CN109346169A - A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium - Google Patents
A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium Download PDFInfo
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- CN109346169A CN109346169A CN201811205971.4A CN201811205971A CN109346169A CN 109346169 A CN109346169 A CN 109346169A CN 201811205971 A CN201811205971 A CN 201811205971A CN 109346169 A CN109346169 A CN 109346169A
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
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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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention discloses a kind of artificial intelligence assisting in diagnosis and treatment system and its construction methods, equipment and storage medium, belong to life and health technical field.It includes disease knowledge map module, diagnosing model module and machine learning decision-making module；Wherein, the disease knowledge map module forms disease knowledge library, for collecting the various resources about disease for inquiring and reading；The diagnosing model module, for receiving patient information, it stores information in system database, and input to machine learning decision-making module, achievement data is extracted from disease knowledge map module by machine learning decision-making module to compare and analyze, it is pushed out patient information collection, the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement is selected for doctor.The bottom knowledge mapping that it is based on is comprehensive, authoritative；There are easy to operate, service efficiency height in interaction；In human-computer interaction level, intelligence degree is high.
Description
Technical field
The present invention relates to life and health technical field more particularly to a kind of artificial intelligence assisting in diagnosis and treatment system and its building sides
Method, equipment and storage medium.
Background technique
World Health Organization's investigation points out that the whole world has a large amount of patients to die of medical malpractice, rather than disease itself.1999
Year, IOM (US National Medical Research Institute) has delivered an epoch-making report " (people is to make mistakes to Toerris Human "
), report shows: first, the incredible amount of medical malpractice, malpractice is lethal to rank the 5th in the ten big causes of the death；Second, greatly
Partial medical malpractice can be avoided from human factor by computer system.In the U.S. " patient safety and doctor in 2013
Treat quality " in analysis on magazine (Patient safety&quality healthcare), medical treatment is died of in estimation the whole America every year
The number of accident is 210,000-44 ten thousand, becomes the third-largest cause of the death of American, is only second to cardiovascular disease and cancer.It can be seen that doctor misses
It is serious to examine the problem of bringing to society.So far from nineteen fifty, it is averaged misdiagnosis rate for the studies have shown that doctor of mistaken diagnosis both at home and abroad
30% or so.Skin related disease susceptible disease, variation is fast, the state of an illness is complicated, diagnoses and treatment as early as possible is needed, so misdiagnosis rate is compared more
It is all higher in general diseases.Certainly, for each specific patient the diagnosis of disease only to mistake, that is, only have
100% mistaken diagnosis and 0% mistaken diagnosis.Various circles of society are more next to quality of medical care, control malpractice, the cry of raising patient safety is improved
It is higher.How under " internet+" business appearance, improving people's health by the advantage of internet must not as medical industry
The urgent problem to be solved not faced.
That there is case amounts is big for the medical big data in China, data are unstructured, difficult without unified standard, follow-up in industry
The problems such as result in medical field application it is very insufficient, only a small amount of data can be applied to clinical research at present.One
Aspect data source is very many and diverse, and another aspect is clinically very high to auxiliary diagnosis demand；It is hoped that there will be data sides for hospital management layer
Offer administrative decision is helped, experts and scholars want to provide support and driving by data when carrying out scientific research exploration.
Assisting in diagnosis and treatment system in prior art includes construction of knowledge base and knowledge acquisition, and multidimensional knowledge shows.It is right
In construction of knowledge base and knowledge acquisition, as shown in Figure 1, the acquisition of medical knowledge is typically derived from medical literature and experience accumulation,
Including term dictionary, semantic dictionary, knowledge model etc..Perfect knowledge base creates system, passes through setting different type, different necks
Domain, different themes knowledge piece cluster, meet requirement (as shown in Figure 1) of each clinical event to Knowledge Decision-making system.Diagnosis is known
Know library, states the contents such as relevant disease name, diagnosis and treatment reference；Drug knowledge base, statement and medication instruction, incompatibility, no
Good reaction, the relevant content of Experts ' Comments；Chemical examination knowledge base is checked, in the knowledge of state collection of specimens, adapt to taboo etc.
