CN107657063A - The construction method and device of medical knowledge collection of illustrative plates - Google Patents
The construction method and device of medical knowledge collection of illustrative plates Download PDFInfo
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Abstract
The present invention relates to a kind of construction method and device of medical knowledge collection of illustrative plates, in this method, firstly for the vocabulary extraction of medical knowledge data source and Entity recognition, and determine the body belonging to entity, then the characteristic vector of the entity pair that any two entity forms in a word is built, in characteristic vector comprising entity information and entity described in body information, then to each entity to being screened and being classified based on the relation between default entity, so as to obtain the first triple data of two entities and entity relationship, finally the first triple data are imported into database and visualized so as to obtain medical knowledge collection of illustrative plates.Method provided in an embodiment of the present invention effectively can sort out medical knowledge from the data such as case history or medical knowledge document, and automatically generate medical knowledge collection of illustrative plates, so as to provide more full and accurate effective reference scheme during diagnosis.
Description
Technical field
The present invention relates to software technology field, and in particular to a kind of construction method and device of medical knowledge collection of illustrative plates.
Background technology
Case history is generation of the medical worker to patient disease, develops, lapses to, and the medical activity such as is checked, diagnosed, treated
The writing record that process is made.Case history is both the summary of clinical practice work, is to explore disease rule and processing medical science dispute again
Legal basis, be country treasure.In clinical medicine, case history is effectively arranged, therefrom excavates doctor's clinical experience, it is right
Medical advance is significant.
However, inventor has found during the embodiment of the present invention is implemented, in actual diagnosis and treatment, due to medical worker's sheet
There is the otherness of stock of knowledge and clinical experience etc. in body, often different medical workers is directed to same disease or disease
The diagnostic mode of shape and medication custom etc. be also not quite similar, and some that brings notable results having take effect it is little.And pass through
The carry out Couple herbs exchange of tissue medical worker in the industry, not only needs substantial amounts of manpower and materials, and without in real time in raw and
Universal sharing.Therefore, how medical knowledge is effectively sorted out from case history or in the data such as medical knowledge document, realizes doctor
Gain knowledge to share and be particularly important.
The content of the invention
The purpose of the embodiment of the present invention is to provide a kind of construction method and device of medical knowledge collection of illustrative plates.
In a first aspect, the embodiments of the invention provide a kind of construction method of medical knowledge collection of illustrative plates,
Word segmentation processing is carried out to the structured text of medical knowledge data source；
Character string identification is carried out to several vocabulary after word segmentation processing based on default medical knowledge collection of illustrative plates dictionary, will
Vocabulary after identification is as entity；Body belonging to the entity is determined based on default ontology library, wrapped in the ontology library
Containing body and ontological relationship, the body is used to describe the classification described in the entity, and the ontological relationship is used to describe respectively
Corresponding relation between individual body；
Choose the characteristic vector of any two entity structure entity pair in a word；Wherein, wrapped in the characteristic vector
Containing the body belonging to each entity, the entity subclass affiliated in its affiliated body, the part of speech of the entity and two
Position relationship between entity on logic of language；
Based on the ontological relationship in the ontology library, to each entity to screening, wherein first instance pair is removed；Institute
State first instance pair, for comprising two entities belonging to body the entity pair of corresponding ontological relationship is not present in ontology library；
To the entity after screening to carrying out matching sort operation based on default ontological relationship, the first triple number is obtained
According to the entity of two entities obtained after two entities and sort operation of the first triple packet centering containing entity
Relation；
Second aspect, the embodiment of the present invention provide a kind of construction device of medical knowledge collection of illustrative plates again, including：
Participle unit, for carrying out word segmentation processing to the structured text of medical knowledge data source；
Recognition unit, for being carried out based on default medical knowledge collection of illustrative plates dictionary to several vocabulary after word segmentation processing
Character string identifies, using the vocabulary after identification as entity；Body belonging to the entity, institute are determined based on default ontology library
State and body and ontological relationship are included in ontology library, the body is used to describe the classification described in the entity, and the body closes
It is for describing the corresponding relation between each body；
Construction unit, for choosing the characteristic vector of any two entity structure entity pair in a word；Wherein, it is described
In characteristic vector comprising the body belonging to each entity, the entity in its affiliated body belonging to subclass, the entity
The structure of position relationship and two entities between part of speech, two entities on logic of language in the medical knowledge data source
Change several front and rear vocabulary in text；
Screening unit, for based on the ontological relationship in the ontology library, to each entity to screening, removing wherein
First instance pair；The first instance pair, for comprising two entities belonging to body corresponding body is not present in ontology library
The entity pair of relation；
Taxon, for, to carrying out matching sort operation based on default ontological relationship, being obtained to the entity after screening
First triple data, two obtained after two entities and sort operation of the first triple packet centering containing entity
The entity relationship of individual entity；
Visualization, visualized operation is carried out for the first triple data to be imported into database, generated
Medical knowledge collection of illustrative plates.
