CN111105877A - Chronic disease accurate intervention method and system based on deep belief network - Google Patents
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Abstract
The embodiment of the invention provides a chronic disease accurate intervention method and system based on a deep belief network, which comprises the following steps: step S1, constructing a chronic disease dynamic gradual change index accurate classification system; step S2, constructing a DBN model and a self-adaptive parameter information structure thereof; and step S3, extracting a model based on the semantic feature information of the sample-trained chronic disease health management accurate intervention model. According to the chronic disease accurate intervention method and system based on the deep belief network, the deep learning theory is creatively applied to the field of chronic disease management, the chronic disease accurate prevention and control efficiency is improved, and the cost is saved. Solves the problem of chronic diseases prevention and control which restrict the improvement of the life quality of human for a long time.
Description
Technical Field
The invention relates to the field of chronic disease intervention, in particular to a chronic disease accurate intervention method and system based on a deep confidence network.
Background
With the rapid development of economic society, chronic diseases become a major public health problem affecting the health of residents in China. According to the statistics of the Ministry of health, more than 2.6 hundred million chronic disease patients are diagnosed in China at present, and more than 300 million people die of chronic diseases every year. More seriously, along with the acceleration of industrialization, urbanization and aging processes and the aggravation of environmental pollution in China, the number of the chronic diseases is rapidly increased by 550 ten thousand every year, the average number is increased by 1.5 ten thousand every day, and the number of the chronic diseases dying is increased to 85% of the total number of the resident deaths, the medical cost burden accounts for 70% of the total disease burden, so the chronic diseases seriously affect the development of the economic society in China and the improvement of the life quality of people.
With the improvement of medical technology level, the harm of the current chronic diseases to residents in China replaces the harm of epidemic infectious diseases to human bodies, and especially in recent years, the awareness rate and the control rate of risk factors causing the chronic diseases in middle-aged and elderly people are low. The long-term accumulation of a large number of risk factors and the injury to human bodies inevitably increase the risk of middle-aged and elderly people suffering from chronic diseases, so that the morbidity of the chronic diseases is increased year by year, and the risk becomes the first factor causing the death of residents in China. Thus, chronic diseases become the first killer which influences the pursuit of people for good life. Accordingly, middle and long term Chinese programming for preventing and treating chronic diseases (2017-2025) is released for the first time in 2017 and 2 months, the programming requires that the premature death rate caused by chronic diseases is reduced by 10% in 2015 and 20% in 2025 by 2020, the premature death rate caused by cardiovascular and cerebrovascular diseases, cancers, chronic respiratory diseases and diabetes of people aged 30-70 is reduced by 20% in 2015, the expected life of the health of residents is gradually prolonged, and the occurrence of chronic diseases is effectively controlled.
The chronic diseases mainly comprise cardiovascular and cerebrovascular diseases, malignant tumors, diabetes, chronic respiratory diseases and the like. According to research, the occurrence of chronic diseases is closely related to personal life style (60%), genetic factors (15%), social conditions (10%), medical conditions (8%), natural environment (7%) and the like, but the existing research depth of prevention and control of chronic diseases in China is far from enough, the pathogenic mechanism of a plurality of chronic diseases is not completely clear, and accurate and effective prevention and control measures and methods need to be researched.
The current chronic disease treatment is a worldwide problem, developed countries mainly rely on health management and adopt measures of taking prevention as main and treatment as auxiliary to reduce the incidence of the chronic disease, which is the same source of 'no treatment of disease and no treatment of disease' advocated by Chinese medical science. Medical practice finds that: the key to treating chronic diseases lies in early discovery and early prevention, namely: accurate intervention can be performed through chronic disease health management. The chronic disease health intervention refers to medical measures and methods for comprehensively treating risk factors such as adverse behaviors, adverse life styles, ecological environments and personal habits which influence the health of chronic diseases by combining genetic genes. Wherein the precise diagnosis is
The accurate intervention is the key point of health management and is the key point of the comprehensive prevention and treatment of chronic diseases. Health management focuses on "accurate prevention",
namely: when the body of the patient is in a sub-health state, various medical measures are adopted in advance for precise intervention, and further deterioration of diseased organs or tissues is blocked.