Hold；The relevant knowledge contents such as operating instruction relevant to operation, points for attention are stated in operation knowledge library.In these knowledge bases
Hold and is converted into certainly with knowledge such as service databases and medical literature such as hospital original HIS, EMR by data mining platform
The contents such as the intelligible term dictionary of plan system, semantic dictionary, knowledge model.
Part is showed for multidimensional knowledge, by the way that Clinical Decision Support Systems to be connect with hospital HIS, doctor is writing electricity
The different phase of sub- doctor's advice and case history can use intelligent extraction, on line analytical processing, by the effective knowledge in level knowledge base
Classify, be converted into the information that should be readily appreciated that and apply, be presented to medical worker, to aid in decision.With infectiousness
For atypical pneumonia diagnosis, doctor can successively click in window right side knowledge element (as shown in Figure 2) and check check item
Mesh examines the contents such as project, operation project, treatment drug, to play the role of certain auxiliary reference.In addition, guide search,
The functions such as knowledge excavation, medical tool, symptom derivation, classification of diseases (ICD) map are also integrated into each client, are not only convenient for
System user searching classification can also pool our ideas and make concerted efforts, and user is allowed to participate in, and form exhibition of knowledge, feedback, processing, open up again
Existing benign cycle.
By analyze the above content it is found that existing medical diagnosis system there are following technical problems: 1) can not be good
Accomplish artificial intelligence assisting in diagnosis and treatment, is only to provide the inspection item, inspection project, operation item of doctor's auxiliary reference class disease
The contents such as mesh, treatment drug；The bottom knowledge mapping being based on is not comprehensive enough, authoritative；2) workload of doctor can not be reduced；3)
The clinic diagnosis path of similar disease can not be provided by system；4) patient can not be allowed to be changed according to certain class of body, pass through shifting
Moved end carries out autodiagnosis and guiding doctor；5) misdiagnosis rate of doctor cannot be greatly lowered, diagnosis and treatment empirical data can not be shared to remote
Mountain area.To sum up, the bottom knowledge mapping that existing interrogation system is based on is not comprehensive enough, authoritative；It is multiple to there is operation in interaction
It is miscellaneous, the problems such as service efficiency is low；In human-computer interaction level, intelligence degree is insufficient.
Summary of the invention
1. technical problems to be solved by the inivention
In order to overcome the above technical problems, the present invention provides a kind of artificial intelligence assisting in diagnosis and treatment system and its building sides
Method, equipment and storage medium.The bottom knowledge mapping that it is based on is comprehensive, authoritative；There are easy to operate in interaction, effect is used
Rate is high；In human-computer interaction level, intelligence degree is high.
2. technical solution
To solve the above problems, technical solution provided by the invention are as follows:
A kind of artificial intelligence assisting in diagnosis and treatment system, including disease knowledge map module, diagnosing model module and machine
Learning decision module；Wherein, the disease knowledge map module forms disease for collecting the various resources about disease
Knowledge base, for inquiring and reading；
The diagnosing model module stores information in system database, and input for receiving patient information
Machine learning decision-making module is given, achievement data is extracted from disease knowledge map module by machine learning decision-making module and carries out pair
Than analysis, it is pushed out patient information collection, prompts the scheme collection of inspection and associated medication therapies that sufferer needs to implement for doctor
Raw selection；
The machine learning decision-making module reads disease knowledge map for receiving patient information and doctor's practical information
Module information carries out machine learning and makes a policy.
Preferably, the disease knowledge library includes disease knowledge set and disease Top-level Ontology.
The building of disease knowledge map includes 2 parts: disease knowledge set and disease Top-level Ontology.Disease knowledge set
(disease treatment Experiential Knowledge Database) is about the various causes of disease of disease treatment, active drug, therapeutic process, treatment cycle, taboo
Etc. knowledge, be the core in disease knowledge library；Disease Top-level Ontology (disease Medical Domain Ontology) is the medical concept about disease
And concept asks the term set of relationship, is to design to realize shared need with scalability in disease knowledge library, wherein greatly
Partial content can be realized by quoting existing clinical term set or disease ontology.
Preferably, the achievement data that extracts from disease knowledge map module compares and analyzes are as follows: passes through disease
A possibility which kind of disease knowledge mapping connection judge and displaying for the previous treatment skill of such disease, administration data and is examined
Treatment expense relevant information, so that the assurance disease that adjuvant clinical doctor, ambulatory care and other medical personnels can more prepare is controlled
The overall process for the treatment of.
