CN108335748A - A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster - Google Patents
A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster Download PDFInfo
- Publication number
- CN108335748A CN108335748A CN201810047136.6A CN201810047136A CN108335748A CN 108335748 A CN108335748 A CN 108335748A CN 201810047136 A CN201810047136 A CN 201810047136A CN 108335748 A CN108335748 A CN 108335748A
- Authority
- CN
- China
- Prior art keywords
- diagnosis
- treatment
- data
- model
- nasopharyngeal carcinoma
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 164
- 238000011282 treatment Methods 0.000 title claims abstract description 163
- 208000002454 Nasopharyngeal Carcinoma Diseases 0.000 title claims abstract description 77
- 206010061306 Nasopharyngeal cancer Diseases 0.000 title claims abstract description 77
- 201000011216 nasopharynx carcinoma Diseases 0.000 title claims abstract description 77
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 29
- 238000004458 analytical method Methods 0.000 claims abstract description 31
- 230000002452 interceptive effect Effects 0.000 claims abstract description 9
- 230000003993 interaction Effects 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000011160 research Methods 0.000 claims description 20
- 238000010276 construction Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 12
- 230000014509 gene expression Effects 0.000 claims description 7
- 208000009565 Pharyngeal Neoplasms Diseases 0.000 claims description 4
- 201000008006 pharynx cancer Diseases 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 108090000623 proteins and genes Proteins 0.000 description 35
- 238000000034 method Methods 0.000 description 20
- 238000004393 prognosis Methods 0.000 description 17
- 238000012546 transfer Methods 0.000 description 14
- 206010028980 Neoplasm Diseases 0.000 description 12
- 230000006870 function Effects 0.000 description 12
- 230000000694 effects Effects 0.000 description 8
- 230000004083 survival effect Effects 0.000 description 8
- 201000010099 disease Diseases 0.000 description 7
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 7
- 206010027476 Metastases Diseases 0.000 description 6
- 238000002560 therapeutic procedure Methods 0.000 description 6
- 238000012795 verification Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000009401 metastasis Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 5
- 201000011510 cancer Diseases 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 239000000523 sample Substances 0.000 description 4
- 230000001225 therapeutic effect Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 239000003147 molecular marker Substances 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 208000005623 Carcinogenesis Diseases 0.000 description 2
- 241001269238 Data Species 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000036952 cancer formation Effects 0.000 description 2
- 231100000504 carcinogenesis Toxicity 0.000 description 2
- 238000013523 data management Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012268 genome sequencing Methods 0.000 description 2
- 238000011478 gradient descent method Methods 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 210000001989 nasopharynx Anatomy 0.000 description 2
- 238000009206 nuclear medicine Methods 0.000 description 2
- 230000007170 pathology Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 238000001959 radiotherapy Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 230000010415 tropism Effects 0.000 description 2
- 238000002604 ultrasonography Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 206010007747 Cataract congenital Diseases 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000006907 apoptotic process Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012455 bioassay technique Methods 0.000 description 1
- 238000007622 bioinformatic analysis Methods 0.000 description 1
- 238000003766 bioinformatics method Methods 0.000 description 1
- 238000001815 biotherapy Methods 0.000 description 1
- 238000002512 chemotherapy Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 230000034994 death Effects 0.000 description 1
- 231100000517 death Toxicity 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000000338 in vitro Methods 0.000 description 1
- 238000011337 individualized treatment Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 230000000069 prophylactic effect Effects 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
- 230000009452 underexpressoin Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The present invention provides a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server clusters, the dicision of diagnosis and treatment server cluster cluster includes user's interactive server, analysis decision server and data server, user's interactive server is used to carry out data interaction with dicision of diagnosis and treatment terminal and carries out user management, the analysis decision server is for providing dicision of diagnosis and treatment service to the user, the data server is provided with nasopharyngeal carcinoma database and interacts for carrying out data processing；The analysis decision server includes the first diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the 4th diagnosis and treatment module and/or the 5th diagnosis and treatment module, and the first diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the 4th diagnosis and treatment module and the 5th diagnosis and treatment module use different dicision of diagnosis and treatment models respectively.The present invention can provide the accurately intelligent diagnostics decision service based on artificial intelligence to the user, have wide prospect of the application.
Description
Technical field
The present invention relates to medical field more particularly to a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server clusters.
Background technology
Artificial intelligence is research, develops intelligent theory, method, technology and the application for simulating, extending and extending people
A special kind of skill science of system.Artificial intelligence is a branch of computer science, it attempts to understand the essence of intelligence, and produces
Go out a kind of new intelligence machine that can be made a response in such a way that human intelligence is similar, the research in the field includes robot, language
Say identification, image recognition, natural language processing and expert system etc..Artificial intelligence medical treatment is that it is answered in the extension of medical field
With.Artificial intelligence medical treatment at present includes mainly virtual assistant, medical big data, medical image, intelligent sound, body-building biology skill
Art, healthy Lifestyle management, medical treatment search, cancer morning sieve, artificial intelligence chip etc..Existing appearance medical treatment is related intelligent
System includes Watson robots of IBM Corporation, middle mountain Eye Center " CC-Cruiser congenital cataract artificial intelligence platform "
Deng.
But existing artificial intelligence medical system is just like the intelligence system in this kind of specialized health field of nasopharyngeal carcinoma,
By taking Watson robots as an example, function is relatively complete, can cover a variety of kinds of tumor, but to the current common spy in some China
The cancer training field of color is deeply insufficient.And remove except Watson robots, the system functions such as some domestic functional platforms compared with
To be single, mainly for a certain specific small range disease or inspection item, far from achieving the effect that systematization assisting in diagnosis and treatment.
