CN113130052A - Doctor recommendation method, doctor recommendation device, terminal equipment and storage medium - Google Patents
Doctor recommendation method, doctor recommendation device, terminal equipment and storage medium Download PDFInfo
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- CN113130052A CN113130052A CN202110253971.7A CN202110253971A CN113130052A CN 113130052 A CN113130052 A CN 113130052A CN 202110253971 A CN202110253971 A CN 202110253971A CN 113130052 A CN113130052 A CN 113130052A
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
The application is applicable to the technical field of data processing, and provides a doctor recommendation method, a doctor recommendation device, terminal equipment and a storage medium, wherein the doctor recommendation method comprises the following steps: acquiring personal information of a target resident, wherein the personal information of the target resident comprises demographic information and clinic history information of the target resident; inputting the personal information of the target residents into a trained first decision tree to obtain target treatment categories, wherein the target treatment categories are treatment categories matched with the personal information of the target residents; determining a doctor list according to the visit category, wherein the doctor list comprises personal information of at least one target doctor; recommending the doctor list to the target resident. Through the application, matched doctors can be recommended to residents.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a doctor recommendation method, a doctor recommendation device, a terminal device and a storage medium.
Background
At present, residents need to go to a hospital to find doctors in relevant departments by themselves or find the main treatment field of the doctors on an official network of the hospital, and determine the doctors relevant to the illness condition to see a doctor. Because resident's medical knowledge is deficient, lacks the understanding to the doctor, has great randomness and blindness when seeking the doctor, leads to finding suitable doctor difficultly.
Disclosure of Invention
The application provides a doctor recommendation method, a doctor recommendation device, terminal equipment and a storage medium, so as to recommend doctors matched with the doctor to residents.
In a first aspect, an embodiment of the present application provides a doctor recommendation method, where the doctor recommendation method includes:
acquiring personal information of a target resident, wherein the personal information of the target resident comprises the demographic information and the clinic history information of the target historical resident;
inputting the personal information of the target residents into a trained first decision tree to obtain target treatment categories, wherein the target treatment categories are treatment categories matched with the personal information of the target residents;
determining a doctor list according to the target clinic category, wherein the doctor list comprises personal information of at least one target doctor;
recommending the doctor list to the target resident.
In a second aspect, an embodiment of the present application provides a doctor recommendation apparatus, including:
the system comprises an information acquisition module, a service management module and a service management module, wherein the information acquisition module is used for acquiring personal information of target residents, and the personal information of the target residents comprises demographic information and clinic history information of the target residents;
the category acquisition module is used for inputting the personal information of the target residents into the trained first decision tree to obtain target treatment categories, wherein the target treatment categories are treatment categories matched with the personal information of the target residents;
a doctor determining module, configured to determine a doctor list according to the target visit category, where the doctor list includes personal information of at least one target doctor;
and the doctor recommending module is used for recommending the doctor list to the target residents.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor, when executing the computer program, implements the steps of the doctor recommendation method according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the doctor recommendation method according to the first aspect.
In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to perform the steps of the doctor recommendation method according to the first aspect.
Therefore, according to the scheme, the personal information of the target residents is input into the trained first decision tree, the treatment categories matched with the personal information of the target residents can be obtained, the doctor list can be obtained according to the treatment categories, the doctor list is recommended to the target residents, doctors matched with the doctor list can be recommended to the target residents, and the health requirements of the target residents are met.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart illustrating an implementation of a doctor recommendation method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating an implementation of a doctor recommendation method according to a second embodiment of the present application;
FIG. 3 is a schematic structural diagram of a recommendation device for doctors according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to a fourth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
In particular implementations, the terminal devices described in embodiments of the present application include, but are not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers having touch sensitive surfaces (e.g., touch screen displays and/or touch pads). It should also be understood that in some embodiments, the device is not a portable communication device, but is a desktop computer having a touch-sensitive surface (e.g., a touch screen display and/or touchpad).
In the discussion that follows, a terminal device that includes a display and a touch-sensitive surface is described. However, it should be understood that the terminal device may include one or more other physical user interface devices such as a physical keyboard, mouse, and/or joystick.
