CN112786182A

CN112786182A - Intelligent diagnosis guiding method and device, electronic equipment and storage medium

Info

Publication number: CN112786182A
Application number: CN202011624017.6A
Authority: CN
Inventors: 黄建玲
Original assignee: Ping An International Smart City Technology Co Ltd
Current assignee: Shenzhen Ping An Smart Healthcare Technology Co ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-05-11
Anticipated expiration: 2040-12-31
Also published as: CN112786182B

Abstract

The invention relates to the technical field of digital medical treatment, and provides an intelligent diagnosis guiding method, an intelligent diagnosis guiding device, electronic equipment and a storage medium, wherein the method comprises the following steps: displaying a plurality of referral models; when detecting that a target diagnosis guide model in the plurality of diagnosis guide models is selected, identifying the type of the target diagnosis guide model; when the type of the target diagnosis guide model is identified to be the image-text guidance diagnosis guide model, displaying a human body model diagram, receiving a first diseased part input by a patient in the human body model diagram, and triggering a first voice question and answer corresponding to the first diseased part; and receiving the first answer voice of the first voice question-answer of the patient, determining the disease type of the patient, and determining the treatment preference of the patient according to the historical treatment information of the patient to generate the treatment recommendation information. According to the invention, the plurality of diagnosis guide models are provided for the patient to select different diagnosis guide models for diagnosis guide according to different requirements, so that the diversified requirements of the patient are met, and the diagnosis guide accuracy is improved.

Description

Intelligent diagnosis guiding method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of digital medical treatment, in particular to an intelligent diagnosis guiding method, an intelligent diagnosis guiding device, electronic equipment and a storage medium.

Background

With the rapid development of online medical treatment, departments are divided more and more finely, and the phenomena of wrong sign hanging and wrong disease watching of patients are caused by the complexity and the diversity of diseases frequently. The application of intelligent diagnosis guiding is already trend, the intelligent diagnosis guiding mode in the prior art generally carries out diagnosis guiding through question pushing and answer selection, for example, an operation and maintenance background is established, and medical staff manually sets a diagnosis guiding tree according to diagnosis and treatment experiences.

The inventor finds that when a patient initiates a diagnosis guide, the intelligent diagnosis guide can not dynamically configure the diagnosis guide tree according to the switching of the patient problems, so that the accuracy of the diagnosis guide is low, the diagnosis guide mode is single, the diagnosis guide can not be performed according to different patient groups, the flexibility is lacked, and the diversified requirements of the patient can not be met.

Disclosure of Invention

In view of the above, there is a need for an intelligent diagnosis guiding method, an intelligent diagnosis guiding device, an electronic device, and a storage medium, which can meet the diversified needs of patients and improve the accuracy of diagnosis guiding by providing a plurality of diagnosis guiding models for patients to select different diagnosis guiding models according to different needs.

A first aspect of the present invention provides an intelligent diagnosis guiding method, comprising:

displaying a plurality of diagnosis guide models in response to a diagnosis guide request of a patient, wherein the diagnosis guide request comprises historical clinic information of the patient;

when detecting that a target diagnosis guide model in the plurality of diagnosis guide models is selected, identifying the type of the target diagnosis guide model;

when the type of the target diagnosis guide model is identified to be a picture-text guidance diagnosis guide model, displaying a human body model diagram, receiving a first diseased part input by the patient in the human body model diagram, and triggering a first voice question and answer corresponding to the first diseased part;

receiving a first answering voice of the patient aiming at the first voice question and answer, determining the disease type of the patient according to the first voice question and the first answering voice, and determining the clinic preference of the patient according to the historical clinic information of the patient;

and generating clinic recommendation information according to the disease type of the patient and the clinic preference of the patient.

Optionally, the method further includes:

when the type of the target diagnosis guide model is identified to be the intelligent voice diagnosis guide model, triggering a second voice question-answer;

acquiring the second voice question and answer and an input signal of second answer voice aiming at the second voice question and answer to perform voice recognition to obtain a first session text;

extracting a plurality of preset first key information from the first session text, and performing entity identification on the plurality of preset first key information to obtain a plurality of first entities;

determining a disease type of the patient and a visit preference of the patient from the plurality of first entities.

