CN113160954B

CN113160954B - Medical resource allocation method and device, storage medium and electronic equipment

Info

Publication number: CN113160954B
Application number: CN202110371213.5A
Authority: CN
Inventors: 张金凤
Original assignee: Taikang Insurance Group Co Ltd
Current assignee: Taikang Insurance Group Co Ltd
Priority date: 2021-04-07
Filing date: 2021-04-07
Publication date: 2023-08-01
Anticipated expiration: 2041-04-07
Also published as: CN113160954A

Abstract

The disclosure relates to a medical resource allocation method and device, a storage medium and electronic equipment, and relates to the technical field of machine learning, wherein the method comprises the following steps: acquiring target attribute information, a target insurance portrait and a target health portrait of a target user according to user identification information included in the medical consultation request; inputting a preset doctor practice portrait, target attribute information, target insurance portrait and target health portrait into a first medical resource allocation model to obtain a first medical resource allocation result; inputting the target disease information, the target attribute information, the target insurance portrait, the target health portrait and the preset doctor practice portrait into a second medical resource allocation model to obtain a second medical resource allocation result; and matching the corresponding target medical resources for the target user according to the first medical resource allocation result and the second medical resource allocation result. The medical resource allocation method and device improve accuracy of medical resource allocation results.

Description

Medical resource allocation method and device, storage medium and electronic equipment

Technical Field

The embodiment of the invention relates to the technical field of machine learning, in particular to a medical resource allocation method, a medical resource allocation device, a computer readable storage medium and electronic equipment.

Background

At present, when a user activates on-line health benefits through some specific application programs, the application programs can provide specific contractual doctor services for the user, namely, a contractual doctor is matched for the user, and the contractual doctor can provide long-term stable health consultation inquiry and management services for the user.

Specifically, when the user activates the health benefits, the contractors allocated to the user are mainly general doctors, and when the contractors encounter the special problems, the contractors can pull the special doctors into the group to solve the problems.

However, the above allocation scheme has the following drawbacks: on the one hand, when medical resources are allocated to users, health portraits and insurance portraits of the users are not considered, so that the accuracy of the allocation result of the medical resources is lower; on the other hand, the corresponding medical resources cannot be directly matched for the user according to the actual demands of the user, and the problem of medical resource waste exists.

Therefore, it is desirable to provide a new medical resource allocation method and apparatus.

It should be noted that the information of the present invention in the above background section is only for enhancing the understanding of the background of the present invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.

Disclosure of Invention

The present invention aims to provide a medical resource allocation method, a medical resource allocation device, a computer-readable storage medium and an electronic apparatus, which further overcome, at least to some extent, the problem of low accuracy of medical resource allocation results due to limitations and drawbacks of the related art.

According to one aspect of the present disclosure, there is provided a medical resource allocation method including:

receiving a medical consultation request sent by a target user through a terminal device, and responding to the medical consultation request to acquire target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request;

inputting a preset doctor practice portrait, the target attribute information, a target insurance portrait and a target health portrait into a first medical resource allocation model to obtain a first medical resource allocation result;

acquiring target disease information corresponding to the target insurance image, and inputting the target disease information, the target attribute information, the target insurance image, the target health image and a preset doctor practice image into a second medical resource allocation model to obtain a second medical resource allocation result;

And matching the corresponding target medical resources for the target user according to the first medical resource allocation result and the second medical resource allocation result, and sending the target medical resources to the terminal equipment.

In one exemplary embodiment of the present disclosure, the first medical resource allocation model includes a first fully connected layer consisting of a plurality of fully connected units connected in parallel, a first vector splice layer, and a first distance calculation layer;

the method for obtaining the first medical resource allocation result comprises the steps of:

encoding the target attribute information and the target health portrait by using a first full-connection unit in the first full-connection layer to obtain a first user information code, and encoding the target insurance portrait by using a second full-connection unit in the first full-connection layer to obtain a first user portrait code;

encoding the preset doctor practice portrait by using a third full-connection unit in the first full-connection layer to obtain a first practice portrait code;

Splicing the first user information code and the first user portrait code by using the first vector splicing layer to obtain a first target user code;

and calculating a first distance between the first target user code and the first medical portrait code by using the first distance calculating layer, and obtaining a first medical resource allocation result according to a first distance calculating result.

In one exemplary embodiment of the present disclosure, the second medical resource allocation model includes a second full-connection layer composed of a plurality of full-connection units connected in parallel, a second vector splice layer, and a second distance calculation layer;

the target disease information, the target attribute information, the target insurance portrait, the target health portrait and the preset doctor practice portrait are input into a second medical resource allocation model to obtain a second medical resource allocation result, which comprises the following steps:

encoding the target attribute information and the target health portrait by using a fourth full-connection unit in the second full-connection layer to obtain a second user information code, and encoding the target insurance portrait by using a fifth full-connection unit in the second full-connection layer to obtain a second user portrait code;

The sixth full-connection unit in the second full-connection layer is utilized to encode the preset doctor practice portrait to obtain a second practice portrait code, and the seventh full-connection unit in the second full-connection layer is utilized to encode the target disease information to obtain a disease information code;

splicing the second user information code and the second user portrait code by using the second vector splicing layer to obtain a second target user code, and splicing the disease information code and the second practice portrait code to obtain a medical information code;

and calculating a second distance between the second target user code and the medical information code by using the second distance calculation layer, and obtaining a second medical resource allocation result according to a second distance calculation result.