In recent years, as the growth momentum of chronic diseases in China is continuously improved, the research on chronic disease prevention and control strategies and measures is also increased in China, but as the pathogenic factors of the chronic diseases are many and the relation among variables is complex, the traditional statistical method is adopted for examination adjustment, monitoring and prevention and control, the tracking period is long, the information amount is large, the internal rules of pathological changes are difficult to find, and the effective implementation of the accurate chronic disease prevention and control measures is seriously influenced.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a chronic disease accurate intervention method and system based on a deep belief network.
In a first aspect, an embodiment of the present invention provides a chronic disease accurate intervention method based on a deep belief network, including the following steps:
step S1, constructing a chronic disease dynamic gradual change index accurate classification system;
step S2, constructing a DBN model and a self-adaptive parameter information structure thereof;
and step S3, extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
Further, in step S1, the system for accurately classifying the chronic disease dynamic gradual change index includes: extracting a chronic disease pathology gradual change feature library, a pathology feature library, an intervention feature library and an accurate intervention strategy library; the accurate intervention strategy library consists of a health intervention scheme set based on chronic disease feature classification.
Further, the precise intervention strategy library comprises: chronic disease diagnosis standard, risk assessment model and accurate intervention model.
Further, in step S2, the bottom layer of the DBN model is formed by stacking multiple layers of limited boltzmann machine frames, the top layer is a BP neural network, the bottom layer algorithm adopts greedy unsupervised learning layer by layer, and the top layer performs supervised learning on the network through labeled data.
Further, the DBN model employs dominant genetic algorithms to determine structure.
Further, in step S3, the DBN learning semantic information feature extraction algorithm is constructed by:
data acquisition and preprocessing: mining and forming an original data set from each data stream of the chronic disease feature classification and risk level evaluation quantization table, then preprocessing the original collected data, and finally dividing the data set into two parts, namely a training sample and test data;
extracting chronic disease DBN characteristic classification parameters based on a multilayer RBM stack: calculating the optimal network structure parameters of the DBN model of the chronic diseases by adopting a genetic dominance evolution algorithm and combining a sample training method, wherein the optimal network structure parameters comprise the number of input layer nodes, the number of hidden layer nodes and the number of hidden layer layers;
determining the weight of the network transmission parameters of the DBN chronic disease accurate intervention model: training a DBN accurate prediction and intervention model by using training data, calculating an error between actual output and target output, expressing the error by using a function related to network weight, adjusting a weight matrix by using a conjugate gradient algorithm, and finally obtaining a network weight matrix with the minimum error function;
and (3) a characteristic semantic information testing stage: inputting the test data into a DBN accurate intervention model, and calculating an accurate intervention result of a chronic disease concept;
and (4) analyzing a prediction result: and comparing the prediction result with the intervention result of the DBN model for the same training data and test data.
In a second aspect, an embodiment of the present invention provides a chronic disease accurate intervention system based on a deep belief network, including:
the classification system construction module is used for constructing a chronic disease dynamic gradual change index accurate classification system;
the model construction module is used for constructing a DBN model and a self-adaptive parameter information structure thereof;
and the extraction model module is used for extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the steps of the method for accurately intervening chronic diseases based on a deep belief network as provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for precise intervention of chronic diseases based on a deep belief network as provided in the first aspect.
The embodiment of the invention provides a chronic disease accurate intervention method and system based on a deep belief network, which creatively apply a deep learning theory to the field of chronic disease management, by monitoring chronic diseases and calculating the model, the epidemic trend and death spectrum of the chronic diseases are mastered, high risk groups and patients of the chronic disease attack are determined, evaluating the status of the target group of chronic diseases, extracting essential characteristics, expressing causal relationship, classifying and grading the relevant data and action result of the chronic diseases according to probability distribution status, analyzing the hidden internal correlation between data by using a correlation data analysis method, and adopts targeted intervention measures to control the prevalence of risk factors of chronic diseases and the occurrence and development of chronic diseases, and the effect of the precise intervention measures of the chronic diseases is continuously evaluated, the efficiency is improved in the precise prevention and control of the chronic diseases, and the cost is saved. Solves the chronic disease prevention and control problem which restricts the improvement of the life quality of human for a long time.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a chronic disease precise intervention method based on a deep belief network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of sample training and practical application of the method according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of a chronic disease precise intervention system based on a deep belief network according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Because the inducement of the chronic diseases has the characteristics of diversity, complexity, long-term, primary and secondary characteristics and progressiveness, the factors such as living environment, living habits, psychological factors, social relationships and the like are combined to correctly understand the pathogenesis of the chronic diseases, a life system is taken as a core to construct a large sample disease case information database, and an efficient data mining technology is selected to discover the intrinsic essential rule for inducing the chronic diseases. The precise medical treatment of the chronic diseases comprises two parts of precise diagnosis and precise prevention and control, wherein the precise diagnosis is the basis. The accurate diagnosis of chronic diseases is to objectively collect and analyze clinical symptoms and signs, discover the biological material basis and biomarkers of the diseases through the technologies of system biology, bioinformatics, big data mining and the like, express the occurrence, development and evolution rules of the biological material basis and biomarkers, and thus objectively and accurately diagnose the diseases. The accurate prevention and control is to adopt effective measures to accurately intervene the evolution of diseases according to the disease evolution rule. The method introduces accurate medical treatment into the field of chronic disease prevention and control, realizes accurate disease classification and diagnosis of chronic diseases, makes a personalized health maintenance, disease prevention, diagnosis and treatment and rehabilitation scheme, analyzes the curative effect and safety mechanism of the novel Chinese and western medical combined medical treatment, and is a challenging system engineering.
