CN109635121A - Medical knowledge map creation method and relevant apparatus - Google Patents
Medical knowledge map creation method and relevant apparatus Download PDFInfo
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- CN109635121A CN109635121A CN201811319666.8A CN201811319666A CN109635121A CN 109635121 A CN109635121 A CN 109635121A CN 201811319666 A CN201811319666 A CN 201811319666A CN 109635121 A CN109635121 A CN 109635121A
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Classifications
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- 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
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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Abstract
The embodiment of the present application provides a kind of medical knowledge map creation method and relevant apparatus, wherein the described method includes: obtaining the medical data in the hospital system of at least one hospital of target area；Multiple medical bodies are extracted from the medical data；By the incidence relation method for building up between preset medical bodies, the incidence relation between the multiple medical bodies is established；According to the incidence relation between the multiple medical bodies, therefore the management difficulty of medical data can reduce according to preset knowledge mapping creation method creation medical knowledge map, to promote convenience when medical data uses.
Description
Technical field
This application involves technical field of data processing, and in particular to a kind of medical knowledge map creation method and related dress
It sets.
Background technique
With gradually being combined for medical system and internet, medical system has obtained development energetically by internet.But
It is while medical system is combined with internet, there is also some areas for improvement.Since medical data compares
Huge, efficiency is lower when handling in existing scheme data, increases so as to cause the difficulty of data management, in medical data
Convenience when use is lower.
Summary of the invention
The embodiment of the present application provides a kind of medical knowledge map creation method and relevant apparatus, can reduce medical data
Management difficulty, to promote convenience when medical data uses.
The first aspect of the embodiment of the present application provides a kind of medical knowledge map creation method, which comprises
Obtain the medical data in the hospital system of at least one hospital of target area；
Multiple medical bodies are extracted from the medical data；
By the incidence relation method for building up between preset medical bodies, the pass between the multiple medical bodies is established
Connection relationship；
According to the incidence relation between the multiple medical bodies, medical treatment is created according to preset knowledge mapping creation method
Knowledge mapping.
The second aspect of the embodiment of the present application provides a kind of medical knowledge map creating device, and described device includes:
Acquiring unit, the medical data in the hospital system of at least one hospital for obtaining target area；
Extraction unit, for extracting multiple medical bodies from the medical data；
First creating unit, for establishing described more by the incidence relation method for building up between preset medical bodies
Incidence relation between a medical bodies；
Second creating unit, for according to the incidence relation between the multiple medical bodies, according to preset knowledge graph
It composes creation method and creates medical knowledge map.
The third aspect of the embodiment of the present application provides a kind of terminal, including processor, input equipment, output equipment and storage
Device, the processor, input equipment, output equipment and memory are connected with each other, wherein the memory is for storing computer
Program, the computer program include program instruction, and the processor is configured for calling described program instruction, are executed such as this
Apply for the step instruction in embodiment first aspect.
The fourth aspect of the embodiment of the present application provides a kind of computer readable storage medium, wherein above-mentioned computer can
Read the computer program that storage medium storage is used for electronic data interchange, wherein above-mentioned computer program executes computer
The step some or all of as described in the embodiment of the present application first aspect.
5th aspect of the embodiment of the present application provides a kind of computer program product, wherein above-mentioned computer program produces
Product include the non-transient computer readable storage medium for storing computer program, and above-mentioned computer program is operable to make to count
Calculation machine executes the step some or all of as described in the embodiment of the present application first aspect.The computer program product can be
One software installation packet.
Implement the embodiment of the present application, at least has the following beneficial effects:
By the embodiment of the present application, the medical data in the hospital system of at least one hospital of target area is obtained, from
Multiple medical bodies are extracted in the medical data, by the incidence relation method for building up between preset medical bodies, are established
Incidence relation between the multiple medical bodies, according to the incidence relation between the multiple medical bodies, according to preset
Knowledge mapping creation method creates medical knowledge map, therefore, the medical data at least one hospital to target area
It extracts, obtains multiple medical bodies, and root medical bodies establish the incidence relation between medical bodies, and successively creation doctor
Knowledge mapping is treated, by medical knowledge map come managed care data, so that medical knowledge can be passed through when using medical data
Map is come convenience when extracting medical data, and then can be lifted to a certain extent using medical data.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of application for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 provides a kind of application scenarios schematic diagram of medical knowledge map creation method for the embodiment of the present application；
Fig. 2 provides a kind of flow diagram of medical knowledge map creation method for the embodiment of the present application；
Fig. 3 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application；
Fig. 4 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application；
Fig. 5 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application；
Fig. 6 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application；
Fig. 7 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application；
Fig. 8 is a kind of structural schematic diagram of terminal provided by the embodiments of the present application；
Fig. 9 provides a kind of structural schematic diagram of medical knowledge map creating device for the embodiment of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
The description and claims of this application and term " first " in above-mentioned attached drawing, " second " etc. are for distinguishing
Different objects, are not use to describe a particular order.In addition, term " includes " and " having " and their any deformations, it is intended that
It is to cover and non-exclusive includes.Such as the process, method, system, product or equipment for containing a series of steps or units do not have
It is defined in listed step or unit, but optionally further comprising the step of not listing or unit, or optionally also wrap
Include other step or units intrinsic for these process, methods, product or equipment.
" embodiment " mentioned in this application is it is meant that a particular feature, structure, or characteristic described can be in conjunction with the embodiments
Included at least one embodiment of the application.The phrase, which occurs, in each position in the description might not each mean phase
Same embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art are explicitly
Implicitly understand, embodiments described herein can be combined with other embodiments.
