CN106250716A - A kind of neurodermatitis Forecasting Methodology based on increment type neural network model and prognoses system - Google Patents
A kind of neurodermatitis Forecasting Methodology based on increment type neural network model and prognoses system Download PDFInfo
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- 201000009053 Neurodermatitis Diseases 0.000 title claims abstract description 126
- 238000003062 neural network model Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 25
- 239000013598 vector Substances 0.000 claims abstract description 56
- 230000007170 pathology Effects 0.000 claims abstract description 29
- 239000000284 extract Substances 0.000 claims abstract description 9
- 238000012937 correction Methods 0.000 claims abstract description 8
- 238000007689 inspection Methods 0.000 claims abstract description 6
- 210000002569 neuron Anatomy 0.000 claims description 111
- 238000012549 training Methods 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 13
- 238000012806 monitoring device Methods 0.000 claims description 12
- 201000004624 Dermatitis Diseases 0.000 claims description 10
- 238000013139 quantization Methods 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 7
- 238000012544 monitoring process Methods 0.000 claims description 7
- 201000010099 disease Diseases 0.000 claims description 6
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 6
- 210000005036 nerve Anatomy 0.000 claims description 6
- 230000036541 health Effects 0.000 claims description 5
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims description 3
- 241000190070 Sarracenia purpurea Species 0.000 claims description 3
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- 230000001537 neural effect Effects 0.000 claims description 3
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- 230000007935 neutral effect Effects 0.000 claims 1
- 230000002354 daily effect Effects 0.000 description 25
- 210000004218 nerve net Anatomy 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 208000024891 symptom Diseases 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 235000013399 edible fruits Nutrition 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 230000001575 pathological effect Effects 0.000 description 2
- 206010013786 Dry skin Diseases 0.000 description 1
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- 230000008878 coupling Effects 0.000 description 1
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Abstract
The invention discloses a kind of neurodermatitis Forecasting Methodology based on increment type neural network model, comprise the steps: to set up the daily data database of neurodermatitis；Neural network model is trained；Gather daily life data to send to server, preserve to the daily data logger of user；The daily data logger of user extracts data on the same day, forms n-dimensional vector, input in neurodermatitis pathology neural network model after doing normalized and carry out neurodermatitis probabilistic forecasting；Wired home neurodermatitis care appliances judges that whether neurodermatitis probit is more than 0.5；User is judged to neurodermatitis, and user removes examination in hospital voluntarily, and by wired home neurodermatitis care appliances, inspection result is sent back server, and server judges to check that result is the most correct；Perform increasable algorithm when checking result mistake, neural network model is carried out dynamic corrections.The present invention predicts accurately, makes neural network model to measure for each user.
Description
Technical field
The invention belongs to field of medical technology, particularly relate to a kind of nerve skin based on increment type neural network model
Scorching Forecasting Methodology and prognoses system.
Background technology
Present Domestic is each health management system arranged is respectively provided with neurodermatitis prediction and evaluation, and its prediction mode used is data
Coupling.Its principle is that by system matches fixed data, then personal lifestyle data entry system is shown ill probability.But due to
Human body and the complexity of disease, unpredictability, in the bio signal form of expression with information, Changing Pattern (Self-variation
Change with after medical intervention) on, it being detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are many
All there is extremely complex non-linear relationship in aspect.So using traditional Data Matching can only be data examination blindly, nothing
Method judges the logic association between data and data and variable, and the codomain deviation obtained is big, causes the specificity of system prediction
The poorest, domestic health management system arranged effectively the neurodermatitis of individual cannot be carried out Accurate Prediction so current.
Major part is all to use BP neural network model to neurodermatitis prediction before this, but when new detection data are produced
The when of raw, it is necessary to again training neural network model, operation efficiency is extremely low.And after system user scale increases, clothes
Business device will be unable to complete in time training mission.
