CN111816301A - Medical inquiry assisting method, device, electronic equipment and medium - Google Patents

Abstract

The invention relates to artificial intelligence and provides a medical inquiry assisting method, a medical inquiry assisting device, electronic equipment and a medium. The method can determine a user to be diagnosed, acquire illness state text information and illness state image information of the user to be diagnosed, identify entities in the illness state text information, obtain a target text entity, determine a first disease entity associated with the target text entity from a pre-constructed text knowledge graph, determine a second disease entity associated with the illness state image information from the pre-constructed image knowledge graph, fuse the first disease entity and the second disease entity to obtain a target set, determine the probability of each disease entity in the target set, determine the disease entity with the highest probability as a main disease of the user to be diagnosed, and determine candidate diseases of the user to be diagnosed from the target set according to the probability. The invention can improve the accuracy of the inquiry assistance. In addition, the invention also relates to a block chain technology, and the main disease and the candidate disease can be stored in the block chain.

Description

Medical inquiry assisting method, device, electronic equipment and medium

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a medical inquiry assisting method, a medical inquiry assisting device, electronic equipment and a medium.

Background

Because the existing disease diagnosis method mainly completes diagnosis according to personal experience of doctors, and simultaneously, more experienced and more qualified doctors are few, the existing disease diagnosis method cannot meet the large number of diagnosis requirements at present. With the rapid development of artificial intelligence and the development of hospital informatization, an intelligent auxiliary inquiry method is generated, however, the method only analyzes text information, ignores the analysis of image information (for example, electrocardiogram) in a medical scene, and thus results in low accuracy of inquiry assistance.

Disclosure of Invention

In view of the above, it is necessary to provide a medical inquiry assisting method, a medical inquiry assisting apparatus, an electronic device, and a medium, which can not only improve the accuracy of the inquiry assisting, but also improve the inquiry efficiency of medical staff.

A medical consultation assistance method, comprising:

when a medical inquiry auxiliary request is received, determining a user to be diagnosed according to the medical inquiry auxiliary request;

acquiring the illness state text information and the illness state image information of the user to be diagnosed;

identifying an entity in the disease condition text information to obtain a target text entity;

determining a first disease entity associated with the target text entity from a pre-constructed text knowledge-graph and a second disease entity associated with the condition image information from a pre-constructed image knowledge-graph;

fusing the first disease entity and the second disease entity to obtain a target set;

determining a probability for each disease entity in the target set;

and determining the disease entity with the maximum probability as the main disease of the user to be diagnosed, and determining the candidate disease of the user to be diagnosed from the target set according to the probability.

According to a preferred embodiment of the present invention, the determining the user to be diagnosed according to the medical inquiry diagnosis assistance request comprises:

acquiring any idle thread from the thread connection pool;

analyzing the method body of the medical inquiry auxiliary request by using any idle thread to obtain data information carried by the medical inquiry auxiliary request;

acquiring a preset label, and acquiring information corresponding to the preset label from the data information as an identification code;

and determining the user to be diagnosed by using the identification code.

According to a preferred embodiment of the present invention, the acquiring of the medical condition text information and the medical condition image information of the user to be diagnosed includes one or more of the following combinations:

downloading a file corresponding to the identification code from a first preset website as the disease condition text information, and downloading a file corresponding to the identification code from a second preset website as the disease condition image information; and/or

And identifying the medical record of the user to be diagnosed by using an optical character recognition algorithm to obtain the medical condition text information, and controlling a medical instrument to acquire the medical condition image information of the user to be diagnosed.

According to a preferred embodiment of the present invention, the identifying the entity in the condition text information to obtain the target text entity includes:

performing word segmentation processing on the illness state text information to obtain a plurality of words;

converting each word segmentation into a word vector, and combining the word vectors according to the sequence of each word segmentation in the disease condition text information to obtain a vector sequence corresponding to the disease condition text information;

performing feature extraction on the vector sequence by using a bidirectional long and short term memory network to obtain a first feature vector corresponding to each participle in a forward long and short term memory network and a second feature vector corresponding to each participle in a reverse long and short term memory network;

splicing the first feature vector and the second feature vector to obtain a target vector corresponding to each word segmentation;

multiplying each target vector by a preset weight matrix, and adding a preset offset value to obtain a score vector of each participle, wherein each element in the score vector represents the score of a corresponding label of each participle;

for each score vector, determining a label corresponding to the element with the highest score as a target label of each word segmentation to obtain a plurality of target labels in the illness state text information;

acquiring a target label corresponding to a symptom entity in a preset symptom library from the plurality of target labels as a first text entity, and acquiring a target label corresponding to a sign entity in a preset sign library from the plurality of target labels as a second text entity;

and combining the first text entity and the second text entity to obtain the target text entity.