The embodiments of the invention provide a kind of construction method and device of medical knowledge collection of illustrative plates, in this method, firstly for
The vocabulary of medical knowledge data source extracts and Entity recognition, and determines the body belonging to entity, and it is real then to build any two
The characteristic vector of the entity pair of body composition, the information of body described in the information comprising entity and entity in characteristic vector, then
To each entity to being screened and being classified based on the relation between default entity, so as to obtain two entities and reality
First triple data of body relation, finally the first triple data are imported into database and visualized so as to obtain
Medical knowledge collection of illustrative plates.Method provided in an embodiment of the present invention can be arranged effectively from the data such as case history or medical knowledge document
Go out medical knowledge, and automatically generate medical knowledge collection of illustrative plates, so as to provide more full and accurate effective reference during diagnosis
Scheme.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area
Technical staff will be clear understanding.Accompanying drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Limitation.And in whole accompanying drawing, identical part is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 is a kind of construction method embodiment flow chart of medical knowledge collection of illustrative plates provided by the invention；
Fig. 2 is body and ontological relationship schematic diagram provided in an embodiment of the present invention；
Fig. 3 is medical knowledge collection of illustrative plates schematic diagram provided in an embodiment of the present invention；
Fig. 4 is a kind of construction method embodiment flow chart of specific medical knowledge collection of illustrative plates provided in an embodiment of the present invention；
Fig. 5 is a kind of construction device example structure schematic diagram of medical knowledge collection of illustrative plates provided by the invention；
Fig. 6 is a kind of computer equipment example structure block diagram provided by the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
In a first aspect, the embodiments of the invention provide a kind of construction method of medical knowledge collection of illustrative plates, as shown in figure 1, including：
S101, the structured text to medical knowledge data source carry out word segmentation processing；
S102, based on default medical knowledge collection of illustrative plates dictionary to after word segmentation processing several vocabulary carry out character string knowledge
Not, using the vocabulary after identification as entity；Body belonging to the entity, the ontology library are determined based on default ontology library
In include body and ontological relationship, the body is used to describe the classification described in the entity, and the ontological relationship is used to retouch
State the corresponding relation between each body；
S103, the characteristic vector for choosing any two entity structure entity pair in a word；Wherein, the characteristic vector
In comprising the body belonging to each entity, the entity in its affiliated body belonging to subclass, the part of speech of the entity, two
Position relationship and two entities between entity on logic of language is in the structured text of the medical knowledge data source
Several front and rear vocabulary；
S104, based on the ontological relationship in the ontology library, to each entity to screening, remove wherein first instance
It is right；The first instance pair, for comprising two entities belonging to body the reality of corresponding ontological relationship is not present in ontology library
Body pair；
S105, to the entity after screening to carrying out matching sort operation based on default ontological relationship, obtain the first ternary
Data are organized, two entities obtained after two entities and sort operation of the first triple packet centering containing entity
Entity relationship；
S106, the first triple data are imported into database carry out visualized operation, generate medical knowledge figure
Spectrum.
In the construction method of medical knowledge collection of illustrative plates provided in an embodiment of the present invention, firstly for the word of medical knowledge data source
Remittance is extracted and Entity recognition, and determines the body belonging to entity, then builds the spy of the entity pair of any two entity composition
Sign vector, the information of body described in the information comprising entity and entity in characteristic vector, then to each entity to sieving
Select and classified based on the relation between default entity, so as to obtain the first triple of two entities and entity relationship
Data, finally the first triple data are imported into database and visualized so as to obtain medical knowledge collection of illustrative plates.This hair
The method that bright embodiment provides effectively can sort out medical knowledge from the data such as case history or medical knowledge document, and automatically
Medical knowledge collection of illustrative plates is generated, so as to provide more full and accurate effective reference scheme during diagnosis.
