CN111883251A - Medical misdiagnosis detection method and device, electronic equipment and storage medium - Google Patents
Medical misdiagnosis detection method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN111883251A CN111883251A CN202010739726.2A CN202010739726A CN111883251A CN 111883251 A CN111883251 A CN 111883251A CN 202010739726 A CN202010739726 A CN 202010739726A CN 111883251 A CN111883251 A CN 111883251A
- Authority
- CN
- China
- Prior art keywords
- disease
- medical
- vector
- diagnosed
- misdiagnosis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 135
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 208
- 201000010099 disease Diseases 0.000 claims abstract description 206
- 239000013598 vector Substances 0.000 claims abstract description 150
- 230000004044 response Effects 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 19
- 208000024891 symptom Diseases 0.000 claims description 30
- 238000013528 artificial neural network Methods 0.000 claims description 27
- 238000003745 diagnosis Methods 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 19
- 230000011218 segmentation Effects 0.000 claims description 16
- 238000013527 convolutional neural network Methods 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 5
- 230000007246 mechanism Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 abstract description 4
- 238000013473 artificial intelligence Methods 0.000 abstract description 3
- 239000000284 extract Substances 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000004590 computer program Methods 0.000 description 13
- 238000012549 training Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 4
- 230000001960 triggered effect Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 206010011224 Cough Diseases 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000001717 pathogenic effect Effects 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 230000010365 information processing Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000011580 syndromic disease Diseases 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Public Health (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to artificial intelligence, and provides a medical misdiagnosis detection method, a medical misdiagnosis detection device, electronic equipment and a storage medium, the method can obtain chief complaint data and a disease to be diagnosed from a medical misdiagnosis detection request, determine a user to be diagnosed, obtain the current medical history of the user to be diagnosed, extract the chief complaint data and entities in the current medical history to obtain a disease entity, obtain a target entity associated with the disease entity, obtain the weight of the target entity, convert the target entity into a medical knowledge characteristic vector based on the weight, process the chief complaint data to obtain a text characteristic vector, splice the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector, input the target vector into a discrimination model to obtain a disease list, when the disease to be diagnosed does not exist in the disease list, the response result of the medical misdiagnosis detection request is determined as misdiagnosis, so that the misdiagnosis detection rate and real-time early warning can be improved. In addition, the invention also relates to a block chain technology, and the response result can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a medical misdiagnosis detection method and device, electronic equipment and a storage medium.
Background
Because the population cardinality of China is huge, the hospital visit amount is also large, doctors are required to make diagnosis in a very short time according to the chief complaint data and related experiences of patients, and the misdiagnosis rate of the doctors is high due to the difficulty and time limitation of the whole-body diagnosis. In order to reduce the misdiagnosis rate, the conventional misdiagnosis detection system is also developed. However, the existing misdiagnosis detection system is often used for post detection, and cannot achieve the effect of real-time early warning.
Disclosure of Invention
In view of the above, it is desirable to provide a medical misdiagnosis detection method, device, electronic device and storage medium, which can not only improve the misdiagnosis detection rate, but also achieve the effect of real-time early warning.
A medical misdiagnosis detection method, comprising:
when a medical misdiagnosis detection request is received, obtaining chief complaint data and a disease to be diagnosed from the medical misdiagnosis detection request;
determining a user to be diagnosed according to the medical misdiagnosis detection request, and acquiring the current medical history of the user to be diagnosed;
extracting entities in the main complaint data and entities in the current medical history to obtain disease entities;
acquiring a target entity associated with the disease entity from a pre-constructed graph neural network, and acquiring the weight of the target entity;
converting the target entity into a medical knowledge feature vector based on the weights;
processing the chief complaint data by using a convolutional neural network to obtain a text feature vector;
splicing the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector;
inputting the target vector into a discrimination model to obtain a disease list;
detecting whether the disease to be diagnosed is present in the disease list;
determining a response result of the medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list.
According to a preferred embodiment of the present invention, the determining a user to be diagnosed according to the medical misdiagnosis detection request, and acquiring a current medical history of the user to be diagnosed includes:
acquiring any idle thread from the thread connection pool;
analyzing the method body of the medical misdiagnosis detection request by using any idle thread to obtain message information carried by the medical misdiagnosis detection request;
acquiring a preset label, and acquiring information corresponding to the preset label from the message information as an identification code;
and determining the user to be diagnosed by using the identification code, and acquiring information corresponding to the user to be diagnosed from a filing database as the current medical history.
According to a preferred embodiment of the present invention, the extracting the entities in the chief complaint data and the entities in the current medical history to obtain the disease entities comprises:
performing word segmentation on the chief complaint data to obtain a first word segmentation, and performing word segmentation on the current medical history to obtain a second word segmentation;
traversing a pre-constructed dictionary, determining the traversed first participle as an entity in the chief complaint data, and determining the traversed second participle as an entity in the current medical history;
and fusing entities in the main complaint data and entities in the current medical history to obtain the disease entities.
According to a preferred embodiment of the present invention, before obtaining the target entity associated with the disease entity from the pre-constructed graph neural network, the medical misdiagnosis detection method further comprises:
obtaining a current disease and obtaining symptom attributes associated with the current disease;
converting the current disease into a disease vector and converting the symptom attribute into a symptom vector;
calculating the degree of association of the symptom vector and the disease vector by using an attention mechanism;
and inputting the current disease, the symptom attribute and the correlation degree into a graph template to obtain the graph neural network.
According to a preferred embodiment of the present invention, the converting the target entity into a medical knowledge feature vector based on the weight comprises:
obtaining a vector value of the target entity to obtain an entity vector;
and carrying out weighting and operation on the entity vector based on the weight to obtain the medical knowledge characteristic vector.