Preferably, the decision are as follows: recommendation is less than or the doubtful rate of the suspected disease close to the threshold value being previously set.
Preferably, the comparative analysis includes helping repeatedly in the patient of hospital admission, all previous diagnosis information is compared
Doctor more quickly and correctly carries out medical diagnosis on disease.The inquiry refers to the title of certain class disease, can inquire the disease
The contents such as concept, the cause of disease, clinical manifestation, complication, inspection, diagnosis, treatment, prevention, facilitate doctor to learn to consult.
A kind of construction method of artificial intelligence assisting in diagnosis and treatment system, step are as follows:
A, disease knowledge map: the various resources about disease of comprehensive collection is constructed, fusion disease knowledge set is formed
With disease Top-level Ontology；
B, it constructs diagnosing model: receiving patient information, store information in system database, and carry out engineering
Decision is practised, achievement data is extracted from disease knowledge map module by machine learning decision and compares and analyzes, be pushed out trouble
Person's information collection, the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement are selected for doctor.
Preferably, the building process of the knowledge collection of the medical diagnosis on disease are as follows: the various moneys about disease of comprehensive collection
Source carries out electronization to it using digitizing technique, and for complicated professional medical practice knowledge, form includes various figures
Piece, audio and video resources are instructed to carry out sorted generalization, are allowed to structuring, systematization by the expert in field.
Preferably, the building process of the Top-level Ontology of the disease are as follows: for the disease resource of text-type, in relevant medical
Under terminology standard, text analyzing, semantic criteria, entity and Relation extraction, association analysis etc. are carried out to it.It obtains about disease
All specifications or nonstandard concept lexical set, and according to the relationship type between the difference and concept of resource content, group
At the Top-level Ontology about disease.
Preferably, the diagnosing model be with using computer technology, the patient's that human-computer interaction is transmitted
Various information by patient basis, symptom, input to clinical medicine knowledge library system, and knowledge base system is by built-in
Machine learning model is pushed out possible patient information, prompts sufferer and needs the inspection implemented and associated medication therapies
Scheme collection is selected for doctor；And the function of intelligent retrieval electronic health record and Medical Knowledge management is provided for clinician.
Preferably, the process of machine learning and decision are as follows: new samples are learnt by variation to disease indicators, and by information
It is stored in system database, contacting between index and disease is interpreted by internal system algorithm, while in learning process
Change judgment mode, constantly to adapt to new environmental requirement.
A kind of equipment, the equipment include:
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of places
Manage method of the device execution as described in any of the above item.
A kind of storage medium being stored with computer program is realized when described program is executed by processor as any of the above
Method described in.
3. beneficial effect
Using technical solution provided by the invention, compared with prior art, have the following beneficial effects:
1) present invention accomplishes artificial intelligence assisting in diagnosis and treatment well, provides doctor's check item of auxiliary reference class disease
Mesh examines the contents such as project, operation project, treatment drug；The bottom knowledge mapping being based on is comprehensive, authoritative；2) doctor is reduced
Workload；3) the clinic diagnosis path of similar disease is provided by system；4) it allows patient to be changed according to certain class of body, passes through
Mobile terminal carries out autodiagnosis and guiding doctor；5) misdiagnosis rate of doctor is greatly lowered, diagnosis and treatment empirical data is shared to remote mountain areas.It is comprehensive
On, the bottom knowledge mapping that interrogation system of the invention is based on is comprehensive, authoritative；There are easy to operate in interaction, effect is used
Rate is high；In human-computer interaction level, intelligence degree is high.
Detailed description of the invention
Fig. 1 is the construction of knowledge base and knowledge acquisition flow diagram of existing Medicine Assist Expert System；
Fig. 2 is that the multidimensional knowledge of existing Medicine Assist Expert System shows window schematic diagram；
Fig. 3 is assisting in diagnosis and treatment system overall logic architecture diagram of the invention；
Fig. 4 is one of disease knowledge map embodiment of the invention；
Fig. 5 is diagnosing model flow diagram of the invention；
Fig. 6 is the process schematic of machine learning of the invention and depth decision；
Fig. 7 is a kind of structural schematic diagram of equipment provided in an embodiment of the present invention.