The terminal device supports various applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disc burning application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an email application, an instant messaging application, an exercise support application, a photo management application, a digital camera application, a web browsing application, a digital music player application, and/or a digital video player application.
Various applications that may be executed on the terminal device may use at least one common physical user interface device, such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and/or changed between applications and/or within respective applications. In this way, a common physical architecture (e.g., touch-sensitive surface) of the terminal can support various applications with user interfaces that are intuitive and transparent to the user.
It should be understood that, the sequence numbers of the steps in this embodiment do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation to the implementation process of the embodiment of the present application.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
Referring to fig. 1, which is a schematic flow chart illustrating an implementation process of a doctor recommendation method provided in an embodiment of the present application, where the doctor recommendation method is applied to a terminal device, as shown in the figure, the doctor recommendation method may include the following steps:
The personal information of the target resident includes, but is not limited to, demographic information and clinic history information of the target resident.
The target resident may refer to a resident who needs a recommended doctor. For example, when a resident is hospitalized, the terminal device is required to recommend a doctor related to the condition of the resident, that is, the target resident.
Demographic information of the targeted resident includes, but is not limited to, age, gender, family demographic configuration, height, weight, and the like.
The visit history information of the target resident includes, but is not limited to, the medical history, the number of times of visits, the time of visits, the doctor of the visit, the medical history of the outpatient clinic, etc. of the target resident.
According to the demographic information and the clinic history information of the target residents, the health requirements of the target residents can be mined, so that doctors meeting the health requirements of the target residents are recommended to the target residents.
Specifically, a doctor recommendation system may be installed on the terminal device, and when a target resident needs to recommend a doctor, the target resident may start the doctor recommendation system, enter an operation interface of the doctor recommendation system, display an information input option on the operation interface, and input demographic information and clinic history information of the target resident on the information input option. Wherein, the information input options include, but are not limited to, age, gender, family population structure, height, weight, medical history, treatment times, treatment time, doctor, outpatient medical history and other options. The doctor recommendation system may refer to software for implementing the solution of the present application.
And 102, inputting the personal information of the target residents into the trained first decision tree to obtain the target diagnosis category.
The target visit category refers to a visit category that matches personal information of the target resident.
The first decision tree is used for predicting the treatment categories matched with the personal information of the residents according to the personal information of the residents, so that the personal information of the target residents is input into the first decision tree, the treatment categories matched with the target residents (namely the target treatment categories) can be predicted, doctors matched with the target residents can be obtained according to the target treatment categories, and the health requirements of the target residents are met.
The first decision tree may be a sparse multivariate decision tree that considers a combination of factors at each decision to better demarcate different health needs. In the first decision tree, the plurality of factors include, but are not limited to, age, gender, family population structure, height, weight, medical history, number of visits, time of visits, doctor visits, medical records from an outpatient clinic, and the like. The sparse multivariate decision tree is also called a sparse diagonal decision tree, and the sparse diagonal decision tree only needs to use fewer training samples.
It should be noted that before the first decision tree is used to predict the visit category matching with the target residents, the first decision tree needs to be trained first, so that the trained first decision tree can accurately predict the visit category matching with the target residents.
Optionally, before training the first decision tree, this embodiment further includes:
acquiring personal information and historical treatment categories of historical residents;
training the first decision tree comprises:
and training a first decision tree according to the personal information and the historical treatment categories of the historical residents.
The historical residents may refer to residents corresponding to personal information used in training the first decision tree, for example, the personal information of the resident a is used in training the first decision tree, and then the resident a is the historical resident. According to the personal information of the history residents, the health requirements of the history residents can be mined, so that doctors meeting the health requirements of the history residents are recommended to the history residents.
The historical visit category refers to a visit category that matches personal information of the historical residents.
In the embodiment, the personal information of the historical residents and the historical visit categories are used as training samples for training the first decision tree. Specifically, personal information of historical residents in the training sample is used as input of the first decision tree, the historical diagnosis category is used as a true value, and the first decision tree is trained. It should be noted that, when the first decision tree is trained, the number of the training samples is at least two, so that more training samples are ensured, and the accuracy of predicting doctors by using the first decision tree can be improved by using more training samples to train the first decision tree.