According to a preferred embodiment of the present invention, the processing the chief complaint data by using the convolutional neural network to obtain the text feature vector includes:
dividing words of the main complaint data according to a preset numerical value to obtain a plurality of main complaint divided words;
vectorizing the plurality of main complaint participles to obtain a plurality of main complaint vectors, wherein each main complaint vector comprises a plurality of dimensions;
determining the dimension with the largest vector value in each main complaint vector to obtain a target dimension, and acquiring a vector value corresponding to the target dimension;
and splicing the obtained vector values to obtain the text characteristic vector.
According to a preferred embodiment of the present invention, after determining the response result of the medical misdiagnosis detection request as misdiagnosis, the medical misdiagnosis detection method further includes:
generating a misdiagnosis report according to the user to be diagnosed, the response result and the disease list;
encrypting the diagnosis report by adopting a symmetric encryption algorithm to obtain a ciphertext;
determining a terminal sending the medical misdiagnosis detection request and sending time of the medical misdiagnosis detection request;
acquiring a log table of the terminal, and acquiring a login account corresponding to the sending time from the log table;
sending the ciphertext to the login account;
and sending an alarm prompt when the feedback information of the login account is not received within the preset time.
A medical misdiagnosis detection apparatus comprising:
the system comprises an acquisition unit, a diagnosis unit and a diagnosis unit, wherein the acquisition unit is used for acquiring chief complaint data and a disease to be diagnosed from a medical misdiagnosis detection request when the medical misdiagnosis detection request is received;
the determining unit is used for determining a user to be diagnosed according to the medical misdiagnosis detection request and acquiring the current medical history of the user to be diagnosed;
the extraction unit is used for extracting entities in the chief complaint data and entities in the current medical history to obtain disease entities;
the acquiring unit is further used for acquiring a target entity associated with the disease entity from a pre-constructed graph neural network and acquiring the weight of the target entity;
a conversion unit for converting the target entity into a medical knowledge feature vector based on the weight;
the processing unit is used for processing the chief complaint data by using a convolutional neural network to obtain a text feature vector;
the splicing unit is used for splicing the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector;
the input unit is used for inputting the target vector into a discrimination model to obtain a disease list;
a detection unit for detecting whether the disease to be diagnosed exists in the disease list;
the determining unit is further configured to determine a response result of the medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list.
An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the medical misdiagnosis detection 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 misdiagnosis detection method.
According to the technical scheme, the user to be diagnosed is determined according to the medical misdiagnosis detection request, the user to be diagnosed can be accurately determined, the entity in the chief complaint data and the entity in the current medical history are extracted to obtain the disease entity, the medical history of the user can influence the diagnosis result, so the current medical history of the user to be diagnosed is considered when the disease entity is analyzed, the misdiagnosis detection accuracy can be improved, the weight of the target entity can be accurately determined by acquiring the target entity related to the disease entity from the pre-constructed graph neural network and acquiring the weight of the target entity, the weight is not arbitrarily set but acquired from the pre-constructed graph neural network, and further the medical knowledge characteristic vector can be accurately generated and further input into the judgment model, the method comprises the steps of obtaining a disease list, considering chief complaint data and the current medical history of a user to be diagnosed in the target vector, therefore, accurately determining the disease list, determining a response result of a medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list, and improving the misdiagnosis detection rate because the disease list comprises a plurality of predicted diseases.
Drawings
FIG. 1 is a flow chart of a medical misdiagnosis detection method according to a preferred embodiment of the present invention.
Fig. 2 is a functional block diagram of a preferred embodiment of the medical misdiagnosis detection apparatus of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a medical misdiagnosis detection method 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 misdiagnosis detection 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 misdiagnosis detection method is applied to the smart city, so that the construction of the smart city is promoted. The medical misdiagnosis detection method is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof 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 misdiagnosis detection method is applied to the field of artificial intelligence.
And S10, when receiving the medical misdiagnosis detection request, acquiring the chief complaint data and the disease to be diagnosed from the medical misdiagnosis detection request.
In at least one embodiment of the present invention, the medical misdiagnosis detection request may be triggered by a medical staff, or may be triggered before the medical staff detects that a diagnosis is given to the patient, which is not limited by the present invention.
Further, the information carried by the medical misdiagnosis detection request includes, but is not limited to: the system comprises an identity identification code, a preset label, the chief complaint data, the disease to be diagnosed and the like.
In at least one embodiment of the present invention, the obtaining, by the electronic device, the chief complaint data and the disease to be diagnosed from the medical misdiagnosis detection request includes:
analyzing the message of the medical misdiagnosis detection request to obtain message information carried by the medical misdiagnosis detection request;
acquiring a first identifier, and acquiring information corresponding to the first identifier from the message information as the main complaint data;
and acquiring a second identifier, and acquiring information corresponding to the second identifier from the message information to serve as the disease to be diagnosed.
The first identifier and the second identifier are predefined identifiers, for example, the first identifier may be an intru.
By the above embodiment, since the message header of the medical misdiagnosis detection request does not need to be analyzed, the analysis efficiency of the medical misdiagnosis detection request can be improved, and in addition, the disease to be diagnosed can be accurately determined from the main complaint data set through the mapping relationship between the first identifier and the main complaint data and the mapping relationship between the second identifier and the disease to be diagnosed.
And S11, determining the user to be diagnosed according to the medical misdiagnosis detection request, and acquiring the current medical history of the user to be diagnosed.
In at least one embodiment of the present invention, the user to be diagnosed refers to any user currently treated by a medical staff, and the current medical history refers to a disease that the user to be diagnosed has developed in the past.
In at least one embodiment of the present invention, the determining, by the electronic device, a user to be diagnosed according to the medical misdiagnosis detection request, and acquiring a current medical history of the user to be diagnosed includes:
acquiring any idle thread from the thread connection pool;
analyzing the method body of the medical misdiagnosis detection request by using any idle thread to obtain message information carried by the medical misdiagnosis detection request;
acquiring a preset label, and acquiring information corresponding to the preset label from the message information as an identification code;
and determining the user to be diagnosed by using the identification code, and acquiring information corresponding to the user to be diagnosed from a filing database as the current medical history.