In an exemplary embodiment of the present disclosure, the medical resource allocation method further includes:

acquiring basic attribute information, a history insurance portrait, a history health portrait of a history user, a first doctor portrait allocated to the history user, and a first matching degree between the first doctor portrait and the history user;

generating first sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait and the first doctor portrait, and generating a first sample pair according to the first sample information and the first matching degree;

And training a first network model to be trained by using the first sample to obtain the first medical resource allocation model.

acquiring a second doctor portrait allocated to the historical user, historical disease information corresponding to the historical insurance portrait and a second matching degree between the second doctor portrait and the historical user;

generating second sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait, the historical disease information and the second doctor portrait, and generating a second sample pair according to the first sample information and the second matching degree;

and training a second network model to be trained by using the second sample to obtain the second medical resource allocation model.

In one exemplary embodiment of the present disclosure, acquiring target disease information corresponding to the target insurance image includes:

acquiring historical claim data, and extracting disease names and disease symptoms of the disease to be claiming from the claim data;

and constructing an easily-risky disease database according to the disease name and the disease symptoms of the disease in the claim, and matching target disease information corresponding to the target insurance image from the easily-risky disease database.

In an exemplary embodiment of the present disclosure, obtaining the target attribute information, the target insurance portrait, and the target health portrait of the target user according to the user identification information included in the medical consultation request includes:

acquiring target attribute information of the target user from a user information database according to user identification information included in the medical consultation request; wherein the target attribute information includes a plurality of names, sexes, ages, cities, practices, industries, and annual incomes of the target users;

acquiring the target health portrait from a medical information database according to the user identification information; wherein the target health representation comprises a plurality of health labels, medical records, current medical history, and family history;

acquiring the target insurance portrait from an insurance information database according to the user identification information; the target insurance portrait comprises a plurality of insurance states, insurance types, insurance ranges, premium standards, customer grades, claim settlement times, occurrence reasons and pay amounts.

According to one aspect of the present disclosure, there is provided a medical resource allocation apparatus including:

The first information acquisition module is used for receiving a medical consultation request sent by a target user through terminal equipment, responding to the medical consultation request and acquiring target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request;

the first medical resource allocation module is used for inputting a preset doctor practice portrait, the target attribute information, a target insurance portrait and a target health portrait into the first medical resource allocation model to obtain a first medical resource allocation result;

the second medical resource allocation module is used for acquiring target disease information corresponding to the target insurance image, and inputting the target disease information, the target attribute information, the target insurance image, the target health image and a preset doctor practice image into a second medical resource allocation model to obtain a second medical resource allocation result;

and the target medical resource matching module is used for matching the corresponding target medical resource for the target user according to the first medical resource allocation result and the second medical resource allocation result and sending the target medical resource to the terminal equipment.

In an exemplary embodiment of the present disclosure, the medical resource allocation apparatus may further include:

the second information acquisition module can be used for acquiring basic attribute information of a historical user, a historical insurance portrait, a historical health portrait, a first doctor portrait allocated for the historical user and a first matching degree between the first doctor portrait and the historical user;

the first sample pair generating module can be used for generating first sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait and the first doctor portrait, and generating a first sample pair according to the first sample information and the first matching degree;

and the first model training module can be used for training a first network model to be trained by using the first sample to obtain the first medical resource allocation model.

In an exemplary embodiment of the present disclosure, the medical resource allocation apparatus further includes:

The third information acquisition module can be used for acquiring a second doctor portrait allocated to the historical user, historical disease information corresponding to the historical insurance portrait and a second matching degree between the second doctor portrait and the historical user;

the second sample pair generating module can be used for generating second sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait, the historical disease information and the second doctor portrait, and generating a second sample pair according to the first sample information and the second matching degree;

and the second model training module can be used for training a second network model to be trained by using the second sample to obtain the second medical resource allocation model.

According to one aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the medical resource allocation method of any one of the above.

According to one aspect of the present disclosure, there is provided an electronic device including:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the medical resource allocation method of any one of the above via execution of the executable instructions.

According to the medical resource allocation method provided by the embodiment of the invention, on one hand, the target attribute information, the target insurance portrait and the target health portrait of the target user are obtained according to the user identification information included in the medical consultation request; inputting the preset doctor practice portrait, target attribute information, target insurance portrait and target health portrait into a first medical resource allocation model to obtain a first medical resource allocation result; then, inputting the target disease information, the target attribute information, the target insurance portrait, the target health portrait and the preset doctor practice portrait into a second medical resource allocation model to obtain a second medical resource allocation result; finally, matching corresponding target medical resources for the target user according to the first medical resource allocation result and the second medical resource allocation result; in the medical resource distribution process, the target salty portraits and the target health portraits of the target users are considered, so that the problem that the accuracy of the medical resource distribution result is lower because the health portraits and insurance portraits of the users are not considered when the medical resources are distributed to the users in the prior art is solved; on the other hand, the problem that the corresponding medical resources cannot be directly matched for the user according to the actual demands of the user in the prior art, and medical resource waste exists is solved; on the other hand, as the target disease information is added in the medical resource allocation process, the target medical resource is finally obtained, and the first medical resource allocation result and the second medical resource allocation result are integrated, so that the accuracy of the medical resource allocation result is further improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

Fig. 1 schematically illustrates a flow chart of a medical resource allocation method according to an example embodiment of the present disclosure.

Fig. 2 schematically illustrates a flowchart of a method for inputting a preset doctor practice representation, the target attribute information, a target insurance representation, and a target health representation into a first medical resource allocation model, resulting in a first medical resource allocation result, according to an example embodiment of the present disclosure.

Fig. 3 schematically illustrates a structural example diagram of a first medical resource allocation model according to an example embodiment of the present disclosure.