For ease of understanding, each step in above method embodiment is described in detail below.
S101, the structured text to medical knowledge data source carry out word segmentation processing；
Wherein, medical knowledge data source here can specifically include：Electronic literature, electronic clinical guide, electronic health record,
Certainly other data sources for recording medical knowledge can also be included, the embodiment of the present invention is not especially limited to this.Here
Participle processing method can carry out word segmentation processing using natural language processing (NLP) technology to medical knowledge data source.
It should be noted that when medical knowledge data source is electronic health record, in the structuring to medical knowledge data source
Before text carries out word segmentation processing, methods described can also include：Non-structured text in electronic health record is converted into structure
Change text.Specifically include：
S1011, default irrelevant information in electronic health record is removed；
By taking the inpatient cases in electronic health record as an example, inpatient cases mainly include main suit, medical history, inspection, diagnosis, treatment,
The parts such as therapeutic effect.First inpatient cases are gone with privacy, remove patient, diagnostician some with structure knowledge mapping without
The information of pass, such as name, phone etc..
S1012, the non-structured text in electronic health record is converted into structured text；
It is structured text by non-structured text processing, the inpatient cases mainly used are initial journey record and discharge abstract
Deng, initial journey record mainly comprising medical history, tentative diagnosis, diagnosis basis, antidiastole, diagnosis and treatment plan, wherein medical history is non-knot
Structure text, but content is broadly divided into patient information, present illness history, past medical histroy, past medical history, human health screening, training inspection, auxiliary
Seven parts such as inspection, after medical history is divided into so several partial informations, then is refined and are extracted to each category information.By extraction
Afterwards, non-structured text reformed into computer it will be appreciated that structured text.
S102, based on default medical knowledge collection of illustrative plates dictionary to after word segmentation processing several vocabulary carry out character string knowledge
Not, using the vocabulary after identification as entity；Body belonging to the entity, the ontology library are determined based on default ontology library
In include body and ontological relationship, the body is used to describe the classification described in the entity, and the ontological relationship is used to retouch
State the corresponding relation between each body；
Wherein, medical knowledge collection of illustrative plates dictionary here is the dictionary pre-set, can be built in the following way：
Based on PCTB part-of-speech tagging specifications, be subject to part supplement, reference uses I2B2, UMLS to define entity scope, using hospital's dictionary as
Basis, build the medical knowledge collection of illustrative plates dictionary of standard criterion.More comprehensive each type medical science has been included in this dictionary to know
Know vocabulary, such as " kidney stone ", " colonoscopy ", " polycystic kindey mid-term ", " infection of the upper respiratory tract ", " Amoxicillin " or " stone-extraction operation " etc.
Deng vocabulary.
And then character string identification can be carried out for the vocabulary in the vocabulary after word segmentation processing and medical knowledge collection of illustrative plates dictionary,
And using the vocabulary after identification as entity.For example, if " infection of the upper respiratory tract A Moxi is included in medical knowledge data source
Sentence as woods ", then after being segmented, entity can as " infection of the upper respiratory tract " and " Amoxicillin "
It is identified.By taking electronic health record as an example, symptom entity can obtain by main suit, disease entity can obtain by diagnostic data, by
Check that inspection data processing can obtain checking entity, can be obtained treating entity by treatment plan data.
After entity is identified, it is possible to the body belonging to determining entity according to ontology library.Here body
Storehouse can also be the database pre-set, and the inside stores the relation between various bodies and each body.
Wherein, body here refers specifically to the classification belonging to entity, it can be understood as and it is the upperseat concept of entity, example
Such as " treatment ", " disease ", " symptom ", " medicine ", other bodies can also be included certainly.Each body can be with storage
Stored by identifying, such as 0 represents treatment, 1 represents disease, and 2 represent symptom.
It can be appreciated that " Amoxicillin " entity and " cephalo " entity belong to " medicine " this body；" tumour goes to push up
Art " and " stone-extraction operation " belong to " treatment " this body；" flu " and " kidney stone " belongs to " disease " this body.
Here ontological relationship is used to illustrate the relation between body and body.For example, for treatment, disease, symptom
These three bodies there may be following relation：
(1)<Treatment, improve, disease>
(2)<Treatment, deteriorate, disease>
(3)<Treatment, causes, disease>
(4)<Treatment, puts on, disease>
(5)<Treatment, improve, symptom>
(6)<Treatment, deteriorate, symptom>
(7)<Treatment, causes, symptom>
(8)<Treatment, puts on, symptom>
(9) non-above-mentioned class
That is, improvement here, to deteriorate, cause etc. be relation between body and body.This relation can
To be set according to actual conditions.Fig. 2 shows a kind of citing of body and ontological relationship.