Nasopharyngeal carcinoma is Guangdong Province's local characteristic cancer kind, and recurrence and transfer are underlying cause of deaths, precisely predicts relapse and metastasis and refers to
It is the key that improve curative effect, and how to carry out based on intelligentized data analysis and thus accurately decision and be to lead individualized treatment
Urgent problem to be solved at present.
Invention content
To solve the above-mentioned problems, the present invention provides a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster.
The present invention is realized with following technical solution：
A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster, the dicision of diagnosis and treatment server cluster include using
Family interactive server, analysis decision server and data server, user's interactive server are used for and dicision of diagnosis and treatment end
End carries out data interaction and carries out user management, and the analysis decision server is for providing dicision of diagnosis and treatment service, institute to the user
Data server is stated for carrying out data processing, nasopharyngeal carcinoma database is provided with and interacts；
The analysis decision server includes the first diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the four methods of diagnosis
Treat module and/or the 5th diagnosis and treatment module, the first diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the 4th diagnosis and treatment mould
Block and the 5th diagnosis and treatment module use different dicision of diagnosis and treatment models respectively.
Further, the first diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the first model；
First model is using the first data as data source.
Further, the second diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the second model；
Second model is using the second data as data source.
Further, the third diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on third model；
The third model is using gene data as data source.
Further, the 4th diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the 4th model；
4th model is research pair with a variety of data in the first data, the second data and third data in nasopharyngeal carcinoma database
As carrying out dicision of diagnosis and treatment guidance for user.
Further, the 5th diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the 5th model；
5th model provides diagnosis and treatment to the user based on neural network and determines using multigroup data in nasopharyngeal carcinoma database as data source
Plan.
Further, the 4th model includes public discovery model, and the public discovery model is based on sparse expression
The Theory Construction.
Further, the 4th model includes public discovery model, and the public discovery model is to be expressed in tensor
Under expression matrix, the public discovery model towards tensor description.
The beneficial effects of the invention are as follows：
The present invention provides a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster, can provide to the user and be based on
The accurately intelligent diagnostics decision service of artificial intelligence, has wide prospect of the application.
Description of the drawings
Fig. 1 is nasopharyngeal carcinoma database schematic diagram provided in an embodiment of the present invention；
Fig. 2 is the forming method flow chart of the first model provided in an embodiment of the present invention；
Fig. 3 is the construction method flow chart of the second model provided in an embodiment of the present invention；
Fig. 4 is the construction method flow chart of third model provided in an embodiment of the present invention；
Fig. 5 is the construction method flow chart of the 4th model provided in an embodiment of the present invention；
Fig. 6 is the method flow diagram provided in an embodiment of the present invention that confluence analysis is carried out to multidimensional data；
Fig. 7 is the construction method flow chart of the 5th model provided in an embodiment of the present invention；
Fig. 8 is cloud system schematic diagram provided in an embodiment of the present invention；
Fig. 9 is analysis decision server schematic diagram provided in an embodiment of the present invention；
Figure 10 is dicision of diagnosis and treatment terminal schematic diagram provided in an embodiment of the present invention；
Figure 11 is application method flow chart in a kind of online assisting in diagnosis and treatment system provided in an embodiment of the present invention.
Specific implementation mode
To make the object, technical solutions and advantages of the present invention clearer, the present invention is made into one below in conjunction with attached drawing
Step ground detailed description.
The embodiment of the present invention provides a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment decision system, to build the dicision of diagnosis and treatment
System, the embodiment of the present invention build nasopharyngeal carcinoma database first.Specifically, in the embodiment of the present invention nasopharyngeal carcinoma database composition
The design of structure is considering based on following research purposes：
Multiple dimensioned isomery association between the macroscopical multi-modality images of further investigation, Clinicopathologic phenotype and microcosmic genotype is closed
System, to establish the interaction relation between clinical phenotypes and microcosmic genotype, to complete to nasopharyngeal carcinoma from macroscopic view to microcosmic each
Multiple dimensioned, multi-modal, the big data description of a state, foundation can accurate, full forecast nasopharynx carcinogenesis DISTANT METASTASES INs and multiple
The prediction model of hair.
Nasopharyngeal Carcinoma Patients clinical data, image data and gene data are collected, large-scale multi-source heterogeneous multigroup number is established
It is final to develop nasopharyngeal carcinoma big data high in the clouds diagnosis and treatment analysis system according to library, and precisely treated for auxiliary direction.
In view of this, the nasopharyngeal carcinoma database provided in the embodiment of the present invention as shown in Figure 1, including clinical large database concept and
Gene database.
Specifically, record has the first data formed by clinical alphanumeric information and root in the clinical large database concept
According to clinical image information extraction and the second data for identifying.Wherein, the first data can be word and/or digital form, institute
State the second data can be picture and/or visual form, and by number, binary large object, link or it is multimedia in the form of into
Row storage, first data and the second data can analyze the data source of data as a kind of multidimensional, for subsequent diagnosis and treatment
Decision uses.
Specifically, clinical large database concept may include word digital data library, and the word digital data library is for storing
First data, corresponding clinic alphanumeric information include but not limited to check survey report, treatment data and treatment feelings
Condition data.Specifically, the inspection survey report includes medical record data, test rating, pathology, ultrasound, image and/or nuclear medicine
Deng inspection survey report；The treatment data includes but not limited to radiotherapy, chemotherapy, operation, targeting, biological therapy and/or Chinese medicine
Treatment, the treatment data include but not limited to follow-up prognostic data and therapeutic effect, complication data.