Optionally, the method further includes:

when the type of the target diagnosis guide model is identified to be the intelligent question-answer diagnosis guide model, configuring a first diagnosis guide tree according to the diagnosis guide request, and starting the inquiry of the first diagnosis guide tree from a first question of the first diagnosis guide tree;

when receiving a first target answer of the first question, configuring a second diagnosis guide tree according to the first target answer, starting the inquiry of the second diagnosis guide tree from a second question of the second diagnosis guide tree, and receiving a second target answer of the second question until the intelligent inquiry guide is completed;

acquiring all target answers of all questions of the intelligent question-answer consultation model;

and determining the disease type of the patient and the visit preference of the patient according to all the target answers.

Optionally, the determining the disease type of the patient according to the first voice question answer and the first answer voice comprises:

performing voice recognition on the first answer voice to obtain a second conversation text;

extracting a plurality of preset second key information from the second session text, and performing entity identification on the plurality of preset second key information to obtain a plurality of second entities;

determining a second diseased part of the patient and symptom information of the patient according to the plurality of second entities;

judging whether the first diseased part of the patient is the same as the second diseased part of the patient;

when the first diseased part of the patient is determined to be the same as the second diseased part of the patient, determining the second diseased part as the target diseased part of the patient, and determining the disease type of the patient according to the target diseased part of the patient and the symptom information of the patient;

when the first diseased part of the patient is determined to be different from the second diseased part of the patient, triggering a third voice question and answer corresponding to the second diseased part, and determining a third patient part of the patient according to the third voice question and answer and a corresponding answer; matching a third diseased part of the patient with the first patient part and the second patient part, determining the third diseased part as a target diseased part of the patient when the third diseased part is matched with any diseased part of the first patient part and the second patient part, and determining the disease type of the patient according to the target diseased part of the patient and the symptom information of the patient.

Optionally, the determining the visit preference of the patient according to the historical visit information of the patient includes:

extracting a plurality of preset third key information from the historical visit information of the patient;

calculating the frequency of seeing a doctor of each element of each third key information;

acquiring a preset visit weight threshold value of each element of each third key message;

calculating the product of the frequency of visit of each element of each third key information and the preset visit weight threshold value to obtain the product of each element of each third key information;

determining a target element of each third key element according to the product of each element of each third key information;

determining the visit preference 5 of the patient according to the plurality of target elements of the plurality of third key information.

Optionally, before the displaying the plurality of diagnosis guide models, the method further comprises:

analyzing the diagnosis guide request to obtain basic information of the patient;

and determining a recommended diagnosis guide model from the plurality of diagnosis guide models according to the basic information of the patient.

Optionally, the method further includes:

when a confirmed seeing operation of the patient is received, extracting a plurality of pieces of target key information from a seeing request of the patient, the first answer voice of the patient and the seeing recommendation information, and sending the plurality of pieces of target key information to corresponding seeing doctors.

A second aspect of the present invention provides an intelligent diagnosis guide apparatus, comprising:

the display module is used for responding to a diagnosis guide request of a patient and displaying a plurality of diagnosis guide models, wherein the diagnosis guide request comprises historical diagnosis information of the patient;

the identification module is used for identifying the type of a target diagnosis guide model when detecting that the target diagnosis guide model in the plurality of diagnosis guide models is selected;

the display module is used for displaying a human body model diagram when the type of the target diagnosis guide model is recognized to be a picture-text guide diagnosis guide model, receiving a first diseased part input by the patient in the human body model diagram, and triggering a first voice question and answer corresponding to the first diseased part;

the determining module is used for receiving a first answering voice of the patient aiming at the first voice question and answer, determining the disease type of the patient according to the first voice question and the first answering voice and determining the treatment preference of the patient according to the historical treatment information of the patient;

and the generation module is used for generating clinic recommendation information according to the disease type of the patient and the clinic preference of the patient.

A third aspect of the invention provides an electronic device comprising a processor and a memory, the processor being configured to implement the intelligent approach when executing a computer program stored in the memory.

