CN109859854A - Prediction Method of Communicable Disease, device, electronic equipment and computer-readable medium - Google Patents
Prediction Method of Communicable Disease, device, electronic equipment and computer-readable medium Download PDFInfo
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- CN109859854A CN109859854A CN201811546053.8A CN201811546053A CN109859854A CN 109859854 A CN109859854 A CN 109859854A CN 201811546053 A CN201811546053 A CN 201811546053A CN 109859854 A CN109859854 A CN 109859854A
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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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- 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
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
Abstract
The present invention provides a kind of Prediction Method of Communicable Disease, device, electronic equipment and computer-readable mediums, are related to computer application technology, and method includes: to obtain the history morbidity data of infectious disease；The space characteristics of history morbidity data are extracted by convolutional neural networks；The first temporal aspect of history morbidity data is extracted by Recognition with Recurrent Neural Network；Space characteristics and the first temporal aspect are merged, the space-time characteristic of history morbidity data is obtained；Risk profile is carried out to infectious disease according to space-time characteristic.The present invention utilizes the prediction network model being made of convolutional neural networks and Recognition with Recurrent Neural Network, carries out depth excavation to the space-time characteristic of Infectious Diseases Data, and then improve the accuracy of infectious disease forecasting.
Description
Technical field
The present invention relates to computer application technologies, set more particularly, to a kind of Prediction Method of Communicable Disease, device, electronics
Standby and computer-readable medium.
Background technique
In recent years, global researcher and medical care are increasingly caused by the disease of respiratory infectious (abbreviation infectious disease)
The attention and concern of worker is especially big several times in SARS, Influenza A H1N1, human hepatic stellate cell etc.
It is highly pathogenic to cause huge society to the more areas in the whole world, country with high lethality rate after the epidemic of scale occurs
It can bear and economic loss.Therefore, infectious disease risk profile is necessary.
It is modeled currently, infectious disease risk profile generallys use conventional machines learning method, still, conventional machines study
Model still has deficiency in terms of description data time and space usage and processing space-time data fusion, causes prediction result not quasi- enough
Really.
Summary of the invention
In view of this, the purpose of the present invention is to provide Prediction Method of Communicable Disease, device, electronic equipments and computer-readable
Medium, to alleviate existing conventional machines learning model in terms of description data time and space usage and processing space-time data fusion
Still there is deficiency, leads to the technical problem that prediction result is not accurate enough.
In a first aspect, the embodiment of the invention provides a kind of Prediction Method of Communicable Disease, comprising:
Obtain the history morbidity data of infectious disease；
The space characteristics of the history morbidity data are extracted by convolutional neural networks；
The first temporal aspect of the history morbidity data is extracted by Recognition with Recurrent Neural Network；
The space characteristics and first temporal aspect are merged, the space-time for obtaining the history morbidity data is special
Sign；
Risk profile is carried out to the infectious disease according to the space-time characteristic.
With reference to first aspect, the embodiment of the invention provides the first possible embodiments of first aspect, wherein institute
State method further include:
Obtain the weather environment data for influencing the infectious disease；
The second temporal aspect of the weather environment data is extracted by the Recognition with Recurrent Neural Network；
It is described to merge the space characteristics and first temporal aspect, obtain history morbidity data when
After the step of empty feature, further includes:
The space-time characteristic of history morbidity data is merged with second temporal aspect, obtains multi-source fusion spy
Sign.
With reference to first aspect, the embodiment of the invention provides second of possible embodiments of first aspect, wherein institute
State the step of risk profile is carried out to the infectious disease according to the space-time characteristic, comprising:
Risk profile is carried out to the infectious disease according to the multi-source fusion feature.
With reference to first aspect, the embodiment of the invention provides the third possible embodiments of first aspect, wherein institute
It states and merges the space characteristics and first temporal aspect, obtain the step of the space-time characteristic of the history morbidity data
Suddenly, comprising:
The space characteristics and first temporal aspect are merged by the period sequentially in time, obtain described go through
The space-time characteristic of history morbidity data；
It is described to merge the space characteristics and first temporal aspect by the period sequentially in time, obtain institute
The step of stating the space-time characteristic of history morbidity data, comprising:
The space-time characteristic of history morbidity data and second temporal aspect are carried out by the period sequentially in time
Fusion, obtains the multi-source fusion feature.