Specifically, clinical large database concept can also include image data base, the second data of described image database purchase,
Corresponding clinical image information includes but not limited to all kinds of clinical images such as image, target of prophylactic radiotherapy, ultrasound, nuclear medicine, pathology.
It is emphasized that the first data, the second data and/or genomics data in nasopharyngeal carcinoma database are all adjoint
Its time attribute records together, and the time attribute is provided with certain effect in various dicision of diagnosis and treatment, when by by each index
Between attribute be included in considering for dicision of diagnosis and treatment, can be provided for survival of patients prognosis according to when the result of decision.
In the case where the data of nasopharyngeal carcinoma database are supported, the dicision of diagnosis and treatment system can be based on a variety of dicision of diagnosis and treatment models pair
Nasopharyngeal carcinoma carries out prediction and decision guidance.
In a feasible embodiment, the dicision of diagnosis and treatment system may include the first diagnosis and treatment module, and described first examines
Nasopharyngeal carcinoma prediction and decision guidance can be provided based on the first model by treating module.First model is with the first data
For data source, the forming method of first model is as shown in Fig. 2, include：
S1. the first data are analyzed, the prognosis of each factor and patient and therapeutic effect in the first data is closed
Connection, removal correlation is stronger, the higher factor of synteny.
S2. filter out has the characteristic factor for determining and significantly affecting to patient's prognosis, establishes the first model.
S3. verification analysis is carried out to the first model.
First model can be input with some or certain several fields in the first data or the first data, with patient
Prognostic indicator be output, the prognostic indicator includes but not limited to：N survival rates and tumor recurrence, transfer and/or simultaneously
Send out disease；Prognostic indicator is used to indicate the final final result of patient and the development trend of the nasopharyngeal carcinoma state of an illness.Further, described
The index and the corresponding weight of the index that there is directive property to patient's prognosis can be obtained in one model in the first data.
Further, with the hair of the medical shared data of the continuous expansion and various regions of clinical large database concept data volume
Exhibition, can also be by from itself, either the data of different data center or medical system are introduced into the first model obtained in S2
Verification analysis is carried out, and corrects relevant parameter.It, can also school in real time while the continuous content for expanding clinical large database concept
The relevant parameter of positive first model so that the first model constantly improve is to be optimal, to for nasopharyngeal carcinoma prediction and certainly
Plan guidance provides more valuable reference.
First model is the model established with the first data bit research object, the first data among clinical position
Acquiring way is relatively simple, has some superiority in data recipient face, therefore, the first model has preferably in clinical position
Application prospect.
In another feasible embodiment, the dicision of diagnosis and treatment system can also include the second diagnosis and treatment module, and described the
Two diagnosis and treatment modules can provide nasopharyngeal carcinoma prediction and decision guidance based on the second model.Second model is with second
Data are data source.The construction method of second model is as shown in figure 3, include：
S10. image data is pre-processed.
S20. image is split and three-dimensional reconstruction.
S30. High-throughput quantitative analysis is carried out to image, builds the second model based on image.
Second model can be input with some or certain several parts in the second data or the second data, with patient
Prognostic indicator be output, the prognostic indicator includes but not limited to：N survival rates and tumor recurrence, transfer and/or simultaneously
Send out disease；Prognostic indicator is used to indicate the final final result of patient and the development trend of the nasopharyngeal carcinoma state of an illness.Further, described
The index and the corresponding weight of the index that there is directive property to patient's prognosis can be obtained in two models in the second data.
Specifically, described image preferably be nasopharyngeal carcinoma CT/MRI images, to image be split and three-dimensional reconstruction after, i.e.,
The High-throughput quantitative analysis of the images such as multi-parameter CT/MRI can be digitized.Concrete analysis content includes but not limited to feature ginseng
The extraction of number data, characteristic parameter data dependence analysis, cluster and statistics Exploration on Characteristics, from large batch of relapse and metastasis nasopharynx
Highly sensitive transfer and relapse image parameters is found in the Multimodal medical image of cancer, in conjunction with the prognosis feelings such as Patients on Recurrence, transfer
Condition builds the second model based on images such as nasopharyngeal carcinoma CT/MRI.
Further, with the hair of the medical shared data of the continuous expansion and various regions of clinical large database concept data volume
Exhibition, can also be by from itself, either the data of different data center or medical system are introduced into the second mould obtained in S30
Type carries out verification analysis, and corrects relevant parameter.Further, while the continuous content for expanding clinical large database concept, also
The relevant parameter of the second model can be corrected in real time so that model constantly improve is to be optimal.
Second model is the improvement with medical condition and image technology using the second data as the model of research object
It is universal, effect of second data among clinical position becomes increasingly conspicuous, the second data for medical diagnosis directive property also by
Step enhancing, therefore the second model is likewise supplied with preferable application prospect in clinical position.
Further, gene database is for storing gene data, compared to the first data and the second data, gene data
Acquisition difficulty higher, analyze difficulty bigger, but it is also very outstanding for the effect of the prognostic analysis of nasopharyngeal carcinoma.Base
Because the foundation of database can be that following work create conditions：Microcosmic point analyzes the genome sequencing of relapse and metastasis nasopharyngeal carcinoma
Data probe into the genotypic variation in tumorigenesis, apoptotic process, to find potential transfer and relapse driving gene and
Molecular marker, to provide important foundation for clinical precisely medical research.