According to a preferred embodiment of the present invention, before determining the second disease entity associated with the condition image information from the pre-constructed image knowledgemap, the medical interrogation assistance method further comprises:

acquiring a plurality of inspection images by using a web crawler technology, and acquiring a plurality of inspection diseases corresponding to the plurality of inspection images;

aligning the plurality of inspection images to obtain a plurality of aligned images;

converting each alignment image into an image vector and each exam disease into a text vector based on pixels in each alignment image;

calculating the similarity of the image vector and the text vector as the relevance of the examination image and the examination disease;

generating the image knowledge-graph based on the image vector, the examination disease and the degree of association.

According to a preferred embodiment of the present invention, said determining the probability of each disease entity in said target set comprises:

acquiring an associated text entity associated with each disease entity from the text knowledge graph, and acquiring a first association degree of each disease entity and the associated text entity;

acquiring a related image corresponding to each disease entity from the image knowledge graph, and acquiring a second association degree of each disease entity and the related image;

multiplying the first relevance by the second relevance to obtain a target value of each disease, wherein the target value represents the probability that the associated text entity and the associated image exist in each disease at the same time;

calculating the sum of the target values of all disease entities in the target set to obtain a total value;

dividing the target value for each disease by the total value to obtain the probability for each disease entity.

According to a preferred embodiment of the present invention, after determining the disease with the highest probability as the main disease of the user to be diagnosed, and determining the candidate disease from the target set according to the probability, the medical inquiry assisting method further includes:

acquiring user information of the user to be diagnosed, and generating information to be confirmed according to the user information, the main disease and the candidate disease;

sending the information to be confirmed to terminal equipment of a designated contact person;

when the confirmation information sent by the terminal equipment is received within the preset time, the confirmed disease is extracted from the confirmation information;

generating a diagnosis report according to the user information and the confirmed diseases;

encrypting the diagnosis report by adopting a symmetric encryption algorithm to obtain a ciphertext;

and storing the mapping relation between the user information and the ciphertext, and sending the ciphertext to the client of the user to be diagnosed.

A medical interrogation assistance device, the medical interrogation assistance device comprising:

the system comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for determining a user to be diagnosed according to a medical inquiry auxiliary request when the medical inquiry auxiliary request is received;

the acquisition unit is used for acquiring the illness state text information and the illness state image information of the user to be diagnosed;

the identification unit is used for identifying the entity in the disease condition text information to obtain a target text entity;

the determining unit is further used for determining a first disease entity associated with the target text entity from a pre-constructed text knowledge graph and determining a second disease entity associated with the disease image information from a pre-constructed image knowledge graph;

a fusion unit for fusing the first disease entity and the second disease entity to obtain a target set;

the determining unit is further configured to determine a probability of each disease entity in the target set;

the determining unit is further configured to determine a disease entity with the highest probability as a main disease of the user to be diagnosed, and determine a candidate disease of the user to be diagnosed from the target set according to the probability.

An electronic device, the electronic device comprising:

a memory storing at least one instruction; and

a processor executing instructions stored in the memory to implement the medical interrogation assistance method.

A computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executable by a processor in an electronic device to implement the medical interrogation assistance method.

According to the technical scheme, when the medical condition text information and the medical condition image information of the user to be diagnosed are analyzed, the medical condition image information is analyzed, so that the medical condition text information and the medical condition image information are not limited to the analysis of the text information, the accuracy of inquiry assistance can be improved, a target set comprising a first disease entity and a second disease entity can be comprehensively obtained through the text knowledge graph and the image knowledge graph, and the comprehensiveness is improved. In addition, by analyzing the probability of each disease entity in the target set, determining the disease entity with the highest probability as the main disease and simultaneously selecting the candidate disease from the target set, the medical staff can confirm the confirmed disease again, and therefore the invention can provide inquiry assistance for the medical staff and further improve the inquiry efficiency of the medical staff.

Drawings

FIG. 1 is a flow chart of a preferred embodiment of the medical inquiry and diagnosis assistance method of the present invention.

FIG. 2 is a functional block diagram of a preferred embodiment of the medical interrogation auxiliary device of the present invention.

Fig. 3 is a schematic structural diagram of an electronic device for implementing an auxiliary method for medical inquiry according to a preferred embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.

Fig. 1 is a flow chart of a medical inquiry assisting method according to a preferred embodiment of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.

The medical inquiry assisting method is applied to one or more electronic devices, wherein the electronic devices are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and hardware of the electronic devices includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), an intelligent wearable device, and the like.