With reference to first aspect, the embodiment of the invention provides the 4th kind of possible embodiments of first aspect, wherein institute
The step of stating the history morbidity data for obtaining infectious disease, comprising:
Infectious disease keyword extraction and abnormal data elimination are carried out to history Outpatient Department data, obtain going through for the infectious disease
History morbidity data.
With reference to first aspect, the embodiment of the invention provides the 5th kind of possible embodiments of first aspect, wherein institute
It states space-time characteristic and includes at least region day disease incidence, region week disease incidence and region one of sense rate again；
Second temporal aspect include at least according to the space-time characteristic time sequencing arrange temperature, precipitation,
Humidity and air pressure.
Second aspect, the embodiment of the present invention also provide a kind of infectious disease forecasting device, and described device includes:
First obtains module, for obtaining the history morbidity data of infectious disease；
Space characteristics extraction module, for extracting the space characteristics of the history morbidity data by convolutional neural networks；
First temporal aspect extraction module, when for extracting the first of the history morbidity data by Recognition with Recurrent Neural Network
Sequence characteristics；
Fusion Features module obtains described go through for merging the space characteristics and first temporal aspect
The space-time characteristic of history morbidity data；
Prediction module obtains prediction morbidity number for carrying out risk profile to the infectious disease according to the space-time characteristic
According to.
In conjunction with second aspect, the embodiment of the invention provides the first possible embodiments of second aspect, wherein institute
State device further include:
Second obtains module, for obtaining the weather environment data for influencing the infectious disease；
Second temporal aspect extraction module, for extracting the of the weather environment data by the Recognition with Recurrent Neural Network
Two temporal aspects；
The Fusion Features module is also used to:
The space-time characteristic of history morbidity data is merged with second temporal aspect, obtains multi-source fusion spy
Sign.
The third aspect, the embodiment of the present invention also provide a kind of electronic equipment, including memory, processor and are stored in described
On memory and the computer program that can run on the processor, the processor are realized when executing the computer program
The step of method as described in relation to the first aspect.
Fourth aspect, the embodiment of the present invention also provide a kind of meter of non-volatile program code that can be performed with processor
Calculation machine readable medium is stored with computer program on the readable storage medium storing program for executing, when the computer program is run by processor
The step of executing method as described in relation to the first aspect.
The embodiment of the present invention bring it is following the utility model has the advantages that
The present invention provides a kind of Prediction Method of Communicable Disease, comprising: obtains the history morbidity data of infectious disease；Pass through convolution
Neural network extracts the space characteristics of history morbidity data；The first timing of history morbidity data is extracted by Recognition with Recurrent Neural Network
Feature；Space characteristics and the first temporal aspect are merged, the space-time characteristic of history morbidity data is obtained；According to space-time characteristic
Risk profile is carried out to infectious disease.The history morbidity data of infectious disease belong to space-time data, using by convolutional neural networks and following
The prediction network model that ring neural network is constituted, can effectively excavate the space-time characteristic of space-time data, pass through convolutional neural networks
The infectious disease information in each area in unit time (as weekly) specified region is combined, circulation nerve net is then passed through
Network carries out the extraction (20 weeks such as continuous) of temporal aspect, to realize that the depth of space-time characteristic is excavated, and then it is pre- to improve infectious disease
The accuracy of survey.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention are in specification, claims
And specifically noted structure is achieved and obtained in attached drawing.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the flow chart of Prediction Method of Communicable Disease provided in an embodiment of the present invention；
Fig. 2 is spatial convoluted schematic diagram provided in an embodiment of the present invention；
Fig. 3 is the flow chart of another Prediction Method of Communicable Disease provided in an embodiment of the present invention；
Fig. 4 is the schematic diagram of prediction network provided in an embodiment of the present invention；
Fig. 5 is the schematic diagram of infectious disease forecasting device provided in an embodiment of the present invention；
Fig. 6 is the schematic diagram of electronic equipment provided in an embodiment of the present invention.
Icon: 10- first obtains module；20- space characteristics extraction module；30- the first temporal aspect extraction module；40-
Fusion Features module；50- prediction module；1000- electronic equipment；500- processor；501- memory；502- bus；503- is logical
Believe interface.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
It is modeled currently, infectious disease risk profile generallys use conventional machines learning method, still, conventional machines study
Model still has deficiency in terms of description data time and space usage and processing space-time data fusion, causes prediction result not quasi- enough
Really.Based on this, a kind of Prediction Method of Communicable Disease, device, electronic equipment and computer-readable medium provided in an embodiment of the present invention,
Depth excavation can be carried out to the space-time characteristic of Infectious Diseases Data, and then improves the accuracy of infectious disease forecasting.
