CN113892909B - Intelligent chronic disease screening system based on cognitive state - Google Patents
Intelligent chronic disease screening system based on cognitive state Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4082—Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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Abstract
The invention discloses an intelligent chronic disease screening system based on cognitive state, which comprises: and a data acquisition module: the method comprises the steps of acquiring a chronic disease clinical diagnosis guideline data set and an expert consensus data set formed by an expert in a chronic disease diagnosis and treatment process; and a data processing module: the method comprises the steps of performing fuzzy cluster analysis on the same type of chronic disease parameters in an expert consensus data set and a clinical diagnosis guide data set to obtain a chronic disease cognitive diagnosis parameter subset; and a model generation module: training a maximum entropy model based on the chronic disease cognitive diagnosis parameter subset to generate a pre-classification model of the chronic disease; and an intelligent screening module: the intelligent screening method is used for intelligently screening the chronic diseases according to the chronic disease cognitive diagnosis model. The invention can carry out the fused cognitive diagnosis of the chronic diseases by acquiring the consensus parameter subset formed by experts in different fields in the diagnosis and treatment process of the chronic diseases and the data parameter subset set related to the clinical diagnosis guide.
Description
Technical Field
The invention belongs to the field of artificial intelligence, and particularly relates to an intelligent chronic disease screening system based on cognitive state.
Background
Chronic diseases are general names of chronic non-infectious diseases, but are general names of diseases which are hidden from a disease, long in course, and not prolonged, lack of evidence of exact infectious biological etiology, complicated etiology, and some of which are not completely confirmed. Common chronic diseases mainly include cardiovascular and cerebrovascular diseases, cancers, diabetes mellitus, chronic respiratory diseases and the like, and the central cerebrovascular diseases comprise hypertension, cerebral apoplexy and coronary heart disease. Medical science is a science, and all modern medical science departments are exploring chronic diseases, but many causes of the chronic diseases are not yet ascertained, and insufficient cognition or cognition defects on the chronic diseases can lead to failure to timely diagnose the chronic diseases.
Disclosure of Invention
In view of the above, the invention provides an intelligent screening system for chronic diseases based on cognitive status, which is used for solving the problem of difficult diagnosis caused by insufficient cognition for the chronic diseases.
In a first aspect of the invention, an intelligent screening system for chronic diseases based on cognitive status is disclosed, the system comprising:
and a data acquisition module: acquiring a chronic disease clinical diagnosis guide data set and an expert consensus data set formed by an expert in the chronic disease diagnosis and treatment process;
and a data processing module: performing fuzzy cluster analysis on the same type of chronic disease parameters in the expert consensus data set and the clinical diagnosis guide data set to obtain a chronic disease cognitive diagnosis parameter subset;
and a model generation module: training a maximum entropy model based on the chronic disease cognitive diagnosis parameter subset to generate a chronic disease cognitive diagnosis model;
and an intelligent screening module: and carrying out intelligent screening on the chronic diseases according to the chronic disease cognitive diagnosis model.
Preferably, the data processing module includes:
feature extraction unit: setting n symptoms and signs corresponding to the same type of chronic diseases, and respectively extracting the characteristics of the n symptoms and signs to form n characteristic vectors x j ，j＝1,2,…,n；
A blurring calculation unit: initializing the membership matrix U with a random number having a value between 0 and 1 to obtain the element U ij Satisfying the requirements
Is a constraint in (a);
calculating c clustering centers c i ：
Wherein i=1, …, c; m represents ambiguity; d, D j Is x j A feature vector set in k-neighborhood of (2); g j For the degree of association of a single eigenvector with a disease, when g j Lambda > 0.5 j =1, otherwise λ j ＝0；
Membership updating unit: calculating a new membership matrix u ij ：
Inputting the new membership matrix into a fuzzy calculation unit;
a value calculation unit: according to
Calculating a cost function value, wherein J represents a cost function, d ij ＝||c i -x j The I is the ith cluster center c i And the j-th data feature vector x j Euclidean distance between them; if the value of the cost function is less than the preset threshold, the algorithm stops.
Preferably, the data processing module is further configured to correct data in each cluster category in the fuzzy cluster analysis result, combine features with the same or high similarity, obtain a common parameter subset of a certain type of chronic disease corresponding to different data sets, use the common parameter subset as a chronic disease cognitive parameter subset set, and generate a mapping relationship with the chronic disease category.
Preferably, the training the maximum entropy model based on the chronic disease cognitive diagnosis parameter subset specifically comprises: by a characteristic function f i (x, y) represents the correspondence between sample input x and output y in the cognitive diagnostic parameter subset of chronic disease, f i (x, y) is a binary function, the value is 1 under the condition that x and y meet, otherwise, the value is 0;
the formalized structure of the maximum entropy model is expressed as:
p (y|x) is the required conditional probability distribution, f i (x, y) is a feature function; w (w) i As a characteristic function f i (x, y); z is Z w (x) Is a normalization factor.