Fig. 4 schematically illustrates a flowchart of a method for inputting the target disease information, the target attribute information, the target insurance representation, the target health representation, and the preset doctor practice representation into a second medical resource allocation model to obtain a second medical resource allocation result according to an exemplary embodiment of the present disclosure.

Fig. 5 schematically illustrates a structural example diagram of a second medical resource allocation model according to an example embodiment of the present disclosure.

Fig. 6 schematically illustrates a flowchart of a method of training a first medical resource allocation model according to an example embodiment of the present disclosure.

Fig. 7 schematically illustrates a flowchart of a method of training a second medical resource allocation model according to an example embodiment of the present disclosure.

Fig. 8 schematically illustrates a flowchart of another medical resource allocation method according to an example embodiment of the present disclosure.

Fig. 9 schematically illustrates a block diagram of a medical resource allocation apparatus according to an example embodiment of the present disclosure.

Fig. 10 schematically illustrates an electronic device for implementing the above-described medical resource allocation method according to an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known aspects have not been shown or described in detail to avoid obscuring aspects of the invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

In some medical resource allocation schemes, the allocated contractors are mainly general doctors, and when the contractors encounter special problems, the contractors can pull the special doctors into groups to solve the problems. However, general doctors have limited resources at present, and special doctor resources are not well utilized.

In addition, the current allocation rule is that when the user activates the equity card, allocation is carried out according to the online/offline, idle/busy indication and scheduling time of the general practitioner; moreover, the conventional allocation rules do not comprehensively consider data such as insurance portraits and health portraits of clients, practice portraits of doctors, disease knowledge bases and the like, but simply allocate according to online/offline, busy/busy indication and scheduling time of general doctors, and have the following problems:

On the one hand, the matching accuracy of the user and the doctor cannot be guaranteed. The specific application is as follows: the quality of the user is unknown and what doctors are assigned to the user is also unknown. For example, a customer with diabetes who has a very high premium should be given a priority to a doctor who is experienced in treating diabetes and who is at a higher level, preferably in the same area as the customer, so that better service is provided to the health of the customer.

On the other hand, the risk prevention capability is low, and the claim settlement factors are not taken into consideration. For example, by the health condition of a patient, it is predicted that other diseases may be caused, and the potentially induced diseases are within the scope of the warranty applied. If the intervention is not performed in time in the health management process, danger can be possibly caused and claims can be settled.

In yet another aspect, the general practitioner resources are insufficient and the specialist resources are idle. When general doctors encounter special problems, the general doctors are also required to enter the group, and the service time is slow.

In this example embodiment, a medical resource allocation method is provided first, where the method may operate on a server, a server cluster, or a cloud server, etc.; of course, those skilled in the art may also operate the method of the present invention on other platforms as required, and this is not a particular limitation in the present exemplary embodiment. Referring to fig. 1, the medical resource allocation method may include the steps of:

S110, receiving a medical consultation request sent by a target user through a terminal device, and responding to the medical consultation request to acquire target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request;

s120, inputting a preset doctor practice portrait, the target attribute information, a target insurance portrait and a target health portrait into a first medical resource allocation model to obtain a first medical resource allocation result;

s130, acquiring target disease information corresponding to the target insurance image, and inputting the target disease information, the target attribute information, the target insurance image, the target health image and a preset doctor practice image into a second medical resource allocation model to obtain a second medical resource allocation result;

and S140, matching the corresponding target medical resources for the target user according to the first medical resource allocation result and the second medical resource allocation result, and sending the target medical resources to the terminal equipment.

In the medical resource allocation method, on one hand, the target attribute information, the target insurance portrait and the target health portrait of the target user are acquired according to the user identification information included in the medical consultation request; inputting the preset doctor practice portrait, the target attribute information, the target insurance portrait, the target health portrait and the preset doctor practice portrait into a first medical resource allocation model to obtain a first medical resource allocation result; then, inputting the target disease information, the target attribute information, the target insurance portrait, the target health portrait and the preset doctor practice portrait into a second medical resource allocation model to obtain a second medical resource allocation result; finally, matching corresponding target medical resources for the target user according to the first medical resource allocation result and the second medical resource allocation result; in the medical resource distribution process, the target salty portraits and the target health portraits of the target users are considered, so that the problem that the accuracy of the medical resource distribution result is lower because the health portraits and insurance portraits of the users are not considered when the medical resources are distributed to the users in the prior art is solved; on the other hand, the problem that the corresponding medical resources cannot be directly matched for the user according to the actual demands of the user in the prior art, and medical resource waste exists is solved; on the other hand, as the target disease information is added in the medical resource allocation process, the target medical resource is finally obtained, and the first medical resource allocation result and the second medical resource allocation result are integrated, so that the accuracy of the medical resource allocation result is further improved.

The medical resource allocation method according to the exemplary embodiment of the present disclosure will be explained and illustrated in detail below with reference to the accompanying drawings.

First, the object of the present disclosure of the exemplary embodiment is explained and explained. Specifically, the invention mainly solves the problem of dynamic combination and allocation of the team of contracted doctors provided for the user, improves the matching degree of the user and doctors, and what contracted doctors should be allocated by what user, is more suitable for the user, better serves the consultation and health management of the client, improves the satisfaction degree of the client and reduces the claims; meanwhile, the doctor who signs a contract is guaranteed to be allocated to the user, so that the current health condition can be well treated and managed, potential health problems can be prevented and intervened, and danger is avoided. Further, based on the treatment and prevention ideas, when the client activates the health rights and interests, a doctor who plays a role in treatment and a doctor who plays a role in prevention are dynamically combined from the dimensions of level, region, expertise, service effect (speed and quality), carrying capacity and the like according to the basic information, insurance portraits and health portraits of the client, the medical portraits of the doctor and the disease knowledge base, and a doctor team who signs up and matches with the client.