Summary of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, it is provided that a kind of based on increment type neural network model
Neurodermatitis Forecasting Methodology and prognoses system, the present invention by neural network model training predict a large amount of patient in hospital pathology
Data, find neurodermatitis pathology and neurodermatitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk
Crowd characteristic, the logic association between these several the causes of disease and variable, ultimately form probability Accurate Prediction ill to neurodermatitis
Neurodermatitis pathology neural network model, the present invention by gather user's daily life data, its data of active analysis
Periodically, regular suffer from neurodermatitis probability eventually through neurodermatitis pathology Neural Network model predictive user, with
The mode of visual effect reminds user's instant hospitalizing, is constantly repaiied by increasable algorithm when Neural Network model predictive is inaccurate
Positive neural network model, trains the neural network model for this user, along with use to set up for each equipment user
The increase of time, the neural network model made this user to measure with foundation, accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of neurodermatitis based on increment type neural network model pre-
Survey method, comprises the steps:
Step (1), acquisition hospital neurodermatitis etiology and pathology data source and patient's daily monitoring data, thus set up god
Through the property daily data database of dermatitis；
Step (2), the daily data database of neurodermatitis set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the neurodermatitis pathology neural network model trained；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life that will gather
Live data sends to server, and the daily life data of user are preserved to the daily data logger of user by server；
Step (4), from the daily data logger of user, extract data on the same day, form n-dimensional vector, and n-dimensional vector is done
The neurodermatitis pathology neural network model trained in input step (2) after normalized carries out neurodermatitis general
Rate is predicted, obtains neurodermatitis probability, and neurodermatitis probability is sent to the nursing of wired home neurodermatitis by server
Equipment；
After step (5), wired home neurodermatitis care appliances receive the neurodermatitis probability that server transmits, sentence
Disconnected neurodermatitis probit, whether more than 0.5, if greater than 0.5, is then judged to that this user obtained neurodermatitis, attention device
Warning is to remind user, if less than 0.5, is then judged to that this user does not obtain neurodermatitis；
Step (6), when user is judged to neurodermatitis, user removes examination in hospital voluntarily, and will check result
Sending back server by wired home neurodermatitis care appliances, server judges to check that result is the most correct, if inspection
Come to an end fruit mistake, then explanation neurodermatitis pathology Neural Network model predictive is inaccurate, if checking that result is correct, then illustrates
Neurodermatitis pathology Neural Network model predictive is accurate；
Step (7), when checking result mistake, extract from the daily data logger of user the record in m days preserve to
In incremental data table, when the record quantity in incremental data table is more than h bar, perform increasable algorithm, sick to neurodermatitis
Reason neural network model carries out dynamic corrections；
Step (8), repetition step (3)～(7).
Further, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1
Node, extracts k bar record from neurodermatitis daily data database table and is trained, and every record is a n-dimensional vector,
All data are before use first through normalized so that it is numerical value is interval in [0,1], then perform following steps to nerve net
Network model is trained:
1) one n-dimensional vector of input is to neural network model, calculates all of weight vector in neural network model defeated to this
Entering the distance of n-dimensional vector, closest neuron is triumph neuron, and its computing formula is as follows:
Wherein: WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) adjusting the weight vector of neuron in triumph neuron and triumph neuron field, formula is as follows:
Wherein: WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Territory；α (t) is learning rate, and it is as the function that the increase of iterations is gradually successively decreased, and span is [01], through repeatedly
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt it is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Further, wired home neurodermatitis care appliances will check that result sends back the object information of server
Form is: { checking whether correct, blood glucose value }, and server is after receiving object information, it is judged that check that result is the most correct.