For convenient for understanding the present embodiment, first to a kind of Prediction Method of Communicable Disease disclosed in the embodiment of the present invention
It describes in detail.
Embodiment one
Fig. 1 shows the flow chart of Prediction Method of Communicable Disease provided in an embodiment of the present invention.
As shown in Figure 1, a kind of Prediction Method of Communicable Disease provided in an embodiment of the present invention, comprising the following steps:
Step S101 obtains the history morbidity data of infectious disease；
In this step, infectious disease can be the disease by respiratory infectious, such as influenza etc. can also be other types
Infectious disease.When needing the infectious disease to specified region to carry out risk profile, the history Outpatient Department data in the specified region is collected,
It can specifically be provided by health information department.Firstly, being pre-processed to the history Outpatient Department data being collected into, including infected
Sick keyword extraction and abnormal data elimination obtain the history morbidity data of infectious disease.Wherein, infectious disease keyword includes hair
Disease symptoms, disease time etc., abnormal data include the Outpatient Department data in sample insufficient season or month.
Step S102 extracts the space characteristics of history morbidity data by convolutional neural networks；
Step S103 extracts the first temporal aspect of history morbidity data by Recognition with Recurrent Neural Network；
The prediction network model based on depth time-space network of the present embodiment building, by convolutional neural networks and circulation nerve
Network is constituted.The history morbidity data of infectious disease belong to typical space-time data, i.e., have the number of time and Spatial Dimension simultaneously
According to, such as the disease symptom for sending out patient in some area, disease time.
In order to effectively excavate the space-time characteristic of history morbidity data, firstly, organizing organization data by planned network convolution kernel
Input form, construct unsupervised convolutional neural networks, can be the value convolution in a region a to point by convolution
Above, the local correlations in the space of short distance are described, as shown in Fig. 2, can be increasingly remoter after multiple convolution
Region convolution describes the correlation apart from farther away space, and with the intensification of network depth, the abstractness of feature to together
It is more obvious, it indicates that ability is more and more stronger, breaches the defect that the difficult point of characterizing definition and space-time isolate in conventional method.Together
When, the reasonable size for controlling convolution kernel, additionally it is possible to the effective dimension-reduction treatment for realizing high dimensional data.
Above-mentioned history morbidity data can be divided into training data and data to be predicted, and training data is for training pre- survey grid
Network model.Specifically, rough estimates are carried out to the infectious disease incidence situation of training data, by statistical nature input prediction network mould
Type is effectively learnt, and statistical nature belongs to space-time characteristic, is to make a definite diagnosis accurate disease the ASSOCIATE STATISTICS of sample, such as area
Domain day disease incidence, region week disease incidence, region sense rate etc. again.
Data to be predicted are inputted into trained prediction network model, extract space characteristics by convolutional neural networks, i.e.,
The information in each area in unit time (as weekly) specified region is combined and is counted, then passes through circulation mind again
Extraction (20 weeks such as continuous) through network implementations temporal aspect.
Step S104 merges space characteristics and the first temporal aspect, obtains the space-time characteristic of history morbidity data；
In this step, space characteristics and the first temporal aspect are merged by the period sequentially in time, obtain history
The space-time characteristic for data of falling ill；
Specifically, space characteristics can be the region disease incidence of unit time, can also be other systems in addition to disease incidence
Count attribute, such as sense rate again；If the unit time is weekly, the first temporal aspect be can be continuous n weeks, such as from t-n
Thoughtful the t weeks, the value of t and n can be set, and certainly, the unit time can also specifically set, for example, can also be it is daily,
Quarterly etc..
The region disease incidence weekly of extraction was subjected to Fusion Features according to the t-n weeks to the t weeks each period, is obtained
To from the disease incidence of region weekly in the t-n weeks to the t weeks, i.e. space-time characteristic.
Step S105 carries out risk profile to infectious disease according to space-time characteristic.
If the space-time characteristic extracted is then to predict network model from the disease incidence of region weekly in the t-n weeks to the t weeks
The prediction result of output can be the region disease incidence in the t weeks next week, be also possible to the t weeks following m weeks area weekly
Domain disease incidence.