Specific embodiment
To further appreciate that the contents of the present invention, the application is made further specifically with reference to the accompanying drawings and examples
It is bright.It is understood that specific embodiment described herein is used only for explaining related invention, rather than to the limit of the invention
It is fixed.It also should be noted that illustrating only part relevant to invention for ease of description, in attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Embodiment 1
A kind of artificial intelligence assisting in diagnosis and treatment system, including disease knowledge map module, diagnosing model module and machine
Learning decision module；Wherein, the disease knowledge map module forms disease for collecting the various resources about disease
Knowledge base, for inquiring and reading；
The diagnosing model module stores information in system database, and input for receiving patient information
Machine learning decision-making module is given, achievement data is extracted from disease knowledge map module by machine learning decision-making module and carries out pair
Than analysis, it is pushed out patient information collection, prompts the scheme collection of inspection and associated medication therapies that sufferer needs to implement for doctor
Raw selection；
The machine learning decision-making module reads disease knowledge map for receiving patient information and doctor's practical information
Module information carries out machine learning and makes a policy.
The disease knowledge library includes disease knowledge set and disease Top-level Ontology.The building of disease knowledge map includes
2 parts: disease knowledge set and disease Top-level Ontology.Disease knowledge set (disease treatment Experiential Knowledge Database) is about disease
The knowledge such as the various causes of disease, active drug, therapeutic process, treatment cycle, the taboo treated, are the cores in disease knowledge library；Disease
Top-level Ontology (disease Medical Domain Ontology) is the term set that relationship is asked about the medical concept and concept of disease, is for reality
The shared of existing disease knowledge library needs with scalability and is designed, and the content of wherein most can pass through the existing clinic operation of reference
Language set or disease ontology are realized.
Embodiment 2
A kind of artificial intelligence assisting in diagnosis and treatment system of the present embodiment, is further improved on the basis of embodiment 1, described
Which kind of extract achievement data from disease knowledge map module to compare and analyze are as follows: disease judged by the connection of disease knowledge map
A possibility that sick, simultaneously shows for the previous treatment skill of such disease, administration data and cost of medical service relevant information, thus auxiliary
The overall process for the assurance disease treatment for helping clinician, ambulatory care and other medical personnels that can more prepare.
The decision are as follows: recommendation is less than or the doubtful rate of the suspected disease close to the threshold value being previously set；Pair
Include to repeatedly in the patient of hospital admission than analysis, all previous diagnosis information compares, help doctor more quickly and correctly into
Row medical diagnosis on disease.The inquiry refers to the title of certain class disease, can inquire the concept of the disease, the cause of disease, clinical manifestation, simultaneously
The contents such as disease, inspection, diagnosis, treatment, prevention are sent out, doctor is facilitated to learn to consult.
Embodiment 3
A kind of construction method of artificial intelligence assisting in diagnosis and treatment system of the present embodiment, one kind according to embodiment 1 or 2 are artificial
Intelligent assisting in diagnosis and treatment system, the steps include:
A, disease knowledge map: the various resources about disease of comprehensive collection is constructed, fusion disease knowledge set is formed
With disease Top-level Ontology；
B, it constructs diagnosing model: receiving patient information, store information in system database, and carry out engineering
Decision is practised, achievement data is extracted from disease knowledge map module by machine learning decision and compares and analyzes, be pushed out trouble
Person's information collection, the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement are selected for doctor.
Method described in the present embodiment can run use on the mobile terminals such as PC, mobile phone, IPAD, can also be in desktop
It runs and uses on brain, same technical effect can be obtained.
Embodiment 4
A kind of construction method of artificial intelligence assisting in diagnosis and treatment system of the present embodiment, is improved on the basis of embodiment 3, also
The building process of knowledge collection including the medical diagnosis on disease are as follows: the various resources about disease of comprehensive collection use number
Change technology carries out electronization to it, and for complicated professional medical practice knowledge, form includes various pictures, audio and view
Frequency resource is instructed to carry out sorted generalization, is allowed to structuring, systematization by the expert in field.
Embodiment 5
A kind of construction method of artificial intelligence assisting in diagnosis and treatment system of the present embodiment, changes on the basis of embodiment 3 or 4
Into the building process of the Top-level Ontology of the disease are as follows: for the disease resource of text-type, under relevant medical terminology standard,
Text analyzing, semantic criteria, entity and Relation extraction, association analysis etc. are carried out to it.Obtain about disease all specifications or
Nonstandard concept lexical set, and according to the relationship type between the difference and concept of resource content, it forms about disease
Top-level Ontology.