Next, in a medical resource allocation method of the present disclosure, referring to fig. 1, there is shown:

in step S110, a medical consultation request sent by a target user through a terminal device is received, and target attribute information, a target insurance portrait and a target health portrait of the target user are acquired according to user identification information included in the medical consultation request in response to the medical consultation request.

Specifically, the target user may log in the application program (for example, tay life) through the terminal device, and the application program may perform real-name authentication on the target user (for example, the user is required to fill in a real name, an identity card number, a telephone number, etc.); when a user carries out medical consultation through an interaction control in the application program, the terminal equipment can generate a medical consultation request according to the real name, the identity card number and the telephone number of the target user and send the medical consultation request to a server side; when the server receives the medical consultation request, the target attribute information, the Mubao insurance image and the target health portrait of the target user can be acquired according to the user identification information (such as an identity card number or a telephone number) included in the medical consultation request.

Further, the target attribute information of the target user can be obtained from a user information database according to the user identification information included in the medical consultation request; wherein the target attribute information includes a plurality of names, sexes, ages, cities, practices, industries, and annual incomes of the target users; meanwhile, the target health portrait can be obtained from a medical information database according to the user identification information; wherein the target health representation comprises a plurality of health labels, medical records, current medical history, and family history; further, the target insurance portrait can be obtained from an insurance information database according to the user identification information; the target insurance portrait comprises a plurality of insurance states, insurance types, insurance ranges, premium standards, customer grades, claim settlement times, occurrence reasons and pay amounts.

For example, the name, sex, age, city, occupation, industry, annual income and other information of the target user can be obtained from a user information database (such as a client management system) according to the identification card number or telephone number of the target user;

In addition, the health label (health, sub-health, illness and the like) of the target user, the main diagnosis result in the history online-offline visit record, the current medical history, the past medical history, the family history and the like can be obtained from the hospital information system and the internet medical system in the medical information database (which can comprise the hospital information system and the internet medical system and can also be other systems, and the example does not limit the special limitation on the hospital information system and the internet medical system) according to the identification card number or the telephone number;

further, the insurance status of the target user (the insurance status may include not being applied, being broken, being stopped, etc.), the insurance policy, the coverage, the standard premium (may include the total number of premiums of the currently effective policy), the bowling, the class (the user class of the target user), the likelihood of whether the target user has secondary development (may include high, general, low, none, etc.), the number of claims, the reason for the risk, the amount of payouts, etc. may also be obtained from an insurance information database (which may be an insurance management system, for example) based on the identification number or the telephone number.

In step S120, a preset doctor practice portrait, the target attribute information, a target insurance portrait, a target health portrait, and a preset doctor practice portrait are input into a first medical resource allocation model, so as to obtain a first medical resource allocation result.

In the present exemplary embodiment, first, a preset doctor practice portrait may be acquired from a doctor resource system, and specifically may include doctor practice departments, doctor expertise, doctor level (expert level or general level, etc.), doctor first practice hospital, practice status, doctor main diagnosis with highest score in patient evaluation, comprehensive evaluation, average response market, whether online, whether busy, shift time, etc.

Further, after the preset doctor practice portrait is obtained, the preset doctor practice portrait, the target attribute information, the target insurance portrait and the target can be input into the first medical resource allocation model, so as to obtain a first medical resource allocation result. The first medical resource allocation model comprises a first full-connection layer, a first vector splicing layer and a first distance calculating layer, wherein the first full-connection layer is composed of a plurality of full-connection units which are connected in parallel.

Specifically, referring to fig. 2, inputting a preset doctor practice portrait, the target attribute information, a target insurance portrait, and a target health portrait into a first medical resource allocation model to obtain a first medical resource allocation result, the method may include the following steps:

Step S210, the target attribute information and the target health portrait are encoded by using a first full-connection unit in the first full-connection layer to obtain a first user information code, and the target insurance portrait is encoded by using a second full-connection unit in the first full-connection layer to obtain a first user portrait code;

step S220, the third full-connection unit in the first full-connection layer is utilized to encode the preset doctor practice portrait, and a first practice portrait code is obtained;

step S230, splicing the first user information code and the first user portrait code by using the first vector splicing layer to obtain a first target user code;

step S240, calculating a first distance between the first target user code and the first medical resource allocation result according to the first distance calculation layer.

Step S210 to step S240 are explained below with reference to fig. 3.

First, referring to fig. 3, the first medical resource allocation model may include a first full connection layer 310 composed of a first input layer 300, a plurality of full connection units (a first full connection unit 311, a second full connection unit 312, and a third full connection unit 313) connected in parallel, a first vector stitching layer 320, a first distance calculation layer 330, and a first output layer 340. Each full-connection unit is connected with the first input layer and the first vector splicing layer respectively, and the first vector splicing layer is connected with the first output layer through the first distance calculation layer.

Based on this, it can be known that the specific calculation process of the first medical resource allocation result may include: firstly, in a user information full-connection layer (a first full-connection unit), each network unit can process user basic information (namely target attribute information) and target health portraits of an input layer at the same time, so as to obtain a first user information code; in the insurance image full-connection layer, each network unit will process the target insurance image of the input layer at the same time, so as to obtain the first user image code (insurance information code); meanwhile, at a doctor information full connection layer (a third full connection unit), each network unit processes all information (doctor practice portrait) of a doctor so as to obtain a first practice portrait code; secondly, splicing the first user information code and the first user portrait code in a first splicing layer to obtain a first target user code; and then, in a first distance calculation layer, performing distance calculation on the first target user code and the first practice portrait code in a fully-connected mode, and finally outputting a value between 0 and 1 by using a sigmoid activation function, and then selecting the value with the largest value as a first medical resource allocation result.