The present embodiment utilizes the prediction network model being made of convolutional neural networks and Recognition with Recurrent Neural Network, passes through convolution mind
The space characteristics of Infectious Diseases Data are extracted through network, and the extraction of temporal aspect, Ke Yishen are then carried out by Recognition with Recurrent Neural Network
Degree excavates the space-time characteristic of space-time data, and then improves the accuracy of infectious disease forecasting.
Embodiment two
In view of the pathogenic bacteria of the breathing sexually transmitted disease such as influenza or the active degree and surrounding meteorology ring of virus
The influence in border is more close, therefore, better prediction result in order to obtain, in the base for the space-time characteristic for extracting history morbidity data
On plinth, the factors such as weather environment relevant to infectious disease can also be taken into account.
Unlike above-described embodiment one, training data and data to be predicted not only include history morbidity data, are also wrapped
Include weather environment data.Specifically, rough estimates, integration two category features input are carried out to the infectious disease incidence situation of training data
Prediction network model is effectively learnt, including statistical nature and direct feature.Statistical nature belongs to space-time characteristic, is to essence
True disease makes a definite diagnosis the ASSOCIATE STATISTICS of sample, such as region day disease incidence, region week disease incidence, region sense rate etc. again.Directly
Feature, which refers to, is used to analyze propagation risk for factors such as the environment being directly associated with infectious disease, meteorologies as temporal aspect, than
Such as temperature (including mean temperature, maximum temperature, minimum temperature, maximum temperature difference), precipitation, humidity and air pressure etc..
As shown in figure 3, it is provided in an embodiment of the present invention another kind Prediction Method of Communicable Disease the following steps are included:
Step S201 obtains the history morbidity data of infectious disease and influences the weather environment data of infectious disease；
Step S202 extracts the space characteristics of history morbidity data by convolutional neural networks；
Step S203, the first temporal aspect of history morbidity data is extracted by Recognition with Recurrent Neural Network, and passes through circulation
Second temporal aspect of neural network extraction weather environment data；
Step S204 merges space characteristics and the first temporal aspect, obtains the space-time characteristic of history morbidity data；
The space-time characteristic of history morbidity data is merged with the second temporal aspect, obtains multi-source fusion by step S205
Feature.
In this step, the space-time characteristic of history morbidity data and the second temporal aspect are carried out by the period sequentially in time
Fusion, obtains multi-source fusion feature.
Specifically, space-time characteristic includes region day disease incidence, region week disease incidence and region sense rate etc. again；Second timing
Feature includes temperature (including mean temperature, maximum temperature, minimum temperature, the maximum arranged according to the time sequencing of space-time characteristic
Temperature difference etc.), precipitation, the weather environments data such as humidity and air pressure.
Step S206 carries out risk profile to infectious disease according to multi-source fusion feature.
Step S201~step S206 is similar with the step of embodiment one S101~detailed process of step S105, herein not
It repeats again.
The prediction network of the present embodiment as shown in figure 4, input convolutional Neural net for the history morbidity data of infectious disease first
Network extracts space characteristics by convolution, then extracts the first temporal aspect by Recognition with Recurrent Neural Network, including the t-n period arrives
Space characteristics and the first temporal aspect are merged by the period, obtain space-time characteristic by the t period；Simultaneously by meteorological ring
Border data input Recognition with Recurrent Neural Network, extract the second temporal aspect, and space-time characteristic and the second temporal aspect are carried out by the period
Fusion, obtains multi-source fusion feature, prediction result is exported according to multi-source fusion feature, to realize infectious disease risk profile.
The present embodiment integrates weather environment on the basis of integrating the space-time characteristic of history morbidity data of infectious disease
Etc. multifactor carry out Spatiotemporal Data Modeling, space-time data is analyzed；The neural network structure constructed by deep learning method includes
Convolutional neural networks and Recognition with Recurrent Neural Network give full play to the ability of Neural Network Data expression；Based on multisource spatio-temporal data
It predicts network model, realizes fine-grained infectious disease spatio-temporal prediction from time, space entirety angle.It reduces based on the single time
The uncertainty of sequence signature prediction, further improves the early stage risk profile precision for region infectious disease, realizes space-time
The depth of reasoning and data mining combines.
As shown in figure 5, the embodiment of the present invention also provides a kind of infectious disease forecasting device, device includes:
First obtains module 10, for obtaining the history morbidity data of infectious disease；
Space characteristics extraction module 20, for extracting the space characteristics of history morbidity data by convolutional neural networks；
First temporal aspect extraction module 30, for extracting the first timing of history morbidity data by Recognition with Recurrent Neural Network
Feature；
Fusion Features module 40 obtains history morbidity data for merging space characteristics and the first temporal aspect
Space-time characteristic；
Prediction module 50 obtains prediction morbidity data for carrying out risk profile to infectious disease according to space-time characteristic.