As it can be seen that nasopharyngeal carcinoma database described in the embodiment of the present invention is to incorporate clinical large database concept and gene database
Multi-source heterogeneous multigroup database, the data of storage, which constitute multidimensional, can analyze data.
In another feasible embodiment, the dicision of diagnosis and treatment system can also include third diagnosis and treatment module, and described the
Three diagnosis and treatment modules can provide nasopharyngeal carcinoma prediction and decision guidance based on third model.The third model is with gene
Data are data source.The construction method of the third model is as shown in figure 4, include：
S100. genome sequencing is carried out to the nasopharyngeal carcinoma of pairing, transfer stove, recurrence stove.
S200. bioinformatic analysis is carried out to the data after sequencing.
Comprehensive biological bioinformatics analysis is carried out to the data after sequencing, height phase is found in nasopharyngeal carcinoma transfer and relapse sample
Correlation gene target spot, difference molecule and molecular marker find significant transfer, recurrence driving gene and abrupt information and turn
It moves, recurrence related pathways.
S300. the third model based on nasopharyngeal carcinoma gene data is built according to analysis result.
The transfer driving gene and related mutation information obtained to analysis carries out functional verification using internal experiment in vitro,
Build the third model based on nasopharyngeal carcinoma gene data.The third model can all or part of gene data be input, with
The prognostic indicator of patient is output, and the prognostic indicator includes but not limited to：N survival rates and tumor recurrence, transfer and/
Or complication；Prognostic indicator is used to indicate the final final result of patient and the development trend of the nasopharyngeal carcinoma state of an illness.Further, in institute
State the index and the corresponding power of the index that can be obtained in gene data in third model and there is directive property to patient's prognosis
Weight.
Further, with the continuous expansion of gene database data volume, and medical shared data in all parts of the country
Development can also will be introduced into the third model obtained in S300 from the data at different data center or medical system and carry out
Verification analysis, and correct relevant parameter.It further, can also be in real time while constantly expanding the content of gene database
Ground corrects the relevant parameter of third model so that model constantly improve is to be optimal.
The third model is the model established by research object of gene data, with gene technology and correlative study
It is universal, effect of the gene data among clinical position becomes increasingly conspicuous, gene data for medical diagnosis directive property also by
Step enhancing, therefore third model is likewise supplied with preferable application prospect in clinical position.
The data source of first model, the second model and third model is different, and based on a variety of data sources into
Various data sources can be included in the dicision of diagnosis and treatment of nasopharyngeal carcinoma by row to be considered, and is referred to provide more comprehensive diagnosis and treatment for patient
It leads.Based on this cognition, in another feasible embodiment, the dicision of diagnosis and treatment system can also include the 4th diagnosis and treatment mould
Block, the 4th diagnosis and treatment module can provide nasopharyngeal carcinoma prediction and decision guidance based on the 4th model.4th mould
Type can be using a variety of data in the first data, the second data and third data in nasopharyngeal carcinoma database as research object
User carries out dicision of diagnosis and treatment guidance.Wherein the construction method of the 4th model is as shown in figure 5, include：
P1. confluence analysis is carried out to multigroup data, establishes the Data Storage Models after quality evaluation.
Specifically, multigroup data of learning can be a variety of in the first data, the second data and gene data, wherein the
One data, the second data and gene data are all a group data.
P2. comprehensive system probes into the relevance between multigroup data, establishes based on multigroup the 4th mould for learning Data Integration
Type.
The 4th model includes public discovery model in the embodiment of the present invention, can specifically be based on sparse expression the Theory Construction
The public discovery model.
In another feasible embodiment, in P2 implementation procedures, the frame with expression matrix can be expressed in tensor
Under frame, the characteristic model described towards tensor is proposed；It establishes the tensor based on Prior Knowledge Constraints and matches energy equation；Analysis is special
Sign tensor describes method, the internal relation of Prior Knowledge Constraints design, tensor matching method and local Common Substructure；For height
The data of noise pollution, the robustness of test model；For mass data, establishes efficient restricted problem and decompose mechanism, to
Rapid Optimum solves, to finally obtain public discovery model.
Specifically, the embodiment of the present invention proposes the method for carrying out confluence analysis to multigroup data, as shown in fig. 6, packet
It includes：
First, it establishes towards multigroup matrix decomposition model for learning data, multidimensional data is decomposed in the same base space,
Interactive relationship between coefficient of analysis matrix, to find the incidence relation between multigroup data.
Specifically, correlation rule between research genotype and phenotype is established, important gene target spot and image phenotype is found, swells
Dynamically associating between the factors such as tumor stage and step.
Secondly, it establishes towards multigroup tensor resolution model for learning data, by using the side in three implicit variable base spaces
Method decomposes multigroup data in three base spaces, extracts independently of multigroup centronucleus tensor for learning data, research core
Public relation between amount, to find its public association.
Specifically, research is under different constraints, the resolution characteristic in implicit variable base space, designs towards multiple constraint centronucleus
The correlation function of tensor.
Again, it establishes the tensor described based on tensor and matches model, the tensor property for inquiring into single group data describes method,
The influence that research tensor property dimension and quantitative description match tensor；The matching method of design feature tensor, structure is based on first
Test the tensor pairing model of knowledge constraints.
Finally, it establishes public module for above-mentioned analysis result and finds model.