In step S130, target disease information corresponding to the target insurance image is acquired, and the target disease information, the target attribute information, the target insurance image, the target health image, and the preset doctor practice image are input into a second medical resource allocation model, so as to obtain a second medical resource allocation result.

In the present exemplary embodiment, first, disease information corresponding to the insurance image is acquired. Specifically, the method comprises the following steps: firstly, acquiring historical claim data, and extracting disease names and disease symptoms of the claimed diseases from the claim data; and secondly, constructing an easily-risky disease database according to the disease name and the disease symptoms of the disease which are already settled, and matching target disease information corresponding to the target insurance image from the easily-risky disease database. For example, the disease name of the disease and the symptom information corresponding to the disease may be extracted from the data of the disease in the last year or 3 years, and then the extracted disease name and the symptom information corresponding to the disease may be sorted, so as to obtain the database of the disease easy to be at risk; furthermore, the corresponding target disease information can be matched from the risk-prone disease database according to the insurance kinds and the insurance ranges in the target insurance portrait. The method is characterized in that the potential diseases of the clients are compared with parameters such as insurance coverage, risk reasons and the like in an insurance portrait, so that the 'health problem easy to risk' of the clients is judged, the 'health problem easy to risk' is matched with parameters such as doctor practice departments, doctor expertise, practice states, consultation main diagnosis with highest score in patient evaluation, comprehensive evaluation and the like in doctor practice portraits, doctors capable of preventing and interfering with the potential health problem of the clients are found, services are provided for the clients, the health problem is tracked timely, the risk is prevented from gradual change, and claims are reduced.

And secondly, after the target disease information is acquired, the target disease information, the target attribute information, the target insurance portrait, the target health portrait and the preset doctor practice portrait can be input into a second medical resource allocation model to obtain a second medical resource allocation result. The second medical resource allocation model comprises a second full-connection layer, a second vector splicing layer and a second distance calculating layer, wherein the second full-connection layer is composed of a plurality of full-connection units which are connected in parallel.

Specifically, referring to fig. 4, the target disease information, the target attribute information, the target insurance portrait, the target health portrait, and the preset doctor practice portrait are input into a second medical resource allocation model to obtain a second medical resource allocation result, and the method may include the following steps:

step S410, the fourth full-connection unit in the second full-connection layer is utilized to encode the target attribute information and the target health portrait to obtain a second user information code, and the fifth full-connection unit in the second full-connection layer is utilized to encode the target insurance portrait to obtain a second user portrait code;

step S420, the sixth full-connection unit in the second full-connection layer is utilized to encode the preset doctor practice portrait to obtain a second practice portrait code, and the seventh full-connection unit in the second full-connection layer is utilized to encode the target disease information to obtain a disease information code;

Step S430, the second vector splicing layer is utilized to splice the second user information code and the second user portrait code to obtain a second target user code, and splice the disease information code and the second practice portrait code to obtain a medical information code;

step S440, calculating a second distance between the second target user code and the medical information code by using the second distance calculating layer, and obtaining a second medical resource allocation result according to a second distance calculating result.

Hereinafter, the steps S410 to S440 will be explained and explained with reference to fig. 5.

First, referring to fig. 5, the second medical resource allocation model may include a second full connection layer 510 composed of a second input layer 500, a plurality of full connection units (a fourth full connection unit 511, a fifth full connection unit 512, a sixth full connection unit 513, and a seventh full connection unit 514) connected in parallel, a second vector concatenation layer 520, a second distance calculation layer 530, and a second output layer 540. Each full-connection unit is connected with the second input layer and the second vector splicing layer respectively, and the second vector splicing layer is connected with the second output layer through the second distance calculation layer.

Based on this, it can be known that the specific calculation process of the second medical resource allocation result may include: firstly, in a customer information full-connection layer (a fourth full-connection unit), each network unit can process target attribute information and target health portrait of an input layer at the same time, so as to obtain a second user information code; at the insurance image full-connection layer (fifth full-connection unit), each network unit will process the target insurance image of the input layer at the same time, so as to obtain the second user image code; at the doctor information full connection layer (sixth full connection unit), each network unit processes all information of the doctor (doctor practice portrait), so as to obtain a second practice portrait code; in the disease library full-connection layer (seventh full-connection unit), each network unit can process the target disease information of the input layer at the same time and process the target disease information, so as to obtain a disease information code; secondly, in a second splicing layer, splicing the second user information code and the second user portrait code together to obtain a second target user code, and splicing the disease information code and the second practice portrait code together to obtain a medical information code; further, in the second distance calculating layer, the distance calculation is performed on the second target user code and the medical information code which are connected together in a full connection mode, a value between 0 and 1 is output by using a sigmoid activation function, and then the value with the largest value is selected as a second medical resource allocation result.

It should be noted that, the difference between the first medical resource allocation model and the second medical resource allocation model is that one more fully connected unit is added in the second medical resource allocation model, and the purposes of the first medical resource allocation model and the second medical resource allocation model are different, so that the included parameters are different; therefore, the encoding result obtained by encoding each input information using the first medical resource allocation model differs from the encoding result obtained by encoding each input information using the second medical resource allocation model.

In step S140, according to the first medical resource allocation result and the second medical resource allocation result, a corresponding target medical resource is matched for the target user, and the target medical resource is sent to the terminal device.