Electronic device involved by the embodiment of the present application may include the various handheld devices with wireless communication function,
Mobile unit, wearable device calculate equipment or are connected to other processing equipments and various forms of radio modem
User equipment (user equipment, UE), mobile station (mobile station, MS), terminal device (terminal
Device) etc..For convenience of description, apparatus mentioned above is referred to as electronic device.
In order to better understand medical knowledge map creation method provided by the embodiments of the present application, medical treatment is known first below
Know map creation method briefly to be introduced.Referring to Fig. 1, Fig. 1 provides a kind of medical knowledge map for the embodiment of the present application
The application scenarios schematic diagram of creation method.As shown in Figure 1, data platform 101 obtains the doctor of at least one hospital of target area
It is real to extract multiple medical treatment after obtaining medical data from medical data for medical data in department's system 102, data platform 101
Body, medical bodies may include drug, disease, symptom, inspection, CT scan (Computed Tomography,
CT) picture etc., data platform 101 establishes the incidence relation between above-mentioned multiple medical bodies, for example, between disease and drug
Incidence relation etc. between incidence relation, disease and symptom, data platform 101 are closed in the association established between multiple medical bodies
After system, therefore medical knowledge map is passed through according to the incidence relation creation medical knowledge map between above-mentioned multiple medical bodies
Carry out managed care data, so that when using medical data medical data can be extracted by medical knowledge map, and then can
Convenience when be lifted to a certain extent using medical data.
Referring to Fig. 2, Fig. 2 provides a kind of process signal of medical knowledge map creation method for the embodiment of the present application
Figure.As shown in Fig. 2, medical knowledge map creation method includes step 201-204, it is specific as follows:
201, the medical data in the hospital system of at least one hospital of target area is obtained.
Wherein, a hospital or multiple hospitals may be only existed in target area, then are obtaining the hospital of target area
It when medical data, needs to obtain the medical data of at least one hospital in the region, is represented alternatively, obtaining and having in target area
The medical data of all hospitals in the medical data of one or more hospitals of property or target area.
Optionally, medical data can include: medical data information, medical data information can include: hospitalization information, nursing letter
Breath, the inspection information of drug use information, disease information, the symptom information of disease, disease, operation names information of disease etc.,
Herein by way of example only, it is not especially limited.
202, multiple medical bodies are extracted from the medical data.
Optionally, medical bodies may include drug, disease, symptom, inspection, operation names, CT picture etc..Wherein, drug
It can extract and obtain from drug use information, disease can be extracted from disease information and be obtained, and symptom can be from the disease of disease
In shape extract obtain, inspection can be extracted from the inspection information of disease obtain, operation names can be from the operation names of disease
Extraction obtains in information, CT picture can be extracted from the inspection information of disease and be obtained.
Optionally, when carrying out medical bodies extraction, medical data can be extracted parallel, i.e., simultaneously to medical number
Multiple entities in extract, for example, can extract simultaneously to drug, disease, the symptom etc. in medical data.
203, it by the incidence relation method for building up between preset medical bodies, establishes between the multiple medical bodies
Incidence relation.
Optionally, medical bodies include disease and CT picture, include CT picture identification in CT picture, CT picture identification is used for
Position captured by CT picture is identified, a kind of method of the possible incidence relation established between medical bodies includes step A1-
A4, specific as follows:
A1, feature extraction is carried out to the CT picture, obtains multiple characteristic values of the CT picture；
Wherein, when carrying out feature extraction to CT picture, it can be and gray value extraction is carried out to CT picture, obtain CT picture
Multiple gray values.Method when carrying out gray value extraction to CT picture may include step A11-A12, specific as follows:
A11, CT picture is subjected to region division according to the matrix of n*n, obtains multiple regions；
Wherein, n is positive integer, after being divided according to the matrix of n*n, obtains n*n region, according to matrix form into
Row divide when, the area in the region obtained after division can be it is identical be also possible to it is different.A kind of possible division mode
Are as follows: after the first row to matrix divides, the mean value of the gray value in the first row in each region is identified, when the first row
The mean value of the gray value in first region or the last one region is greater than preset mean value, then remaining region is carried out non-equal faces
Product divides.Wherein presetting gray value is the gray value stored in system, can also can be set by user by system sets itself
It is fixed.
The gray value of the pixel in each region in A12, the multiple region of acquisition.
Optionally, the pixel in CT picture can be determined according to the resolution ratio of input, differentiate after picture input
After rate determines, then the number of the pixel of image can be obtained by the relationship between resolution ratio and pixel.
A2, disease information corresponding with the CT picture is determined according to the multiple characteristic value；
It wherein, may include step A21- according to the method that multiple characteristic values determine disease information corresponding with CT picture
A23, specific as follows:
A21, according to the gray value in each region, determine gray value interval in the region；
Optionally, in the gray value of the pixel in each region, there are the smallest gray values of maximum sum of the grayscale values, then
The gray value interval that minimum gray value and gray scale maximum value can be constituted is as the gray value interval in the region.It is, of course, also possible to
In the upper limit value and lower limit value of determination section, remove the maximum value and minimum value in gray value, second largest value and sub-minimum are made
For the upper limit value and lower limit value in section.Gray value interval is determined by the above method, can be reduced and be determined in section to a certain extent
When interference, promote accuracy of section when determining.
Optionally, each region has its area identity information (identification, ID), and region ID can be it
Corresponding coordinate in a matrix, coordinate for example can be (1,1) etc..
A22, according to the CT picture identification obtain the CT picture captured by position；
Wherein, the mapping relations between position captured by CT picture identification and CT picture are by hospital system in shooting CT figure
It is established when piece.Any part that position can be user's body is shot, for example, head, hand, lung, heart etc..Different bats
Position is taken the photograph with different CT picture identifications.