The rapid development of the current precise medicine for chronic diseases benefits from the establishment of a large-scale human genome biological database, the rise of high-throughput proteomics, metabonomics and various detection means, and the development of artificial intelligence, computational analysis and large-scale data processing technology. If the work adopts the traditional manual prevention and control mode, the workload is large, the efficiency is low, the coverage is small, and the popularization is difficult or even impossible. With the rapid development of artificial intelligence, data mining, big data, internet of things and cloud super computing technology, the Deep Belief Network (DBN) learning algorithm is widely applied in the fields of artificial intelligence, computer accurate medical treatment and the like, and provides technical support for accurate prevention and control of chronic diseases.
Fig. 1 is a flowchart of a chronic disease accurate intervention method based on a deep belief network according to an embodiment of the present invention, as shown in fig. 1, for complex multivariate chronic disease pathogenic factors, the method employs a DBN algorithm simulating human brain learning, and includes the following steps:
step S1, constructing a chronic disease dynamic gradual change index accurate classification system;
the method aims to research the correlation between chronic diseases and individual pathogenic factors such as genetic genes, social and natural environments, medical conditions, life styles and the like, and because common chronic diseases are subjected to a gradual change process from health to diseases, the process can be very long and usually needs several years to ten years or even decades. Therefore, the pathogenic factors of the chronic disease population need to be tracked, monitored and sample data collected for a long time, the incidence trend and the internal change rule of the chronic disease are analyzed and mastered, the association degree between the risk pathogenic factors and the factors of the chronic disease is screened, and a classification system of the dynamic gradual change indexes of the chronic disease is established.
The system for accurately classifying the dynamic gradual change indexes of the chronic diseases mainly comprises: and extracting a chronic disease pathology gradual change feature library, a pathology feature library, an intervention feature library and an accurate intervention strategy library. The accurate intervention strategy library is the core for constructing an accurate classification system of the chronic disease dynamic gradual change indexes. The accurate intervention strategy library is mainly based on a chronic disease health management sample library, individual genes, constitutions, living environments and habits, physiological health parameters and accurate classification characteristics of chronic diseases, and an accurate intervention scheme set of chronic diseases is constructed. The health intervention scheme set extracts individual medical record features based on the health condition of a patient in a dynamic process, describes the multi-dimensional relationship between potential medicines, prevention and control measures and pathology, and organizes the extracted entity relationship by taking the chronic disease prevention and control problem as the center to form system expression of concepts such as medicines, treatment and examination related to the precise intervention of the chronic disease. The DBN learning model converts a chronic disease dynamic gradual change index accurate classification system into a multi-classification problem, performs characteristic extraction on entity information, converts the entity information into a characteristic vector, performs training learning of a classifier, and can extract potential relations among dimensions such as chronic disease pathological characteristics, intervention strategies, treatment schemes and the like through label learning, so that the chronic disease accurate classification system is gradually enriched. Through data mining, a deep learning network is utilized to simulate a doctor health management diagnosis and analysis method, risk assessment, classification and danger level identification are carried out on chronic patient groups, and automatic classification and accurate intervention of the groups are achieved. The accurate intervention strategy library is a dynamic characteristic library and is enriched and corrected in the continuous learning process of the system.