Specifically, after the first medical resource allocation result and the second medical resource allocation result are obtained, the first medical resource allocation result and the second medical resource allocation result can be combined, so that a final target medical resource can be obtained, the target medical resource can be a doctor team, doctors in the doctor team can carry out treatment management on the current medical consultation request of the target user, and potential medical risks of the target user can be prevented and intervened, so that the problem of risk avoidance is achieved.

The specific training process of the first medical resource allocation model and the second medical resource allocation model according to the exemplary embodiment of the present disclosure is explained and explained below with reference to fig. 6 and 7.

First, referring to FIG. 6, a specific training process for a first medical resource allocation model may include the steps of:

step S610, obtaining basic attribute information of a history user, a history insurance portrait, a history health portrait, a first doctor portrait allocated for the history user, and a first matching degree between the first doctor portrait and the history user.

Step S620, generating first sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait and the first doctor portrait, and generating a first sample pair according to the first sample information and the first matching degree.

And step 630, training a first network model to be trained by using the first sample to obtain the first medical resource allocation model.

Hereinafter, step S610 to step S630 will be explained and explained. First, data samples are collected for training model 1 (first medical resource allocation model), each sample being a data pair (first sample pair) shaped as x, y. Wherein x is a multidimensional vector containing basic attribute information, health information (historical health portrait) of a certain user, historical insurance portrait of the historical user, and information (first doctor portrait) of a certain doctor allocated to the historical user; specifically, the first doctor portrait requires to select a doctor suitable for the current health condition of the customer and the quality (acquired by the index of the insurance portrait, etc.) of the customer; then, normalizing all the dimension information of the history user, the dimension information of the insurance portrait and the dimension information of the doctor to be converted into a number between 0 and 1; where y is a real number, a value of 0 indicates that the client is not reasonable to assign to the doctor, and a value of 1 indicates that the client is reasonable to assign to the doctor.

Further, the model is trained based on the first sample until the accuracy of the first network model to be trained is substantially unchanged. In the training process, the first sample pair of the bar can be divided into a training set, a verification set and a test set, wherein the specific ratio can be 7:2:1; moreover, binary cross entropy can be adopted as a loss function, and a random gradient descent method with momentum is adopted as an optimization method; moreover, some regular constraints can be added to avoid overfitting (such as L2 regularization); training on the training set, gradually increasing the training round number until the accuracy on the verification data set is basically unchanged; and finally, testing is carried out on the testing set, and the testing result can be used as the real accuracy of future online service.

Next, referring to fig. 7, a specific training process of the second medical resource allocation model may include the steps of:

step S710, obtaining a second doctor portrait allocated to the history user, history disease information corresponding to the history insurance portrait, and a second matching degree between the second doctor portrait and the history user;

step S720, generating second sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait, the historical disease information and the second doctor portrait, and generating a second sample pair according to the first sample information and the second matching degree;

And step S730, training a second network model to be trained by using the second sample, so as to obtain the second medical resource allocation model.

Hereinafter, step S710 to step S730 will be explained and explained. Specifically, for the second sample pair of training model 2 (second medical resource allocation model), where x is a multidimensional vector containing basic attribute information of the historical user, a historical health representation, a historical insurance representation, historical disease information, and a second doctor representation; wherein the matching second physician representation is to select a physician that is appropriate for the potential health condition of the historic user (e.g., risk-prone disease information); simultaneously, normalizing all the dimension information of the history user, the dimension information of the insurance portrait, the dimension information of the disease library and the dimension information of the second doctor portrait to be converted into numbers between 0 and 1; y is a real number, a value of 0 indicates that the historical user is not reasonably assigned to the doctor, and a value of 1 indicates that the historical user is reasonably assigned to the doctor.

Further, the second sample is divided into three parts of a training set, a verification set and a test set, wherein the specific ratio can be 7:2:1; and then training a second network model to be trained by using the second sample until the accuracy of the model is basically unchanged. Specifically, binary cross entropy can be used as a loss function, a random gradient descent method with momentum is used as an optimization method, and regular constraints can be added to avoid overfitting (such as L2 regularization). Training on the training set, gradually increasing the training round number until the accuracy on the verification data set is basically unchanged; and finally, testing is carried out on the testing set, and the testing result can be used as the real accuracy of future online service.

The medical resource allocation method according to the exemplary embodiment of the present disclosure is further explained and illustrated below with reference to fig. 8. Referring to fig. 8, the medical resource allocation method may include the steps of:

step S801, inquiring about user basic information (name, gender, age, city);

step S802, inquiring insurance image (insurance state, standard premium, bowling, grade) of user;

step S803, inquiring the user health portrait (health label, main diagnosis in history online and offline visit record, present medical history in health file, family history);

step S804, inquiring doctor ' S practice portraits (doctor ' S practice department, doctor expertise, doctor ' S principal diagnosis with highest score in patient evaluation, doctor level, doctor ' S first practice hospital, practice status), finding out doctors with higher degree of matching with users, and then distributing according to doctor ' S on-line/off-line, idle/busy indication and shift time;

step S805, after the user basic information, the health information, the user insurance portrait information and the doctor information are coded, inputting a first medical resource allocation model to obtain a doctor with highest matching degree as doctor 1 matched with the current health condition of the user;

Step S806, after the user basic information, the health information, the user insurance portrait information, the disease information and the doctor information are coded, inputting a second medical resource allocation model to obtain a doctor with highest matching degree as doctor 2 matched with the potential health condition of the user;

step S807, doctor 1 and doctor 2 are set as contracted doctor groups of the user.