Illustratively, the first decision tree is trained using n training samples, one training sample including personal information of a historical resident and a historical encounter category, the n training samples may be expressed as { (x)1,y1),(x1,y1),…,(xi,yi),…,(xn,yn) X represents personal information of historical residents, and y represents historical clinic categories. The personal information of the historical residents can also be called as feature vectors of the historical residents, and the feature vectors of the historical residents include but are not limited to the attributes of the historical residents such as age, sex, height, weight, medical records, treatment times, treatment time, doctors, outpatient medical records and the like.
In training the first decision tree using the training samples,the optimal test condition is determined aiming at the linear combination of different attributes based on an objective function until a training termination condition is met. In the training process, each decision tree node utilizes a sigmoid function to replace an indication function, so that a first decision tree is trained by utilizing a continuous optimization algorithm, and the optimal model parameters of the separation training samples are determined. Assume a total of N training samples, training sample x, at a decision tree nodeiThe probability of a child node pointing to its left side is
The probability of pointing to its right node is
Require that
And
the calculation method of (c) is as follows:
wherein σ (·) denotes a sigmoid function,
z represents a variable of the sigmoid function, p represents the attribute quantity of the ith training sample in the N training samples, theta represents a model parameter of the first decision tree, and theta representsjJ-th model parameter, x, representing a first decision treei,jRepresenting the jth attribute of the ith training sample of the N training samples.
The objective function can be expressed as follows:
wherein, WL(theta) and WR(theta) represents the weight of the weight,
λ1and λ2The regular coefficients are represented and are positive numbers; | θ | non-woven phosphor1Is the sum of the absolute values of all model parameters;
represents the sum of the squares of all model parameters; hL(theta) and HR(θ) represents a non-convex function of the model parameters.
HL(theta) and HRThe expression (θ) is defined as follows:
wherein K represents the number of historical encounter categories, K represents the kth historical encounter category, if training sample yiK, then
For the convenience of analysis, the objective function can be decomposed into a smooth objective function and a non-smooth objective function, which are specifically expressed as follows:
E(θ)＝S(θ)+||θ||1 (6)
wherein S (theta) represents a smooth objective function,
||θ||1representing a non-smooth objective function.
For the non-smooth objective function with the absolute value, continuous optimization algorithms such as normal-finite-Memory Quasi-Newton (OWL-QN), adjacent gradient and the like can be used for analysis. And determining a model parameter which enables the objective function to be minimum by utilizing a continuous optimization algorithm, and constructing a decision tree node division condition.
The first decision tree is constructed as follows:
training a sample S, the maximum depth D and the depth D of a node as input;
step 1, for a node, if all training samples in the node belong to the same diagnosis condition or D is greater than D, finishing construction;
step 2, otherwise, adding a new node, and calculating a target function corresponding to the node according to the formula (3);
step 3, determining a model parameter theta (namely an optimal model parameter theta) when the objective function is minimum according to a continuous optimization algorithm;
and 5, continuing to execute the steps 1 to 4 until all the nodes meet the step 1, namely completing the construction of the first decision tree.
Optionally, the obtaining of the personal information and the historical encounter categories of the historical residents comprises:
acquiring collected public information and clinic appointment data of a clinic appointment platform, wherein the public information comprises related information of historical residents, and the clinic appointment data comprises data related to personal information and classes of the historical residents;
and determining personal information and historical clinic types of the historical residents according to the public information and the clinic appointment data.
The information related to the historical residents may refer to all information related to the historical residents, such as the ages, sexes, family population structures, heights, weights, residential addresses, and the like of the historical residents. In this embodiment, the public information may be artificially collected and the collected public information may be transmitted to the terminal device. For example, the collected public information is filled in the terminal device, or the collected public information is filled in another device, and the other device transmits the public information to the terminal device. The public information can be collected from the registration information when the historical residents register the appointment platform or other applications.