Embodiment 6
A kind of construction method of artificial intelligence assisting in diagnosis and treatment system of the present embodiment changes on the basis of embodiment 3,4,5
Into the diagnosing model is so that using computer technology, the various information for the patient that human-computer interaction is transmitted pass through
Patient basis, symptom input to clinical medicine knowledge library system, and knowledge base system passes through built-in machine learning model,
It is pushed out possible patient information, the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement is selected for doctor
It selects；And the function of intelligent retrieval electronic health record and Medical Knowledge management is provided for clinician.
Embodiment 7
The construction method of a kind of artificial intelligence assisting in diagnosis and treatment system of the present embodiment, on the basis of embodiment 3,4,5,6
It improves, the process of machine learning and decision are as follows: new samples are learnt by the variation to disease indicators, and store information in system
Database interprets contacting between index and disease by internal system algorithm, while constantly changing in learning process and sentencing
Disconnected mode, to adapt to new environmental requirement.
Embodiment 8
The present embodiment proposes a kind of equipment, and the equipment includes:
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of places
Manage method of the device execution as described in any one of embodiment 3-7.
The present embodiment also proposed a kind of storage medium for being stored with computer program, real when which is executed by processor
The now method as described in any one of embodiment 3-7.
Fig. 7 is a kind of structural schematic diagram for equipment that one embodiment of the invention provides.
As shown in fig. 7, present invention also provides a kind of equipment 500, including one or more centres as on the other hand
Unit (CPU) 501 is managed, can be added according to the program being stored in read-only memory (ROM) 502 or from storage section 508
The program that is downloaded in random access storage device (RAM) 503 and execute various movements appropriate and processing.In RAM503, also deposit
It contains equipment 500 and operates required various programs and data.CPU501, ROM502 and RAM503 pass through the phase each other of bus 504
Even.Input/output (I/O) interface 505 is also connected to bus 504.
I/O interface 505 is connected to lower component: the importation 506 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 507 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 508 including hard disk etc.；
And the communications portion 509 of the network interface card including LAN card, modem etc..Communications portion 509 via such as because
The network of spy's net executes communication process.Driver 510 is also connected to I/O interface 505 as needed.Detachable media 511, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 510, in order to read from thereon
Computer program be mounted into storage section 508 as needed.
Particularly, in accordance with an embodiment of the present disclosure, the method for any of the above-described embodiment description may be implemented as computer
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be tangibly embodied in machine readable
Computer program on medium, the computer program include the program generation for executing the method for any of the above-described embodiment description
Code.In such embodiments, which can be downloaded and installed from network by communications portion 509, and/or
It is mounted from detachable media 511.
As another aspect, present invention also provides a kind of computer readable storage medium, the computer-readable storage mediums
Matter can be computer readable storage medium included in the device of above-described embodiment；It is also possible to individualism, it is unassembled
Enter the computer readable storage medium in equipment.Computer-readable recording medium storage has one or more than one program, should
Program is used to execute by one or more than one processor is described in the present processes.
Flow chart and block diagram in attached drawing are illustrated according to the system of various embodiments of the invention, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, this is depending on related function.Also it wants
It is noted that the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, Ke Yitong
The dedicated hardware based system of functions or operations as defined in executing is crossed to realize, or by specialized hardware and can be calculated
The combination of machine instruction is realized.
Being described in the embodiment of the present application involved unit or module can be realized by way of software, can also be with
It is realized by way of hardware.Described unit or module also can be set in the processor, for example, each unit can
To be the software program being arranged in computer or intelligent movable equipment, it is also possible to the hardware device being separately configured.Wherein, this
The title of a little units or module does not constitute the restriction to the unit or module itself under certain conditions.
Above description is only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the application, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from the application design, appointed by above-mentioned technical characteristic or its equivalent feature
Other technical solutions of meaning combination and formation.Such as features described above and (but being not limited to) disclosed herein have similar functions
Technical characteristic replaced mutually and the technical solution that is formed.