S103, the characteristic vector for choosing any two entity structure entity pair in a word；Wherein, the characteristic vector
In comprising the body belonging to each entity, the entity in its affiliated body belonging to subclass, the part of speech of the entity, two
Position relationship and two entities between entity on logic of language is in the structured text of the medical knowledge data source
Several front and rear vocabulary；
Specifically, for the entity extracted, using any two entity E1 and E2 therein as an entity
It is right<E1,E2>, then construct the entity is to corresponding characteristic vector, a kind of optional make of its characteristic vector：
(E1.Ontology；E2.Ontology；E1.Type；E2.Type；E1.vn；E2.vn；Order Wi-w；Wi-w-
1；…Wi-1；Wi+1；…Wi+w；Ti-w；…Ti-1；Ti+1；…,Ti+w Wj-w,Wj-w-1,…Wj-1,Wj+1,…Wj+w,
Tj-w,…Tj-1,Tj+1,…,Tj+w)
Wherein, by taking E1 as an example：
E1.Ontology：E1 body is represented, can be represented here with the mark of body for example, 0 represents treatment, 1 generation
Surface diseases, 2 represent symptom.
E1.Type：Represent that subclass of this entity of E1 belonging in affiliated body, such as E1 belong to treatment body, then
Type means that E1 subclasses affiliated in body is treated, such as drug therapy or operative treatment, and also likely to be present certainly does not have
The situation of subclass.Here equally subclass can be represented using mark, such as 0 represents no subclass, 1 represents drug therapy, 2 tables
Show operative treatment.
Order：E1 and orders of the E2 in data source are represented, 0 represents E1 in E2 fronts, and 1 represents E1 in E2 back.
W：The vocabulary around entity is converted into vector using word2vec instruments, wherein (0,0,0,0,0,0,0,0) generation
Vocabulary is not present around table entity.Such as it will use and be converted to (0,1,0,0,0,1,1,0).
T：Part of speech feature (T)：Part of speech table D={ d1, d2 ... ..., dn } is built first, is by part of speech feature vector representation：T
={ T1, T2 ... ..., Tn }, if the part of speech of word is m, the part of speech feature vector representation of the word is：(0,0,0……1,…
..0), m-th is 1.
Give one example below to illustrate said process.
There are such one section of word in Medical guidelines：Unroofing of cyst is used to polycystic kindey mid-term patient, help to reduce blood pressure,
Mitigate pain and improve renal function, stone-extraction operation is implemented with Stone obstruction person.
Carry out entity extraction first (according to existing dictionary).If the vocabulary extracted is：Polycystic kindey mid-term (disease
Entity), unroofing of cyst (operative treatment entity), Stone obstruction (illness entity), four realities of stone-extraction operation (operative treatment entity)
Body, then following 6 entities pair will occur：
<Polycystic kindey mid-term unroofing of cyst>
<Polycystic kindey mid-term Stone obstruction>
<Polycystic kindey mid-term stone-extraction operation>
<Unroofing of cyst Stone obstruction>
<Unroofing of cyst stone-extraction operation>
<Stone obstruction stone-extraction operation>
For<Unroofing of cyst stone-extraction operation>, because ontology library does not store this<Treatment treatment>Ontological relationship, because
This its 9th class for just belonging in ontological relationship.
In above-mentioned six entities pair, with structure<Polycystic kindey mid-term unroofing of cyst>The characteristic vector of this entity pair is
Example, E1 are polycystic kindey mid-term, and E2 is that tumour goes to push up, for the w in vector, if choosing a word around E1 and E2.Then it is special
Levying vector is：
【1 (representing that polycystic kindey mid-term belongs to disease body), 0 (representing that unroofing of cyst belongs to treatment body), 0 (represents disease
Sick body does not have subclass), 2 (operative treatments belonged in treatment), 0 (E1 is before E2), the term vector of " to ", the word of " patient "
Vector, the part of speech feature of " to ", the part of speech feature of " patient ", the term vector (0,1,0,0,0,1,1,0) of " use ", " contributing to "
Part of speech feature, the part of speech feature of " use ", the part of speech feature of " contributing to "】.