The diagnosis appointment platform may be a diagnosis appointment platform of each of a plurality of hospitals, or may be a diagnosis appointment platform integrating a plurality of hospitals, which is not limited herein.
The terminal device may acquire the appointment data from the appointment platform.
Specifically, the method comprises the following steps: the terminal equipment sends a data acquisition request to the diagnosis reservation platform, and the diagnosis reservation platform acquires diagnosis reservation data stored by the diagnosis reservation platform after receiving the data acquisition request and feeds the diagnosis reservation data back to the terminal equipment. The data acquisition request comprises the type of the data to be acquired, so that the diagnosis appointment platform can feed back the required data to the terminal equipment conveniently. The appointment data includes, but is not limited to, medical history of the historical residents, appointment doctors, appointment time, number of times of treatment, medical history of the doctor, outpatient medical history, and identity information of the historical residents. The historical clinic categories matched with the personal information of the historical residents can be obtained according to the clinic appointment data.
Since the public information includes the related information of the historical residents, and the appointment data includes the medical history of the historical residents, the appointment doctor, the appointment time, the number of times of the appointment, the doctor, the medical record of the outpatient service, the identity information of the historical residents and the like, the personal information and the historical appointment category of the historical residents can be obtained by analyzing and integrating the public information and the appointment data.
Specifically, the historical visiting category of the historical residents can be obtained by analyzing the relevant information of the historical residents, the appointment doctors in the visiting appointment data and/or departments to which the visiting doctors belong, and the disease types in the outpatient medical records. For example, the age of the historical population is four years, the department to which the doctor belongs is pediatrics, the disease type in the outpatient medical record is respiratory disease, and since respiratory disease usually belongs to the internal medicine, the historical population is children, the historical visiting category of the historical population can be determined to be pediatric internal medicine under the pediatrics.
Optionally, after acquiring the public information and the visit reservation data, the embodiment further includes:
cleaning the public information and the appointment data to obtain cleaned data;
determining the personal information and the historical encounter category of the historical resident based on the public information and the encounter appointment data includes:
and determining personal information and historical clinic types of the historical residents according to the data after cleaning.
Since there may be duplicated information or data in the public information and the visit reservation data, and there may also be missing values or abnormal values in part of the information or data, the quality of the personal information of the history resident and the category of the history visit can be improved by cleaning the public information and the visit reservation data.
And step 103, determining a doctor list according to the target clinic category.
Wherein the doctor list includes personal information of at least one target doctor.
The personal information of the target doctor includes, but is not limited to, information of the target doctor's education background, working experience, title, position, etc. The target doctor refers to a doctor matched with the target visit category, namely, a doctor matched with the health requirement of the target resident.
Specifically, the doctor list may be recommended to the target resident by a preset recommendation manner. The preset recommendation mode refers to a preset recommendation mode. For example, a doctor list is displayed on a screen of the terminal device, or the doctor list is transmitted to a mailbox of a target resident, or the like.
According to the embodiment of the application, the personal information of the target residents is input into the trained first decision tree, the treatment categories matched with the personal information of the target residents can be obtained, the doctor list can be obtained according to the treatment categories, the doctor list is recommended to the target residents, doctors matched with the doctor list can be recommended to the target residents, and the health requirements of the target residents are met.
Referring to fig. 2, which is a schematic flow chart illustrating an implementation process of a doctor recommendation method provided in the second embodiment of the present application, where the doctor recommendation method is applied to a terminal device, as shown in the figure, the doctor recommendation method may include the following steps:
in step 201, personal information of a target resident is acquired.
The step is the same as step 101, and reference may be made to the related description of step 101, which is not described herein again.
The step is the same as step 102, and reference may be made to the related description of step 102, which is not repeated herein.
The step is the same as step 103, and reference may be made to the related description of step 103, which is not described herein again.
And step 204, acquiring the health service level of the target doctor in the doctor list.
Wherein the health service level of the target doctor can be understood as the medical level of the target doctor.
In one embodiment, the terminal device may obtain the health service level of the target doctor from the health service level evaluation device.