In step S1 of the embodiment, the precision intervention strategy library includes: chronic disease diagnosis standard, risk assessment model and accurate intervention model. Aiming at solid cases, according to the diagnosis standard of chronic diseases, the risk assessment of the chronic diseases can be divided into three levels, wherein the first level belongs to healthy constitutions, the second level belongs to excessive constitutions, the third level belongs to malignant pathological constitutions, and the accurate intervention of health management is mainly to intervene on the second-level constitutions. And the second level is that before the disease occurs, the physiological characteristic parameters of the individual are extracted through the model, the type and the risk level of the pathological changes are found, and a health management scheme is generated by combining the individual condition with a chronic disease intervention strategy library so as to block the further spread of the disease in the body. The accurate intervention model is a main risk factor aiming at a chronic disease feature classification and risk level evaluation index system, calculates the time-gradient-based functional relation of each related factor to the chronic disease, and scientifically expresses the nonlinear corresponding relation between the type of the chronic disease and accurate intervention.
As shown in fig. 2, step S2, constructing a DBN model and an adaptive parameter information structure thereof;
the Deep Belief Network (DBN) learning algorithm is widely applied to the fields of artificial intelligence, computer accurate medical treatment and the like, and provides technical support for accurate prevention and control of chronic diseases. The DBN is one of artificial neural networks, simulates the learning mechanism of the human brain to explain data, and has the motivation of establishing and simulating the human brain to analyze and learn.
The DBN adopts a multilayer Restricted Boltzmann Machine (RBM) frame, and the interlayer training is adopted to solve the optimal problem of feature extraction, and simultaneously, the classification precision problem is also ensured. The deep learning algorithm is a new technology of an artificial intelligence algorithm, aims to establish and simulate a neural network system for human brain learning, and compared with the traditional neural network, the system mainly comprises a multi-layer network structure of an input layer, a plurality of hidden layers (single layer or multi-layer) and an output layer.
Considering that the medical mechanism of chronic diseases is complex, related influence factors are more, the factors have self-influence and mutual influence, are qualitative and quantitative, and the influence factors and the disease result have nonlinear relation, the formed medical data has the characteristics of diversity, complexity, nonlinearity, overlarge data quantity and the like, so that the traditional BP (back propagation) neural network is a multilayer feedforward neural network trained according to an error back propagation algorithm, and the learning model is not applicable to solving the problem. The DBN is one of deep learning algorithms, and is a deep learning algorithm composed of a multilayer Restricted Boltzmann Machine (RBM) and a BP neural network, wherein the bottom layer is formed by stacking a plurality of RBMs to realize abstract representation of data, the top layer is the BP neural network, the bottom layer algorithm adopts layer-by-layer greedy unsupervised learning to carry out layered learning on the DBN, and then supervised learning is carried out on the network through labeled data on the top layer, so that the reconstruction error of the network is minimized. The DBN learning framework can realize the approximation of complex functions only by a simple network structure through learning a deep nonlinear network structure, and shows the strong capability of learning the essential characteristics of the data set from a large number of unmarked sample sets. Due to the deep hierarchy of the model (usually, hidden layer nodes with 5 layers, 6 layers and even 10 layers), the expression capability is strong, and the characteristics which can better represent large-scale data can be obtained. Therefore, the model has the advantages of processing large-scale data through unsupervised deep learning and being compatible with high-precision identification characteristic data.
The DBN can finally achieve the purposes of knowledge discovery and accurate classification through a mode of combining bottom layer unsupervised learning and top layer supervised parameter adjustment. Meanwhile, the learning algorithm of the DBN can realize the goal of fast learning when processing a large amount of data, thereby improving the efficiency and accuracy of the model. At present, most of disease diagnosis and prevention and control technologies mainly adopt algorithms such as classification and regression, and adopt DBN to hierarchically express original data through unsupervised learning, realize simulation of complex functions through multi-layer propagation, and finally realize the function of learning essential features from a large amount of sample data.