S104, based on the ontological relationship in the ontology library, to each entity to screening, remove wherein first instance
It is right；The first instance pair, for comprising two entities belonging to body the reality of corresponding ontological relationship is not present in ontology library
Body pair；
As an example it is assumed that the body corresponding to two entities of entity centering is respectively to check and treat, and in ontology library
In do not store<Check, treatment>Such ontological relationship, therefore by this entity to removing.
S105, to the entity after screening to carrying out matching sort operation based on default ontological relationship, obtain the first ternary
Data are organized, two entities obtained after two entities and sort operation of the first triple packet centering containing entity
Entity relationship；
Wherein, the relation between entity and entity here is that the pre- personnel's contingency table for first passing through specialty is poured in, such as<
Treatment, puts on, disease>, therefore need exist for according to marked ontological relationship (such as treatment body and disease body between
Relation) to the entity after screening to carrying out matching classification.Specifically can by decision tree, random forest, logistic regression, SVM,
The sorter models such as neutral net are trained, and take 70% data to carry out model training as training set, 30% data are made
Tested for test set, the grader of good classification effect is chosen after trained.If individualized training device classification accuracy rate can not reach pre-
Phase requires, then can be integrated multiple graders using integrated study, to improve classifying quality.
After sort operation, finally give<Entity, relation, entity>Such triple data, such as obtain<Take stone
Art, put on, Stone obstruction>Such triple data.
S106, the first triple data are imported into database carry out visualized operation, generate medical knowledge figure
Spectrum.
It can specifically import in Neo4j chart databases, and carry out visualized operation, ultimately generate medical knowledge figure
Spectrum, Fig. 3 show a kind of example of medical knowledge collection of illustrative plates.
So as to after doctor have input the substance parameter such as illness, disease, be segmented to these substance parameters
And determine that doctor wants the entity of input after semantic parsing, then based on the medical knowledge collection of illustrative plates that this has been generated, can be automatic
Generate and export the therapeutic scheme on the substance parameter, so that doctor refers to.
In the specific implementation, the rule that default entity needs to observe also is included in ontology library here.Specifically, this
In rule for example can be that, if patient suffers from enterobrosis, colonoscopy (source can not be done《Digestive endoscopy》).
Therefore, method provided in an embodiment of the present invention also includes：
S107, the second triple data of structure, the second triple data include：After participle operation identification
Associated another entity in the regular and described rule that entity, the default entity needs are observed；
That is, for each by segmenting the entity identified, it is necessary to judge that the entity is in ontology library
The rule of the restricted entity whether is stored, is obtained if having based on another entity associated by the rule.It is final to obtain<It is real
Body, rule, entity>Triple.
For example, if being " enterobrosis " by segmenting the entity identified, find to be stored with " intestines by searching ontology library
Rule corresponding to perforation ", its rule is that can not do colonoscopy if patient suffers from enterobrosis, then by another entity " intestines of association
Mirror " also extracts.Ultimately forming triple is<Enterobrosis, contraindication, colonoscopy>, wherein contraindication here is rule point
One kind under class.
Whole modeling and application process may be referred to Fig. 4, due to hereinbefore each step being illustrated,
This is repeated no more.
Second aspect, the embodiment of the present invention additionally provide a kind of construction device of medical knowledge collection of illustrative plates, as shown in figure 5, bag
Include：
Participle unit 201, for carrying out word segmentation processing to the structured text of medical knowledge data source；
Recognition unit 202, for based on default medical knowledge collection of illustrative plates dictionary to several vocabulary after word segmentation processing
Character string identification is carried out, using the vocabulary after identification as entity；Sheet belonging to the entity is determined based on default ontology library
Body, includes body and ontological relationship in the ontology library, and the body is used to describing the classification described in the entity, described
Body relation is used to describe the corresponding relation between each body；
Construction unit 203, for choosing the characteristic vector of any two entity structure entity pair in a word；Wherein,
The body belonging to each entity, the entity subclass affiliated in its affiliated body, the reality are included in the characteristic vector
Position relationship and two entities between the part of speech of body, two entities on logic of language is in the medical knowledge data source
Several front and rear vocabulary in structured text；
Screening unit 204, for based on the ontological relationship in the ontology library, to each entity to screening, removing
Wherein first instance pair；The first instance pair, for comprising two entities belonging to body be not present in ontology library it is corresponding
The entity pair of ontological relationship；
Taxon 205, for, to carrying out matching sort operation based on default ontological relationship, being obtained to the entity after screening
To the first triple data, obtained after two entities and sort operation of the first triple packet centering containing entity
The entity relationship of two entities；
Visualization 206, visualized operation is carried out for the first triple data to be imported into database, it is raw
Into medical knowledge collection of illustrative plates.