The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a cloud computing (cloud computing) based cloud consisting of a large number of hosts or network servers.

The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.

In at least one embodiment of the invention, the medical inquiry assisting method is applied to the field of artificial intelligence.

And S10, when the medical inquiry assisting request is received, determining the user to be diagnosed according to the medical inquiry assisting request.

In at least one embodiment of the present invention, the medical inquiry service request may be triggered by a medical staff or may be triggered at a preset time, which is not limited by the present invention.

Further, the information carried by the medical inquiry assistance request includes, but is not limited to: an identification code, a preset tag, etc.

In at least one embodiment of the present invention, the electronic device determining, according to the medical inquiry diagnosis assistance request, a user to be diagnosed includes:

acquiring any idle thread from the thread connection pool;

analyzing the method body of the medical inquiry auxiliary request by using any idle thread to obtain data information carried by the medical inquiry auxiliary request;

acquiring a preset label, and acquiring information corresponding to the preset label from the data information as an identification code;

and determining the user to be diagnosed by using the identification code.

The identity identification code has uniqueness, so that the user to be diagnosed can be accurately determined through the identity identification code, in addition, the idle thread is directly acquired from the thread connection pool to analyze the medical inquiry auxiliary request, and the creation time of the thread is saved, so that the analysis speed of the medical inquiry auxiliary request is improved, and the determination efficiency of the user to be diagnosed is further improved.

And S11, acquiring the disease condition text information and the disease condition image information of the user to be diagnosed.

In at least one embodiment of the present invention, the medical condition text information includes symptom information of the user to be diagnosed communicating with the medical care personnel, and sign information of the user to be diagnosed. Further, the condition image information includes, but is not limited to: the electrocardiogram and color ultrasound images of the user to be diagnosed, and the like.

In at least one embodiment of the present invention, the acquiring, by the electronic device, the medical condition text information and the medical condition image information of the user to be diagnosed includes one or more of the following combinations:

(1) downloading a file corresponding to the identification code from a first preset website as the disease condition text information, and downloading a file corresponding to the identification code from a second preset website as the disease condition image information;

(2) and identifying the medical record of the user to be diagnosed by using an optical character recognition algorithm to obtain the medical condition text information, and controlling a medical instrument to acquire the medical condition image information of the user to be diagnosed.

Through the embodiment, the disease condition text information and the disease condition image information of the user to be diagnosed can be accurately acquired.

And S12, identifying the entity in the disease condition text information to obtain a target text entity.

In at least one embodiment of the present invention, the target text entities include symptom entities as well as sign entities.

In at least one embodiment of the present invention, the electronic device identifies an entity in the condition text information, and obtaining the target text entity includes:

performing word segmentation processing on the illness state text information to obtain a plurality of words;

converting each word segmentation into a word vector, and combining the word vectors according to the sequence of each word segmentation in the disease condition text information to obtain a vector sequence corresponding to the disease condition text information;

performing feature extraction on the vector sequence by using a bidirectional long and short term memory network to obtain a first feature vector corresponding to each participle in a forward long and short term memory network and a second feature vector corresponding to each participle in a reverse long and short term memory network;

splicing the first feature vector and the second feature vector to obtain a target vector corresponding to each word segmentation;

multiplying each target vector by a preset weight matrix, and adding a preset offset value to obtain a score vector of each participle, wherein each element in the score vector represents the score of a corresponding label of each participle;

for each score vector, determining a label corresponding to the element with the highest score as a target label of each word segmentation to obtain a plurality of target labels in the illness state text information;

acquiring a target label corresponding to a symptom entity in a preset symptom library from the plurality of target labels as a first text entity, and acquiring a target label corresponding to a sign entity in a preset sign library from the plurality of target labels as a second text entity;

and combining the first text entity and the second text entity to obtain the target text entity.

Wherein the target label can be B-PER, E-PER, B-ORG, I-ORG, E-ORG, B-EVE, I-EVE, E-EVE, O, etc.

The label with the highest score is determined as the target label, so that the plurality of target labels in the illness state text information can be accurately determined, and the target text entity can be accurately determined according to the relation between the entities in the preset symptom library and the preset sign library and the plurality of target labels.

S13, determining a first disease entity associated with the target textual entity from a pre-constructed textual knowledge-map and a second disease entity associated with the condition image information from a pre-constructed image knowledge-map.

In at least one embodiment of the invention, the textual knowledge-graph includes a degree of association of a textual entity with a disease entity.

Further, the image knowledge map includes a correlation of the examination image with the examination disease.

