CN106295238A - A kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model and prognoses system - Google Patents

A kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model and prognoses system Download PDF

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CN106295238A
CN106295238A CN201610861208.1A CN201610861208A CN106295238A CN 106295238 A CN106295238 A CN 106295238A CN 201610861208 A CN201610861208 A CN 201610861208A CN 106295238 A CN106295238 A CN 106295238A
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neuron
hypertensive nephropathy
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nephropathy
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杨滨
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Hunan Old Code Information Technology Co Ltd
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    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

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Abstract

The invention discloses a kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model, comprise the steps: to set up the daily data database of hypertensive nephropathy；Neural network model is trained；Gather daily life data to send to server；The daily data logger of user extracts data on the same day, forms n-dimensional vector, input in hypertensive nephropathy pathology neural network model after doing normalized and carry out hypertensive nephropathy degree of danger probabilistic forecasting；Wired home hypertensive nephropathy care appliances judges that whether hypertensive nephropathy degree of danger value W is more than or equal to 3；When user receives attention device warning, user removes examination in hospital voluntarily, and by wired home hypertensive nephropathy 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

A kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model and prediction System
Technical field
The invention belongs to field of medical technology, particularly relate to a kind of hypertensive renal based on increment type neural network model Disease forecasting method and prognoses system.
Background technology
Present Domestic is each health management system arranged is respectively provided with hypertensive nephropathy 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 hypertensive nephropathy of individual cannot be carried out Accurate Prediction so current.
Major part is all to use BP neural network model to hypertensive renal disease forecasting 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 Hypertensive nephropathy Forecasting Methodology and prognoses system, the present invention by neural network model training predict a large amount of patient in hospital pathology Data, find hypertensive nephropathy pathology and hypertensive nephropathy 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 hypertensive nephropathy Hypertensive nephropathy pathology neural network model, the present invention by gather user's daily life data, its data of active analysis Periodically, regular suffer from hypertensive nephropathy probability eventually through hypertensive nephropathy 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 hypertensive nephropathy based on increment type neural network model pre- Survey method, comprises the steps:
Step (1), acquisition hospital hypertensive nephropathy etiology and pathology data source and patient's daily monitoring data, thus set up height The daily data database of blood pressure nephropathy；
Step (2), the daily data database of hypertensive nephropathy set up according to step (1) are off-line manner to nerve net Network model is trained, to obtain the hypertensive nephropathy 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 hypertensive nephropathy pathology neural network model trained in input step (2) after normalized carries out hypertensive renal be critically ill Danger degree probabilistic forecasting, obtains hypertensive nephropathy probability results array P, and server is by high blood corresponding for maximum probability in array P Pressure nephropathy danger degree value W sends wired home hypertensive nephropathy care appliances to；
Step (5), wired home hypertensive nephropathy care appliances receive the hypertensive nephropathy degree of danger that server transmits After value W, it is judged that whether hypertensive nephropathy degree of danger value W is more than or equal to 3, and if greater than equal to 3, then attention device warning is to remind User, if less than 3, then attention device does not warns；
Step (6), when user receive attention device warning time, user removes examination in hospital voluntarily, and inspection result is passed through Wired home hypertensive nephropathy care appliances sends back server, and server judges to check that result is the most correct, if checking knot Really mistake, then explanation hypertensive nephropathy pathology Neural Network model predictive is inaccurate, if checking that result is correct, then high blood is described Pressure nephropathy 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 hypertensive nephropathy 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 hypertensive nephropathy 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 neutral net 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:
| | x i - W k | | = min j ( | | x i - W j | | ) ;
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 [0 1], through repeatedly It is 0.72 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 hypertensive nephropathy care appliances will check that result sends back the object information of server Form is: { the hypertensive nephropathy degree of danger value of the actual judgement of doctor }, and server is after receiving object information, it is judged that check Result is the most correct.
Further, the increasable algorithm that hypertensive nephropathy 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:
QE i = Σ X j ∈ C i { | | W i - X j | | } ;
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
E i = Σ i ∈ T ( - l b n i m ) × n i m ;
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, in step (4), hypertensive nephropathy probability results array P obtained is one 5 dimension variable, its 5 dimension Variable is respectively 5 hypertensive nephropathy degree of danger probability, and maximum probability in 5 hypertensive nephropathy degree of danger probability is corresponding Hypertensive nephropathy degree of danger value W send wired home hypertensive nephropathy care appliances to；In step (5), work as hypertension Represent preferable during nephropathy danger degree value W=1, represent normal during W=2, during W=3, represent subhealth state, represent dangerous during W=4, W Represent abnormally dangerous when=5.
Further, if user includes health check-up by other means and checks oneself, learn and oneself have suffered from hypertensive nephropathy, and The attention device of wired home hypertensive nephropathy care appliances does not warn, then it represents that wired home hypertensive nephropathy care appliances is sentenced Disconnected inaccurate, now perform step (6)～(7), wired home hypertensive nephropathy care appliances is sent to service object information On device.
Present invention also offers the prognoses system of a kind of described hypertensive nephropathy Forecasting Methodology, including intelligent monitoring device, Smart machine data acquisition unit, server and wired home hypertensive nephropathy 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 hypertensive nephropathy care appliances is by communication device two and described server network communication.