Based on the research method of above-mentioned public discovery model, an embodiment of the present invention provides originality Integrated Models to optimize letter
Number, the Integrated Models majorized function is the core content of public discovery model, for being picked out to nose in multigroup data
Pharynx cancer patient's prognosis has the index of directive property：
Subjectto:Xi,j∈{0,1}
XI1≤b1
XTI2≤b2
Wherein H expressions group data S1And S2Between similarity, X indicate allocation matrix,Indicate data S1It is internal special
Incidence relation is levied,Indicate data S2Internal feature incidence relation, it is similar that formula first item indicates that allocation matrix X meets as possible
The distribution of matrix H is spent, Section 2 and Section 3 indicate to have incorporated data S respectively1With data S2Priori, improve accuracy rate,
Section 4 indicates that allocation matrix has sparsity so that can preferably explain data S1With data S2Between incidence relation.
Wherein LMIt is following to indicate to normalize Laplacian Matrix
According to Laplacian Matrix property, following formula can be obtained
Above-mentioned optimization function is solved eventually by gradient descent method, obtains Data Integration as a result, shown in following algorithm 1
Algorithm 1
Input：M1,M2,
1：Initialize λ1,λ2,β,X(0),γ(1),M,ρ>1,δ∈(0,1),ε>0, k=1
2：Normalize M1,M2
3：Following formula update allocation matrix X is solved using gradient descent method(k)
4：M=ρ M are set, if
5：Update γ(k+1)=min (γ(k)-Mg(X(k)),0)
6：K=k+1 is set
7：Repeat step 3,4,5,6 until | | g (X(k))||<ε
Output：X
Further, have on the basis of directive property index based on the public discovery model acquisition prognosis, in order to
Comprehensive treatment effect is promoted, weight of the various directive property indexs in Index for diagnosis can also be included in and be examined in the 4th model
Amount.The method for obtaining weight based on the 4th model has very much, and the embodiment of the present invention generally lifts three feasible embodiments.
In a feasible embodiment, the output of the 4th model can be simply represented as P=K1A1+K2A2+
K3A3+……+KmAm.P is in A1+A2+A3+ ... under the conditions of+Am, without all kinds of existence such as progression of disease in 5-year Survival/5 year
Possible probability of happening.And according to the value of the data acquisition A1 ... Am in nasopharyngeal carcinoma database, you can obtain various directive property
Weight of the index in Index for diagnosis.
In another feasible embodiment, the output P of the 4th model can be expressed as to multiple index comprehensive effects
As a result, and the comprehensive function to include each prognosis have directive property index while acting on, acting on and mix stage by stage and make
With.By analyzing the data acquisition in nasopharyngeal carcinoma database to various directive property indexs in Index for diagnosis in this representation method
In weight.
In another feasible embodiment, research that can be based on the first model, the second model and/or third model
As a result, obtaining weight of the various directive property indexs in Index for diagnosis.In this embodiment, the structure of the 4th model includes
Following step：
The index for having directive property for prognosis is picked out according to public discovery model；
Judge that the index belongs to the first data, the second data or gene data；
If the index belongs to the first data, the weight of the index is obtained according to the first model；
If the index belongs to the second data, the weight of the index is obtained according to the second model；
If the index belongs to gene data, the weight of the index is obtained according to third model；
After the weight of the index to be obtained for all referring to tropism, dicision of diagnosis and treatment is carried out according to the weighted results of index and its weight
Guidance.
4th model is that dicision of diagnosis and treatment system realizes that the dicision of diagnosis and treatment of generalization creates condition, it is clear that in dicision of diagnosis and treatment
System in actual use, needs the feedback for constantly obtaining doctor and patient, in conjunction with the data and document of continuous renewal,
It constantly corrects public module and finds model, the first model, the second model and third model, to be artificial intelligence assisting in diagnosis and treatment
More power-assisteds are provided.
More comprehensively medical treatment result is obtained in order to be based on nasopharyngeal carcinoma database, in another feasible embodiment, institute
It can also includes the 5th diagnosis and treatment module to state dicision of diagnosis and treatment system, and the 5th diagnosis and treatment module can provide nose based on the 5th model
Pharynx cancer prediction and decision guidance.5th model equally can be using multigroup data in nasopharyngeal carcinoma database as data
Source, is different from the construction method of the 4th model, and the 5th model provides dicision of diagnosis and treatment to the user based on neural network.Wherein the 5th mould
The construction method of type as shown in fig. 7, comprises：
P10 extracts the valuable feature of generation for the 5th model in different group data.
Extracting method specifically can there are many, can also refer to the 4th model in public discovery model carry out feature
Extraction.
P20 obtains high dimensional feature to associated characteristic feature progress higher-dimension is abstract in different groups of data.
P30 completes the filtering to redundancy feature of noise.
In above three step, bottom can be extracted to height using convolutional neural networks to the first data and gene data
The feature representation of layer；Useful feature is excavated using integrated approach to the second data, and is combined not by traditional logistic regression
Same norm constraint filters unrelated interference characteristic.
By being carried out to associated characteristic feature in different groups of data, higher-dimension is abstract to obtain high dimensional feature, and completion pair
The filtering of redundancy feature of noise；To create conditions for P40.
All high dimensional features are attached by P40 by full articulamentum, to generate the 5th model.
Specifically, all high dimensional features can be attached by full articulamentum, generate dicision of diagnosis and treatment model, when with
When the data volume of training pattern newly collected reaches regulation magnitude, model training, Optimized model parameter so that mould are restarted
The continuous self-teaching of type, self adjustment, self-perfection, to form the 5th model.