And storing medical histories corresponding to a plurality of users in the profiling library.
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 misdiagnosis detection request, and the creation time of the thread is saved, so that the analysis speed of the medical misdiagnosis detection request is increased, and the misdiagnosis detection efficiency is further increased.
And S12, extracting entities in the chief complaint data and entities in the current medical history to obtain disease entities.
In at least one embodiment of the present invention, the disease entity refers to a disease that may occur to the user to be diagnosed, for example, the disease entity may be a cough.
In at least one embodiment of the present invention, the electronic device extracting the entities in the chief complaint data and the entities in the current medical history to obtain the disease entities includes:
performing word segmentation on the chief complaint data to obtain a first word segmentation, and performing word segmentation on the current medical history to obtain a second word segmentation;
traversing a pre-constructed dictionary, determining the traversed first participle as an entity in the chief complaint data, and determining the traversed second participle as an entity in the current medical history;
and fusing entities in the main complaint data and entities in the current medical history to obtain the disease entities.
Wherein the dictionary stores a plurality of pathogenic entities therein.
And S13, acquiring a target entity associated with the disease entity from the pre-constructed neural network of the map, and acquiring the weight of the target entity.
In at least one embodiment of the invention, the graph neural network comprises a plurality of entities, attributes of each entity and association degrees of the entities and the attributes.
In at least one embodiment of the present invention, before obtaining the target entity associated with the disease entity from the pre-constructed graph neural network, the medical misdiagnosis detection method further includes:
obtaining a current disease and obtaining symptom attributes associated with the current disease;
converting the current disease into a disease vector and converting the symptom attribute into a symptom vector;
calculating the degree of association of the symptom vector and the disease vector by using an attention mechanism;
and inputting the current disease, the symptom attribute and the correlation degree into a graph template to obtain the graph neural network.
With the above embodiment, since the graph template is constructed in advance, the above embodiment does not need to repeatedly create the graph template, and thus, the determination efficiency of the graph neural network can be improved.
In at least one embodiment of the present invention, the obtaining the weight of the target entity includes:
acquiring the association degree of the disease entity and the target entity from the graph neural network as a target association degree;
and carrying out normalization processing on the target association degree to obtain the weight.
S14, converting the target entity into a medical knowledge feature vector based on the weight.
In at least one embodiment of the invention, the electronic device converting the target entity into a medical knowledge feature vector based on the weights comprises:
obtaining a vector value of the target entity to obtain an entity vector;
and carrying out weighting and operation on the entity vector based on the weight to obtain the medical knowledge characteristic vector.
And S15, processing the chief complaint data by using a convolutional neural network to obtain a text feature vector.
In at least one embodiment of the present invention, the processing, by the electronic device, the complaint data by using a convolutional neural network to obtain a text feature vector includes:
dividing words of the main complaint data according to a preset numerical value to obtain a plurality of main complaint divided words;
vectorizing the plurality of main complaint participles to obtain a plurality of main complaint vectors, wherein each main complaint vector comprises a plurality of dimensions;
determining the dimension with the largest vector value in each main complaint vector to obtain a target dimension, and acquiring a vector value corresponding to the target dimension;
and splicing the obtained vector values to obtain the text characteristic vector.
The preset value may be configured in a customized manner, for example, the preset value may be 2.
Through the embodiment, the loss of the chief complaint data can be reduced.
And S16, splicing the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector.
In at least one embodiment of the present invention, the target vector is generated by stitching the medical knowledge feature vector and the text feature vector.
And S17, inputting the target vector into a discriminant model to obtain a disease list.
In at least one embodiment of the present invention, the disease list includes a plurality of predicted diseases with a higher probability.
In at least one embodiment of the present invention, before inputting the target vector into a discriminant model, resulting in a disease list, the method further comprises:
acquiring a plurality of training data, wherein each training data comprises symptom data of each training user, medical history of each training user and corresponding symptoms;
converting the symptom data into symptom vectors, converting the medical history into medical history vectors, and converting the corresponding symptoms into the disease vectors;
splicing the symptom vector and the medical history vector to obtain a spliced vector;
and constructing the discrimination model according to the splicing vector and the disease vector.
Through the embodiment, the training data are derived from the real data, so that an accurate discriminant model can be generated, and the determination of a subsequent disease list is facilitated.
In at least one embodiment of the present invention, the inputting the target vector into a discriminant model to obtain a disease list includes:
inputting the target vector into the discrimination model to obtain various predicted diseases and the probability of each predicted disease;
sequencing the plurality of predicted diseases according to the sequence of the probability from large to small to obtain a queue;
and selecting the first N predicted diseases from the queue, and fusing the selected predicted diseases to obtain the disease list, wherein N is a positive integer.
An accurate disease list can be obtained by fusing predicted diseases with high probability.
S18, detecting whether the disease to be diagnosed exists in the disease list.
In at least one embodiment of the present invention, the electronic device traverses the disease list, determines that the disease to be diagnosed exists in the disease list when the disease to be diagnosed is the same as the predicted disease traversed in the disease list, or determines that the disease to be diagnosed does not exist in the disease list when the disease to be diagnosed is different from all the predicted diseases traversed in the disease list.
In other embodiments, when the disease to be diagnosed exists in the disease list, the response result of the medical misdiagnosis detection request is determined as a correct diagnosis, reward information is generated, an issuing user of the disease to be diagnosed is determined, and the reward information is sent to the terminal device of the issuing user.
Through the implementation mode, the reward information can be sent to the sending user when the sending user makes an accurate diagnosis, so that the experience of the sending user is improved.
And S19, determining the response result of the medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list.
It is emphasized that, in order to further ensure the privacy and security of the response result, the response result may also be stored in a node of a block chain.