A23, gray value interval corresponding to each region in the preset standard CT picture at shooting position is extracted, is schemed according to CT
Gray value interval corresponding to each region is determined and CT in the gray value interval in each region and standard CT picture in piece
The corresponding disease information of picture.
Wherein, the region division mode of preset standard CT picture is consistent with the region division mode of above-mentioned CT picture, simultaneously
Also ID setting method in region having the same.
Optionally, gray value interval comparison is carried out according to region ID, that is, region ID having the same carries out gray value ratio
It is right, when the gray value interval of the CT picture in some region is not belonging to corresponding standard gray angle value section, it is determined that go out with
The corresponding disease information in the region, will disease information corresponding with the region as the corresponding disease information of CT picture,
Therefore, a CT picture can correspond to multiple disease informations.
Wherein, each region can correspond to different disease informations, can also correspond to identical disease information, such as adjacent
Region can correspond to identical disease information, certainly when different regions corresponds to disease information, with the disease verified in actual medical
Subject to sick information, that is, when region corresponding with the region occurs abnormal in some region and standard CT picture, can determine that out
Subject to disease information.For example, the corresponding position of CT picture is nose, in CT picture, the gray value interval at nasal sinus position occurs
Abnormal, then disease information corresponding thereto can be nasal sinus inflammation, nasal sinus routed purulence, sinus infection etc..
A3, according to the disease information, determine disease corresponding with the disease information；
Wherein, it is illustrated so that disease information is nasal sinus inflammation, the routed purulence of nasal sinus, sinus infection as an example, with nasal sinus inflammation, nose
Sinus bursts the corresponding disease of purulence, sinus infection as nasosinusitis.
Disease corresponding with the disease information described in A4, determination and the CT picture are associated.
Optionally, the alternatively possible method for establishing the incidence relation between medical bodies are as follows: by the multiple medical treatment
Entity is input in medical bodies correlation model, is obtained between the multiple medical bodies by the medical bodies correlation model
Incidence relation, the medical bodies correlation model is the pass between the medical bodies prestored by supervised learning model training
Connection relationship obtains.Wherein, medical bodies correlation model is obtained after being learnt using machine learning to sample data, wherein machine
Device learning model is supervised learning model, and supervised learning model for example can be weight model in artificial neural network method
Deng a kind of method for building up of medical bodies correlation model are as follows: sample is carried out feature extraction first, obtains feature set, then will
Feature set inputs in training pattern, and training pattern is learnt according to the algorithm in training pattern, which for example can be ladder
Descent method, Newton's algorithm, conjugate gradient algorithms etc. are spent, medical bodies correlation model are finally obtained, in this approach, by a large amount of
Sample study, finally obtain medical bodies correlation model.By machine learning model, a large amount of sample is learnt,
Medical bodies correlation model can be accurately determined, to improve the accuracy when incidence relation obtains.Sample
Data for example can be, the incidence relation between medical bodies artificially inputted, be also possible to use with crawler software in network
In crawl incidence relation between medical bodies, specifically, crawl tool crawled from Baidupedia, wikipedia etc. it is medical real
Incidence relation between body.Wherein, the incidence relation between medical bodies prestored may be to be inputted by artificial mode
Incidence relation between medical bodies, can also be from the monologue story-telling with gestures of drug, medical treatment obtained in the specification and purposes of medical instrument
Incidence relation between entity.
204, it according to the incidence relation between the multiple medical bodies, is created according to preset knowledge mapping creation method
Medical knowledge map.
Optionally, a kind of possible method according to the incidence relation creation medical knowledge map between multiple medical bodies
It is specific as follows including step B1-B5:
B1, feature extraction is carried out to the multiple medical bodies, obtains each medical bodies in the multiple medical bodies
Characteristic, the characteristic includes keyword, and the keyword includes medicine name, disease name and/or symptom name
Claim；
Optionally, multiple classifications include: drug categories, disease category, symptom classification etc..Classify to medical bodies,
Obtaining multiple class method for distinguishing can be with are as follows: the characteristic of medical bodies is extracted, then according to this feature data to medical bodies
Classify.Wherein, characteristic can be the data for including keyword, and keyword for example can be disease name, medication name
Title, symptom title etc..
B2, classify according to the characteristic of the multiple medical bodies to the multiple medical bodies, obtain multiple
Medical bodies classification；
Optionally, being classified according to title to medical bodies can be with are as follows:, will if medical bodies title belongs to drug class
Medical bodies are as drug categories, if medical bodies title belongs to disease class, using medical bodies as disease class, if medical treatment is real
Body title belongs to medical conditions, then using medical bodies as symptom class.Certainly it can also include other classification methods, be only herein
For example, being not specifically limited.
Optionally, since medical bodies have diversity, the then another method when classifying to medical bodies are as follows:
Be illustrated by taking medicine name as an example, drug can be divided into following first category: antibiotics drug, cardiovascular and cerebrovascular medication,
Digestive system surgical procedures, respiratory medicine, drug for urinary system, hematological system medication, ENT dept.'s medication, antirheumatic drug,
Parenteralia drug, diabetic, hormone drugs, bentoquatam, Amino-Cerv, anti-tumor drug, antipsychotic drug
Product, clearing heat and detoxicating drug, Shou Tijidong blocking agent and antiallergic, nourishing similar drug, vitamin, minerals drug etc..Wherein
In each category, it can also further be classified, be illustrated by taking antibiotics drug as an example, antibiotic medicine can be divided into
Following subclass: cephalosporins, aminoglycoside, macrolides, Tetracyclines, lincomycin class etc..