Specifically, the 5th model of the embodiment of the present invention can be by establishing convolutional neural networks and convolutional neural networks
It practises and realizing.
In the building process of the 5th model, the data in nasopharyngeal carcinoma database are collected by a full articulamentum
At by error backpropagation algorithm combination stochastic gradient descent come adjusting parameter, and using dropout, leaky relu are sharp
Function living and norm constraint come the case where limiting the adjusting range of parameter, ensure the sparsity of feature, avoid over-fitting, to
It is further ensured that the efficiency of the 5th model.By the 5th model model is adjusted in the prediction efficiency of training set and verification collection most
The number and relevant parameter of full articulamentum afterwards, to provide valuable diagnostic comments for doctor.
4th model and the 5th model are that can use nasopharyngeal carcinoma number comprehensively with multigroup model for learning data bit research object
It is desirably to obtain the more comprehensive result of decision according to the content in library, the first model, the second model and third model are with single group number
According to the model for research object, diagnosis and treatment can be carried out on the basis of a certain kind organizes data, in a kind of preferred embodiment
In, the first model, the second model, third model, the 4th model and the 5th model can have or preferentially exist one kind parallel
Or it is a variety of, to provide more good dicision of diagnosis and treatment service to the user from multiple angles.
On the basis of the above, the embodiment of the present invention further provides for a kind of nasopharyngeal carcinoma artificial intelligence auxiliary and examines
The logical architecture of decision system is treated, nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment decision system is specifically as follows a kind of online diagnosis and treatment system
System, specifically, the online diagnosis and therapy system can be a kind of cloud system, the data supporter as the online diagnosis and therapy system
(nasopharyngeal carcinoma database) can also be laid beyond the clouds, it is clear that be more advantageous to Data Integration using cloud storage and diagnosis and treatment are analyzed.
The dicision of diagnosis and treatment system as shown in figure 8, may include setting dicision of diagnosis and treatment server beyond the clouds and with it is described
Dicision of diagnosis and treatment server communication connection dicision of diagnosis and treatment terminal, the dicision of diagnosis and treatment system specifically can use B-S frameworks or
C-S frameworks, the dicision of diagnosis and treatment terminal are sent out to the dicision of diagnosis and treatment server cluster corresponding to described in response to user instruction
The request of data of user instruction；For the dicision of diagnosis and treatment server in response to the request of data, the response of generation data simultaneously will be described
Data response is transmitted to the dicision of diagnosis and treatment terminal, in order to which the dicision of diagnosis and treatment terminal corresponds to the user to user's displaying
The result of instruction.
Further, in order to provide consulting services to the user comprehensively, the dicision of diagnosis and treatment server is specifically as follows service
Device cluster (dicision of diagnosis and treatment server cluster), the dicision of diagnosis and treatment server cluster include user's interactive server, analysis decision
Server and data server, user's interactive server are used to carry out data interaction with dicision of diagnosis and treatment terminal to be used in combination
Family manages, and the analysis decision server is used for for providing dicision of diagnosis and treatment service, the data server to the user into line number
According to processing, it is provided with nasopharyngeal carcinoma database and interacts.The analysis decision server is as shown in figure 9, preferably include
One diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the 4th diagnosis and treatment module and the 5th diagnosis and treatment module, first diagnosis and treatment
Module, the second diagnosis and treatment module, third diagnosis and treatment module, the function of the 4th diagnosis and treatment module and the 5th diagnosis and treatment module are as previously mentioned, herein
No longer superfluous words.
Further, in the dicision of diagnosis and treatment terminal, it is provided with user's registration, data management, inquiry, annotates, compare, examining
The functions such as rope, displaying.Specifically, the dicision of diagnosis and treatment terminal includes as shown in Figure 10：
User registration module, for being registered to the dicision of diagnosis and treatment server cluster, in order to which user uses diagnosis and treatment
The various services that policy server cluster provides.
Data management module, the personal data for managing user.
Enquiry module inquires target data for user to dicision of diagnosis and treatment server cluster.
Annotations module is marked for user in display interface.
Comparison module for being compared for similar data, and identifies comparison result in order to which user quickly has found
Difference between similar data.
Module is retrieved, user's fast search target data is used for.
Display module, for carrying out data visualization.
The dicision of diagnosis and treatment terminal can be to be mounted with the arbitrary smart machine of the software interacted with dicision of diagnosis and treatment server,
Such as the equipment such as PC, mobile phone or tablet.
Further include changing interface module in the dicision of diagnosis and treatment terminal, the changing interface module according to user for stepping on
The difference of record identity provides different display interfaces to the user.Specifically, according to the identity difference for using user, changing interface mould
Block is also additionally provided different functions.If user identity is patient user, provides a user and access data, check diagnostic result
With the functions such as therapeutic scheme；If user identity is clinician user, user, which provides, to be accessed data, makes diagnosis decision and prognosis
The functions such as assessment.
It specifically, can be by, using easy friendly operation interface, high in the clouds being divided in Website front-end in B-S frameworks
The medical treatment result of analysis visualizes.The visualization can specifically realize by the visualization model of dicision of diagnosis and treatment terminal, it is described can
Include data early warning unit, chart linkage unit and comparison split cells depending on changing module.Clinical speciality doctor is not only facilitated to operate,
And clear medical image display interface and case-data display interface are additionally provided, it can be with to clinical speciality doctor
Preferably make assessment and diagnosis.