In at least one embodiment of the present invention, after determining the response result of the medical misdiagnosis detection request as misdiagnosis, the medical misdiagnosis detection method further includes:
generating a misdiagnosis report according to the user to be diagnosed, the response result and the disease list;
encrypting the diagnosis report by adopting a symmetric encryption algorithm to obtain a ciphertext;
determining a terminal sending the medical misdiagnosis detection request and sending time of the medical misdiagnosis detection request;
acquiring a log table of the terminal, and acquiring a login account corresponding to the sending time from the log table;
sending the ciphertext to the login account;
and sending an alarm prompt when the feedback information of the login account is not received within the preset time.
Through the embodiment, after misdiagnosis is determined, the ciphertext can be timely generated and sent to achieve the effect of timely early warning, in addition, feedback can be not received within the preset time, an alarm can be timely sent out, the effect of real-time early warning can be achieved, the ciphertext can be timely received, and accordingly damage is avoided.
According to the technical scheme, the user to be diagnosed is determined according to the medical misdiagnosis detection request, the user to be diagnosed can be accurately determined, the entity in the chief complaint data and the entity in the current medical history are extracted to obtain the disease entity, the medical history of the user can influence the diagnosis result, so the current medical history of the user to be diagnosed is considered when the disease entity is analyzed, the misdiagnosis detection accuracy can be improved, the weight of the target entity can be accurately determined by acquiring the target entity related to the disease entity from the pre-constructed graph neural network and acquiring the weight of the target entity, the weight is not arbitrarily set but acquired from the pre-constructed graph neural network, and further the medical knowledge characteristic vector can be accurately generated and further input into the judgment model, the method comprises the steps of obtaining a disease list, considering chief complaint data and the current medical history of a user to be diagnosed in the target vector, therefore, accurately determining the disease list, determining a response result of a medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list, and improving the misdiagnosis detection rate because the disease list comprises a plurality of predicted diseases.
Fig. 2 is a functional block diagram of a medical misdiagnosis detection device according to a preferred embodiment of the present invention. The medical misdiagnosis detection device 11 includes an acquisition unit 110, a determination unit 111, an extraction unit 112, a conversion unit 113, a processing unit 114, a concatenation unit 115, an input unit 116, a detection unit 117, a calculation unit 118, an entry unit 119, a construction unit 120, a generation unit 121, an encryption unit 122, and a transmission unit 123. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
When receiving a medical misdiagnosis detection request, the obtaining unit 110 obtains the chief complaint data and the disease to be diagnosed from the medical misdiagnosis detection request.
In at least one embodiment of the present invention, the medical misdiagnosis detection request may be triggered by a medical staff, or may be triggered before the medical staff detects that a diagnosis is given to the patient, which is not limited by the present invention.
Further, the information carried by the medical misdiagnosis detection request includes, but is not limited to: the system comprises an identity identification code, a preset label, the chief complaint data, the disease to be diagnosed and the like.
In at least one embodiment of the present invention, the obtaining unit 110 obtains the chief complaint data and the disease to be diagnosed from the medical misdiagnosis detection request includes:
analyzing the message of the medical misdiagnosis detection request to obtain message information carried by the medical misdiagnosis detection request;
acquiring a first identifier, and acquiring information corresponding to the first identifier from the message information as the main complaint data;
and acquiring a second identifier, and acquiring information corresponding to the second identifier from the message information to serve as the disease to be diagnosed.
The first identifier and the second identifier are predefined identifiers, for example, the first identifier may be an intru.
By the above embodiment, since the message header of the medical misdiagnosis detection request does not need to be analyzed, the analysis efficiency of the medical misdiagnosis detection request can be improved, and in addition, the disease to be diagnosed can be accurately determined from the main complaint data set through the mapping relationship between the first identifier and the main complaint data and the mapping relationship between the second identifier and the disease to be diagnosed.
The determining unit 111 determines the user to be diagnosed according to the medical misdiagnosis detection request, and obtains the current medical history of the user to be diagnosed.
In at least one embodiment of the present invention, the user to be diagnosed refers to any user currently treated by a medical staff, and the current medical history refers to a disease that the user to be diagnosed has developed in the past.
In at least one embodiment of the present invention, the determining unit 111 determines the user to be diagnosed according to the medical misdiagnosis detection request, and acquiring the current medical history of the user to be diagnosed includes:
acquiring any idle thread from the thread connection pool;
analyzing the method body of the medical misdiagnosis detection request by using any idle thread to obtain message information carried by the medical misdiagnosis detection request;
acquiring a preset label, and acquiring information corresponding to the preset label from the message information as an identification code;
and determining the user to be diagnosed by using the identification code, and acquiring information corresponding to the user to be diagnosed from a filing database as the current medical history.
And storing medical histories corresponding to a plurality of users in the profiling library.
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 misdiagnosis detection request, and the creation time of the thread is saved, so that the analysis speed of the medical misdiagnosis detection request is increased, and the misdiagnosis detection efficiency is further increased.
The extraction unit 112 extracts the entities in the chief complaint data and the entities in the current medical history to obtain disease entities.
In at least one embodiment of the present invention, the disease entity refers to a disease that may occur to the user to be diagnosed, for example, the disease entity may be a cough.
In at least one embodiment of the present invention, the extracting unit 112 extracts the entities in the chief complaint data and the entities in the current medical history, and obtaining the disease entities includes:
performing word segmentation on the chief complaint data to obtain a first word segmentation, and performing word segmentation on the current medical history to obtain a second word segmentation;
traversing a pre-constructed dictionary, determining the traversed first participle as an entity in the chief complaint data, and determining the traversed second participle as an entity in the current medical history;
and fusing entities in the main complaint data and entities in the current medical history to obtain the disease entities.
Wherein the dictionary stores a plurality of pathogenic entities therein.