Further, device further include:
Second obtains module, for obtaining the weather environment data for influencing infectious disease；
Second temporal aspect extraction module, the second timing for extracting weather environment data by Recognition with Recurrent Neural Network are special
Sign；
Further, Fusion Features module 40 is also used to:
The space-time characteristic of history morbidity data is merged with the second temporal aspect, obtains multi-source fusion feature.
The technical effect and preceding method embodiment phase of device provided by the embodiment of the present invention, realization principle and generation
Together, to briefly describe, Installation practice part does not refer to place, can refer to corresponding contents in preceding method embodiment.
Infectious disease forecasting device provided in an embodiment of the present invention, has with Prediction Method of Communicable Disease provided by the above embodiment
Identical technical characteristic reaches identical technical effect so also can solve identical technical problem.
The embodiment of the present invention also provides a kind of electronic equipment, including memory, processor, and being stored in memory can locate
The computer program run on reason device, processor realize infectious disease forecasting side provided by the above embodiment when executing computer program
The step of method.
The embodiment of the present invention also provides a kind of computer readable storage medium, and meter is stored on computer readable storage medium
Calculation machine program, the step of Prediction Method of Communicable Disease of above-described embodiment is executed when computer program is run by processor.
Referring to Fig. 6, the embodiment of the present invention also provides a kind of electronic equipment 1000, comprising: processor 500, memory 501,
Bus 502 and communication interface 503, processor 500, communication interface 503 and memory 501 are connected by bus 502；Memory
501 for storing program；Processor 500 is used to call the program being stored in memory 501 by bus 502, executes above-mentioned
The Prediction Method of Communicable Disease of embodiment.
Wherein, memory 501 may include high-speed random access memory (RAM, Random Access Memory),
It may further include non-labile memory (non-volatile memory), for example, at least a magnetic disk storage.By extremely
A few communication interface 503 (can be wired or wireless) is realized logical between the system network element and at least one other network element
Letter connection, can be used internet, wide area network, local network, Metropolitan Area Network (MAN) etc..
Bus 502 can be isa bus, pci bus or eisa bus etc..The bus can be divided into address bus, number
According to bus, control bus etc..Only to be indicated with a four-headed arrow in Fig. 6, it is not intended that an only bus convenient for indicating
Or a type of bus.
Wherein, memory 501 is for storing program, and processor 500 executes described program after receiving and executing instruction,
Method performed by the device that the stream process that aforementioned any embodiment of the embodiment of the present invention discloses defines can be applied to processor
In 500, or realized by processor 500.
Processor 500 may be a kind of IC chip, the processing capacity with signal.It is above-mentioned during realization
Each step of method can be completed by the integrated logic circuit of the hardware in processor 500 or the instruction of software form.On
The processor 500 stated can be general processor, including central processing unit (Central Processing Unit, abbreviation
CPU), network processing unit (Network Processor, abbreviation NP) etc.；It can also be digital signal processor (Digital
Signal Processing, abbreviation DSP), specific integrated circuit (Application Specific Integrated
Circuit, abbreviation ASIC), ready-made programmable gate array (Field-Programmable Gate Array, abbreviation FPGA) or
Person other programmable logic device, discrete gate or transistor logic, discrete hardware components.It may be implemented or execute sheet
Disclosed each method, step and logic diagram in inventive embodiments.General processor can be microprocessor or the processing
Device is also possible to any conventional processor etc..The step of method in conjunction with disclosed in the embodiment of the present invention, can be embodied directly in
Hardware decoding processor executes completion, or in decoding processor hardware and software module combination execute completion.Software mould
Block can be located at random access memory, flash memory, read-only memory, programmable read only memory or electrically erasable programmable storage
In the storage medium of this fields such as device, register maturation.The storage medium is located at memory 501, and processor 500 reads memory
Information in 501, in conjunction with the step of its hardware completion above method.
In the description of the present invention, it should be noted that term " first ", " second ", " third " are used for description purposes only,
It is not understood to indicate or imply relative importance.