For example, a user YY, male, 40 years old, the urban armed forces, IT industry, private company manager, annual income about 80 ten thousand, insurance health under guarantee about serious illness risks (115 serious illness risks, 60 light illness risks in guarantee range), long-term accident risks, life risks and the like, standard insurance costs 60 ten thousand, bowling 10 years, grade digger, secondary development possibility high, claim number 0, no reason for risk emergence and pay amount 0 are added.

Five years after the allergy of the health label and the age of the cigarette, allergic rhinitis is ranked first in the main diagnosis of the history visit, allergic rhinitis is found in the current medical history, and asthma is found in the father of the family history.

When the user performs real-name authentication in Tai life and activates health equity, the system screens out a doctor in the first medical resource allocation model according to the basic information of the user, the health portrait of the user and the practice portrait of the insurance portrait and the doctor through combined comprehensive calculation. (Wang someone doctor matches the user the highest in model 1, matching 0.92, 1 highest).

The king doctor is specially used for treating the allergic rhinitis, the highest scoring in the evaluation of the patients is also the treatment of the allergic rhinitis, the king doctor is the dominant doctor, the first medical practice is Tay Kang Tongji (Wuhan) medical practice, the medical practice department is the otorhinolaryngology department, and the medical practice state is in use. Five stars are comprehensively evaluated. The average response time is 30 seconds, when YY activates health rights, the wang doctor is online and idle, and the day has been shifted.

In the system, in the model 2, a doctor is selected based on the basic information of the user, the health portrait and insurance portrait of the user, the medical portrait of the doctor, and the disease knowledge base. (some doctor matches the user the highest in model 2, matching 0.9, 1 at the highest).

A doctor is a doctor of the respiratory department auxiliary staff of the Tay Kang Tongji (Wuhan) hospital, because in the serious danger purchased by the user YY, serious asthma exists in the guarantee range, the user YY has asthma in family history, the user YY is allergic constitution and smoked for 5 years, and although the doctor suffers from allergic rhinitis, the possibility of inducing asthma is high, and an asthma expert is added into a signing team for the system. The health change condition of the user can be known in time, and the health guidance and the intervention can be performed on the user in time, so that asthma is prevented from being induced; finally, a doctor and a doctor form a team of contracted doctors, and the team of contracted doctors are matched with the user and contracted.

The medical resource allocation model provided by the embodiment of the disclosure not only ensures doctors who sign up with user allocation, but also can carry out good treatment management on the current health condition, improves the health service quality and improves the user satisfaction; moreover, doctors who sign up with the user distribution can be guaranteed, potential health problems can be prevented and intervened, danger is avoided, claims are reduced, and the purposes of fully integrating and optimally configuring doctor resources are achieved; furthermore, the insurance image of the user is further added, so that the user quality can be judged according to the insurance state, standard premium, bowling, grade, secondary development possibility, claim settlement times and pay amount parameters in the insurance image, the grade of doctors and the selection interpretation of service effect dimensions are acted, the high-quality users are enabled to be matched with the high-quality doctors, the health service quality is guaranteed, and the user experience is further improved.

The example embodiments of the present disclosure also provide a medical resource allocation apparatus. Referring to fig. 9, the medical resource allocation apparatus may include a first information acquisition module 910, a first medical resource allocation module 920, a second medical resource allocation module 930, and a target medical resource matching module 940.

Wherein:

the first information obtaining module 910 may be configured to receive a medical consultation request sent by a target user through a terminal device, and obtain, in response to the medical consultation request, target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request;

the first medical resource allocation module 920 may be configured to input a preset doctor practice portrait, the target attribute information, a target insurance portrait, and a target health portrait into the first medical resource allocation model, to obtain a first medical resource allocation result;

the second medical resource allocation module 930 may be configured to obtain target disease information corresponding to the target insurance image, and input the target disease information, the target attribute information, the target insurance image, the target health image, and a preset doctor practice image into a second medical resource allocation model to obtain a second medical resource allocation result;

the target medical resource matching module 940 may be configured to match a corresponding target medical resource for the target user according to the first medical resource allocation result and the second medical resource allocation result, and send the target medical resource to the terminal device.

The specific details of each module in the above medical resource allocation device are already described in detail in the corresponding medical resource allocation method, so that they will not be described in detail here.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the steps of the methods of the present invention are depicted in the accompanying drawings in a particular order, this is not required to either imply that the steps must be performed in that particular order, or that all of the illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

In an exemplary embodiment of the present invention, an electronic device capable of implementing the above method is also provided.

Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 10. The electronic device 1000 shown in fig. 10 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 10, the electronic device 1000 is embodied in the form of a general purpose computing device. Components of electronic device 1000 may include, but are not limited to: the at least one processing unit 1010, the at least one memory unit 1020, a bus 1030 connecting the various system components (including the memory unit 1020 and the processing unit 1010), and a display unit 1040.

Wherein the storage unit stores program code that is executable by the processing unit 1010 such that the processing unit 1010 performs steps according to various exemplary embodiments of the present invention described in the above section of the "exemplary method" of the present specification. For example, the processing unit 1010 may perform step S110 as shown in fig. 1: receiving a medical consultation request sent by a target user through a terminal device, and responding to the medical consultation request to acquire target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request; step S120: inputting a preset doctor practice portrait, the target attribute information, a target insurance portrait and a target health portrait into a first medical resource allocation model to obtain a first medical resource allocation result; step S130: acquiring target disease information corresponding to the target insurance image, and inputting the target disease information, the target attribute information, the target insurance image, the target health image and a preset doctor practice image into a second medical resource allocation model to obtain a second medical resource allocation result; step S140: and matching the corresponding target medical resources for the target user according to the first medical resource allocation result and the second medical resource allocation result, and sending the target medical resources to the terminal equipment.