The obtaining unit 110 obtains a target entity associated with the disease entity from a pre-constructed graph neural network, and obtains a weight of the target entity.
In at least one embodiment of the invention, the graph neural network comprises a plurality of entities, attributes of each entity and association degrees of the entities and the attributes.
In at least one embodiment of the present invention, before obtaining the target entity associated with the disease entity from the pre-constructed graph neural network, the obtaining unit 110 obtains the current disease and obtains the symptom attribute associated with the current disease;
the conversion unit 113 converts the current disease into a disease vector and converts the symptom attribute into a symptom vector;
the calculation unit 118 calculates the association degree of the symptom vector and the disease vector by using an attention mechanism;
the entry unit 119 enters the current disease, the symptom attribute, and the degree of association into a graph template to obtain the graph neural network.
With the above embodiment, since the graph template is constructed in advance, the above embodiment does not need to repeatedly create the graph template, and thus, the determination efficiency of the graph neural network can be improved.
In at least one embodiment of the present invention, the obtaining unit 110 obtains the weight of the target entity, including:
acquiring the association degree of the disease entity and the target entity from the graph neural network as a target association degree;
and carrying out normalization processing on the target association degree to obtain the weight.
The conversion unit 113 converts the target entity into a medical knowledge feature vector based on the weights.
In at least one embodiment of the present invention, the converting unit 113 converts the target entity into a medical knowledge feature vector based on the weight includes:
obtaining a vector value of the target entity to obtain an entity vector;
and carrying out weighting and operation on the entity vector based on the weight to obtain the medical knowledge characteristic vector.
The processing unit 114 processes the chief complaint data by using a convolutional neural network to obtain a text feature vector.
In at least one embodiment of the present invention, the processing unit 114 processes the complaint data by using a convolutional neural network, and obtaining a text feature vector includes:
dividing words of the main complaint data according to a preset numerical value to obtain a plurality of main complaint divided words;
vectorizing the plurality of main complaint participles to obtain a plurality of main complaint vectors, wherein each main complaint vector comprises a plurality of dimensions;
determining the dimension with the largest vector value in each main complaint vector to obtain a target dimension, and acquiring a vector value corresponding to the target dimension;
and splicing the obtained vector values to obtain the text characteristic vector.
The preset value may be configured in a customized manner, for example, the preset value may be 2.
Through the embodiment, the loss of the chief complaint data can be reduced.
The splicing unit 115 splices the medical knowledge feature vector and the text feature vector to obtain a target vector.
In at least one embodiment of the present invention, the target vector is generated by stitching the medical knowledge feature vector and the text feature vector.
The input unit 116 inputs the target vector into a discriminant model to obtain a disease list.
In at least one embodiment of the present invention, the disease list includes a plurality of predicted diseases with a higher probability.
In at least one embodiment of the present invention, before the target vector is input into the discriminant model to obtain the disease list, the obtaining unit 110 obtains a plurality of training data, each of which includes symptom data of each training user, a medical history of each training user, and a corresponding disease;
the conversion unit 113 converts the symptom data into a symptom vector, converts the medical history into a medical history vector, and converts the corresponding disorder into a disorder vector;
the stitching unit 115 stitches the symptom vector and the medical history vector to obtain a stitched vector;
the construction unit 120 constructs the discriminant model according to the stitching vector and the syndrome vector.
Through the embodiment, the training data are derived from the real data, so that an accurate discriminant model can be generated, and the determination of a subsequent disease list is facilitated.
In at least one embodiment of the present invention, the inputting unit 116 inputs the target vector into a discriminant model, and obtaining the disease list includes:
inputting the target vector into the discrimination model to obtain various predicted diseases and the probability of each predicted disease;
sequencing the plurality of predicted diseases according to the sequence of the probability from large to small to obtain a queue;
and selecting the first N predicted diseases from the queue, and fusing the selected predicted diseases to obtain the disease list, wherein N is a positive integer.
An accurate disease list can be obtained by fusing predicted diseases with high probability.
The detection unit 117 detects whether the disease to be diagnosed is present in the disease list.
In at least one embodiment of the present invention, the detecting unit 117 traverses the disease list, determines that the disease to be diagnosed exists in the disease list when the disease to be diagnosed is the same as the predicted disease traversed in the disease list, or determines that the disease to be diagnosed does not exist in the disease list when the disease to be diagnosed is different from all the predicted diseases traversed in the disease list.
In other embodiments, when the disease to be diagnosed exists in the disease list, the response result of the medical misdiagnosis detection request is determined as a correct diagnosis, reward information is generated, an issuing user of the disease to be diagnosed is determined, and the reward information is sent to the terminal device of the issuing user.
Through the implementation mode, the reward information can be sent to the sending user when the sending user makes an accurate diagnosis, so that the experience of the sending user is improved.
When the disease to be diagnosed does not exist in the disease list, the determination unit 111 determines a response result of the medical misdiagnosis detection request as misdiagnosis.
It is emphasized that, in order to further ensure the privacy and security of the response result, the response result may also be stored in a node of a block chain.
In at least one embodiment of the present invention, after determining the response result of the medical misdiagnosis detection request as misdiagnosis, the generating unit 121 generates a misdiagnosis report according to the user to be diagnosed, the response result, and the disease list;
the encryption unit 122 encrypts the diagnosis report by using a symmetric encryption algorithm to obtain a ciphertext;
the determination unit 111 determines a terminal that issues the medical misdiagnosis detection request and an issuance time of the medical misdiagnosis detection request;
the obtaining unit 110 obtains a log table of the terminal, and obtains a login account corresponding to the sending time from the log table;
the sending unit 123 sends the ciphertext to the login account;
when the feedback information of the login account is not received within the preset time, the sending unit 123 sends out an alarm prompt.