Further, the increasable algorithm that neurodermatitis pathology neural network model carries out dynamic corrections is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, it is sent in neural network model learning function enter
Row study, learning procedure is as follows:
1) first each to output layer weight vector is composed little random number and does normalized, then utilizes input mode vector V
Meansigma methods Avg (V), be initialized as in neural network model the 0th layer the weights of unique neuron, and be set to nerve of winning
Unit, calculates its quantization error QE；
2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the power of these 4 neurons is initialized
Value；The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) from VX, select a vectorial VXiDo following judgement:
If VXiFor the data of not tape label, then calculating the Euclidean distance of it and each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron work that value is maximum
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ that wins
{VXi, calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. the most predetermined frequency of training, then
Go to step 4)；
6) calculate adjusted after this neural network model in quantization error QE of each neuroni, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label kind set on neuron；
Then judge:
If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, turn step
Rapid 4)；
If Ei> E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly grown increases in the subnet queue of Layer+1 layer；
If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；
7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) to it again
Being trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
Further, if user includes health check-up by other means and checks oneself, learn and oneself have suffered from neurodermatitis, and
The attention device of wired home neurodermatitis care appliances does not warn, then it represents that wired home neurodermatitis care appliances is sentenced
Disconnected inaccurate, now perform step (6)～(7), wired home neurodermatitis care appliances is sent to service object information
On device.
Present invention also offers the prognoses system of a kind of described neurodermatitis Forecasting Methodology, including intelligent monitoring device,
Smart machine data acquisition unit, server and wired home neurodermatitis care appliances, described intelligent monitoring device is with described
Smart machine data acquisition unit is connected, and described smart machine data acquisition unit is by communication device one and described server network
Communication, described wired home neurodermatitis care appliances is by communication device two and described server network communication.
Further, described wired home neurodermatitis care appliances is provided with attention device.
Further, described intelligent monitoring device include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligence horse
Bucket and Intelligent light sensing equipment.
Beneficial effects of the present invention:
1, the present invention predicts a large amount of patient in hospital pathological data by neural network model training, finds neurodermatitis sick
Reason and neurodermatitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, these several causes of disease it
Between logic association and variable, ultimately form the neurodermatitis pathology nerve net of probability Accurate Prediction ill to neurodermatitis
Network model, the present invention is by gathering user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through god
Suffer from neurodermatitis probability through property dermatitis pathology Neural Network model predictive user, remind user i.e. in the way of visual effect
Time seek medical advice and prevent.
2, constantly revise neural network model when Neural Network model predictive is inaccurate by increasable algorithm, with for
Each equipment user sets up and trains the neural network model for this user, along with the increase of the time of use, to set up this
The neural network model that user makes to measure, accuracy rate is greatly improved.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
In having technology to describe, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to
Other accompanying drawing is obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.
Embodiment
As it is shown in figure 1, a kind of based on increment type neural network model the neurodermatitis Forecasting Methodology that the present invention provides,
Comprise the steps:
Step (1), acquisition hospital neurodermatitis etiology and pathology data source and patient's daily monitoring data, thus set up god
Through the property daily data database of dermatitis；
The most daily monitoring data are 21 item data, and its 21 item data is the age, sex, the course of disease time, recurrence frequency, body
Dry skin situation, erythra image, body weight, the erythra order of severity, sufferings degree, time for falling asleep, smoking capacity (every day), limbs skin
Situation, 21 item data such as every day, travel distance, pursued an occupation, temperature, humidity, air quality index, the present invention is with 21 item data
Set up 21 dimensional vectors；
Step (2), the daily data database of neurodermatitis set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the neurodermatitis pathology neural network model trained；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life that will gather
Live data sends to server, and the daily life data of user are preserved to the daily data logger of user by server；
Step (4), from the daily data logger of user, extract data on the same day, form 21 dimensional vectors, and to 21 dimensional vectors
The neurodermatitis pathology neural network model trained in input step (2) after doing normalized carries out neurodermatitis
Probabilistic forecasting, obtains neurodermatitis probability, and server sends neurodermatitis probability to wired home neurodermatitis and protects
Reason equipment；
After step (5), wired home neurodermatitis care appliances receive the neurodermatitis probability that server transmits, sentence
Disconnected neurodermatitis probit, whether more than 0.5, if greater than 0.5, is then judged to that this user obtained neurodermatitis, attention device
Warning is to remind user, if less than 0.5, is then judged to that this user does not obtain neurodermatitis；
Step (6), when user is judged to neurodermatitis, user removes examination in hospital voluntarily, and will check result
Sending back server by wired home neurodermatitis care appliances, server judges to check that result is the most correct, if inspection
Come to an end fruit mistake, then explanation neurodermatitis pathology Neural Network model predictive is inaccurate, if checking that result is correct, then illustrates
Neurodermatitis pathology Neural Network model predictive is accurate；
Step (7), when checking result mistake, extract from the daily data logger of user the record in 7 days preserve to
In incremental data table, when the record quantity in incremental data table is more than 100, perform increasable algorithm, to neurodermatitis
Pathology neural network model carries out dynamic corrections；
Step (8), repetition step (3)～(7).