Claims (10)
1. a kind of artificial intelligence assisting in diagnosis and treatment system, which is characterized in that including disease knowledge map module, diagnosing model mould
Block and machine learning decision-making module；Wherein, the disease knowledge map module, for collecting the various resources about disease,
Disease knowledge library is formed, for inquiring and reading；
The diagnosing model module stores information in system database, and input to machine for receiving patient information
Device learning decision module is extracted achievement data from disease knowledge map module by machine learning decision-making module and compared point
Analysis, is pushed out patient information collection, and the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement is selected for doctor
It selects；
The machine learning decision-making module reads disease knowledge map module for receiving patient information and doctor's practical information
Information carries out machine learning and makes a policy.
2. a kind of artificial intelligence assisting in diagnosis and treatment system according to claim 1, which is characterized in that the disease knowledge library
Including disease knowledge set and disease Top-level Ontology.
3. a kind of artificial intelligence assisting in diagnosis and treatment system according to claim 1, which is characterized in that described from disease knowledge
Which kind of extract achievement data in map module to compare and analyze are as follows: a possibility that judging disease by the connection of disease knowledge map
And show for the previous treatment skill of such disease, administration data and cost of medical service relevant information, thus adjuvant clinical doctor,
The overall process for the assurance disease treatment that ambulatory care and other medical personnels can more prepare.
4. a kind of artificial intelligence assisting in diagnosis and treatment system according to claim 1, which is characterized in that the decision are as follows: recommend
It is less than or approaches the doubtful rate of the suspected disease for the threshold value being previously set.
5. a kind of artificial intelligence assisting in diagnosis and treatment system according to claim 1, which is characterized in that the comparative analysis packet
It includes to repeatedly in the patient of hospital admission, all previous diagnosis information is compared.
6. a kind of construction method of artificial intelligence assisting in diagnosis and treatment system, it is characterised in that:
A, disease knowledge map: the various resources about disease of comprehensive collection is constructed, fusion disease knowledge set and disease are formed
Sick Top-level Ontology；
B, it constructs diagnosing model: receiving patient information, store information in system database, and carry out machine learning and determine
Plan is extracted achievement data from disease knowledge map module by machine learning decision and compared and analyzed, and patient's letter is pushed out
Breath collection, the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement are selected for doctor.
7. a kind of construction method of artificial intelligence assisting in diagnosis and treatment system according to claim 6, which is characterized in that the disease
The building process of the knowledge collection of disease diagnosis are as follows: the various resources about disease of comprehensive collection, using digitizing technique to it
Electronization is carried out, for complicated professional medical practice knowledge, form includes various pictures, audio and video resources, by
The expert in field instructs to carry out sorted generalization, is allowed to structuring, systematization；Further, the structure of the Top-level Ontology of the disease
Build process are as follows: for the disease resource of text-type, under relevant medical terminology standard, it is carried out text analyzing, semantic criteria,
Entity and Relation extraction, association analysis etc..All specifications or nonstandard concept lexical set of the acquisition about disease, and according to
Relationship type between the difference and concept of resource content forms the Top-level Ontology about disease.
8. a kind of construction method of artificial intelligence assisting in diagnosis and treatment system according to claim 6, which is characterized in that the disease
Sick diagnostic model is so that using computer technology, the various information for the patient that human-computer interaction is transmitted are basic by patient
Information, symptom input to clinical medicine knowledge library system, and knowledge base system passes through built-in machine learning model, and being pushed out can
The patient information of energy, the scheme collection for prompting inspection and associated medication therapies that sufferer needs to implement are selected for doctor；And it is
The function of clinician offer intelligent retrieval electronic health record and Medical Knowledge management；Further, the machine learning and decision
Process are as follows: new samples are learnt by variation to disease indicators, and store information in system database, pass through internal system
Algorithm interprets contacting between index and disease, while constantly changing judgment mode in learning process, adapting to new ring
Border requires.
9. a kind of equipment, the equipment include:
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors
Execute the method as described in any one of embodiment 6-8.
10. a kind of storage medium for being stored with computer program, which is characterized in that realized such as when described program is executed by processor
Method described in any one of embodiment 6-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201811205971.4A CN109346169A (en) | 2018-10-17 | 2018-10-17 | A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium |
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| CN201811205971.4A CN109346169A (en) | 2018-10-17 | 2018-10-17 | A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium |
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