Through the embodiment, after misdiagnosis is determined, the ciphertext can be timely generated and sent to achieve the effect of timely early warning, in addition, feedback can be not received within the preset time, an alarm can be timely sent out, the effect of real-time early warning can be achieved, the ciphertext can be timely received, and accordingly damage is avoided.
According to the technical scheme, the user to be diagnosed is determined according to the medical misdiagnosis detection request, the user to be diagnosed can be accurately determined, the entity in the chief complaint data and the entity in the current medical history are extracted to obtain the disease entity, the medical history of the user can influence the diagnosis result, so the current medical history of the user to be diagnosed is considered when the disease entity is analyzed, the misdiagnosis detection accuracy can be improved, the weight of the target entity can be accurately determined by acquiring the target entity related to the disease entity from the pre-constructed graph neural network and acquiring the weight of the target entity, the weight is not arbitrarily set but acquired from the pre-constructed graph neural network, and further the medical knowledge characteristic vector can be accurately generated and further input into the judgment model, the method comprises the steps of obtaining a disease list, considering chief complaint data and the current medical history of a user to be diagnosed in the target vector, therefore, accurately determining the disease list, determining a response result of a medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list, and improving the misdiagnosis detection rate because the disease list comprises a plurality of predicted diseases.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for detecting medical misdiagnosis of the present invention.
In one embodiment of the present invention, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and a computer program, such as a medical misdiagnosis detection program, stored in the memory 12 and executable on the processor 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.
The Processor 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. The processor 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.
The processor 13 executes an operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in each of the above-described embodiments of the medical misdiagnosis detection method, 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 the memory 12 and executed by the processor 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 an acquisition unit 110, a determination unit 111, an extraction unit 112, a conversion unit 113, a processing unit 114, a concatenation unit 115, an input unit 116, a detection unit 117, a calculation unit 118, an entry unit 119, a construction unit 120, a generation unit 121, an encryption unit 122, and a transmission unit 123.
The memory 12 can be used for storing the computer programs and/or modules, and the processor 13 implements various functions of the electronic device 1 by running or executing the computer programs and/or modules stored in the memory 12 and calling data stored in the memory 12. The memory 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, the memory 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.
The memory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, the memory 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, the memory 12 in the electronic device 1 stores a plurality of instructions to implement a medical misdiagnosis detection method, and the processor 13 can execute the plurality of instructions to implement:
when a medical misdiagnosis detection request is received, obtaining chief complaint data and a disease to be diagnosed from the medical misdiagnosis detection request;
determining a user to be diagnosed according to the medical misdiagnosis detection request, and acquiring the current medical history of the user to be diagnosed;
extracting entities in the main complaint data and entities in the current medical history to obtain disease entities;
acquiring a target entity associated with the disease entity from a pre-constructed graph neural network, and acquiring the weight of the target entity;
converting the target entity into a medical knowledge feature vector based on the weights;
processing the chief complaint data by using a convolutional neural network to obtain a text feature vector;
splicing the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector;
inputting the target vector into a discrimination model to obtain a disease list;
detecting whether the disease to be diagnosed is present in the disease list;
determining a response result of the medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list.
Specifically, the processor 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 misdiagnosis detection method is characterized by comprising the following steps:
when a medical misdiagnosis detection request is received, obtaining chief complaint data and a disease to be diagnosed from the medical misdiagnosis detection request;
determining a user to be diagnosed according to the medical misdiagnosis detection request, and acquiring the current medical history of the user to be diagnosed;
extracting entities in the main complaint data and entities in the current medical history to obtain disease entities;
acquiring a target entity associated with the disease entity from a pre-constructed graph neural network, and acquiring the weight of the target entity;
converting the target entity into a medical knowledge feature vector based on the weights;
processing the chief complaint data by using a convolutional neural network to obtain a text feature vector;
splicing the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector;
inputting the target vector into a discrimination model to obtain a disease list;
detecting whether the disease to be diagnosed is present in the disease list;
determining a response result of the medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list.
2. The medical misdiagnosis detection method according to claim 1, wherein the determining a user to be diagnosed according to the medical misdiagnosis detection request and the obtaining the current medical history of the user to be diagnosed comprises:
acquiring any idle thread from the thread connection pool;
analyzing the method body of the medical misdiagnosis detection request by using any idle thread to obtain message information carried by the medical misdiagnosis detection request;
acquiring a preset label, and acquiring information corresponding to the preset label from the message information as an identification code;
and determining the user to be diagnosed by using the identification code, and acquiring information corresponding to the user to be diagnosed from a filing database as the current medical history.
3. The medical misdiagnosis detection method of claim 1, wherein the extracting entities in the chief complaint data and entities in the current medical history to obtain disease entities comprises:
performing word segmentation on the chief complaint data to obtain a first word segmentation, and performing word segmentation on the current medical history to obtain a second word segmentation;
traversing a pre-constructed dictionary, determining the traversed first participle as an entity in the chief complaint data, and determining the traversed second participle as an entity in the current medical history;
and fusing entities in the main complaint data and entities in the current medical history to obtain the disease entities.
4. The medical misdiagnosis detection method according to claim 1, wherein before obtaining the target entity associated with the disease entity from a pre-constructed graph neural network, the medical misdiagnosis detection method further comprises:
obtaining a current disease and obtaining symptom attributes associated with the current disease;
converting the current disease into a disease vector and converting the symptom attribute into a symptom vector;
calculating the degree of association of the symptom vector and the disease vector by using an attention mechanism;
and inputting the current disease, the symptom attribute and the correlation degree into a graph template to obtain the graph neural network.
5. The medical misdiagnosis detection method according to claim 1, wherein the converting the target entity into a medical knowledge feature vector based on the weight comprises:
obtaining a vector value of the target entity to obtain an entity vector;
and carrying out weighting and operation on the entity vector based on the weight to obtain the medical knowledge characteristic vector.