A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the intelligent diagnosis guide method.

In summary, according to the intelligent diagnosis guiding method, the intelligent diagnosis guiding device, the electronic device and the storage medium, on one hand, by displaying a plurality of diagnosis guiding models simultaneously, a patient can select different diagnosis guiding models for diagnosis guiding according to needs, so that diversified needs of the patient are met, and the accuracy of diagnosis guiding and the flexibility of selecting the diagnosis guiding models by the patient are improved; on the other hand, the disease type of the patient is determined according to the diseased part and the symptom information of the patient, so that the accuracy of determining the disease type of the patient is improved, the accuracy of diagnosis guide recommendation is improved, the diagnosis preference of the patient is determined according to the historical diagnosis information of the patient, the accuracy of determining the diagnosis preference of the patient is improved, and meanwhile the satisfaction degree of the patient is improved; and finally, generating the diagnosis recommendation information according to the disease type of the patient and the diagnosis preference of the patient, avoiding the phenomena of wrong number hanging and wrong diagnosis of the patient, and improving the utilization rate of the diagnosis guide model and the satisfaction degree of the patient.

Drawings

Fig. 1 is a flowchart of an intelligent diagnosis guiding method according to an embodiment of the present invention.

Fig. 2 is a structural diagram of an intelligent diagnosis guiding apparatus according to a second embodiment of the present invention.

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Example one

In this embodiment, the intelligent diagnosis guiding method may be applied to an electronic device, and for an electronic device that needs to perform intelligent diagnosis guiding, the intelligent diagnosis guiding function provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in the form of a Software Development Kit (SKD).

As shown in fig. 1, the intelligent diagnosis guiding method specifically includes the following steps, and the order of the steps in the flowchart can be changed and some steps can be omitted according to different requirements.

And S11, responding to the diagnosis guide request of the patient, and displaying a plurality of diagnosis guide models, wherein the diagnosis guide request comprises the historical diagnosis information of the patient.

In this embodiment, a diagnosis guide request input by a patient is received, specifically, the diagnosis guide request is used for the patient to request an inquiry guide before a visit for a disease, for example, before the patient makes a visit, a doctor who can be ordered is determined, a list of the doctors who can be ordered can be obtained by sending the diagnosis guide request, and the list includes information such as an age of each doctor, a medical age, a good field, a doctor position, a historical inquiry amount, and an evaluation of the patient.

In this embodiment, when a diagnosis guide request of the patient is received, a plurality of diagnosis guide models are displayed in response to the diagnosis guide request of the patient, and specifically, the types of the diagnosis guide models include, but are not limited to, an intelligent voice diagnosis guide model, a graphical guidance diagnosis guide model, and an intelligent question-answering diagnosis guide model.

Optionally, after displaying the plurality of lead models, the method further comprises:

and displaying the diagnosis guide explanation of each diagnosis guide model.

In this embodiment, the diagnosis guide explanation is used to explain the advantages and disadvantages of each diagnosis guide model, for example, the intelligent voice diagnosis guide model is suitable for the patient population with weak eyesight, and the image-text guidance diagnosis guide model is suitable for the patient population with weak hearing.

Further, before displaying the plurality of referral models, the method further comprises:

In this embodiment, the basic information of the patient is obtained by analyzing the transmitted request for medical consultation of the patient, and specifically, the basic information includes information such as the age, sex, place of birth, history, and current medical history of the patient. The recommended diagnosis guide model of the patient can be determined according to the age of the patient and the birth place of the patient, for example, if the age of the patient is 65 years, the vision of the patient is determined to be weak and the recommended diagnosis guide model of the patient is determined to be an intelligent voice diagnosis guide model.

Optionally, after determining the recommended diagnosis guidance model according to the basic information of the patient, the method further comprises:

and displaying the recommended diagnosis guiding model according to a preset display rule.

In this embodiment, the display rule may be preset, for example, a recommendation identifier may be added to the recommended diagnosis guide model, specifically, the recommendation identifier may be a red arrow, or a dynamic frame may be configured for the recommended diagnosis guide model.

