. In the clinical medicine field, the volume of medical data has been expanding in recent years due to the diversity of medical records and the heterogeneity of medical information systems, and the large-scale application of medical information systems in hospitals around the world. According to the three-layer model design of the medical cloud aiming at medical big data processing, in the third stage
The distributed computing technology is used as a core cloud computing technology, so that the processing and 5 application of medical big data in medical cloud are realized, and the core concept of cloud computing, namely 'cloud computing', is introduced into hospital informatization construction. Hidden in
The secret behind the medical big data contributes to the development of clinical medicine and medical information technology.
At present, the service of our national hospitals is better to implement the development concept centering on patients, and the informatization construction and the corresponding management method in our hospitals are realized to a certain extent. Hospital's general medicine
By introducing department content on the WeChat client, the content and the characteristics of the department 0 room can be provided for the patient who sees the doctor according to certain content, and a medical environment and a doctor seeing mode can be better provided for the patient. For patients in hospitals
The patient can perfect own information, including some online payments, bank deposit and the like, when seeing a doctor, so that the patient can pay treatment cost more conveniently, and certain medical services are provided for the patient, including printing some payment sheets and the like.
In addition, the embodiment of the application can also be used for constructing cases based on the similarity of the patient bodies by analyzing the similarity of the patient bodies 5 such as illness, disease, symptoms and the like and the known body, screening proper prescription sets
And (5) an inference model.
It should be noted that the method according to the embodiment of the present application may be performed by a single apparatus, such as a meter
A computer or server, etc. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices 0. In the case of such a distributed scenario, one of the devices may be
To perform only one or more steps of the methods of embodiments of the present application, the devices interact with each other to accomplish the methods.
It should be noted that the foregoing describes some embodiments of the present application. Other embodiments are in
Within the scope of the appended claims. In some cases, the actions recited in the claims or the steps of step 5 may be performed in a different order than in the above embodiments and still achieve desirable results.
In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
0 based on the same inventive concept, corresponding to the method of any embodiment, the application also provides
A prescription recommendation device.
Referring to fig. 2, the prescription recommendation device includes:
an acquisition module 21 configured to acquire first user information;
a comparison module 22 configured to compare with at least one second user information other than the first user information based on the first user information, resulting in a first similarity;
a determining module 23 configured to determine third user information from the first similarity based on the second user information;
and a recommending module 24 configured to determine a recommended prescription according to the third user information and recommend the recommended prescription to a user corresponding to the first user information.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
The device of the foregoing embodiment is configured to implement the corresponding prescription recommendation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the application also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the prescription recommendation method of any embodiment when executing the program.
Fig. 3 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the corresponding prescription recommendation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the present application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the prescription recommendation method according to any of the above embodiments, corresponding to the method of any of the above embodiments.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the above embodiment stores computer instructions for causing the computer to execute the prescription recommendation method according to any one of the above embodiments, and has the advantages of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the application as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, and the like, which are within the spirit and principles of the embodiments of the application, are intended to be included within the scope of the application.
Claims (10)
1. A prescription recommendation method, comprising:
acquiring first user information;
comparing the first user information with at least one piece of second user information except the first user information based on the first user information to obtain a first similarity;
determining third user information according to the first similarity based on the second user information;
and determining a recommended prescription according to the third user information, and recommending the recommended prescription to the user corresponding to the first user information.
2. The method of claim 1, wherein the first user information comprises: disease conditions, disease types, and symptoms.
3. The method of claim 2, wherein comparing the first user information with at least one second user information other than the first user information to obtain a first similarity comprises:
comparing the disease condition in the first user information with the disease condition in the second user information to obtain a second similarity;
comparing the disease type in the first user information with the disease type in the second user information to obtain a third degree of similarity;
comparing the symptoms in the first user information with the symptoms in the second user information based on the symptoms in the first user information to obtain a fourth similarity;
and determining to obtain the first similarity based on the second similarity, the third similarity and the fourth similarity.
4. The method of claim 3, wherein the determining the first similarity based on the second similarity, a third similarity, and the fourth similarity comprises:
acquiring a first preset weight corresponding to the disease condition, a second preset weight corresponding to the disease type and a third preset weight corresponding to the symptom;
and correspondingly carrying out weighted calculation on the second similarity, the third similarity and the fourth similarity based on the first preset weight, the second preset weight and the third preset weight to obtain the first similarity.
5. The method of claim 1, wherein the determining third user information from the first similarity based on the second user information comprises:
and taking the second user information corresponding to the first similarity with the largest similarity as the third user information, so as to determine the third user information from the second user information.
6. The method of claim 1, wherein determining a recommended prescription from the third user information and recommending the recommended prescription to the user corresponding to the first user information comprises:
acquiring the prescription corresponding to the third user information, and taking the prescription as the recommended prescription;
and recommending the recommended prescription to the user corresponding to the first user information.
7. The method according to claim 1, characterized in that the method further comprises:
and responding to the situation that a first doctor name in the prescriptions corresponding to one piece of second user information is higher than a second doctor name in the prescriptions corresponding to the other pieces of second user information, and taking the prescriptions corresponding to the first doctor name as the recommended prescriptions.
8. A prescription recommendation device, comprising:
the acquisition module is configured to acquire first user information;
a comparison module configured to compare with at least one second user information other than the first user information based on the first user information, resulting in a first similarity;
a determining module configured to determine third user information from the first similarity based on the second user information;
and the recommending module is configured to determine a recommending prescription according to the third user information and recommend the recommending prescription to a user corresponding to the first user information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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| CN202211552304.XA CN116631574A (en) | 2022-12-05 | 2022-12-05 | Prescription recommendation method and related equipment |
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| CN202211552304.XA CN116631574A (en) | 2022-12-05 | 2022-12-05 | Prescription recommendation method and related equipment |
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