In at least one embodiment of the invention, prior to determining a second disease entity associated with the condition image information from the pre-constructed image knowledgemap, the medical interrogation assistance method further comprises:

acquiring a plurality of inspection images by using a web crawler technology, and acquiring a plurality of inspection diseases corresponding to the plurality of inspection images;

aligning the plurality of inspection images to obtain a plurality of aligned images;

converting each alignment image into an image vector and each exam disease into a text vector based on pixels in each alignment image;

calculating the similarity of the image vector and the text vector as the relevance of the examination image and the examination disease;

generating the image knowledge-graph based on the image vector, the examination disease and the degree of association.

By aligning the inspection images, the generated image vectors can be prevented from being inaccurate due to the problems of inclination and the like of the inspection images, and the accuracy of image vector conversion is improved.

In at least one embodiment of the invention, the determining a second disease entity associated with the condition image information from a pre-constructed image knowledgemap comprises:

converting the disease condition image information into a disease condition image vector;

acquiring a disease vector corresponding to each disease in all diseases from the image knowledge graph;

calculating the similarity of the disease condition image vector and each disease vector;

and selecting a disease vector with the similarity larger than a preset threshold value as a target disease vector, and determining the disease corresponding to the target disease vector as the second disease entity.

S14, fusing the first disease entity and the second disease entity to obtain a target set.

In at least one embodiment of the present invention, the elements in the target set include the first disease entity and the second disease entity.

S15, determining the probability of each disease entity in the target set.

In at least one embodiment of the invention, the electronic device determining the probability of each disease entity in the target set comprises:

acquiring an associated text entity associated with each disease entity from the text knowledge graph, and acquiring a first association degree of each disease entity and the associated text entity;

acquiring a related image corresponding to each disease entity from the image knowledge graph, and acquiring a second association degree of each disease entity and the related image;

multiplying the first relevance by the second relevance to obtain a target value of each disease, wherein the target value represents the probability that the associated text entity and the associated image exist in each disease at the same time;

calculating the sum of the target values of all disease entities in the target set to obtain a total value;

dividing the target value for each disease by the total value to obtain the probability for each disease entity.

And S16, determining the disease entity with the highest probability as the main disease of the user to be diagnosed, and determining the candidate disease of the user to be diagnosed from the target set according to the probability.

It is emphasized that the main disease and the candidate disease may also be stored in a node of a blockchain in order to further ensure privacy and security of the main disease and the candidate disease.

In at least one embodiment of the present invention, the determining, by the electronic device, the candidate disease of the user to be diagnosed from the target set according to the probability includes:

sequencing all disease entities in the target set according to the probability from large to small to obtain a target queue;

extracting the first N disease entities from the target queue as target disease entities, wherein the value of N is a configuration value;

deleting the main disease from the target disease entity to obtain the candidate disease.

Through the above embodiment, the candidate diseases can be quickly determined.

In at least one embodiment of the present invention, after determining the disease with the highest probability as the main disease of the user to be diagnosed, and determining the candidate disease from the target set according to the probability, the medical inquiry assisting method further includes:

acquiring user information of the user to be diagnosed, and generating information to be confirmed according to the user information, the main disease and the candidate disease;

sending the information to be confirmed to terminal equipment of a designated contact person;

when the confirmation information sent by the terminal equipment is received within the preset time, the confirmed disease is extracted from the confirmation information;

generating a diagnosis report according to the user information and the confirmed diseases;

encrypting the diagnosis report by adopting a symmetric encryption algorithm to obtain a ciphertext;

and storing the mapping relation between the user information and the ciphertext, and sending the ciphertext to the client of the user to be diagnosed.

By analyzing the confirmation information, the accuracy of the diagnosis report can be ensured, and the accuracy of the inquiry assistance can be improved.

According to the technical scheme, when the medical condition text information and the medical condition image information of the user to be diagnosed are analyzed, the medical condition image information is analyzed, so that the medical condition text information and the medical condition image information are not limited to the analysis of the text information, the accuracy of inquiry assistance can be improved, a target set comprising a first disease entity and a second disease entity can be comprehensively obtained through the text knowledge graph and the image knowledge graph, and the comprehensiveness is improved. In addition, by analyzing the probability of each disease entity in the target set, determining the disease entity with the highest probability as the main disease and simultaneously selecting the candidate disease from the target set, the medical staff can confirm the confirmed disease again, and therefore the invention can provide inquiry assistance for the medical staff and further improve the inquiry efficiency of the medical staff.