The input layer of the neural network model of the present invention is 21 nodes, and hidden layer number is 43, and output layer is 1 node
(i.e. the probability of neurodermatitis), extracts 400000 records from neurodermatitis daily data database table and is trained,
Every record is 21 dimensional vectors, and all data are before use first through normalized so that it is numerical value is interval in [0,1], so
Neural network model is trained by rear execution following steps:
1) one 21 dimensional vector of input are to neural network model, calculate all of weight vector in neural network model defeated to this
Entering the distance of 21 dimensional vectors, closest neuron is triumph neuron, and its computing formula is as follows:
Wherein: WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) adjusting the weight vector of neuron in triumph neuron and triumph neuron field, formula is as follows:
Wherein: WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Territory；α (t) is learning rate, and it is as the function that the increase of iterations is gradually successively decreased, and span is [01], through repeatedly
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt it is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when reaching changing of regulation
After generation number, neural network model training terminates.
The wired home neurodermatitis care appliances of the present invention will check that result sends back the object information of server
Form is: { checking whether correct, blood glucose value }, and server is after receiving object information, it is judged that check that result is the most correct.
The increasable algorithm that neurodermatitis pathology neural network model carries out dynamic corrections of the present invention is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, it is sent in neural network model learning function enter
Row study, learning procedure is as follows:
1) first each to output layer weight vector is composed little random number and does normalized, then utilizes input mode vector V
Meansigma methods Avg (V), be initialized as in neural network model the 0th layer the weights of unique neuron, and be set to nerve of winning
Unit, calculates its quantization error QE；
2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the power of these 4 neurons is initialized
Value；The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) from VX, select a vectorial VXiDo following judgement:
If VXiFor the data of not tape label, then calculating the Euclidean distance of it and each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron work that value is maximum
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ that wins
{VXi, calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. the most predetermined frequency of training, then
Go to step 4)；
6) calculate adjusted after this neural network model in quantization error QE of each neuroni, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label kind set on neuron；
Then judge:
If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, turn step
Rapid 4)；
If Ei> E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly grown increases in the subnet queue of Layer+1 layer；
If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；
7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) to it again
Being trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
If the user of the present invention includes health check-up by other means and checks oneself, learn and oneself have suffered from neurodermatitis, and
The attention device of wired home neurodermatitis care appliances does not warn, then it represents that wired home neurodermatitis care appliances is sentenced
Disconnected inaccurate, now perform step (6)～(7), wired home neurodermatitis care appliances is sent to service object information
On device.
Present invention also offers the prognoses system of a kind of described neurodermatitis Forecasting Methodology, including intelligent monitoring device,
Smart machine data acquisition unit, server and wired home neurodermatitis care appliances, described intelligent monitoring device is with described
Smart machine data acquisition unit is connected, and described smart machine data acquisition unit is by communication device one and described server network
Communication, described wired home neurodermatitis care appliances is by communication device two and described server network communication.
It is provided with attention device on the described wired home neurodermatitis care appliances of the present invention.