B3, classification logotype building is carried out to the multiple medical bodies classification, obtains the multiple medical bodies classification
Classification logotype；
Optionally, by taking first category as an example, classification logotype can be set to category mark, second level classification logotype, three
Grade classification logotype etc., category mark can be the mark of the 4-digit numbers such as 0001,0002, and second level classification logotype can be six
The mark of bit digital, such as 000001,000002,010000 etc., three-level classification logotype can be the mark of eight-digit number word, example
Such as 00000001,00000003,00000005, if there is subsequent framework, then with the progress of three-level classification logotype set-up mode
Setting.
B4, pass through the classification logotype of the multiple medical bodies, building medical knowledge map framework；
Optionally, when constructing mark, medical knowledge map framework can be constructed in advance, according to the number of first category,
The level framework for constructing medical knowledge map constructs medical knowledge map according to the subclass of each classification in first category
Two level frameworks can construct the three-level structure of medical knowledge map if each subclass further includes the subclass of its own, with
This analogizes, and completes the foundation of medical knowledge map framework.
Wherein, a level framework is corresponding with category, and two level frameworks are corresponding with second level classification, three-level structure and three-level
Classification is corresponding, is corresponded with this.
B5, the incidence relation between the multiple medical bodies is stored in the medical knowledge map framework, is obtained
Medical knowledge map.
It optionally, is that index stores the incidence relation between multiple medical bodies to medical knowledge map framework with entity
In, wherein a kind of possible storage mode is that incidence relation and entity are stored in identical position in map.
The storage form of incidence relation between medical bodies for example can be " entity-relationship-entity ", such as: " medicine
Product-relationship-disease ", " drug group-relationship-disease ", " symptom-relationship-disease ", " inspection-relationship-disease ", " inspection
Look into-relationship-illness ", " disease-relationship-disease " etc., wherein relationship can lead to certain symptom for improvement or deterioration, disease
(either certain symptoms), inspection have found certain symptom (perhaps certain symptoms) certain disease cause certain other disease generation (or
Certain diseases), be specifically as follows: relationship between drug and disease it is to be understood that drug the disease can be carried out improve or
Person cures the disease, then the relationship between the drug and disease is to improve, conversely, drug has deterioration effect to the disease, then
Relationship between the drug and disease is to deteriorate, and does not generate any effect to the disease there are also drug certainly, is not then included in drug
Relationship between disease；Drug group that is, different drugs combination, the relationship between disease can refer to drug and disease
Relationship between disease；Symptom is with the relationship of disease it is to be understood that symptom corresponding with the disease；It checks between disease
Relationship is it is to be understood that the medical examination corresponding with the disease of certain disease, for example, calculus, then need to carry out CT examination etc.；
Relationship between disease and disease is it is to be understood that a kind of generation of disease, will lead to the generation of another disease.
Optionally, the alternatively possible side according to the incidence relation creation medical knowledge map between multiple medical bodies
Method includes step C1-C6, specific as follows:
C1, data conversion process is carried out to the incidence relation between the multiple medical bodies, obtains the multiple medical treatment
The relation identity of incidence relation between entity；
Wherein, when data variation is handled, and incidence relation is transformed to incidence relation and is identified, above-mentioned mark has unique
Property, i.e., incidence relation is identified with unique incidence relation.Incidence relation mark can be set according to the mark set, be preset
Mark for example can be A0001, A0002, A0003 to any mark between A9999.
C2, classified to the multiple medical bodies using preset classification method, obtain multiple medical bodies classifications；
Optionally, the classification method that preset classification method can refer to step B1 to B2 is classified, and can also be joined certainly
Classify according to classification method preset in hospital system, obtains medical bodies classification.For example, hospital's tumour class, interior class, outer
Class etc..
C3, data conversion process is carried out to the multiple medical bodies classification, obtains the multiple medical bodies classification
Classification logotype；
Optionally, conversion process is carried out to medical bodies categorical data, can be arranged according to preset classification logotype and carries out
Conversion process, preset classification logotype for example can be 001,002 to 999, and different classification logotypes is arranged in different classifications.
C4, the classification logotype of the multiple medical bodies classification is subjected to hash conversion, obtains the multiple medical bodies
The cryptographic Hash of the classification logotype of classification；
C5, using the cryptographic Hash of the classification logotype of the multiple medical bodies classification as the rope of medical knowledge data link table
Draw, creates medical knowledge data link table；
Optionally, medical knowledge data link table is hash hash table, for the doubly linked list array arranged with hashed value.hash
Hash table includes multiple chained lists, and each chained list has a node, and the node of each chained list is corresponding with class indication.Hash dissipates
List includes multiple entrances, in the corresponding incidence relation mark of inquiry hash value, needs to be traversed for the corresponding hash of hash value and dissipates
The chained list that list entries are directed toward, wherein the chained list that hash hash table entry is directed toward can have multiple chained lists, and chained list is that association is closed
System's mark.
C6, by the incidence relation between the multiple medical bodies, be stored in the medical knowledge data link table, obtain
Medical knowledge map.
In a possible example, the medical bodies include multiple diseases, the incidence relation packet between medical bodies
The incidence relation between each disease in multiple diseases is included, knowledge mapping creation method may also include step D1-D3, specifically such as
Under:
D1, the first disease for receiving input, and it is associated with first disease with reference to association disease；
Wherein, the first disease of input can input for user, or system operator is inputted, and certainly may be used
To have other input modes.
D2, according to the medical knowledge map, obtain target association disease associated with first disease；
If D3, the reference association disease and the target association disease are inconsistent, add described with reference to association disease
It is added in the incidence relation of first disease.