When a DBN model is constructed, two very important parameters need to be determined, namely the number of nodes of a hidden layer and a hidden layer, and the weight of each node is also determined when the number of layers and the number of nodes of a system are calculated. The more hidden layers, the stronger the processing capability, but the lower the calculation efficiency, and the excessive number of nodes of the hidden layers also causes overfitting, thereby causing calculation results to be wrong. The project adopts a self-optimization interlayer characteristic loss transfer function based on sparse characteristic constraint condition limitation to ensure that the interlayer information transfer loss is minimum. The deep learning process is to obtain the representation of the learning features layer by layer, each layer of learning can obtain a new representation, and the new representation can be represented into the original data in a certain way. For the representation of a feature, if the representation is more sparse, the feature is activated by only a few upper nodes, and the feature plays an abstract role to a certain extent. Therefore, the model based on sparse feature constraint condition limitation is selected, and the obtained feature discrimination effect is better. And sparsity of the obtained features of the model can be improved by adjusting the weight of the inter-layer feature loss function. Therefore, the model adopts layered unsupervised learning, and then the network is supervised-learned through labeled data on the top layer, so that the reconstruction error of the network is minimized. And determining the optimal structure of the DBN model by adopting an advantage genetic algorithm according to the inheritance of the characteristics among the layers. When the number of hidden layers of the DBN model is 1, the number of input nodes is set to 10 different values varying between 1 and 10 in the input layer. The number of nodes of the hidden layer is set to five different values, 4, 8, 12, 16 and 20. The result of this setting is: the network prediction effect is more sensitive to the change of the number of the nodes of the hidden layer relative to the change of the number of the input nodes. In the experimental result of the data set, the number of nodes of the input layer and the number of nodes of the hidden layer corresponding to the highest recognition rate are found out by combining the advantage genetic algorithm with the constraint condition of the minimum loss function process, then a new hidden layer is added, and the influence of the change of the number of the nodes in the new hidden layer on the prediction effect is judged, so that the optimal number of the nodes is determined, and the number of layers of the hidden layer is also determined.
For state (v, h), the energy function of the RBM takes the minimum calculation formula:
in the formula: wi,j-weight between the ith node of the explicit layer and the jth node of the implicit layer;
ai-the offset size of the apparent layer node i;
bi-bias size of hidden layer node j.
And the RBM model parameters are theta, W, a and b, and the nodes of the visible layer and the hidden layer are substituted into the formula of the energy function, so that the energy of the whole RBM connection structure can be obtained.
From the Gibbs distribution (Gibbs) it follows: the probability of the RBM in the current state (v, h) is:
the probability can be regarded as the joint probability distribution of the apparent layer state and the hidden layer state, and the edge distribution of the apparent layer state can be obtained according to the joint probability distribution as follows:
and step S3, extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
The method comprises the following steps of constructing a low-probability (RBM) classification model by using a unsupervised greed layer-by-layer algorithm, transmitting the learned weight theta ═ W, a and b } to a DBN to form a chronic disease feature classification library, and using the RBM hidden layer state obtained by training data calculation as input data of the next RBM to further learn the dependency relationship between RBM hidden layer units, repeating the learning process for multiple times, combining the high-probability and loss function minimum constraint conditions of information features in the repeating process, adopting an advantage-propagation algorithm to realize the optimization problem of an optimal path so as to determine the optimal layer structure of a limited Boltzmann machine and learn complex semantic structure information in data, after completing layer-by-layer RBM pre-training, forming a DBM stack to form a DBN, adding a classification layer BP after the top layer-by layer pre-training is completed, forming a self-downward and upward feedforward neural network, and using a backward propagation algorithm for modifying the characteristic-classification algorithm, wherein the RBM hidden layer-classification algorithm is used as an input end of a backward propagation algorithm, the RBM hidden layer-training algorithm, the RBM classification algorithm is selected, the algorithm, the RBM hidden layer algorithm is used as an evolutionary training process, the backward propagation algorithm, the algorithm is selected, the algorithm is used for modifying the algorithm, the algorithm is used for modifying the algorithm for modifying the step of modifying the steps of modifying the steps of:
1) data acquisition and preprocessing. And mining each data stream of the chronic disease feature classification and risk level evaluation quantization table to form an original data set, then preprocessing the original collected data, and finally dividing the data set into two parts, namely a training sample and test data.
2) And extracting chronic disease DBN characteristic classification parameters based on the multilayer RBM stack. And (3) calculating the optimal network structure parameters of the DBN model of the chronic diseases by adopting a genetic advantage evolution algorithm and combining a sample training method, wherein the optimal network structure parameters comprise the number of input layer nodes, the number of hidden layer nodes and the number of layers of hidden layers.