Specifically, after acquiring the doctor list, the terminal device sends the doctor list to the health service level evaluation device, and the health service level evaluation device can determine the health service levels of all target doctors in the doctor list and feed back the health service levels of all target doctors to the terminal device. For example, the doctor list includes personal information of three target doctors, the terminal device may send the doctor list to the health service level evaluation device, and the health service level evaluation device may evaluate the health service levels of the three target doctors according to the respective personal information of the three target doctors, obtain the respective health service levels of the three target doctors, and feed back the respective health service levels of the three target doctors to the terminal device. The health service level evaluation device is used for evaluating the health service level of a doctor according to personal information of the doctor.
In another embodiment, before obtaining the health service level of the target doctor, the terminal device may first obtain a corresponding relationship, where the corresponding relationship at least includes a mapping relationship between personal information of the target doctor and the health service level thereof; and then the health service level corresponding to the personal information of the target doctor can be found from the corresponding relation, and the health service level is the health service level of the target doctor. The correspondence may be stored in the terminal device in advance, or the terminal device may obtain the correspondence from another device, which is not limited herein.
In yet another embodiment, personal information of the target doctor may be input into the trained second decision tree, resulting in a health service level of the target doctor.
The second decision tree is used for predicting the health service level of the doctor according to the personal information of the doctor, so that the personal information of all target doctors in the doctor list is input into the second decision tree, and the health service level of all the target doctors can be predicted. For example, the doctor list includes personal information of three target doctors, and the respective health service levels of the three target doctors can be obtained by inputting the personal information of all the three target doctors into the second decision tree.
The second decision tree may be a sparse multivariate decision tree that considers a combination of factors at each decision, allowing for better differentiation of different health service levels. In the second decision tree, the above factors include, but are not limited to, information of educational background, experience of work, title, position, etc.
It should be noted that before the second decision tree is used to predict the health service level of the target doctor, the second decision tree needs to be trained first, so that the trained second decision tree can accurately predict the health service level of the target doctor.
When the training sample is used for training the second decision tree, the training process and the construction process of the first decision tree can be referred to, and the details are not repeated herein. The personal information of the historical doctors includes, but is not limited to, information of education background, working experience, titles, positions and the like of the historical doctors. According to the personal information of the historical doctors, the professional specialties of the historical doctors can be mined, and the health service level of the historical doctors can be obtained.
Specifically, the personal information of all target doctors in the doctor list can be sorted according to the sequence of the health service level from high to low, so that the personal information of the target doctors with higher health service level is arranged at the front, and the target residents can quickly find the doctors with higher matching degree.
It should be noted that, in this embodiment, the personal information of all target doctors in the doctor list may also be sorted in the order from the low health service level to the high health service level, or in other orders, which is not limited herein.
And step 206, recommending the sorted doctor list to the target residents.
It should be noted that, when the terminal device recommends the ranked doctor list to the target residents, the preset recommendation manner described in the first embodiment may also be used, and specific description may refer to the first embodiment, which is not described herein again.
According to the embodiment of the application, on the basis of the first embodiment, the personal information of all target doctors in the doctor list is sorted according to the health service level to obtain the sorted doctor list, and the sorted doctor list is recommended to the target residents, so that the target residents can conveniently and quickly find doctors with high matching degree.
Fig. 3 is a schematic structural diagram of a doctor recommendation device provided in the third embodiment of the present application, and for convenience of description, only the parts related to the third embodiment of the present application are shown.
The doctor recommending apparatus includes:
the information acquisition module 31 is used for acquiring personal information of a target resident, wherein the personal information of the target resident comprises demographic information and clinic history information of the target resident;
a category obtaining module 32, configured to input the personal information of the target residents into the trained first decision tree, so as to obtain a target treatment category, where the target treatment category is a treatment category matched with the personal information of the target residents;
a doctor determining module 33, configured to determine a doctor list according to the target clinic category, where the doctor list includes personal information of at least one target doctor;
a doctor recommending module 34 for recommending the doctor list to the target residents.
Optionally, the doctor recommending apparatus further includes:
the data acquisition module is used for acquiring personal information and historical treatment categories of historical residents;
and the decision tree training module is used for training the first decision tree according to the personal information of the historical residents and the historical clinic categories.