Fig. 2 is a functional block diagram of a preferred embodiment of the auxiliary device for medical inquiry of the present invention. The medical inquiry assisting apparatus 11 includes adetermination unit 110, anacquisition unit 111, anidentification unit 112, afusion unit 113, aprocessing unit 114, aconversion unit 115, acalculation unit 116, ageneration unit 117, atransmission unit 118, anextraction unit 119, and anencryption unit 120. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by theprocessor 13 and that can perform a fixed function, and that are stored in thememory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.

When receiving the medical consultation assistance request, thedetermination unit 110 determines the user to be diagnosed according to the medical consultation assistance request.

In at least one embodiment of the present invention, the medical inquiry service request may be triggered by a medical staff or may be triggered at a preset time, which is not limited by the present invention.

Further, the information carried by the medical inquiry assistance request includes, but is not limited to: an identification code, a preset tag, etc.

In at least one embodiment of the present invention, the determiningunit 110 determines the user to be diagnosed according to the medical inquiry diagnosis assistance request, including:

acquiring any idle thread from the thread connection pool;

analyzing the method body of the medical inquiry auxiliary request by using any idle thread to obtain data information carried by the medical inquiry auxiliary request;

acquiring a preset label, and acquiring information corresponding to the preset label from the data information as an identification code;

and determining the user to be diagnosed by using the identification code.

The identity identification code has uniqueness, so that the user to be diagnosed can be accurately determined through the identity identification code, in addition, the idle thread is directly acquired from the thread connection pool to analyze the medical inquiry auxiliary request, and the creation time of the thread is saved, so that the analysis speed of the medical inquiry auxiliary request is improved, and the determination efficiency of the user to be diagnosed is further improved.

The obtainingunit 111 obtains the illness state text information and the illness state image information of the user to be diagnosed.

In at least one embodiment of the present invention, the medical condition text information includes symptom information of the user to be diagnosed communicating with the medical care personnel, and sign information of the user to be diagnosed. Further, the condition image information includes, but is not limited to: the electrocardiogram and color ultrasound images of the user to be diagnosed, and the like.

In at least one embodiment of the present invention, the acquiringunit 111 acquires the medical condition text information and the medical condition image information of the user to be diagnosed, which includes one or more of the following combinations:

(1) downloading a file corresponding to the identification code from a first preset website as the disease condition text information, and downloading a file corresponding to the identification code from a second preset website as the disease condition image information;

(2) and identifying the medical record of the user to be diagnosed by using an optical character recognition algorithm to obtain the medical condition text information, and controlling a medical instrument to acquire the medical condition image information of the user to be diagnosed.

Through the embodiment, the disease condition text information and the disease condition image information of the user to be diagnosed can be accurately acquired.

Theidentification unit 112 identifies the entity in the disease condition text information to obtain a target text entity.

In at least one embodiment of the present invention, the target text entities include symptom entities as well as sign entities.

In at least one embodiment of the present invention, the identifyingunit 112 identifies the entity in the condition text information, and obtaining the target text entity includes:

performing word segmentation processing on the illness state text information to obtain a plurality of words;

converting each word segmentation into a word vector, and combining the word vectors according to the sequence of each word segmentation in the disease condition text information to obtain a vector sequence corresponding to the disease condition text information;

performing feature extraction on the vector sequence by using a bidirectional long and short term memory network to obtain a first feature vector corresponding to each participle in a forward long and short term memory network and a second feature vector corresponding to each participle in a reverse long and short term memory network;

splicing the first feature vector and the second feature vector to obtain a target vector corresponding to each word segmentation;

multiplying each target vector by a preset weight matrix, and adding a preset offset value to obtain a score vector of each participle, wherein each element in the score vector represents the score of a corresponding label of each participle;

for each score vector, determining a label corresponding to the element with the highest score as a target label of each word segmentation to obtain a plurality of target labels in the illness state text information;

acquiring a target label corresponding to a symptom entity in a preset symptom library from the plurality of target labels as a first text entity, and acquiring a target label corresponding to a sign entity in a preset sign library from the plurality of target labels as a second text entity;

and combining the first text entity and the second text entity to obtain the target text entity.

Wherein the target label can be B-PER, E-PER, B-ORG, I-ORG, E-ORG, B-EVE, I-EVE, E-EVE, O, etc.

The label with the highest score is determined as the target label, so that the plurality of target labels in the illness state text information can be accurately determined, and the target text entity can be accurately determined according to the relation between the entities in the preset symptom library and the preset sign library and the plurality of target labels.

Thedetermination unit 110 determines a first disease entity associated with the target text entity from a pre-constructed text knowledgemap and a second disease entity associated with the condition image information from a pre-constructed image knowledgemap.