In this embodiment, the recommended intelligent diagnosis guide model is displayed according to the preset display rule, so that the patient can be assisted to quickly select the diagnosis guide model, and the efficiency of determining the diagnosis guide model is improved.

S12, when detecting that a target diagnosis guide model in the plurality of diagnosis guide models is selected, identifying the type of the target diagnosis guide model.

In this embodiment, the target diagnosis guide model refers to a diagnosis guide model selected by the patient, and when a diagnosis guide request of the patient is received and a plurality of diagnosis guide models are displayed, the patient may select one diagnosis guide model from the plurality of diagnosis guide models as the target diagnosis guide model according to needs, and perform diagnosis guide according to the type of the target diagnosis guide model.

And S13, when the type of the target diagnosis guide model is recognized to be the image-text guide diagnosis guide model, displaying a human body model diagram, receiving a first diseased part input by the patient in the human body model diagram, and triggering a first voice question and answer corresponding to the first diseased part.

In this embodiment, the image-text guidance diagnosis model guides the patient to see a doctor by an image-text manner, and when the image-text guidance diagnosis model is determined to be selected by the patient, a human model diagram is displayed, specifically, the human model diagram is pre-stored, the patient can select a part of body discomfort in the human model diagram, determine the part of body discomfort as a diseased part, and when the diseased part input by the patient in the human model diagram is received, trigger a first language question and answer corresponding to the diseased part.

In some other embodiments, the method further comprises:

In this embodiment, the intelligent voice diagnosis guidance model is configured to consult information such as a chief complaint symptom, a present medical history, a past history of the patient through an AI intelligent assistant language, acquire a symptom description of the patient through voice recognition, extract a plurality of preset key information from the symptom description of the patient, input the plurality of key information into a preset entity recognition model, perform entity recognition to obtain a plurality of first entities, specifically, the first entities include disease entities and symptom entities, determine a disease type of the patient according to the plurality of first entities, and determine a visit preference of the patient from a doctor attribute and a visit manner of a tendency of the patient, specifically, the doctor attribute includes: historical inquiry amount, doctor job title, fastest visit time and the like; the inclined visit modes comprise: appointment registration, field registration, image-text inquiry, telephone inquiry, video inquiry and the like.

In some other embodiments, the method further comprises:

In this embodiment, different diagnosis guide trees may be configured in advance according to different diseases, questions in a conventional diagnosis guide tree are configured in advance, a diagnosis guide tree is configured according to the diagnosis guide request, when a question answered by a patient deviates, different diagnosis guide trees cannot be switched according to answers answered by the patient, unlike the conventional diagnosis guide tree model, in the intelligent question-answering diagnosis guide model according to this embodiment, a question is configured in the diagnosis guide tree, specifically, the question includes a plurality of standard answers, a standard answer corresponding to the question by the patient is determined by calculating a similarity between an answer answered by the patient and the standard answer, and the whole diagnosis guide question-answering model may be determined according to a key value pair configured in the standard answer.

In this embodiment, after a diagnosis guide request of a patient is received, a first diagnosis guide tree is configured according to the diagnosis guide request, a second diagnosis guide tree is configured according to a first target answer of a first question of the first diagnosis guide tree, and the diagnosis guide trees can be switched according to answers of the questions of the patient, so that the accuracy of obtaining diagnosis information after diagnosis guide is improved, and the utilization rate of a diagnosis guide model is further improved.

In the embodiment, the plurality of diagnosis guide models are displayed simultaneously, and the patient can select different diagnosis guide models for diagnosis guide according to the requirement, so that the flexibility and the satisfaction of selecting the diagnosis guide models by the patient are improved.

S14, receiving a first answer voice of the patient for the first voice question and answer, determining the disease type of the patient according to the first voice question and answer voice and determining the visit preference of the patient according to the historical visit information of the patient.

In this embodiment, the first voice question-answer is used for asking symptom information of the patient for a question preset on a first diseased part of the patient by an intelligent voice assistant, the patient answers according to the first voice question-answer, answer voice of the patient is obtained, the answer voice is converted into text information after being subjected to voice recognition, and then the text information is input into a preset entity recognition model to be subjected to entity recognition so as to determine the disease type of the patient.