It is apparent to those skilled in the art that for convenience and simplicity of description, the method for foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.The apparatus embodiments described above are merely exemplary, for example, the division of the unit,
Only a kind of logical function partition, there may be another division manner in actual implementation, in another example, multiple units or components can
To combine or be desirably integrated into another system, or some features can be ignored or not executed.Another point, it is shown or beg for
The mutual coupling, direct-coupling or communication connection of opinion can be through some communication interfaces, device or unit it is indirect
Coupling or communication connection can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in the executable non-volatile computer-readable storage medium of a processor.Based on this understanding, of the invention
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the present invention
State all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can be with
Store the medium of program code.
Finally, it should be noted that embodiment described above, only a specific embodiment of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, those skilled in the art should understand that: anyone skilled in the art
In the technical scope disclosed by the present invention, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover in protection of the invention
Within the scope of.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (10)
1. a kind of Prediction Method of Communicable Disease characterized by comprising
Obtain the history morbidity data of infectious disease；
The space characteristics of the history morbidity data are extracted by convolutional neural networks；
The first temporal aspect of the history morbidity data is extracted by Recognition with Recurrent Neural Network；
The space characteristics and first temporal aspect are merged, the space-time characteristic of the history morbidity data is obtained；
Risk profile is carried out to the infectious disease according to the space-time characteristic.
2. the method according to claim 1, wherein the method also includes:
Obtain the weather environment data for influencing the infectious disease；
The second temporal aspect of the weather environment data is extracted by the Recognition with Recurrent Neural Network；
Described to merge the space characteristics and first temporal aspect, the space-time for obtaining the history morbidity data is special
After the step of sign, further includes:
The space-time characteristic of history morbidity data is merged with second temporal aspect, obtains multi-source fusion feature.
3. according to the method described in claim 2, it is characterized in that, described carry out the infectious disease according to the space-time characteristic
The step of risk profile, comprising:
Risk profile is carried out to the infectious disease according to the multi-source fusion feature.
4. according to the method described in claim 2, it is characterized in that, described by the space characteristics and first temporal aspect
The step of being merged, obtaining the space-time characteristic of the history morbidity data, comprising:
The space characteristics and first temporal aspect are merged by the period sequentially in time, obtain the history hair
The space-time characteristic of sick data；
It is described to merge the space characteristics and first temporal aspect by the period sequentially in time, obtain described go through
The step of space-time characteristic of history morbidity data, comprising:
The space-time characteristic of history morbidity data is merged by the period sequentially in time with second temporal aspect,
Obtain the multi-source fusion feature.
5. the method according to claim 1, wherein the history for obtaining infectious disease is fallen ill the step of data,
Include:
Infectious disease keyword extraction and abnormal data elimination are carried out to history Outpatient Department data, obtain the history hair of the infectious disease
Sick data.
6. according to the method described in claim 2, it is characterized in that, the space-time characteristic includes at least region day disease incidence, area
Domain week disease incidence and region one of sense rate again；
Second temporal aspect includes at least temperature, the precipitation, humidity arranged according to the time sequencing of the space-time characteristic
And air pressure.
7. a kind of infectious disease forecasting device, which is characterized in that described device includes:
First obtains module, for obtaining the history morbidity data of infectious disease；
Space characteristics extraction module, for extracting the space characteristics of the history morbidity data by convolutional neural networks；
First temporal aspect extraction module, the first timing for extracting the history morbidity data by Recognition with Recurrent Neural Network are special
Sign；
Fusion Features module obtains the history hair for merging the space characteristics and first temporal aspect
The space-time characteristic of sick data；
Prediction module obtains prediction morbidity data for carrying out risk profile to the infectious disease according to the space-time characteristic.
8. device according to claim 7, which is characterized in that described device further include:
Second obtains module, for obtaining the weather environment data for influencing the infectious disease；
Second temporal aspect extraction module, when for extracting the second of the weather environment data by the Recognition with Recurrent Neural Network
Sequence characteristics；
The Fusion Features module is also used to:
The space-time characteristic of history morbidity data is merged with second temporal aspect, obtains multi-source fusion feature.
9. a kind of electronic equipment, including memory, processor and it is stored on the memory and can transports on the processor
Capable computer program, which is characterized in that the processor realizes such as claim 1 to 6 times when executing the computer program
The step of method described in one.
10. a kind of computer-readable medium for the non-volatile program code that can be performed with processor, which is characterized in that described
It is stored with computer program on readable storage medium storing program for executing, such as claim 1 to 6 is executed when the computer program is run by processor
The step of described in any item methods.
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