In at least one embodiment of the invention, the textual knowledge-graph includes a degree of association of a textual entity with a disease entity.

Further, the image knowledge map includes a correlation of the examination image with the examination disease.

In at least one embodiment of the present invention, before determining the second disease entity associated with the disease condition image information from the pre-constructed image knowledge graph, the obtainingunit 111 obtains a plurality of examination images using a web crawler technique, and obtains a plurality of examination diseases corresponding to the plurality of examination images;

theprocessing unit 114 performs alignment processing on the plurality of inspection images to obtain a plurality of aligned images;

theconversion unit 115 converts each alignment image into an image vector and converts each examination disease into a text vector based on the pixels in each alignment image;

thecalculation unit 116 calculates the similarity between the image vector and the text vector as the association degree between the examination image and the examination disease;

thegeneration unit 117 generates the image knowledge map based on the image vector, the examination disease, and the degree of association.

By aligning the inspection images, the generated image vectors can be prevented from being inaccurate due to the problems of inclination and the like of the inspection images, and the accuracy of image vector conversion is improved.

In at least one embodiment of the present invention, the determiningunit 110 determines the second disease entity associated with the condition image information from a pre-constructed image knowledge-map comprises:

converting the disease condition image information into a disease condition image vector;

acquiring a disease vector corresponding to each disease in all diseases from the image knowledge graph;

calculating the similarity of the disease condition image vector and each disease vector;

and selecting a disease vector with the similarity larger than a preset threshold value as a target disease vector, and determining the disease corresponding to the target disease vector as the second disease entity.

Thefusion unit 113 fuses the first disease entity and the second disease entity to obtain a target set.

In at least one embodiment of the present invention, the elements in the target set include the first disease entity and the second disease entity.

Thedetermination unit 110 determines the probability of each disease entity in the target set.

In at least one embodiment of the present invention, the determiningunit 110 determines the probability of each disease entity in the target set comprises:

acquiring an associated text entity associated with each disease entity from the text knowledge graph, and acquiring a first association degree of each disease entity and the associated text entity;

acquiring a related image corresponding to each disease entity from the image knowledge graph, and acquiring a second association degree of each disease entity and the related image;

multiplying the first relevance by the second relevance to obtain a target value of each disease, wherein the target value represents the probability that the associated text entity and the associated image exist in each disease at the same time;

calculating the sum of the target values of all disease entities in the target set to obtain a total value;

dividing the target value for each disease by the total value to obtain the probability for each disease entity.

The determiningunit 110 determines the disease entity with the highest probability as the main disease of the user to be diagnosed, and determines the candidate disease of the user to be diagnosed from the target set according to the probability.

It is emphasized that the main disease and the candidate disease may also be stored in a node of a blockchain in order to further ensure privacy and security of the main disease and the candidate disease.

In at least one embodiment of the present invention, the determiningunit 110 determines the candidate diseases of the user to be diagnosed from the target set according to the probability includes:

sequencing all disease entities in the target set according to the probability from large to small to obtain a target queue;

extracting the first N disease entities from the target queue as target disease entities, wherein the value of N is a configuration value;

deleting the main disease from the target disease entity to obtain the candidate disease.

Through the above embodiment, the candidate diseases can be quickly determined.

In at least one embodiment of the present invention, after determining the disease with the highest probability as the main disease of the user to be diagnosed, and determining a candidate disease from the target set according to the probability, the obtainingunit 111 obtains the user information of the user to be diagnosed, and generates information to be confirmed according to the user information, the main disease, and the candidate disease;

the sendingunit 118 sends the information to be confirmed to the terminal device of the designated contact;

when receiving the confirmation information sent by the terminal device within the preset time, the extractingunit 119 extracts the diagnosed disease from the confirmation information;

the generatingunit 117 generates a diagnosis report according to the user information and the diagnosed disease;

theencryption unit 120 encrypts the diagnostic report by using a symmetric encryption algorithm to obtain a ciphertext;

the sendingunit 118 stores the mapping relationship between the user information and the ciphertext, and sends the ciphertext to the client of the user to be diagnosed.

By analyzing the confirmation information, the accuracy of the diagnosis report can be ensured, and the accuracy of the inquiry assistance can be improved.

According to the technical scheme, when the medical condition text information and the medical condition image information of the user to be diagnosed are analyzed, the medical condition image information is analyzed, so that the medical condition text information and the medical condition image information are not limited to the analysis of the text information, the accuracy of inquiry assistance can be improved, a target set comprising a first disease entity and a second disease entity can be comprehensively obtained through the text knowledge graph and the image knowledge graph, and the comprehensiveness is improved. In addition, by analyzing the probability of each disease entity in the target set, determining the disease entity with the highest probability as the main disease and simultaneously selecting the candidate disease from the target set, the medical staff can confirm the confirmed disease again, and therefore the invention can provide inquiry assistance for the medical staff and further improve the inquiry efficiency of the medical staff.

Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for assisting medical inquiry in the present invention.

In one embodiment of the present invention, the electronic device 1 includes, but is not limited to, amemory 12, aprocessor 13, and a computer program, such as a medical interrogation assistant program, stored in thememory 12 and executable on theprocessor 13.

It will be appreciated by a person skilled in the art that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and that it may comprise more or less components than shown, or some components may be combined, or different components, e.g. the electronic device 1 may further comprise an input output device, a network access device, a bus, etc.

TheProcessor 13 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. Theprocessor 13 is an operation core and a control center of the electronic device 1, and is connected to each part of the whole electronic device 1 by various interfaces and lines, and executes an operating system of the electronic device 1 and various installed application programs, program codes, and the like.

Theprocessor 13 executes an operating system of the electronic device 1 and various installed application programs. Theprocessor 13 executes the application program to implement the steps of the various medical inquiry assistance method embodiments described above, such as the steps shown in fig. 1.

Illustratively, the computer program may be divided into one or more modules/units, which are stored in thememory 12 and executed by theprocessor 13 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into adetermination unit 110, anacquisition unit 111, arecognition unit 112, afusion unit 113, aprocessing unit 114, aconversion unit 115, acalculation unit 116, ageneration unit 117, atransmission unit 118, anextraction unit 119, and anencryption unit 120.

Thememory 12 can be used for storing the computer programs and/or modules, and theprocessor 13 implements various functions of the electronic device 1 by running or executing the computer programs and/or modules stored in thememory 12 and calling data stored in thememory 12. Thememory 12 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, thememory 12 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.

Thememory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, thememory 12 may be a memory having a physical form, such as a memory stick, a TF Card (Trans-flash Card), or the like.

The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.

Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

With reference to fig. 1, thememory 12 of the electronic device 1 stores a plurality of instructions to implement a medical inquiry assisting method, and theprocessor 13 can execute the plurality of instructions to implement:

when a medical inquiry auxiliary request is received, determining a user to be diagnosed according to the medical inquiry auxiliary request;

acquiring the illness state text information and the illness state image information of the user to be diagnosed;

identifying an entity in the disease condition text information to obtain a target text entity;

determining a first disease entity associated with the target text entity from a pre-constructed text knowledge-graph and a second disease entity associated with the condition image information from a pre-constructed image knowledge-graph;

fusing the first disease entity and the second disease entity to obtain a target set;

determining a probability for each disease entity in the target set;

and determining the disease entity with the maximum probability as the main disease of the user to be diagnosed, and determining the candidate disease of the user to be diagnosed from the target set according to the probability.

Specifically, theprocessor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed system, 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.

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 signs in the claims shall not be construed as limiting the claim concerned.

Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms 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 (10)

1. A medical consultation assistance method, characterized by comprising:

when a medical inquiry auxiliary request is received, determining a user to be diagnosed according to the medical inquiry auxiliary request;

acquiring the illness state text information and the illness state image information of the user to be diagnosed;

identifying an entity in the disease condition text information to obtain a target text entity;

determining a first disease entity associated with the target text entity from a pre-constructed text knowledge-graph and a second disease entity associated with the condition image information from a pre-constructed image knowledge-graph;

fusing the first disease entity and the second disease entity to obtain a target set;

determining a probability for each disease entity in the target set;

and determining the disease entity with the maximum probability as the main disease of the user to be diagnosed, and determining the candidate disease of the user to be diagnosed from the target set according to the probability.

2. The medical interrogation assistance method of claim 1, wherein said determining a user to be diagnosed according to said medical interrogation assistance request comprises:

acquiring any idle thread from the thread connection pool;

analyzing the method body of the medical inquiry auxiliary request by using any idle thread to obtain data information carried by the medical inquiry auxiliary request;

acquiring a preset label, and acquiring information corresponding to the preset label from the data information as an identification code;

and determining the user to be diagnosed by using the identification code.

3. The medical inquiry diagnosis assisting method according to claim 2, wherein the acquiring of the medical condition text information and the medical condition image information of the user to be diagnosed comprises one or more of the following ways:

downloading a file corresponding to the identification code from a first preset website as the disease condition text information, and downloading a file corresponding to the identification code from a second preset website as the disease condition image information; and/or

And identifying the medical record of the user to be diagnosed by using an optical character recognition algorithm to obtain the medical condition text information, and controlling a medical instrument to acquire the medical condition image information of the user to be diagnosed.