The online diagnosis and therapy system is not only that clinical speciality doctor provides recurrent nasopharyngeal carcinoma transfer stage and step tentative prediction
As a result, additionally providing management, annotating, compare, retrieve and show PB or more magnitude group data and clinical information function.It is described
Line diagnosis and therapy system can provide direction guidance for gene target, image tumor region, accurate Personalized medicine, be doctor and patient
Medical diagnosis on disease is provided and visualizes platform, a reliable and effective model system is provided for tumor research treatment, is also other
Tumor research provides the source that can refer to.
Specifically, on the basis of the above, the embodiment of the present invention provides a kind of online diagnosis and therapy system application method work
For diagnosis and treatment example, as shown in figure 11, including：
S101. clinical alphanumeric information, clinical image information and/or gene data are obtained, and combines the time of data
Attribute is included in nasopharyngeal carcinoma database.
Specifically, database table structure can be as illustrated in chart 1：
Table 1
S102. selection can be used in the dicision of diagnosis and treatment model of decision, and carry out survival region, complication, analysis using it.
Specifically, the dicision of diagnosis and treatment model for decision can be the first model, the second model, third model, the
It is one or more in four models and the 5th model.
In this step, the model for decision can be the 4th model, and the output of the 4th model having finger to be a kind of to prognosis
The weighted results of the index of tropism temporarily can simply be represented as P=K1A1+K2A2+K3A3+ ...+KmAm.P is in A1+A2+
Under the conditions of A3+ ...+Am, without the possible probability of happening of all kinds of existence such as progression of disease in 5-year Survival/5 year.P is bigger, it was demonstrated that suffers from
It is obtained under the conditions of person is in A1+A2+A3+ ...+Am more long.Alignment graph model etc. can be established to be used to predict A1+A2+A3+ ...+Am
The Survival datas such as the probability survived how many years.
And wherein, according to the size of disparity items Ai COEFFICIENT Ks i, directionality adjustment can be carried out to Ai, to reach more preferably raw
Deposit benefit.
S103. according to having model, in conjunction with current case, dicision of diagnosis and treatment reference scheme is obtained, and obtain follow up data.
S104. with the addition of new case, constantly learn and optimize existing dicision of diagnosis and treatment model.
In the specific implementation process of the embodiment of the present invention, following achievement can be obtained：
(1) in terms of patient：
Patient fills in itself essential information after logging in, input coherence check, inspection result can obtain relevant disease and examine
The related contents such as points for attention during disconnected, prognosis estimation, treatment.And with therapeutic advance, inputted according to its difference, it obtains different
It is recommended that.
(2) in terms of researcher
According to data with existing library, the correlation models such as treatment recommendations, prognosis are established.And it according to patient's input information, is formed new
Big data, and Parameters in Mathematical Model is corrected, obtains the model that can most react truth.
Data information of both clinical data and gene data is integrated, to find macrophenotypic --- microcosmic genotype
The marker of multilevel lower highlights correlations to establish contact of the gene target to image phenotype, and then is established and is based on big data
The accurate medical system of artificial intelligence.
In addition, following implementation steps in the embodiment of the present invention are put forward for the first time in the related art：
(1) structure has multigroup database of time attribute, establish it is multigroup learn according to when prognosis and complication etc. predict mould
Type.
(2) highly sensitive transfer and relapse image mark is found from the Multimodal medical image of batch relapse and metastasis nasopharyngeal carcinoma
Will；
(3) highly relevant gene target is found in nasopharyngeal carcinoma transfer and relapse sample using genomics bioassay technique
Point, difference molecule and molecular marker；
(4) the multilevel isomeric datas such as clinical alphanumeric information, clinical image information and gene data are integrated, to send out
The marker of the multilevel lower highlights correlations of existing macrophenotypic-sight genotype, to establish gene target to the pass of image phenotype
Connection；
(5) database for building up the extensive different levels group data of nasopharyngeal carcinoma, have developed collection manage, annotate, comparing,
Retrieval, presentation group data and relapse and metastasis are by stages and prediction prognosis is in the online diagnosis and therapy system of one.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (8)
1. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster, which is characterized in that the dicision of diagnosis and treatment server
Cluster includes user's interactive server, analysis decision server and data server, user's interactive server be used for
Dicision of diagnosis and treatment terminal carries out data interaction and carries out user management, and the analysis decision server is determined for providing diagnosis and treatment to the user
Plan service, the data server are provided with nasopharyngeal carcinoma database and interact for carrying out data processing；
The analysis decision server includes the first diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the 4th diagnosis and treatment mould
Block and/or the 5th diagnosis and treatment module, the first diagnosis and treatment module, the second diagnosis and treatment module, third diagnosis and treatment module, the 4th diagnosis and treatment module and
5th diagnosis and treatment module uses different dicision of diagnosis and treatment models respectively.
2. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 1, it is characterised in that：
The first diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the first model；First model
Using the first data as data source.
3. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 1, it is characterised in that：
The second diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the second model；Second model is with
Two data are data source.
4. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 1, it is characterised in that：
The third diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on third model；The third model is with base
Because data are data source.
5. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 1, it is characterised in that：
The 4th diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the 4th model；4th model is with nose
A variety of data in pharynx cancer database in the first data, the second data and third data are research object, are examined for user
Treat decision guidance.
6. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 1, which is characterized in that
The 5th diagnosis and treatment module provides nasopharyngeal carcinoma prediction and decision guidance based on the 5th model；5th model is with nose
Multigroup data are data source in pharynx cancer database, and provide dicision of diagnosis and treatment to the user based on neural network.
7. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 5, it is characterised in that：
4th model includes public discovery model, and the public discovery model is based on sparse expression the Theory Construction.
8. a kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster according to claim 5, it is characterised in that：
4th model includes public discovery model, the public discovery model be tensor expression and expression matrix under, towards
The public discovery model of tensor description.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810047136.6A CN108335748A (en) | 2018-01-18 | 2018-01-18 | A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810047136.6A CN108335748A (en) | 2018-01-18 | 2018-01-18 | A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108335748A true CN108335748A (en) | 2018-07-27 |
Family
ID=62925254
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810047136.6A Pending CN108335748A (en) | 2018-01-18 | 2018-01-18 | A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108335748A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112397172A (en) * | 2020-12-24 | 2021-02-23 | 上海墩庐生物医学科技有限公司 | Intelligent consultant internet application system for breast cancer survival |
| CN113808724A (en) * | 2021-11-17 | 2021-12-17 | 北京因数健康科技有限公司 | Data analysis method and device, storage medium and electronic terminal |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103690240A (en) * | 2013-09-16 | 2014-04-02 | 上海华美络信息技术有限公司 | Medical system |
| US9095266B1 (en) * | 2010-08-02 | 2015-08-04 | Chi Yung Fu | Method for treating a patient |
| CN106997389A (en) * | 2017-03-30 | 2017-08-01 | 浙江鸿程计算机系统有限公司 | Recommend method in a kind of tourist attractions based on many data sets and collaboration tensor resolution |
| CN107103207A (en) * | 2017-04-05 | 2017-08-29 | 浙江大学 | Based on the multigroup accurate medical knowledge search system and implementation method for learning variation features of case |
-
2018
- 2018-01-18 CN CN201810047136.6A patent/CN108335748A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9095266B1 (en) * | 2010-08-02 | 2015-08-04 | Chi Yung Fu | Method for treating a patient |
| CN103690240A (en) * | 2013-09-16 | 2014-04-02 | 上海华美络信息技术有限公司 | Medical system |
| CN106997389A (en) * | 2017-03-30 | 2017-08-01 | 浙江鸿程计算机系统有限公司 | Recommend method in a kind of tourist attractions based on many data sets and collaboration tensor resolution |
| CN107103207A (en) * | 2017-04-05 | 2017-08-29 | 浙江大学 | Based on the multigroup accurate medical knowledge search system and implementation method for learning variation features of case |
Non-Patent Citations (2)
| Title |
|---|
| 石乐明、郑媛婷等: "《大数据与精准医学》", 31 December 2017, 上海交通大学出版社 * |
| 裴红斌: "基于张量的人口接触时空模式建模与发现方法", 《中国优秀硕士学位论文全文数据库（电子期刊）医药卫生科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112397172A (en) * | 2020-12-24 | 2021-02-23 | 上海墩庐生物医学科技有限公司 | Intelligent consultant internet application system for breast cancer survival |
| CN113808724A (en) * | 2021-11-17 | 2021-12-17 | 北京因数健康科技有限公司 | Data analysis method and device, storage medium and electronic terminal |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108198621A (en) | A kind of database data synthesis dicision of diagnosis and treatment method based on neural network | |
| CN108320807A (en) | A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment decision cloud system | |
| Tang et al. | CapSurv: Capsule network for survival analysis with whole slide pathological images | |
| Huang et al. | Integration of patch features through self-supervised learning and transformer for survival analysis on whole slide images | |
| CN108335756A (en) | The synthesis dicision of diagnosis and treatment method in nasopharyngeal carcinoma database and based on the data library | |
| CN108206056A (en) | A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment decision terminal | |
| US7809660B2 (en) | System and method to optimize control cohorts using clustering algorithms | |
| CN109830303A (en) | Clinical data mining analysis and aid decision-making method based on internet integration medical platform | |
| Puppala et al. | METEOR: an enterprise health informatics environment to support evidence-based medicine | |
| Huang et al. | Drug–drug similarity measure and its applications | |
| CN109599157B (en) | Accurate intelligent diagnosis and treatment big data system | |
| CN108520778A (en) | A kind of screening lung cancer system and method based on big data | |
| CN114003734B (en) | Knowledge system and knowledge map system of breast cancer risk factors and construction method | |
| CN109785927A (en) | Clinical document structuring processing method based on internet integration medical platform | |
| WO2021143781A1 (en) | Multi-center synergetic cancer prognosis prediction system based on multi-source migration learning | |
| CN108320797A (en) | A kind of nasopharyngeal carcinoma database and based on the data the synthesis dicision of diagnosis and treatment method in library | |
| CN117271804B (en) | Method, device, equipment and medium for generating common disease feature knowledge base | |
| Dai et al. | Patient similarity: methods and applications | |
| CN108335748A (en) | A kind of nasopharyngeal carcinoma artificial intelligence assisting in diagnosis and treatment policy server cluster | |
| Sethi et al. | Long Short-Term Memory-Deep Belief Network based Gene Expression Data Analysis for Prostate Cancer Detection and Classification | |
| Liu et al. | Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images | |
| Lu et al. | M 2 F: A Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis | |
| Yuan et al. | Application of machine learning in the management of lymphoma: Current practice and future prospects | |
| CN117457065A (en) | Method and system for identifying phenotype-associated cell types based on single-cell multi-set chemical data | |
| Wu et al. | A personalized diagnostic generation framework based on multi-source heterogeneous data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180727 |