It can be realized by the above method and incidence relation is updated, so as to promotion knowledge mapping to a certain extent
Stability after creation, and then the practicability of knowledge mapping can be promoted to a certain extent.
In a possible example, the medical bodies include drug and disease, user need voluntarily according to disease
Disease obtains medicine information associated with the disease following method when obtaining drug relevant to the disease, can be used, specifically
It may include step E1-E3, as follows:
E1, the second disease for receiving target user's input；
E2, the medicine information of drug associated with second disease is obtained according to the medical knowledge map；
It wherein, can be by the second disease when obtaining the medicine information of drug associated with the second disease according to knowledge mapping
Disease is directly searched in knowledge mapping, and extracts being associated between the second disease corresponding with the second disease and drug
Relationship, extracts the drug of the incidence relation of " improvements " from the incidence relation between the second disease and drug, to obtain and the
The medicine information of the associated drug of two diseases.
E3, the medicine information of the drug associated with the second disease is sent to the target user.
Medicine information relevant to disease is obtained by knowledge mapping, enables to the one of the acquisition disease of user voluntarily
A little treatment methods, such as some simple diseases, the urgent disease of some comparisons then promote user and carry out emergency processing to disease
Efficiency, so as to promote user experience to a certain extent.
Referring to Fig. 3, Fig. 3 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application.
As shown in figure 3, knowledge mapping creation method includes step 301-306, it is specific as follows:
301, the medical data in the hospital system of at least one hospital of target area is obtained；
302, multiple medical bodies are extracted from the medical data；
303, it by the incidence relation method for building up between preset medical bodies, establishes between the multiple medical bodies
Incidence relation；
304, feature extraction is carried out to the multiple medical bodies, obtains each medical bodies in the multiple medical bodies
Characteristic, the characteristic includes keyword, and the keyword includes medicine name, disease name and/or symptom name
Claim；
305, classify according to the characteristic of the multiple medical bodies to the multiple medical bodies, obtain multiple
Medical bodies classification；
306, classification logotype building is carried out to the multiple medical bodies classification, obtains the multiple medical bodies classification
Classification logotype；
307, by the classification logotype of the multiple medical bodies, medical knowledge map framework is constructed；
308, the incidence relation between the multiple medical bodies is stored in the medical knowledge map framework, is obtained
Medical knowledge map.
In this example, by classifying to medical bodies, multiple classifications are obtained, and construct medical treatment by multiple classifications
Knowledge mapping framework constructs medical knowledge map framework according to classification, the knot for reflecting medical data that can be relatively clear
Structure, so as to faster extract medical data when using medical data, it is convenient when medical data uses to improve
Property and medical data classification it is clear, improve efficiency when medical data uses to a certain extent.
Referring to Fig. 4, Fig. 4 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application.
As shown in figure 4, knowledge mapping creation method includes step 401-405, it is specific as follows:
401, the medical data in the hospital system of at least one hospital of target area is obtained；
402, multiple medical bodies are extracted from the medical data；
403, it by the incidence relation method for building up between preset medical bodies, establishes between the multiple medical bodies
Incidence relation；
404, data conversion process is carried out to the incidence relation between the multiple medical bodies, obtains the multiple medical treatment
The relation identity of incidence relation between entity；
405, classified to the multiple medical bodies using preset classification method, obtain multiple medical bodies classes
Not；
406, data conversion process is carried out to the multiple medical bodies classification, obtains the multiple medical bodies classification
Classification logotype；
407, the classification logotype of the multiple medical bodies classification is subjected to hash conversion, obtains the multiple medical bodies
The cryptographic Hash of the classification logotype of classification；
408, using the cryptographic Hash of the classification logotype of the multiple medical bodies classification as the rope of medical knowledge data link table
Draw, creates medical knowledge data link table；
409, it by the incidence relation between the multiple medical bodies, is stored in the medical knowledge data link table, obtains
To medical knowledge map.
In this example, by constructing medical knowledge data link table, by the incidence relation between medical bodies, the doctor is arrived in storage
It treats in knowledge data chained list, obtains medical knowledge map, medical knowledge map is constructed by way of chained list, using medical treatment
When data, it is capable of providing efficiently medical data inquiry, it is convenient when medical data uses so as to be promoted to a certain extent
Property.
Referring to Fig. 5, Fig. 5 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application.
As shown in figure 5, knowledge mapping creation method includes step 501-507, it is specific as follows:
501, the medical data in the hospital system of at least one hospital of target area is obtained；
502, multiple medical bodies are extracted from the medical data, the multiple medical bodies include that disease and CT scheme
Piece；
503, feature extraction is carried out to the CT picture, obtains multiple characteristic values of the CT picture；
504, disease information corresponding with the CT picture is determined according to the multiple characteristic value；
505, according to the disease information, disease corresponding with the disease information is determined；
506, determine that the disease corresponding with the disease information and the CT picture are associated；
507, it according to the incidence relation between the multiple medical bodies, is created according to preset knowledge mapping creation method
Medical knowledge map.
In this example, medical bodies include disease and CT picture, are determined by CT picture corresponding with the CT picture
Disease, and according to the corresponding relationship determined, to create medical knowledge map, by automatically extracting the feature of CT picture and true
Fixed disease corresponding with CT picture, can promote accuracy when incidence relation determines, while can also simplify to a certain extent
The acquisition process of incidence relation improves the accuracy of incidence relation acquisition, and then improves the accurate of medical knowledge map
Property.
Referring to Fig. 6, Fig. 6 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application.