The described intelligent monitoring device of the present invention include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool
With Intelligent light sensing equipment etc..
The present invention predicts a large amount of patient in hospital pathological data by neural network model training, finds neurodermatitis sick
Reason and neurodermatitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, these several causes of disease it
Between logic association and variable, ultimately form the neurodermatitis pathology nerve net of probability Accurate Prediction ill to neurodermatitis
Network model, the present invention is by gathering user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through god
Suffer from neurodermatitis probability through property dermatitis pathology Neural Network model predictive user, remind user i.e. in the way of visual effect
Time seek medical advice and prevent.
All data of the present invention preserve to server, can significantly save calculating cost, and hardware configuration is low, thus sells
Valency is the lowest.
The present invention carries communication device one and communication device two, by wifi from being dynamically connected the Internet, and can protect for a long time
Hold online.Various intelligent monitoring devices can easily access present device by the mode such as network or bluetooth, sets in acquisition
The daily life data of the monitoring of intelligent monitoring device, the data that therefore present device obtains can be automatically uploaded after standby mandate
Be real-time, accurately, polynary.
Owing to everyone physical trait is different, the data characteristics shown during neurodermatitis morbidity can not yet
With.The most conventional is the highest by the method accuracy rate of neural network prediction neurodermatitis.The present invention is directed to each equipment use
Family is set up and is trained the neural network model for this user, is running after a period of time, and generation is fixed to this customer volume body
Doing neural network prediction model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to service by wired home neurodermatitis care appliances
Device, for this user's dynamic corrections neural network model, when next time, similar characteristics data occurred in this user, will not miss again
Sentence.Therefore, along with the increase of the time of use, the judgement of the wired home neurodermatitis care appliances of the present invention will be increasingly
Accurately.
The ultimate principle of the present invention, principal character and advantages of the present invention have more than been shown and described.The technology of the industry
Personnel, it should be appreciated that the present invention is not restricted to the described embodiments, simply illustrating this described in above-described embodiment and description
The principle of invention, the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these become
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and
Equivalent defines.
Claims (8)
1. a neurodermatitis Forecasting Methodology based on increment type neural network model, it is characterised in that comprise the steps:
Step (1), obtain hospital neurodermatitis and cure the disease etiology and pathology data source and patient's daily monitoring data, thus set up god
Through the property daily data database of dermatitis；
Step (2), the daily data database of neurodermatitis set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the neurodermatitis pathology neural network model trained；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life number that will gather
According to sending to server, the daily life data of user are preserved to the daily data logger of user by server；
Step (4), from the daily data logger of user, extract data on the same day, form n-dimensional vector, and n-dimensional vector is done normalizing
The neurodermatitis pathology neural network model trained in input step (2) after change process carries out neurodermatitis probability pre-
Surveying, obtain neurodermatitis probability, neurodermatitis probability is sent to wired home neurodermatitis care appliances by server；
After step (5), wired home neurodermatitis care appliances receive the neurodermatitis probability that server transmits, it is judged that god
Through property dermatitis probit whether more than 0.5, if greater than 0.5, being then judged to that this user obtained neurodermatitis, attention device warns
To remind user, if less than 0.5, then it is judged to that this user does not obtain neurodermatitis；
Step (6), when user is judged to neurodermatitis, user removes examination in hospital voluntarily, and inspection result is passed through
Wired home neurodermatitis care appliances sends back server, and server judges to check that result is the most correct, if checking knot
Really mistake, then explanation neurodermatitis pathology Neural Network model predictive is inaccurate, if checking that result is correct, then nerve is described
Property dermatitis pathology Neural Network model predictive is accurate；
Step (7), when checking result mistake, the record extracted from the daily data logger of user in m days preserves to increment
In tables of data, when the record quantity in incremental data table is more than h bar, perform increasable algorithm, to neurodermatitis pathology god
Dynamic corrections is carried out through network model；
Step (8), repetition step (3)～(7).