Optionally, the data acquiring module includes:
the acquisition sub-module is used for acquiring collected public information and clinic appointment data of a clinic appointment platform, wherein the public information comprises related information of the historical residents, and the clinic appointment data comprises data related to personal information of the historical residents and historical clinic categories;
and the determining submodule is used for determining the personal information and the historical clinic categories of the historical residents according to the public information and the clinic appointment data.
Optionally, the data obtaining module further includes:
the cleaning submodule is used for cleaning the public information and the appointment data to obtain cleaned data;
the determining submodule is specifically configured to determine the personal information of the historical residents and the historical clinic categories according to the post-cleaning data.
Optionally, the doctor recommending apparatus further includes:
the level acquisition module is used for acquiring the health service level of the target doctor;
the list sorting module is used for sorting the doctor list according to the health service level of the target doctor to obtain the sorted doctor list;
the doctor recommendation module 34 is specifically configured to:
recommending the ranked doctor list to the target resident.
Optionally, the doctor recommending apparatus further includes:
the relation acquisition module is used for acquiring a corresponding relation, and the corresponding relation at least comprises a mapping relation between the personal information of the target doctor and the health service level of the target doctor;
the level acquiring module is specifically configured to determine the health service level of the target doctor according to the personal information of the target doctor and the corresponding relationship.
Optionally, the level obtaining module is specifically configured to:
and inputting the personal information of the target doctor into the trained second decision tree to obtain the health service level of the target doctor.
The doctor recommendation device provided in the embodiment of the present application can be applied to the first method embodiment and the second method embodiment, and for details, reference is made to the description of the first method embodiment and the second method embodiment, and details are not repeated here.
Fig. 4 is a schematic structural diagram of a terminal device according to a fourth embodiment of the present application. As shown in fig. 4, the terminal device 4 of this embodiment includes: one or more processors 40 (only one of which is shown), a memory 41, and a computer program 42 stored in the memory 41 and executable on the processors 40. The processor 40, when executing the computer program 42, implements the steps in the various physician recommendation method embodiments described above.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output. It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
When the computer program product runs on a terminal device, the terminal device can implement the steps in the method embodiments.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A doctor recommendation method, characterized in that the doctor recommendation method comprises:
acquiring personal information of a target resident, wherein the personal information of the target resident comprises demographic information and clinic history information of the target resident;
inputting the personal information of the target residents into a trained first decision tree to obtain target treatment categories, wherein the target treatment categories are treatment categories matched with the personal information of the target residents;
determining a doctor list according to the target clinic category, wherein the doctor list comprises personal information of at least one target doctor;
recommending the doctor list to the target resident.
2. The physician recommendation method of claim 1 further comprising, prior to training the first decision tree:
acquiring personal information and historical treatment categories of historical residents, wherein the historical treatment categories refer to treatment categories matched with the personal information of the historical residents;
the training the first decision tree comprises:
and training the first decision tree according to the personal information of the historical residents and the historical clinic categories.
3. The doctor recommendation method as claimed in claim 2, wherein said obtaining personal information and historical encounter categories of historical residents comprises:
acquiring collected public information and clinic appointment data of a clinic appointment platform, wherein the public information comprises related information of the historical residents, and the clinic appointment data comprises data related to personal information and historical clinic categories of the historical residents;
and determining the personal information of the historical residents and the historical clinic categories according to the public information and the clinic appointment data.
4. The physician recommendation method of claim 3 further comprising, after obtaining the collected public information and the visit appointment data of the visit appointment platform:
cleaning the public information and the appointment data to obtain cleaned data;
the determining the personal information and the historical clinic category of the historical resident according to the public information and the clinic appointment data comprises:
and determining the personal information of the historical residents and the historical clinic types according to the cleaned data.
5. The doctor recommendation method according to any one of claims 1 to 4, further comprising, after determining the doctor list:
acquiring the health service level of the target doctor;
sorting the doctor list according to the health service level of the target doctor to obtain the sorted doctor list;
the recommending the doctor list to the target resident includes:
recommending the ranked doctor list to the target resident.