Further, described wired home hypertensive nephropathy 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 hypertensive nephropathy sick Reason and hypertensive nephropathy 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 hypertensive nephropathy pathology nerve net of probability Accurate Prediction ill to hypertensive nephropathy 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 height Blood pressure nephropathy pathology Neural Network model predictive user suffers from hypertensive nephropathy degree of danger probability, carries in the way of visual effect Awake user's instant hospitalizing and prevention.
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 hypertensive nephropathy Forecasting Methodology that the present invention provides, Comprise the steps:
Step (1), acquisition hospital hypertensive nephropathy etiology and pathology data source and patient's daily monitoring data, thus set up height The daily data database of blood pressure nephropathy；
The most daily monitoring data are 13 item data, and its 13 item data is the age, sex, heart rate, body fat, and body temperature drinks water Amount and frequency, body weight, the length of one's sleep and quality, travel distance (every day), shrink pressure, 13 item data such as diastolic pressure, the present invention with 13 item data set up 13 dimensional vectors；
Step (2), the daily data database of hypertensive nephropathy set up according to step (1) are off-line manner to nerve net Network model is trained, to obtain the hypertensive nephropathy 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 hypertensive nephropathy pathology neural network model trained in input step (2) after normalized carries out hypertensive renal be critically ill Danger degree probabilistic forecasting, obtains hypertensive nephropathy probability results array P, and server is by high blood corresponding for maximum probability in array P Pressure nephropathy danger degree value W sends wired home hypertensive nephropathy care appliances to；
Wherein in step (4), hypertensive nephropathy probability results array P obtained is one 5 dimension variable, its 5 dimension variable It is respectively 5 hypertensive nephropathy degree of danger probability, height corresponding for maximum probability in 5 hypertensive nephropathy degree of danger probability Blood pressure nephropathy danger degree value W sends wired home hypertensive nephropathy care appliances to.
Step (5), wired home hypertensive nephropathy care appliances receive the hypertensive nephropathy degree of danger that server transmits After value W, it is judged that whether hypertensive nephropathy degree of danger value W is more than or equal to 3, and if greater than equal to 3, then attention device warning is to remind User, if less than 3, then attention device does not warns；
Wherein in step (5), represent preferable when hypertensive nephropathy degree of danger value W=1, represent normal during W=2, W Represent subhealth state when=3, represent dangerous during W=4, represent abnormally dangerous during W=5.I.e. the present invention is by hypertensive nephropathy danger journey Degree is divided into 5 grades, more accurate and visual.
Step (6), when user receive attention device warning time, user removes examination in hospital voluntarily, and inspection result is passed through Wired home hypertensive nephropathy care appliances sends back server, and server judges to check that result is the most correct, if checking knot Really mistake, then explanation hypertensive nephropathy pathology Neural Network model predictive is inaccurate, if checking that result is correct, then high blood is described Pressure nephropathy pathology Neural Network model predictive is accurate；
Wherein wired home hypertensive nephropathy care appliances will check that result sends back the form of the object information of server For: { the hypertensive nephropathy degree of danger value of the actual judgement of doctor }, server is after receiving object information, it is judged that check result The most correct.
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 hypertensive nephropathy 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 13 nodes, and hidden layer number is 27, and output layer is 5 nodes (i.e. 5 hypertensive nephropathy degree of danger probability), extracts 400000 records from hypertensive nephropathy daily data database table Being trained, every record is 13 dimensional vectors, and all data are before use first through normalized so that it is numerical value [0, 1] interval, then perform following steps and neural network model is trained:
1) one 13 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 13 dimensional vectors, closest neuron is triumph neuron, and its computing formula is as follows:
| | x i - W k | | = min j ( | | x i - W j | | ) ;
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 [0 1], through repeatedly It is 0.72 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.
The increasable algorithm that hypertensive nephropathy 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:
QE i = Σ X j ∈ C i { | | W i - X j | | } ;
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
E i = Σ i ∈ T ( - l b n i m ) × n i m ;
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 user includes health check-up by other means and checks oneself, learn and oneself have suffered from hypertensive nephropathy, and wired home The attention device of hypertensive nephropathy care appliances does not warn, then it represents that wired home hypertensive nephropathy care appliances judges inaccurate Really, now performing step (6)～(7), wired home hypertensive nephropathy care appliances is sent to object information on server.
Present invention also offers the prognoses system of a kind of described hypertensive nephropathy Forecasting Methodology, including intelligent monitoring device, Smart machine data acquisition unit, server and wired home hypertensive nephropathy 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 hypertensive nephropathy care appliances is by communication device two and described server network communication.
It is provided with attention device on the described wired home hypertensive nephropathy 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 hypertensive nephropathy pathology With hypertensive nephropathy earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, between these several the causes of disease Logic association and variable, ultimately form the hypertensive nephropathy pathology neutral net of probability Accurate Prediction ill to hypertensive nephropathy 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 high blood Pressure nephropathy pathology Neural Network model predictive user suffers from hypertensive nephropathy probability, reminds user instant in the way of visual effect 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 hypertensive nephropathy morbidity can not yet With.The most conventional is the highest by the method accuracy rate of neural network prediction hypertensive nephropathy.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 hypertensive nephropathy 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 hypertensive nephropathy 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 (9)