As shown in fig. 6, knowledge mapping creation method includes step 601-607, it is specific as follows:
601, the medical data in the hospital system of at least one hospital of target area is obtained；
602, multiple medical bodies are extracted from the medical data, the multiple medical bodies include multiple diseases；
603, it by the incidence relation method for building up between preset medical bodies, establishes between the multiple medical bodies
Incidence relation, the incidence relation between the medical bodies includes that the association between each disease in the multiple disease is closed
System；
604, it according to the incidence relation between the multiple medical bodies, is created according to preset knowledge mapping creation method
Medical knowledge map；
605, the first disease of input is received, and associated with first disease with reference to association disease；
606, according to the medical knowledge map, target association disease associated with first disease is obtained；
If 607, described inconsistent with reference to association disease and the target association disease, add described with reference to association disease
It is added in the incidence relation of first disease.
In this example, by receive input the first disease and it is associated with first with reference to association disease, pass through by
First disease is input in medical knowledge map, obtains target association disease corresponding with the first disease, is referred to by comparing
Be associated with disease and target association disease it is whether consistent, to be updated to the incidence relation of the first disease, with this, by it is above-mentioned more
New mechanism can promote the reliability of medical knowledge map to a certain extent.
Referring to Fig. 7, Fig. 7 provides the flow diagram of another knowledge mapping creation method for the embodiment of the present application.
As shown in fig. 7, knowledge mapping creation method includes step 701-707, it is specific as follows:
701, the medical data in the hospital system of at least one hospital of target area is obtained；
702, multiple medical bodies are extracted from the medical data, the multiple medical bodies include drug and disease；
703, it by the incidence relation method for building up between preset medical bodies, establishes between the multiple medical bodies
Incidence relation, the incidence relation between the multiple medical bodies includes the incidence relation between disease and drug；
704, it according to the incidence relation between the multiple medical bodies, is created according to preset knowledge mapping creation method
Medical knowledge map；
705, the second disease of target user's input is received；
706, the medicine information of drug associated with second disease is obtained according to the medical knowledge map；
707, the medicine information of the drug associated with the second disease is sent to the target user.
In this example, after the completion of medical knowledge map construction, the second disease of target user's input is received, medical treatment is passed through
Knowledge mapping obtains the medicine information of drug associated with the second disease, and the medicine information is sent to target user, energy
It is enough so that the acquisition disease of user voluntarily some treatment methods, such as some simple diseases, the urgent disease of some comparisons,
The efficiency that user carries out emergency processing to disease is promoted, then so as to promote user experience to a certain extent.
It is consistent with above-described embodiment, referring to Fig. 8, Fig. 8 is that a kind of structure of terminal provided by the embodiments of the present application is shown
It is intended to, as shown, including processor, input equipment, output equipment and memory, the processor, input equipment, output are set
Standby and memory is connected with each other, wherein for the memory for storing computer program, the computer program includes that program refers to
It enables, the processor is configured for calling described program instruction, and above procedure includes the instruction for executing following steps；
Obtain the medical data in the hospital system of at least one hospital of target area；
Multiple medical bodies are extracted from the medical data；
By the incidence relation method for building up between preset medical bodies, the pass between the multiple medical bodies is established
Connection relationship；
According to the incidence relation between the multiple medical bodies, medical treatment is created according to preset knowledge mapping creation method
Knowledge mapping.
By the embodiment of the present application, the medical data in the hospital system of at least one hospital of target area is obtained, from
Multiple medical bodies are extracted in the medical data, by the incidence relation method for building up between preset medical bodies, are established
Incidence relation between the multiple medical bodies, according to the incidence relation between the multiple medical bodies, according to preset
Knowledge mapping creation method creates medical knowledge map, therefore, the medical data at least one hospital to target area
It extracts, obtains multiple medical bodies, and root medical bodies establish the incidence relation between medical bodies, and successively creation doctor
Knowledge mapping is treated, by medical knowledge map come managed care data, so that medical knowledge can be passed through when using medical data
Map is come convenience when extracting medical data, and then can be lifted to a certain extent using medical data.
It is above-mentioned that mainly the scheme of the embodiment of the present application is described from the angle of method side implementation procedure.It is understood that
, in order to realize the above functions, it comprises execute the corresponding hardware configuration of each function and/or software module for terminal.This
Field technical staff should be readily appreciated that, in conjunction with each exemplary unit and algorithm of embodiment description presented herein
Step, the application can be realized with the combining form of hardware or hardware and computer software.Some function actually with hardware also
It is the mode of computer software driving hardware to execute, the specific application and design constraint depending on technical solution.Profession
Technical staff can specifically realize described function to each using distinct methods, but this realization should not be recognized
For beyond scope of the present application.
The embodiment of the present application can carry out the division of functional unit according to above method example to terminal, for example, can be right
The each functional unit of each function division is answered, two or more functions can also be integrated in a processing unit.
Above-mentioned integrated unit both can take the form of hardware realization, can also realize in the form of software functional units.It needs
Illustrate, is schematical, only a kind of logical function partition to the division of unit in the embodiment of the present application, it is practical to realize
When there may be another division manner.
Consistent with the above, referring to Fig. 9, Fig. 9 provides a kind of medical knowledge map creation dress for the embodiment of the present application
The structural schematic diagram set.As shown in figure 9, described device includes acquiring unit 901, extraction unit 902, the first creating unit 903
With the second creating unit 904, wherein
Acquiring unit 901, the medical data in the hospital system of at least one hospital for obtaining target area；
Extraction unit 901, for extracting multiple medical bodies from the medical data；
First creating unit 903, for by the incidence relation method for building up between preset medical bodies, described in foundation
Incidence relation between multiple medical bodies；
Second creating unit 904, for according to the incidence relation between the multiple medical bodies, according to preset knowledge
Map creation method creates medical knowledge map.