6. The doctor recommendation method according to claim 5, further comprising, before obtaining the health service level of the target doctor:
acquiring a corresponding relation, wherein the corresponding relation at least comprises a mapping relation between the personal information of the target doctor and the health service level of the target doctor;
the acquiring the health service level of the target doctor comprises the following steps:
and determining the health service level of the target doctor according to the personal information of the target doctor and the corresponding relation.
7. The doctor recommendation method of claim 5, wherein said obtaining the health service level of the target doctor comprises:
and inputting the personal information of the target doctor into the trained second decision tree to obtain the health service level of the target doctor.
8. A doctor recommendation device, characterized in that it comprises:
the system comprises an information acquisition module, a service management module and a service management module, wherein the information acquisition module is used for acquiring personal information of target residents, and the personal information of the target residents comprises demographic information and clinic history information of the target residents;
the category acquisition module is used for inputting the personal information of the target residents into the trained first decision tree to obtain target treatment categories, wherein the target treatment categories are treatment categories matched with the personal information of the target residents;
a doctor determining module, configured to determine a doctor list according to the target visit category, where the doctor list includes personal information of at least one target doctor;
and the doctor recommending module is used for recommending the doctor list to the target residents.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the doctor recommendation method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the doctor recommendation method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113707286A (en) * | 2021-08-30 | 2021-11-26 | 康键信息技术（深圳）有限公司 | Inquiry allocation method, device, equipment and storage medium based on decision tree |
| CN114283932A (en) * | 2022-03-03 | 2022-04-05 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
| WO2023155441A1 (en) * | 2022-02-15 | 2023-08-24 | 康键信息技术（深圳）有限公司 | Medical resource recommendation method and apparatus, device, and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140143233A1 (en) * | 2012-11-20 | 2014-05-22 | Alibaba Group Holding Limited | Method and system for recommending target object information |
| CN109637651A (en) * | 2018-10-31 | 2019-04-16 | 北京春雨天下软件有限公司 | More doctor's recommended methods and device, online consultation system |
| CN110930030A (en) * | 2019-11-22 | 2020-03-27 | 北京雅丁信息技术有限公司 | Doctor skill level rating method |
| CN110942098A (en) * | 2019-11-28 | 2020-03-31 | 江苏电力信息技术有限公司 | Power supply service quality analysis method based on Bayesian pruning decision tree |
| CN111241265A (en) * | 2020-01-09 | 2020-06-05 | 平安国际智慧城市科技股份有限公司 | Information recommendation method, equipment, storage medium and device |
-
2021
- 2021-03-09 CN CN202110253971.7A patent/CN113130052A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140143233A1 (en) * | 2012-11-20 | 2014-05-22 | Alibaba Group Holding Limited | Method and system for recommending target object information |
| CN109637651A (en) * | 2018-10-31 | 2019-04-16 | 北京春雨天下软件有限公司 | More doctor's recommended methods and device, online consultation system |
| CN110930030A (en) * | 2019-11-22 | 2020-03-27 | 北京雅丁信息技术有限公司 | Doctor skill level rating method |
| CN110942098A (en) * | 2019-11-28 | 2020-03-31 | 江苏电力信息技术有限公司 | Power supply service quality analysis method based on Bayesian pruning decision tree |
| CN111241265A (en) * | 2020-01-09 | 2020-06-05 | 平安国际智慧城市科技股份有限公司 | Information recommendation method, equipment, storage medium and device |
Non-Patent Citations (1)
| Title |
|---|
| 冯菁等: "ID3算法在教学质量评价中的应用研究", 《计算机技术与发展》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113707286A (en) * | 2021-08-30 | 2021-11-26 | 康键信息技术（深圳）有限公司 | Inquiry allocation method, device, equipment and storage medium based on decision tree |
| WO2023155441A1 (en) * | 2022-02-15 | 2023-08-24 | 康键信息技术（深圳）有限公司 | Medical resource recommendation method and apparatus, device, and storage medium |
| CN114283932A (en) * | 2022-03-03 | 2022-04-05 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
| CN114283932B (en) * | 2022-03-03 | 2022-06-10 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
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