1. a hypertensive nephropathy Forecasting Methodology based on increment type neural network model, it is characterised in that comprise the steps:
Step (1), obtain hospital hypertensive nephropathy and cure the disease etiology and pathology data source and patient's daily monitoring data, thus set up height The daily data database of blood pressure nephropathy；
Step (2), the daily data database of hypertensive nephropathy set up according to step (1) are off-line manner to neutral net mould Type is trained, to obtain the hypertensive nephropathy 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 hypertensive nephropathy pathology neural network model trained in input step (2) after change process carries out hypertensive nephropathy danger journey Degree probabilistic forecasting, obtains hypertensive nephropathy probability results array P, and server is by hypertensive renal corresponding for maximum probability in array P Sick dangerous degree value W sends wired home hypertensive nephropathy care appliances to；
Step (5), wired home hypertensive nephropathy care appliances receive hypertensive nephropathy degree of danger value W that server transmits After, it is judged that whether hypertensive nephropathy degree of danger value W is more than or equal to 3, and if greater than equal to 3, then attention device warns to remind use Family, if less than 3, then attention device does not warns；
Step (6), when user receives attention device warning, user removes examination in hospital voluntarily, and will check that result is by intelligence Family's hypertensive nephropathy care appliances sends back server, and server judges to check that result is the most correct, if checking that result is wrong By mistake, then explanation hypertensive nephropathy pathology Neural Network model predictive is inaccurate, if checking that result is correct, then hypertensive renal is described Sick 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 hypertensive nephropathy pathology god Dynamic corrections is carried out through network model；
Step (8), repetition step (3)～(7).
A kind of hypertensive nephropathy 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 high blood Extracting k bar record in pressure nephropathy daily data database table to 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 0.72；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 hypertensive nephropathy 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 hypertensive nephropathy care appliances: { doctor The hypertensive nephropathy degree of danger value that raw reality judges }, server is after receiving object information, it is judged that the most just checking result Really.
A kind of hypertensive nephropathy 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 hypertensive nephropathy 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 hypertensive nephropathy Forecasting Methodology based on increment type neural network model the most according to claim 1, it is special Levying and be, in step (4), hypertensive nephropathy probability results array P obtained is one 5 dimension variable, and its 5 dimension variable is respectively 5 hypertensive nephropathy degree of danger probability, hypertensive renal corresponding for maximum probability in 5 hypertensive nephropathy degree of danger probability Sick dangerous degree value W sends wired home hypertensive nephropathy care appliances to；In step (5), when hypertensive nephropathy danger journey Represent preferable during angle value W=1, represent normal during W=2, during W=3, represent subhealth state, represent dangerous during W=4, represent during W=5 Abnormally dangerous.
A kind of hypertensive nephropathy 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 hypertensive nephropathy, and wired home is high The attention device of blood pressure nephropathy care appliances does not warn, then it represents that wired home hypertensive nephropathy care appliances judges inaccurate, Now performing step (6)～(7), wired home hypertensive nephropathy care appliances is sent to object information on server.
7. one kind uses the prognoses system of hypertensive nephropathy Forecasting Methodology described in claim 1～7, it is characterised in that include intelligence Energy monitoring device, smart machine data acquisition unit, server and wired home hypertensive nephropathy 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 hypertensive nephropathy care appliances is led to described server network by communication device two News.
The prognoses system of hypertensive nephropathy Forecasting Methodology the most according to claim 8, it is characterised in that described wired home is high It is provided with attention device on blood pressure nephropathy care appliances.
The prognoses system of hypertensive nephropathy Forecasting Methodology the most according to claim 8, 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.
CN201610861208.1A 2016-09-28 2016-09-28 A kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model and prognoses system Pending CN106295238A (en)

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