6. The medical misdiagnosis detection method of claim 1, wherein the processing the chief complaint data with a convolutional neural network to obtain a text feature vector comprises:
dividing words of the main complaint data according to a preset numerical value to obtain a plurality of main complaint divided words;
vectorizing the plurality of main complaint participles to obtain a plurality of main complaint vectors, wherein each main complaint vector comprises a plurality of dimensions;
determining the dimension with the largest vector value in each main complaint vector to obtain a target dimension, and acquiring a vector value corresponding to the target dimension;
and splicing the obtained vector values to obtain the text characteristic vector.
7. The medical misdiagnosis detection method according to claim 1, wherein after determining a result of the response of the medical misdiagnosis detection request as a misdiagnosis, the medical misdiagnosis detection method further comprises:
generating a misdiagnosis report according to the user to be diagnosed, the response result and the disease list;
encrypting the diagnosis report by adopting a symmetric encryption algorithm to obtain a ciphertext;
determining a terminal sending the medical misdiagnosis detection request and sending time of the medical misdiagnosis detection request;
acquiring a log table of the terminal, and acquiring a login account corresponding to the sending time from the log table;
sending the ciphertext to the login account;
and sending an alarm prompt when the feedback information of the login account is not received within the preset time.
8. A medical misdiagnosis detection apparatus, characterized by comprising:
the system comprises an acquisition unit, a diagnosis unit and a diagnosis unit, wherein the acquisition unit is used for acquiring chief complaint data and a disease to be diagnosed from a medical misdiagnosis detection request when the medical misdiagnosis detection request is received;
the determining unit is used for determining a user to be diagnosed according to the medical misdiagnosis detection request and acquiring the current medical history of the user to be diagnosed;
the extraction unit is used for extracting entities in the chief complaint data and entities in the current medical history to obtain disease entities;
the acquiring unit is further used for acquiring a target entity associated with the disease entity from a pre-constructed graph neural network and acquiring the weight of the target entity;
a conversion unit for converting the target entity into a medical knowledge feature vector based on the weight;
the processing unit is used for processing the chief complaint data by using a convolutional neural network to obtain a text feature vector;
the splicing unit is used for splicing the medical knowledge characteristic vector and the text characteristic vector to obtain a target vector;
the input unit is used for inputting the target vector into a discrimination model to obtain a disease list;
a detection unit for detecting whether the disease to be diagnosed exists in the disease list;
the determining unit is further configured to determine a response result of the medical misdiagnosis detection request as misdiagnosis when the disease to be diagnosed does not exist in the disease list.
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 misdiagnosis detection method according to any one 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 misdiagnosis detection method of any one of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010739726.2A CN111883251A (en) | 2020-07-28 | 2020-07-28 | Medical misdiagnosis detection method and device, electronic equipment and storage medium |
| PCT/CN2020/112235 WO2021120688A1 (en) | 2020-07-28 | 2020-08-28 | Medical misdiagnosis detection method and apparatus, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010739726.2A CN111883251A (en) | 2020-07-28 | 2020-07-28 | Medical misdiagnosis detection method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111883251A true CN111883251A (en) | 2020-11-03 |
Family
ID=73201859
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010739726.2A Pending CN111883251A (en) | 2020-07-28 | 2020-07-28 | Medical misdiagnosis detection method and device, electronic equipment and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111883251A (en) |
| WO (1) | WO2021120688A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112435745A (en) * | 2020-12-18 | 2021-03-02 | 深圳赛安特技术服务有限公司 | Consultation strategy recommendation method and device, electronic equipment and storage medium |
| CN112541066A (en) * | 2020-12-11 | 2021-03-23 | 清华大学 | Text-structured-based medical and technical report detection method and related equipment |
| CN113327691A (en) * | 2021-06-01 | 2021-08-31 | 平安科技（深圳）有限公司 | Query method and device based on language model, computer equipment and storage medium |
| CN116682551A (en) * | 2023-07-27 | 2023-09-01 | 腾讯科技（深圳）有限公司 | Disease prediction method, disease prediction model training method and device |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113921144A (en) * | 2021-09-23 | 2022-01-11 | 清华大学 | Disease prediction set processing method and device, electronic equipment and storage medium |
| CN115830017B (en) * | 2023-02-09 | 2023-07-25 | 智慧眼科技股份有限公司 | Tumor detection system, method, equipment and medium based on image-text multi-mode fusion |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107833629A (en) * | 2017-10-25 | 2018-03-23 | 厦门大学 | Aided diagnosis method and system based on deep learning |
| WO2018171919A1 (en) * | 2017-03-24 | 2018-09-27 | Clinova Limited | Apparatus, method and computer program for providing medical advice based on self-reported symptoms of a user |
| CN110162779A (en) * | 2019-04-04 | 2019-08-23 | 北京百度网讯科技有限公司 | Appraisal procedure, device and the equipment of quality of case history |
| CN110534206A (en) * | 2019-08-26 | 2019-12-03 | 北京好医生云医院管理技术有限公司 | A kind of working method of medical diagnosis auxiliary system |
| CN110675951A (en) * | 2019-08-26 | 2020-01-10 | 北京百度网讯科技有限公司 | Intelligent disease diagnosis method and device, computer equipment and readable medium |
| CN111091906A (en) * | 2019-10-31 | 2020-05-01 | 中电药明数据科技(成都)有限公司 | Auxiliary medical diagnosis method and system based on real world data |