The memory unit 1020 may include readable media in the form of volatile memory units such as Random Access Memory (RAM) 10201 and/or cache memory unit 10202, and may further include Read Only Memory (ROM) 10203.

The storage unit 1020 may also include a program/utility 10204 having a set (at least one) of program modules 10205, such program modules 10205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Bus 1030 may be representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1000 can also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1000, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1050. Also, electronic device 1000 can communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 1060. As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 over the bus 1030. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with the electronic device 1000, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present invention.

In an exemplary embodiment of the present invention, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.

A program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.

Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims

1. A medical resource allocation method, comprising:

receiving a medical consultation request sent by a target user through a terminal device, and responding to the medical consultation request to acquire target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request; wherein the target health representation comprises a plurality of health labels, medical records, current medical history, and family history; the target insurance portrait comprises a plurality of insurance states, insurance kinds, insurance ranges, premium standards, customer grades, the number of times of claims, occurrence reasons and pay amounts;

Encoding the target attribute information and the target health portrait by using a first full-connection unit in a first full-connection layer in a first medical resource allocation model to obtain a first user information code, and encoding the target insurance portrait by using a second full-connection unit in the first full-connection layer to obtain a first user portrait code; encoding a preset doctor practice portrait by using a third full-connection unit in the first full-connection layer to obtain a first practice portrait code; splicing the first user information code and the first user portrait code by using a first vector splicing layer in a first medical resource allocation model to obtain a first target user code; calculating a first distance between the first target user code and the first medical portrait code by using a first distance calculation layer in a first medical resource allocation model, and obtaining a first medical resource allocation result according to a first distance calculation result;

acquiring target disease information corresponding to the target insurance image, coding the target attribute information and the target health image by using a fourth full-connection unit in a second full-connection layer in a second medical resource allocation model to obtain a second user information code, and coding the target insurance image by using a fifth full-connection unit in the second full-connection layer to obtain a second user image code; the sixth full-connection unit in the second full-connection layer is utilized to encode the preset doctor practice portrait to obtain a second practice portrait code, and the seventh full-connection unit in the second full-connection layer is utilized to encode the target disease information to obtain a disease information code; splicing the second user information code and the second user portrait code by using a second vector splicing layer in a second medical resource allocation model to obtain a second target user code, and splicing the disease information code and the second medical portrait code to obtain a medical information code; calculating a second distance between the second target user code and the medical information code by using a second distance calculation layer in a second medical resource allocation model, and obtaining a second medical resource allocation result according to a second distance calculation result;

2. The medical resource allocation method according to claim 1, wherein the medical resource allocation method further comprises:

3. The medical resource allocation method according to claim 2, wherein the medical resource allocation method further comprises:

Generating second sample information according to the basic attribute information, the historical insurance portrait, the historical health portrait, the historical disease information and the second doctor portrait, and generating a second sample pair according to the second sample information and the second matching degree;

4. The medical resource allocation method according to claim 1, wherein acquiring target disease information corresponding to the target insurance image includes:

5. The medical resource allocation method according to claim 1, wherein acquiring the target attribute information, the target insurance portrait, and the target health portrait of the target user according to the user identification information included in the medical consultation request includes:

6. A medical resource allocation device, comprising:

the first information acquisition module is used for receiving a medical consultation request sent by a target user through terminal equipment, responding to the medical consultation request and acquiring target attribute information, a target insurance portrait and a target health portrait of the target user according to user identification information included in the medical consultation request; wherein the target health representation comprises a plurality of health labels, medical records, current medical history, and family history; the target insurance portrait comprises a plurality of insurance states, insurance kinds, insurance ranges, premium standards, customer grades, the number of times of claims, occurrence reasons and pay amounts;

The first medical resource allocation module is used for coding the target attribute information and the target health portrait by using a first full-connection unit in a first full-connection layer in a first medical resource allocation model to obtain a first user information code, and coding the target insurance portrait by using a second full-connection unit in the first full-connection layer to obtain a first user portrait code; encoding a preset doctor practice portrait by using a third full-connection unit in the first full-connection layer to obtain a first practice portrait code; splicing the first user information code and the first user portrait code by using a first vector splicing layer in a first medical resource allocation model to obtain a first target user code; calculating a first distance between the first target user code and the first medical portrait code by using a first distance calculation layer in a first medical resource allocation model, and obtaining a first medical resource allocation result according to a first distance calculation result;

the second medical resource allocation module is used for acquiring target disease information corresponding to the target insurance image, coding the target attribute information and the target health image by using a fourth full-connection unit in a second full-connection layer in a second medical resource allocation model to obtain a second user information code, and coding the target insurance image by using a fifth full-connection unit in the second full-connection layer to obtain a second user image code; the sixth full-connection unit in the second full-connection layer is utilized to encode the preset doctor practice portrait to obtain a second practice portrait code, and the seventh full-connection unit in the second full-connection layer is utilized to encode the target disease information to obtain a disease information code; splicing the second user information code and the second user portrait code by using a second vector splicing layer in a second medical resource allocation model to obtain a second target user code, and splicing the disease information code and the second medical portrait code to obtain a medical information code; calculating a second distance between the second target user code and the medical information code by using a second distance calculation layer in a second medical resource allocation model, and obtaining a second medical resource allocation result according to a second distance calculation result;

7. A computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the medical resource allocation method of any of claims 1-5.

8. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the medical resource allocation method of any one of claims 1-5 via execution of the executable instructions.