By the embodiment of the present application, the medical data in the hospital system of at least one hospital of target area is obtained, from
Multiple medical bodies are extracted in the medical data, by the incidence relation method for building up between preset medical bodies, are established
Incidence relation between the multiple medical bodies, according to the incidence relation between the multiple medical bodies, according to preset
Knowledge mapping creation method creates medical knowledge map, therefore, the medical data at least one hospital to target area
It extracts, obtains multiple medical bodies, and root medical bodies establish the incidence relation between medical bodies, and successively creation doctor
Knowledge mapping is treated, by medical knowledge map come managed care data, so that medical knowledge can be passed through when using medical data
Map is come convenience when extracting medical data, and then can be lifted to a certain extent using medical data.
Optionally, it in the incidence relation according between the multiple medical bodies, is created according to preset knowledge mapping
In terms of construction method creates medical knowledge map, second creating unit 904 is specifically used for:
Feature extraction is carried out to the multiple medical bodies, obtains the spy of each medical bodies in the multiple medical bodies
Data are levied, the characteristic includes keyword, and the keyword includes medicine name, disease name and/or symptom title；
Classify according to the characteristic of the multiple medical bodies to the multiple medical bodies, obtains multiple medical treatment
Entity class；
Classification logotype building is carried out to the multiple medical bodies classification, obtains the classification of the multiple medical bodies classification
Mark；
By the classification logotype of the multiple medical bodies, medical knowledge map framework is constructed；
Incidence relation between the multiple medical bodies is stored in the medical knowledge map framework, medical treatment is obtained
Knowledge mapping
Optionally, it in the incidence relation according between the multiple medical bodies, is created according to preset knowledge mapping
Construction method create medical knowledge map in terms of, second creating unit 904 also particularly useful for:
Data conversion process is carried out to the incidence relation between the multiple medical bodies, obtains the multiple medical bodies
Between incidence relation relation identity；
Classified to the multiple medical bodies using preset classification method, obtains multiple medical bodies classifications；
Data conversion process is carried out to the multiple medical bodies classification, obtains the classification of the multiple medical bodies classification
Mark；
The classification logotype of the multiple medical bodies classification is subjected to hash conversion, obtains the multiple medical bodies classification
Classification logotype cryptographic Hash；
Using the cryptographic Hash of the classification logotype of the multiple medical bodies classification as the index of medical knowledge data link table, wound
Build medical knowledge data link table；
By the incidence relation between the multiple medical bodies, it is stored in the medical knowledge data link table, is cured
Treat knowledge mapping.
Optionally, the multiple medical bodies include disease and CT picture, described by between preset medical bodies
Incidence relation method for building up, in terms of establishing incidence relation between the multiple medical bodies, first creating unit 903
It is specifically used for:
Feature extraction is carried out to the CT picture, obtains multiple characteristic values of the CT picture；
Disease information corresponding with the CT picture is determined according to the multiple characteristic value；
According to the disease information, disease corresponding with the disease information is determined；
Determine that the disease corresponding with the disease information and the CT picture are associated.
Optionally, in the incidence relation method for building up by between preset medical bodies, the multiple doctor is established
Treat entity between incidence relation in terms of, first creating unit 903 also particularly useful for:
The multiple medical bodies are input in medical bodies correlation model, are obtained by the medical bodies correlation model
Incidence relation between the multiple medical bodies, the medical bodies correlation model are to pass through supervised learning model training
The incidence relation between medical bodies prestored obtains.
Optionally, the multiple medical bodies include multiple diseases, the incidence relation packet between the multiple medical bodies
Include the incidence relation between each disease in the multiple disease, the medical knowledge map creating device also particularly useful for:
The first disease of input is received, and associated with first disease with reference to association disease；
According to the medical knowledge map, target association disease associated with first disease is obtained；
If described inconsistent with reference to association disease and the target association disease, it is added to described with reference to association disease
In the incidence relation of first disease.
Optionally, the multiple medical bodies include drug and disease, the incidence relation between the multiple medical bodies
Including the incidence relation between disease and drug, the medical knowledge map creating device also particularly useful for: receive target user
Second disease of input；
The medicine information of drug associated with second disease is obtained according to the medical knowledge map；
The medicine information of the drug associated with the second disease is sent to the target user.
The embodiment of the present application also provides a kind of computer storage medium, wherein computer storage medium storage is for electricity
The computer program of subdata exchange, it is as any in recorded in above method embodiment which execute computer
A kind of some or all of medical knowledge map creation method step.
The embodiment of the present application also provides a kind of computer program product, and the computer program product includes storing calculating
The non-transient computer readable storage medium of machine program, the computer program make computer execute such as above method embodiment
Some or all of any medical knowledge map creation method of middle record step.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the application is not limited by the described action sequence because
According to the application, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, related actions and modules not necessarily the application
It is necessary.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed device, it can be by another way
It realizes.For example, the apparatus embodiments described above are merely exemplary, such as the division of the unit, it is only a kind of
Logical function partition, there may be another division manner in actual implementation, such as multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be through some interfaces, the indirect coupling or communication connection of device or unit,
It can be electrical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, applying for that each functional unit in bright each embodiment can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also be realized in the form of software program module.
If the integrated unit is realized in the form of software program module and sells or use as independent product
When, it can store in a computer-readable access to memory.Based on this understanding, the technical solution of the application substantially or
Person says that all or part of the part that contributes to existing technology or the technical solution can body in the form of software products
Reveal and, which is stored in a memory, including some instructions are used so that a computer equipment
(can be personal computer, server or network equipment etc.) executes all or part of each embodiment the method for the application
Step.And memory above-mentioned includes: USB flash disk, read-only memory (read-only memory, ROM), random access memory
The various media that can store program code such as (randomaccess memory, RAM), mobile hard disk, magnetic or disk.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can store in a computer-readable memory, memory
It may include: flash disk, read-only memory, random access device, disk or CD etc..