| CN111383769A (en) * | 2020-01-08 | 2020-07-07 | 科大讯飞股份有限公司 | Method, device, equipment and storage medium for detecting complaint and diagnosis consistency |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951719A (en) * | 2017-04-10 | 2017-07-14 | 荣科科技股份有限公司 | The construction method and constructing system of clinical diagnosis model, clinical diagnosing system |
| AU2018350984A1 (en) * | 2017-10-17 | 2020-05-07 | Satish Rao | Machine learning based system for identifying and monitoring neurological disorders |
| CN109545384A (en) * | 2018-11-21 | 2019-03-29 | 上海依智医疗技术有限公司 | A kind of medical diagnostic method and device |
| EP3895178A4 (en) * | 2018-12-11 | 2022-09-14 | K Health Inc. | System and method for providing health information |
| CN110277165B (en) * | 2019-06-27 | 2021-06-04 | 清华大学 | Auxiliary diagnosis method, device, equipment and storage medium based on graph neural network |
| CN111063429A (en) * | 2019-10-25 | 2020-04-24 | 中国科学院自动化研究所 | Medical consultation method, device, equipment and computer-readable storage medium |
| CN111161819B (en) * | 2019-12-31 | 2023-06-30 | 重庆亚德科技股份有限公司 | System and method for processing medical record data of traditional Chinese medicine |
-
2020
- 2020-07-28 CN CN202010739726.2A patent/CN111883251A/en active Pending
- 2020-08-28 WO PCT/CN2020/112235 patent/WO2021120688A1/en active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018171919A1 (en) * | 2017-03-24 | 2018-09-27 | Clinova Limited | Apparatus, method and computer program for providing medical advice based on self-reported symptoms of a user |
| CN107833629A (en) * | 2017-10-25 | 2018-03-23 | 厦门大学 | Aided diagnosis method and system based on deep learning |
| CN110162779A (en) * | 2019-04-04 | 2019-08-23 | 北京百度网讯科技有限公司 | Appraisal procedure, device and the equipment of quality of case history |
| CN110534206A (en) * | 2019-08-26 | 2019-12-03 | 北京好医生云医院管理技术有限公司 | A kind of working method of medical diagnosis auxiliary system |
| CN110675951A (en) * | 2019-08-26 | 2020-01-10 | 北京百度网讯科技有限公司 | Intelligent disease diagnosis method and device, computer equipment and readable medium |
| CN111091906A (en) * | 2019-10-31 | 2020-05-01 | 中电药明数据科技(成都)有限公司 | Auxiliary medical diagnosis method and system based on real world data |
| CN111383769A (en) * | 2020-01-08 | 2020-07-07 | 科大讯飞股份有限公司 | Method, device, equipment and storage medium for detecting complaint and diagnosis consistency |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112541066A (en) * | 2020-12-11 | 2021-03-23 | 清华大学 | Text-structured-based medical and technical report detection method and related equipment |
| CN112541066B (en) * | 2020-12-11 | 2022-10-25 | 清华大学 | Text-structured-based medical and technical report detection method and related equipment |
| CN112435745A (en) * | 2020-12-18 | 2021-03-02 | 深圳赛安特技术服务有限公司 | Consultation strategy recommendation method and device, electronic equipment and storage medium |
| CN112435745B (en) * | 2020-12-18 | 2024-04-05 | 深圳赛安特技术服务有限公司 | Method and device for recommending treatment strategy, electronic equipment and storage medium |
| CN113327691A (en) * | 2021-06-01 | 2021-08-31 | 平安科技（深圳）有限公司 | Query method and device based on language model, computer equipment and storage medium |
| CN113327691B (en) * | 2021-06-01 | 2022-08-12 | 平安科技（深圳）有限公司 | Query method and device based on language model, computer equipment and storage medium |
| CN116682551A (en) * | 2023-07-27 | 2023-09-01 | 腾讯科技（深圳）有限公司 | Disease prediction method, disease prediction model training method and device |
| CN116682551B (en) * | 2023-07-27 | 2023-12-22 | 腾讯科技（深圳）有限公司 | Disease prediction method, disease prediction model training method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021120688A1 (en) | 2021-06-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021114736A1 (en) | Medical consultation assistance method and apparatus, electronic device, and medium | |
| CN111883251A (en) | Medical misdiagnosis detection method and device, electronic equipment and storage medium | |
| CN111488363B (en) | Data processing method, device, electronic equipment and medium | |
| WO2022134466A1 (en) | Data processing method and related device | |
| CN111985241B (en) | Medical information query method, device, electronic equipment and medium | |
| CN113032528B (en) | Case analysis method, case analysis device, case analysis equipment and storage medium | |
| CN111986793A (en) | Diagnosis guide processing method and device based on artificial intelligence, computer equipment and medium | |
| CN111986763A (en) | Disease data analysis method and device, electronic device and storage medium | |
| CN112363923A (en) | Test method, device, computer equipment and medium based on questionnaire system | |
| CN112214588A (en) | Multi-intention recognition method and device, electronic equipment and storage medium | |
| CN113536770B (en) | Text analysis method, device and equipment based on artificial intelligence and storage medium | |
| CN113918467A (en) | Financial system testing method, device, equipment and storage medium | |
| CN113724830B (en) | Medication risk detection method based on artificial intelligence and related equipment | |
| CN113268597B (en) | Text classification method, device, equipment and storage medium | |
| CN114360732B (en) | Medical data analysis method, device, electronic equipment and storage medium | |
| CN112199494A (en) | Medical information searching method and device, electronic equipment and storage medium | |
| CN112435745A (en) | Consultation strategy recommendation method and device, electronic equipment and storage medium | |
| CN113470775B (en) | Information acquisition method, device, equipment and storage medium | |
| CN116468043A (en) | Nested entity identification method, device, equipment and storage medium | |
| CN111933241B (en) | Medical data analysis method, device, electronic equipment and storage medium | |
| CN113408265B (en) | Semantic analysis method, device and equipment based on human-computer interaction and storage medium | |
| CN113282218A (en) | Multi-dimensional report generation method, device, equipment and storage medium | |
| CN113688119B (en) | Medical database construction method based on artificial intelligence and related equipment | |
| CN113283677A (en) | Index data processing method, device, equipment and storage medium | |
| CN113764061B (en) | Medication detection method based on multidimensional data analysis and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201103 |