In this embodiment, the visit preference of the patient can be confirmed by the historical visit information of the patient, and specifically, the historical visit information of the patient includes, but is not limited to, the doctor information and the historical medical history of the historical visits.

In this embodiment, since the patient may not correctly confirm the diseased part, the diseased part selected in the human model may not be the target diseased part, and specifically, the target diseased part is determined according to the first diseased part of the patient and the second diseased part determined by the patient through the answer after the first voice question-answer, and is determined according to the determination result.

In this embodiment, when it is determined that the first affected part of the patient is the same as the second affected part of the patient, it is determined that the first affected part selected by the patient in the human model is the same as the second affected part determined by the patient after the answer description after the first voice question-answer, and it is determined that the target affected part of the patient is the first affected part; when the first diseased part of the patient is determined to be different from the second diseased part of the patient, the first diseased part selected by the patient in the human body model is determined to be different from the second diseased part determined by the patient after the answer description of the first voice question-answer, a third voice question-answer needs to be initiated aiming at the second diseased part again, a third diseased part is determined according to the third voice question-answer and the corresponding answer of the patient, the matched diseased part is determined to be the target diseased part of the patient by matching the third diseased part with the first diseased part and the second diseased part, the accuracy of determining the target diseased part is improved, the disease type of the patient is determined according to the symptom information of the target diseased part and the patient, and the accuracy of determining the disease type of the patient is improved, thereby improving the accuracy of the diagnosis guide.

Optionally, the determining the visit preference of the patient according to the historical visit information of the patient comprises:

determining a visit preference of the patient based on a plurality of target elements of the plurality of third key information.

In this embodiment, the visit preference of the patient is determined according to a visit weight threshold and a frequency of a plurality of preset third critical information in the historical visit information of the patient, specifically, the third critical information is preset, and the third critical information includes: the grade of the historical doctor, the age of the historical doctor, the sex of the historical doctor, the inquiry amount of the historical doctor, the gold of the historical doctor and the historical diseased part.

Illustratively, the plurality of third key information includes: doctor's rating, doctor's gender, patient history 4 visits, doctor's rating: 2 experts and 2 ordinary doctors, wherein the preset treatment weight threshold of the expert is obtained and 0.6 is obtained, the preset treatment weight threshold of the ordinary doctor is obtained and 0.4 is obtained, the grade of the doctor is calculated to be the product of the experts by 1.2, the grade of the doctor is calculated to be the product of the ordinary doctor by 0.8, the target element corresponding to the grade of the doctor is determined to be the expert, and the sex of the doctor is determined: 3 times of female, 1 time of male, obtaining a preset treatment weight threshold value of 0.5 for the female doctor, a preset treatment weight threshold value of 0.5 for the male doctor, calculating the product of the sex of the treating doctor to be female to be 1.5, calculating the product of the sex of the treating doctor to be male to be 0.5, determining that the target element corresponding to the sex of the treating doctor is female, and determining the treatment preference of the patient according to the target elements of the third key information as follows: specialist-woman.

In the embodiment, the diagnosis preference of the patient is determined according to the historical diagnosis information of the patient, so that the accuracy of determining the diagnosis preference of the patient is improved, and the satisfaction of the patient is improved.

And S15, generating the visit recommendation information according to the disease type of the patient and the visit preference of the patient.

In this embodiment, the visit recommendation information is used to guide the patient to make a quick and accurate visit, and the visit recommendation information is generated according to the disease type of the patient and the visit preference of the patient, so that the phenomena of wrong number hanging and wrong disease watching of the patient are avoided, and the utilization rate of the diagnosis guide model and the satisfaction degree of the patient are improved.

Further, the method further comprises:

In this embodiment, when receiving a confirmation visit operation of the patient, determining that the patient has placed an order, generating visit recommendation information, obtaining target key information of all information of the patient in the use guidance model, and sending the target key information to the target doctor, specifically, the target key information includes basic information of the patient obtained from a request for a visit of the patient, disease diagnosis information of the patient obtained from an answer to a first voice question of the patient, and a visit preference of the patient obtained from the visit recommendation information, and sending the obtained target key information to the corresponding target doctor.