3) And determining the weight of the transmission parameters of the accurate intervention model network for the DBN chronic diseases. Training a DBN accurate prediction and intervention model by using training data, calculating the error of actual output and target output in order to accelerate training, expressing the error by using a function related to network weight W, adjusting a weight matrix by using a conjugate gradient algorithm, and finally obtaining the network weight matrix W with the minimum error function.
4) And a characteristic semantic information testing stage. And inputting the test data into a DBN accurate intervention model, and calculating an accurate intervention result of the chronic disease concept.
5) And (5) analyzing a prediction result. And predicting the same training data and test data by using a classical prediction method, and comparing a prediction result with an intervention result of the DBN model. Accordingly: the learning training core of the DBN model comprises unsupervised autonomous training of a limited Boltzmann machine and supervised training of a BP algorithm. When the DBN model is trained, if all layers of the whole network are trained simultaneously, the time complexity is too high, and if a greedy layer-by-layer learning algorithm is adopted, the problem can be solved.
The semantic information mining algorithm in the DBN based on sample training comprises the following steps: the method comprises the following steps of a semantic abstract genetic optimization algorithm from a low layer to a high layer, high-layer semantic information generation and network middle-layer semantic feature mining, and a semantic information base of each layer is constructed. In the aspect of high-level semantic information generation, how to learn and select a RBM template set extracted from bottom-level feature information under a deep learning framework is researched, then high-level semantic information is generated by adopting a combination function, the obtained high-level semantic information is analyzed by using a DBN (database network), so that finally available high-level semantic information is obtained, and the obtained high-level semantic information is fused into a DBN learning model framework to form an abstract semantic information feature library of a chronic disease health management accurate intervention system.
The representation of the RBM network needs to fit the input data as closely as possible. The calculation is as follows:
let us say for a set of sample sets that satisfy independent co-distributions: d ═ V (1), V (2),. ·, V (n), learning parameter θ ═ { W, a, b }, where S denotes a sample space, q denotes an input sample distribution, q (a) denotes a probability of input sample a, p is an edge distribution expressed by the RBM network, and q and p are KL distances:
for the RBM network, namely, the RBM network is enabled to randomly generate a plurality of (v, h) states, and the probability of the occurrence of the training samples is highest. Selecting a parameter for the probability model to maximize the probability of the current observation sample, wherein the optimization problem is the parameter which maximizes the data likelihood value:
θ*＝argmaxln(P(v；θ)) (13)
because there is no connection between the inside of the explicit layer and the implicit layer, the input variables and the conditional expectation values of the states under the model distribution are given, and the states of the implicit layer nodes are as follows:
P(hj＝1|v)＝σ(bj+∑iviwi,j) (14)
and obtaining the reconstruction state of the apparent layer calculated by the hidden layer according to the CD-k algorithm as follows:
P(vi＝1|h)＝σ(ai+∑ihjwi,j) (15)
where σ (a) ═ 1/(1+ exp (-x)), this is the Sigmoid function.
According to the formula, an approximate gradient can be obtained, and the RBM parameters are updated by using a gradient descent method:
the parameter optimization process of the RBM model can be regarded as a process for minimizing the model energy, namely minimizing the reconstruction error. The weighting parameters are usually updated in a Contrast Divergence (CD) manner during the unsupervised training process, i.e. the weighting parameters are updated
Wherein ε is the learning rate, EdataThe expectation value of the dependent data obtained when the visible state value is taken as the training sample value is taken as the expectation value of the joint probability distribution of the whole network unit; edata(. cndot.) is the expected value for a visible cell in a random binary state.
Based on any one of the above embodiments, fig. 3 is a schematic diagram of a chronic disease precise intervention system based on a deep confidence network according to an embodiment of the present invention, where the system includes:
the classification system construction module is used for constructing a chronic disease dynamic gradual change index accurate classification system;
the model construction module is used for constructing a DBN model and a self-adaptive parameter information structure thereof;
and the extraction model module is used for extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
In summary, the accurate intervention method and system for chronic diseases based on the deep belief network provided by the embodiment of the invention creatively apply the deep learning theory to the field of chronic disease management, grasp the epidemic trend and death spectrum of chronic diseases through the chronic disease monitoring and the calculation of the model, determine the high-risk crowd and patients with chronic disease outbreak, evaluate the conditions of the target crowd of chronic diseases, extract essential characteristics, express causal relationship, classify and grade the relevant data and action results of chronic diseases according to probability distribution conditions, analyze the hidden internal correlation among data by using a correlation data analysis method, and adopt targeted intervention measures, control the prevalence of the risk factors of chronic diseases and the occurrence and development of chronic diseases, continuously evaluate the effect of accurate intervention measures of chronic diseases, improve the efficiency in the aspect of accurate prevention and control of chronic diseases, the cost is saved. Solves the chronic disease prevention and control problem which restricts the improvement of the life quality of human for a long time.