A kind of neurodermatitis Forecasting Methodology based on increment type neural network model the most according to claim 1, it is special
Levying and be, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1 node, from nerve
Property dermatitis daily data database table in extract k bar record be trained, every record is a n-dimensional vector, and all data exist
First through normalized before using so that it is numerical value is interval in [0,1], then perform following steps and neural network model is carried out
Training:
1) one n-dimensional vector of input is to neural network model, calculates all of weight vector in neural network model and ties up to this input n
The distance of vector, closest neuron is triumph neuron, and its computing formula is as follows:
Wherein: WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) adjusting the weight vector of neuron in triumph neuron and triumph neuron field, formula is as follows:
Wherein: WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron field；α
T () is learning rate, it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through many experiments
Choosing Optimal learning efficiency is 0.62；DjIt it is the distance of neuron j and triumph neuron；σ (t) is as the function that the time successively decreases；
All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when the iteration time reaching regulation
After number, neural network model training terminates.
A kind of neurodermatitis Forecasting Methodology based on increment type neural network model the most according to claim 1, it is special
Levying and be, the form of the object information that inspection result sends back server is by wired home neurodermatitis care appliances: { inspection
Look into the most correct, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.
A kind of neurodermatitis Forecasting Methodology based on increment type neural network model the most according to claim 1, it is special
Levying and be, the increasable algorithm that neurodermatitis pathology neural network model carries out dynamic corrections is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, it is sent in neural network model learning function learn
Practising, learning procedure is as follows:
1) first each to output layer weight vector is composed little random number and does normalized, then utilizes the flat of input mode vector V
Average Avg (V), is initialized as in neural network model the 0th layer the weights of unique neuron, and is set to triumph neuron, meter
Calculate its quantization error QE；
2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the weights of these 4 neurons are initialized；Will
The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron iiIt is set to
0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) from VX, select a vectorial VXiDo following judgement:
If VXiFor the data of not tape label, then calculate the Euclidean distance of it and each neuron, the nerve that chosen distance is the shortest
Unit is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron of value maximum is as obtaining
Victory neuron, updates this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest neuron i is as obtaining
Victory neuron；
5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ { VX that winsi,
Calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. the most predetermined frequency of training, then go to step
4)；
6) calculate adjusted after this neural network model in quantization error QE of each neuroni, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting to fall that label is the number of samples of i on neuron, m represents to fall the total of label data on neuron
Number, T represents to fall the sample label kind set on neuron；
Then judge:
If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, go to step
4)；
If Ei> E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will be the longest
The subnet gone out increases in the subnet queue of Layer+1 layer；
If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；
7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) it is re-started
Training, until neural network model no longer produces new neuron and new layering, whole training terminates.
A kind of neurodermatitis Forecasting Methodology based on increment type neural network model the most according to claim 1, it is special
Levy and be, if user includes health check-up by other means and checks oneself, learn and oneself have suffered from neurodermatitis, and wired home is refreshing
Do not warn through the attention device of property dermatitis care appliances, then it represents that wired home neurodermatitis care appliances judges inaccurate,
Now performing step (6)～(7), wired home neurodermatitis care appliances is sent to object information on server.
6. one kind uses the prognoses system of neurodermatitis Forecasting Methodology described in claim 1～6, it is characterised in that include intelligence
Energy monitoring device, smart machine data acquisition unit, server and wired home neurodermatitis care appliances, described intelligent monitoring
Equipment is connected with described smart machine data acquisition unit, and described smart machine data acquisition unit passes through communication device one with described
Server network communication, described wired home neurodermatitis care appliances is led to described server network by communication device two
News.
The prognoses system of neurodermatitis Forecasting Methodology the most according to claim 7, it is characterised in that described wired home god
It is provided with attention device on property dermatitis care appliances.
The prognoses system of neurodermatitis Forecasting Methodology the most according to claim 7, it is characterised in that described intelligent monitoring sets
Standby include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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