In the embodiment, the target key information is sent to the target doctor, so that the doctor can be assisted to quickly master the disease condition of the patient, the doctor seeing time is shortened, and the patient seeing efficiency and satisfaction are improved.

In summary, the intelligent diagnosis guiding method according to this embodiment displays a plurality of diagnosis guiding models in response to a diagnosis guiding request of a patient, where the diagnosis guiding request includes historical diagnosis information of the patient; when detecting that a target diagnosis guide model in the plurality of diagnosis guide models is selected, identifying the type of the target diagnosis guide model; when the type of the target diagnosis guide model is identified to be a picture-text guidance diagnosis guide model, displaying a human body model diagram, receiving a first diseased part input by the patient in the human body model diagram, and triggering a first voice question and answer corresponding to the first diseased part; receiving a first answering voice of the patient aiming at the first voice question and answer, determining the disease type of the patient according to the first voice question and the first answering voice, and determining the clinic preference of the patient according to the historical clinic information of the patient; and generating clinic recommendation information according to the disease type of the patient and the clinic preference of the patient.

In this embodiment, on one hand, by displaying a plurality of diagnosis guide models simultaneously, the patient can select different diagnosis guide models for diagnosis guide according to the requirements, so that the diversified requirements of the patient are met, and the accuracy of diagnosis guide and the flexibility of selecting the diagnosis guide model by the patient are improved; on the other hand, the disease type of the patient is determined according to the diseased part and the symptom information of the patient, so that the accuracy of determining the disease type of the patient is improved, the accuracy of diagnosis guide recommendation is improved, the diagnosis preference of the patient is determined according to the historical diagnosis information of the patient, the accuracy of determining the diagnosis preference of the patient is improved, and meanwhile the satisfaction degree of the patient is improved; and finally, generating the diagnosis recommendation information according to the disease type of the patient and the diagnosis preference of the patient, avoiding the phenomena of wrong number hanging and wrong diagnosis of the patient, and improving the utilization rate of the diagnosis guide model and the satisfaction degree of the patient.

In addition, the recommended intelligent diagnosis guide model is dynamically displayed, so that the patient can be assisted to quickly select the diagnosis guide model, and the efficiency of determining the diagnosis guide model is improved.

Example two

In some embodiments, the intelligent diagnosis guiding apparatus 20 may include a plurality of functional modules composed of program code segments. Program code of various program segments in the intelligent lead 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform the functions of intelligent lead (described in detail with reference to fig. 1).

In this embodiment, the intelligent diagnosis guiding apparatus 20 may be divided into a plurality of functional modules according to the functions performed by the intelligent diagnosis guiding apparatus. The functional module may include: a display module 201, an identification module 202, a presentation module 203, a determination module 204, and a generation module 205. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.

The display module 201 is configured to display a plurality of diagnosis guide models in response to a diagnosis guide request of a patient, where the diagnosis guide request includes historical diagnosis information of the patient.

Optionally, after displaying the plurality of lead models, a lead interpretation for each lead model is presented.

Further, before displaying a plurality of diagnosis guide models, analyzing the diagnosis guide request to obtain basic information of the patient; and determining a recommended diagnosis guide model from the plurality of diagnosis guide models according to the basic information of the patient.

Optionally, after determining the recommended diagnosis guide model according to the basic information of the patient, the display module 201 is further configured to display the recommended diagnosis guide model according to a preset display rule.

The identification module 202 is configured to identify a type of a target diagnosis guidance model among the plurality of diagnosis guidance models when it is detected that the target diagnosis guidance model is selected.

The display module 203 is configured to display a human body model map, receive a first diseased part input by the patient in the human body model map, and trigger a first voice question and answer corresponding to the first diseased part when the type of the target diagnosis guide model is identified as the image-text guidance diagnosis guide model.