Alternatively, when the medical knowledge data source is electronic health record, the participle unit is additionally operable to：
Default irrelevant information in electronic health record is removed；
Non-structured text in electronic health record is converted into structured text.
Alternatively, described device also includes：
Output unit 207, for when receiving the substance parameter of user's input, based on the medical knowledge collection of illustrative plates, giving birth to
Into and export therapeutic scheme on the substance parameter.
Alternatively, the rule that default entity needs to observe also is included in the ontology library；
Described device also includes：
Regular construction unit 208, for building the second triple data, the second triple data include：By dividing
Associated another reality in the regular and described rule that entity, the default entity needs after word operation identification are observed
Body；
The visualization 206 is additionally operable to：
The first triple data and the second triple data are imported into database and carry out visualization behaviour
Make, generate medical knowledge collection of illustrative plates.
The construction device for the medical knowledge collection of illustrative plates introduced by the present embodiment is can perform in the embodiment of the present invention
The device of the construction method of medical knowledge collection of illustrative plates, so the structure based on the medical knowledge collection of illustrative plates described in the embodiment of the present invention
Method, those skilled in the art can understand the specific embodiment party of the construction device of the medical knowledge collection of illustrative plates of the present embodiment
Formula and its various change form, so how to realize the embodiment of the present invention for the construction device of the medical knowledge collection of illustrative plates herein
In the construction method of medical knowledge collection of illustrative plates be no longer discussed in detail.As long as those skilled in the art implement the embodiment of the present invention
Device used by the construction method of traditional Chinese medicine knowledge mapping, belong to the scope to be protected of the application.
In addition, Fig. 6 shows the structured flowchart of computer equipment provided in an embodiment of the present invention.
Reference picture 6, the computer equipment, including：Processor (processor) 301, memory (memory) 302 and
Bus 303；
Wherein, the processor 301 and memory 302 complete mutual communication by the bus 303；
The processor 301 is used to call the programmed instruction in the memory 302, to perform above-mentioned each method embodiment
The method provided.
A kind of computer program product is also disclosed in the embodiment of the present invention, and the computer program product is non-temporary including being stored in
Computer program on state computer-readable recording medium, the computer program include programmed instruction, when described program instructs
When being computer-executed, computer is able to carry out the method that above-mentioned each method embodiment is provided.
The embodiment of the present invention also provides a kind of non-transient computer readable storage medium storing program for executing, and the non-transient computer is readable to deposit
Storage media stores computer instruction, and the computer instruction makes the computer perform the side that above-mentioned each method embodiment is provided
Method.
In the specification that this place provides, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect,
Above in the description to the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor
The application claims of shield features more more than the feature being expressly recited in each claim.It is more precisely, such as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself
Separate embodiments all as the present invention.
Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit requires, summary and accompanying drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation
Replace.
In addition, it will be appreciated by those of skill in the art that although some embodiments in this include institute in other embodiments
Including some features rather than further feature, but the combination of the feature of different embodiments means to be in the scope of the present invention
Within and form different embodiments.For example, in the following claims, embodiment claimed it is any it
One mode can use in any combination.