4. The medical interrogation assistance method of claim 1, wherein said identifying entities in said medical condition textual information and obtaining target textual entities comprises:

performing word segmentation processing on the illness state text information to obtain a plurality of words;

converting each word segmentation into a word vector, and combining the word vectors according to the sequence of each word segmentation in the disease condition text information to obtain a vector sequence corresponding to the disease condition text information;

performing feature extraction on the vector sequence by using a bidirectional long and short term memory network to obtain a first feature vector corresponding to each participle in a forward long and short term memory network and a second feature vector corresponding to each participle in a reverse long and short term memory network;

splicing the first feature vector and the second feature vector to obtain a target vector corresponding to each word segmentation;

multiplying each target vector by a preset weight matrix, and adding a preset offset value to obtain a score vector of each participle, wherein each element in the score vector represents the score of a corresponding label of each participle;

for each score vector, determining a label corresponding to the element with the highest score as a target label of each word segmentation to obtain a plurality of target labels in the illness state text information;

acquiring a target label corresponding to a symptom entity in a preset symptom library from the plurality of target labels as a first text entity, and acquiring a target label corresponding to a sign entity in a preset sign library from the plurality of target labels as a second text entity;

and combining the first text entity and the second text entity to obtain the target text entity.

5. The medical interrogation assistance method of claim 1, wherein prior to determining a second disease entity associated with the condition image information from a pre-constructed image knowledgemap, the medical interrogation assistance method further comprises:

acquiring a plurality of inspection images by using a web crawler technology, and acquiring a plurality of inspection diseases corresponding to the plurality of inspection images;

aligning the plurality of inspection images to obtain a plurality of aligned images;

converting each alignment image into an image vector and each exam disease into a text vector based on pixels in each alignment image;

calculating the similarity of the image vector and the text vector as the relevance of the examination image and the examination disease;

generating the image knowledge-graph based on the image vector, the examination disease and the degree of association.

6. The medical interrogation assistance method of claim 1, wherein said determining the probability of each disease entity in said target set comprises:

acquiring an associated text entity associated with each disease entity from the text knowledge graph, and acquiring a first association degree of each disease entity and the associated text entity;

acquiring a related image corresponding to each disease entity from the image knowledge graph, and acquiring a second association degree of each disease entity and the related image;

multiplying the first relevance by the second relevance to obtain a target value of each disease, wherein the target value represents the probability that the associated text entity and the associated image exist in each disease at the same time;

calculating the sum of the target values of all disease entities in the target set to obtain a total value;

dividing the target value for each disease by the total value to obtain the probability for each disease entity.

7. The medical consultation assistance method according to claim 1, wherein after determining a disease with the highest probability as a main disease of the user to be diagnosed and determining candidate diseases from the target set according to the probability, the medical consultation assistance method further comprises:

acquiring user information of the user to be diagnosed, and generating information to be confirmed according to the user information, the main disease and the candidate disease;

sending the information to be confirmed to terminal equipment of a designated contact person;

when the confirmation information sent by the terminal equipment is received within the preset time, the confirmed disease is extracted from the confirmation information;

generating a diagnosis report according to the user information and the confirmed diseases;

encrypting the diagnosis report by adopting a symmetric encryption algorithm to obtain a ciphertext;

and storing the mapping relation between the user information and the ciphertext, and sending the ciphertext to the client of the user to be diagnosed.

8. A medical interrogation assistance device, characterized in that it comprises:

the system comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for determining a user to be diagnosed according to a medical inquiry auxiliary request when the medical inquiry auxiliary request is received;

the acquisition unit is used for acquiring the illness state text information and the illness state image information of the user to be diagnosed;

the identification unit is used for identifying the entity in the disease condition text information to obtain a target text entity;

the determining unit is further used for determining a first disease entity associated with the target text entity from a pre-constructed text knowledge graph and determining a second disease entity associated with the disease image information from a pre-constructed image knowledge graph;

a fusion unit for fusing the first disease entity and the second disease entity to obtain a target set;

the determining unit is further configured to determine a probability of each disease entity in the target set;

the determining unit is further configured to determine a disease entity with the highest probability as a main disease of the user to be diagnosed, and determine a candidate disease of the user to be diagnosed from the target set according to the probability.

9. An electronic device, characterized in that the electronic device comprises:

a memory storing at least one instruction; and

a processor executing instructions stored in the memory to implement the medical interrogation assistance method of any of claims 1 to 7.

10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in an electronic device to implement the medical interrogation assistance method of any one of claims 1 to 7.

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