In other embodiments, when the type of the target diagnosis guide model is identified as the intelligent voice diagnosis guide model, triggering a second voice question and answer; acquiring the second voice question and answer and an input signal of second answer voice aiming at the second voice question and answer to perform voice recognition to obtain a first session text; extracting a plurality of preset first key information from the first session text, and performing entity identification on the plurality of preset first key information to obtain a plurality of first entities; determining a disease type of the patient and a visit preference of the patient from the plurality of first entities.

In other embodiments, when the type of the target diagnosis guide model is identified as the intelligent question-answer diagnosis guide model, configuring a first diagnosis guide tree according to the diagnosis guide request, and starting the inquiry of the first diagnosis guide tree from a first question of the first diagnosis guide tree; when receiving a first target answer of the first question, configuring a second diagnosis guide tree according to the first target answer, starting the inquiry of the second diagnosis guide tree from a second question of the second diagnosis guide tree, and receiving a second target answer of the second question until the intelligent inquiry guide is completed; acquiring all target answers of all questions of the intelligent question-answer consultation model; and determining the disease type of the patient and the visit preference of the patient according to all the target answers.

A determining module 204, configured to receive a first answer voice of the patient for the first voice question and answer, determine a disease type of the patient according to the first voice question and the first answer voice, and determine a visit preference of the patient according to historical visit information of the patient.

Optionally, the determining module 204 determining the disease type of the patient according to the first voice question answer and the first answer voice comprises:

Optionally, the determining module 204 determining the visit preference of the patient according to the historical visit information of the patient comprises:

A generating module 205, configured to generate the visit recommendation information according to the disease type of the patient and the visit preference of the patient.

Further, when a confirmed visit operation of the patient is received, a plurality of target key information are extracted from the patient's request for a guide, the first reply voice of the patient and the visit recommendation information, and are sent to the corresponding doctor.

In summary, the intelligent diagnosis guide device according to this embodiment displays a plurality of diagnosis guide models in response to a diagnosis guide request of a patient, where the diagnosis guide request includes historical diagnosis information of the patient; when detecting that a target diagnosis guide model in the plurality of diagnosis guide models is selected, identifying the type of the target diagnosis guide model; when the type of the target diagnosis guide model is identified to be a picture-text guidance diagnosis guide model, displaying a human body model diagram, receiving a first diseased part input by the patient in the human body model diagram, and triggering a first voice question and answer corresponding to the first diseased part; receiving a first answering voice of the patient aiming at the first voice question and answer, determining the disease type of the patient according to the first voice question and the first answering voice, and determining the clinic preference of the patient according to the historical clinic information of the patient; and generating clinic recommendation information according to the disease type of the patient and the clinic preference of the patient.

EXAMPLE III

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.

It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less other hardware or software than those shown, or a different arrangement of components.

In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.

It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.

In some embodiments, the memory 31 is used for storing program codes and various data, such as the intelligent diagnosis guide apparatus 20 installed in the electronic device 3, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by using various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.

In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.

In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute an operating device of the electronic device 3 and various installed application programs (such as the intelligent diagnosis guide device 20), program codes, and the like, for example, the above modules.

The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules illustrated in fig. 2 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of intelligent diagnosis guidance.

In one embodiment of the present invention, the memory 31 stores a plurality of instructions that are executed by the at least one processor 32 to implement the functionality of intelligent referral.

Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention 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, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of modules, units or devices recited in the present invention may also be implemented by one unit or device through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent referral method, comprising:

2. The intelligent referral method of claim 1, further comprising:

3. The intelligent referral method of claim 1, further comprising:

4. The intelligent diagnostic guidance method of claim 1, wherein said determining the type of illness of the patient from the first spoken question-answer and the first answered speech comprises:

5. The intelligent referral method of claim 1 wherein said determining the referral preferences of the patient based on the patient's historical referral information comprises:

6. The intelligent referral method of claim 1, wherein prior to the displaying the plurality of referral models, the method further comprises:

7. The intelligent referral method of any one of claims 1-6, further comprising:

8. An intelligent diagnostic guide apparatus, the apparatus comprising:

9. An electronic device, comprising a processor and a memory, wherein the processor is configured to implement the intelligent diagnosis guiding method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the intelligent diagnosis guiding method according to any one of claims 1 to 7.