Some unit embodiments of the present invention can be realized with hardware, or to be run on one or more processor
Software module realize, or realized with combinations thereof.It will be understood by those of skill in the art that it can use in practice
Microprocessor or digital signal processor (DSP) are realized in gateway according to embodiments of the present invention, proxy server, system
Some or all parts some or all functions.The present invention is also implemented as being used to perform side as described herein
The some or all equipment or program of device (for example, computer program and computer program product) of method.It is such
Realizing the program of the present invention can store on a computer-readable medium, or can have the shape of one or more signal
Formula.Such signal can be downloaded from internet website and obtained, and either be provided or with any other shape on carrier signal
Formula provides.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of some different elements and being come by means of properly programmed computer real
It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
Claims (8)
- A kind of 1. construction method of medical knowledge collection of illustrative plates, it is characterised in that including：Word segmentation processing is carried out to the structured text of medical knowledge data source；Character string identification is carried out to several vocabulary after word segmentation processing based on default medical knowledge collection of illustrative plates dictionary, will be identified Vocabulary afterwards is as entity；Body belonging to the entity is determined based on default ontology library, this is included in the ontology library Body and ontological relationship, the body are used to describe the classification described in the entity, and the ontological relationship is used to describe each Corresponding relation between body；Choose the characteristic vector of any two entity structure entity pair in a word；Wherein, comprising every in the characteristic vector Between subclass belonging in its affiliated body of body, the entity belonging to individual entity, the part of speech of the entity, two entities If before and after the position relationship and two entities on logic of language are in the structured text of the medical knowledge data source Dry vocabulary；Based on the ontological relationship in the ontology library, to each entity to screening, wherein first instance pair is removed；Described One entity pair, for comprising two entities belonging to body the entity pair of corresponding ontological relationship is not present in ontology library；To the entity after screening to carrying out matching sort operation based on default ontological relationship, the first triple data, institute are obtained State the entity relationship of two entities obtained after two entities and sort operation of first triple packet centering containing entity；The first triple data are imported into database and carry out visualized operation, generate medical knowledge collection of illustrative plates.
- 2. according to the method for claim 1, it is characterised in that when the medical knowledge data source is electronic health record, Before the structured text progress word segmentation processing of medical knowledge data source, methods described also includes：Default irrelevant information in electronic health record is removed；Non-structured text in electronic health record is converted into structured text.
- 3. according to the method for claim 1, it is characterised in that methods described also includes：When receiving the substance parameter of user's input, based on the medical knowledge collection of illustrative plates, generate and export on the entity The therapeutic scheme of parameter.
- 4. according to the method for claim 1, it is characterised in that also including default entity in the ontology library needs to observe Rule；Methods described also includes：The second triple data are built, the second triple data include：By the entity after participle operation identification, preset The entity need another entity associated in the regular and described rule observed；The first triple data and the second triple data are imported into database and carry out visualized operation, it is raw Into medical knowledge collection of illustrative plates.
- A kind of 5. construction device of medical knowledge collection of illustrative plates, it is characterised in that including：Participle unit, for carrying out word segmentation processing to the structured text of medical knowledge data source；Recognition unit, for entering line character to several vocabulary after word segmentation processing based on default medical knowledge collection of illustrative plates dictionary String identification, using the vocabulary after identification as entity；Body belonging to the entity is determined based on default ontology library, described Body and ontological relationship are included in body storehouse, the body is used to describe the classification described in the entity, and the ontological relationship is used Corresponding relation between each body is described；Construction unit, for choosing the characteristic vector of any two entity structure entity pair in a word；Wherein, the feature In vector comprising the body belonging to each entity, the entity in its affiliated body belonging to subclass, the part of speech of the entity, Position relationship and two entities between two entities on logic of language is literary in the structuring of the medical knowledge data source Several front and rear vocabulary in this；Screening unit, for based on the ontological relationship in the ontology library, to each entity to screening, removing wherein first Entity pair；The first instance pair, for comprising two entities belonging to body corresponding ontological relationship is not present in ontology library Entity pair；Taxon, for, to carrying out matching sort operation based on default ontological relationship, obtaining first to the entity after screening Triple data, two realities obtained after two entities and sort operation of the first triple packet centering containing entity The entity relationship of body；Visualization, visualized operation is carried out for the first triple data to be imported into database, generate medical science Knowledge mapping.
- 6. device according to claim 5, it is characterised in that when the medical knowledge data source is electronic health record, institute Participle unit is stated to be additionally operable to：Default irrelevant information in electronic health record is removed；Non-structured text in electronic health record is converted into structured text.
- 7. device according to claim 5, it is characterised in that described device also includes：Output unit, for when receiving the substance parameter of user's input, based on the medical knowledge collection of illustrative plates, generating and exporting Therapeutic scheme on the substance parameter.
- 8. according to the method for claim 8, it is characterised in that also including default entity in the ontology library needs to observe Rule；Described device also includes：Regular construction unit, for building the second triple data, the second triple data include：Known by segmenting operation Associated another entity in the regular and described rule that entity, the default entity needs after not are observed；The visualization is additionally operable to：The first triple data and the second triple data are imported into database and carry out visualized operation, it is raw Into medical knowledge collection of illustrative plates.
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