The embodiment of the present application is described in detail above, specific case used herein to the principle of the application and
Embodiment is expounded, the description of the example is only used to help understand the method for the present application and its core ideas；
At the same time, for those skilled in the art can in specific embodiments and applications according to the thought of the application
There is change place, in conclusion the contents of this specification should not be construed as limiting the present application.
Claims (10)
1. a kind of medical knowledge map creation method, which is characterized in that the described method includes:
Obtain the medical data in the hospital system of at least one hospital of target area；
Multiple medical bodies are extracted from the medical data；
By the incidence relation method for building up between preset medical bodies, the association established between the multiple medical bodies is closed
System；
According to the incidence relation between the multiple medical bodies, medical knowledge is created according to preset knowledge mapping creation method
Map.
2. the method according to claim 1, wherein the association according between the multiple medical bodies is closed
System creates medical knowledge map according to preset knowledge mapping creation method, comprising:
Feature extraction is carried out to the multiple medical bodies, obtains the characteristic of each medical bodies in the multiple medical bodies
According to the characteristic includes keyword, and the keyword includes medicine name, disease name and/or symptom title；
Classify according to the characteristic of the multiple medical bodies to the multiple medical bodies, obtains multiple medical bodies
Classification；
Classification logotype building is carried out to the multiple medical bodies classification, obtains the classification mark of the multiple medical bodies classification
Know；
By the classification logotype of the multiple medical bodies, medical knowledge map framework is constructed；
Incidence relation between the multiple medical bodies is stored in the medical knowledge map framework, medical knowledge is obtained
Map.
3. the method according to claim 1, wherein the association according between the multiple medical bodies is closed
System creates medical knowledge map according to preset knowledge mapping creation method, comprising:
Data conversion process is carried out to the incidence relation between the multiple medical bodies, is obtained between the multiple medical bodies
Incidence relation relation identity；
Classified to the multiple medical bodies using preset classification method, obtains multiple medical bodies classifications；
Data conversion process is carried out to the multiple medical bodies classification, obtains the classification mark of the multiple medical bodies classification
Know；
The classification logotype of the multiple medical bodies classification is subjected to hash conversion, obtains the class of the multiple medical bodies classification
The cryptographic Hash not identified；
Using the cryptographic Hash of the classification logotype of the multiple medical bodies classification as the index of medical knowledge data link table, creation doctor
Treat knowledge data chained list；
By the incidence relation between the multiple medical bodies, it is stored in the medical knowledge data link table, obtains medical treatment and know
Know map.
4. method according to any one of claims 1 to 3, which is characterized in that the multiple medical bodies include disease with
CT picture, the incidence relation method for building up by between preset medical bodies, is established between the multiple medical bodies
Incidence relation, comprising:
Feature extraction is carried out to the CT picture, obtains multiple characteristic values of the CT picture；
Disease information corresponding with the CT picture is determined according to the multiple characteristic value；
According to the disease information, disease corresponding with the disease information is determined；
Determine that the disease corresponding with the disease information and the CT picture are associated.
5. method according to any one of claims 1 to 3, which is characterized in that described by between preset medical bodies
Incidence relation method for building up, establish the incidence relation between the multiple medical bodies, comprising:
The multiple medical bodies are input in medical bodies correlation model, institute is obtained by the medical bodies correlation model
The incidence relation between multiple medical bodies is stated, the medical bodies correlation model is to prestore by supervised learning model training
Medical bodies between incidence relation obtain.
6. method according to any one of claims 1 to 3, which is characterized in that the multiple medical bodies include multiple diseases
Disease, the incidence relation between the multiple medical bodies include the incidence relation between each disease in the multiple disease,
The method also includes:
The first disease of input is received, and associated with first disease with reference to association disease；
According to the medical knowledge map, target association disease associated with first disease is obtained；
If described inconsistent with reference to association disease and the target association disease, the reference association disease is added to described
In the incidence relation of first disease.
7. described according to the method described in claim 4, it is characterized in that, the multiple medical bodies include drug and disease
Incidence relation between multiple medical bodies includes the incidence relation between disease and drug, the method also includes:
Receive the second disease of target user's input；
The medicine information of drug associated with second disease is obtained according to the medical knowledge map；
The medicine information of the drug associated with the second disease is sent to the target user.
8. a kind of medical knowledge map creating device, which is characterized in that described device includes:
Acquiring unit, the medical data in the hospital system of at least one hospital for obtaining target area；
Extraction unit, for extracting multiple medical bodies from the medical data；
First creating unit, for establishing the multiple doctor by the incidence relation method for building up between preset medical bodies
Treat the incidence relation between entity；
Second creating unit, for being created according to preset knowledge mapping according to the incidence relation between the multiple medical bodies
Construction method creates medical knowledge map.
9. a kind of terminal, which is characterized in that the processor, defeated including processor, input equipment, output equipment and memory
Enter equipment, output equipment and memory to be connected with each other, wherein the memory is for storing computer program, the computer
Program includes program instruction, and the processor is configured for calling described program instruction, is executed such as any one of claim 1-7
The method.
10. a kind of computer readable storage medium, which is characterized in that the computer storage medium is stored with computer program,
The computer program includes program instruction, and described program instruction makes the processor execute such as right when being executed by a processor
It is required that the described in any item methods of 1-7.
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