Fig. 4 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. The processor 301 may call a computer program stored on the memory 303 and executable on the processor 301 to perform the methods provided by the above embodiments, for example, including:
step S1, constructing a chronic disease dynamic gradual change index accurate classification system;
step S2, constructing a DBN model and a self-adaptive parameter information structure thereof;
and step S3, extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method provided in the foregoing embodiments when executed by a processor, and the method includes:
step S1, constructing a chronic disease dynamic gradual change index accurate classification system;
step S2, constructing a DBN model and a self-adaptive parameter information structure thereof;
and step S3, extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and of course, can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A chronic disease accurate intervention method based on a deep belief network is characterized by comprising the following steps:
step S1, constructing a chronic disease dynamic gradual change index accurate classification system;
step S2, constructing a DBN model and a self-adaptive parameter information structure thereof;
and step S3, extracting a model based on the semantic feature information of the sample-trained chronic disease health management accurate intervention model.
2. The method for accurately intervening chronic diseases based on the deep belief network of claim 1, wherein in step S1, the system for accurately classifying the dynamic gradual change indexes of chronic diseases comprises: extracting a chronic pathological gradual change feature library, a pathological feature library, an intervention feature library and an accurate intervention strategy library; the accurate intervention strategy library consists of a health intervention scheme set based on chronic disease feature classification.
3. The method for precise intervention of chronic diseases based on deep belief network as claimed in claim 2, wherein the precise intervention strategy library comprises: chronic disease diagnosis standard, risk assessment model and accurate intervention model.
4. The method for accurately intervening chronic diseases based on the deep belief network of claim 3, wherein in the step S2, the bottom layer of the DBN model is formed by stacking a plurality of layers of limited Boltzmann machine frames, the top layer is a BP neural network, the algorithm of the bottom layer adopts layer-by-layer greedy unsupervised learning, and the top layer conducts supervised learning on the network through labeled data.
5. The deep belief network-based precise intervention for chronic diseases as claimed in claim 4, wherein the DBN model employs a dominant genetic algorithm to determine structure.
6. The method for accurately intervening chronic diseases based on the deep belief network as claimed in claim 1, wherein in the step S3, the DBN learning semantic information feature extraction algorithm is constructed by:
data acquisition and preprocessing: mining and forming an original data set from each data stream of the chronic disease feature classification and risk level evaluation quantization table, then preprocessing the original collected data, and finally dividing the data set into two parts, namely a training sample and test data;
extracting chronic disease DBN characteristic classification parameters based on a multilayer RBM stack: calculating the optimal network structure parameters of the DBN model of the chronic diseases by adopting a genetic dominance evolution algorithm and combining a sample training method, wherein the optimal network structure parameters comprise the number of input layer nodes, the number of hidden layer nodes and the number of hidden layer layers;
determining the weight of the network transmission parameters of the DBN chronic disease accurate intervention model: training a DBN accurate prediction and intervention model by using training data, calculating an error between actual output and target output, expressing the error by using a function related to network weight, adjusting a weight matrix by using a conjugate gradient algorithm, and finally obtaining a network weight matrix with the minimum error function;
and (3) a characteristic semantic information testing stage: inputting the test data into a DBN accurate intervention model, and calculating an accurate intervention result of a chronic disease concept;
and (4) analyzing a prediction result: and comparing the prediction result with the intervention result of the DBN model for the same training data and test data.
7. An accurate chronic disease intervention system based on a deep belief network, comprising:
the classification system construction module is used for constructing a chronic disease dynamic gradual change index accurate classification system;
the model construction module is used for constructing a DBN model and a self-adaptive parameter information structure thereof;
and the extraction model module is used for extracting a model based on the semantic feature information of the chronic disease health management accurate intervention model trained by the sample.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for precise intervention of chronic diseases based on deep belief network as claimed in any of the claims 1 to 6.
9. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for precise intervention of chronic diseases based on deep belief networks as claimed in any of the claims 1 to 6.
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