Preferably, the characteristic function f is calculated by IIS algorithm i Weights w of (x, y) i And performing iterative updating.
Preferably, the intelligent screening module specifically includes:
and classifying the input chronic disease parameter subset to be identified through the chronic disease cognitive diagnosis model to obtain the corresponding relation between the chronic disease parameter subset to be identified and the chronic disease, and matching and outputting the corresponding chronic disease and solution through the maximum entropy model.
Preferably, the system further comprises a matching output module: the method is used for weighting external factors such as gender, age and the like of the chronic disease patient as a characteristic factor or variable of the identification model, taking the influence of physical factors of the patient on disease judgment into consideration, establishing the association relationship between the gender, age and the like of the patient and the matched output disease, and guaranteeing the accuracy and reliability of the identification result.
In a second aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete communication with each other through the bus;
the memory stores program instructions executable by the processor that the processor invokes to implement the system according to the first aspect of the present invention.
In a third aspect of the present invention, a computer-readable storage medium is disclosed, the computer-readable storage medium storing computer instructions that cause the computer to implement the system according to the first aspect of the present invention.
Compared with the prior art, the invention has the following beneficial effects:
1) The method comprises the steps of performing fuzzy clustering analysis on data and performing fused cognitive diagnosis through a maximum entropy model by acquiring a consensus parameter subset and a clinical diagnosis guideline related data parameter subset set formed by experts in different fields in the diagnosis and treatment process of the chronic diseases, and performing intelligent screening of the chronic diseases according to the chronic disease cognitive diagnosis model; the method can enrich disease data sets, expand the cognitive range of diseases, realize chronic disease cognition under the condition of no clinical experience or limited clinical experience, improve the efficiency of chronic disease screening and diagnosis, and supplement insufficient cognition and cognitive deficiency of medical workers for chronic diseases;
2) And carrying out fuzzy clustering analysis on the same type of chronic disease parameters in the expert consensus data set and the clinical diagnosis guide data set, introducing a correlation degree factor and neighborhood calculation in fuzzy calculation, improving the accuracy of fuzzy clustering, finally obtaining a chronic disease cognitive diagnosis parameter subset, further correcting data in each clustering category in a fuzzy clustering analysis result on the basis, effectively removing noise interference, improving the accuracy of training samples, and further improving the classification accuracy of disease screening.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a cognitive state-based chronic disease intelligent screening system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Referring to fig. 1, the invention discloses a cognitive state-based chronic disease intelligent screening system, which comprises a data acquisition module 100, a data processing module 200, a model generation module 300, an intelligent screening module 400 and a matching output module 500.
The data acquisition module 100 is used for acquiring a chronic disease clinical diagnosis guide data set and an expert consensus data set formed by an expert in the chronic disease diagnosis and treatment process;
this example illustrates a specific embodiment of the present invention, taking parkinson's disease, a common disease among chronic diseases, as an example.
First, a data set of a clinical diagnosis guideline for parkinson's disease is acquired. Clinical diagnostic guidelines data Parkinson's Disease (PD) is predominantly manifested by: 1) Symptoms of exercise: bradykinesia, myotonia, resting tremor, abnormal postural gait, etc.; 2) Non-motor symptoms: cognitive/mental abnormalities, sleep disorders, autonomic dysfunction, sensory disorders, and the like;
and then acquiring a parkinsonism rehabilitation expert consensus data set. Parkinson's Disease (PD) in expert consensus data is mainly manifested as: 1) Motor symptoms: bradykinesia, resting tremor, muscle stiffness, postural gait disorder, and the like; 2) Non-motor symptoms: cognitive mood disorders, sleep disorders, abnormal urination, pain, fatigue, and the like.
The data processing module 200 is configured to perform fuzzy cluster analysis on the same type of chronic disease parameters in the expert consensus data set and the clinical diagnosis guideline data set to obtain a chronic disease cognitive diagnosis parameter subset;
the data processing module specifically comprises a feature extraction unit 201, a fuzzy calculation unit 202, a membership degree updating unit 203, a value calculation unit 204 and a data correction unit 205.
The feature extraction unit 201 is configured to perform feature extraction on n symptoms and signs corresponding to the same chronic disease, to form n feature vectors x j ，j＝1,2,…,n；
The fuzzy calculation unit 202 is used for initializing the membership matrix U by using the random number with the value between 0 and 1, so that the element U ij Satisfying the requirements
Is a constraint in (a); calculating c clustering centers c i ：
Wherein i=1, …, c; m represents ambiguity; d, D j Is x j A feature vector set in k-neighborhood of (2); g j As the degree of association of a single feature vector with a disease, when g j Lambda > 0.5 j =1, otherwise λ j ＝0；
The membership degree updating unit 203: for calculating a new membership matrix u ij ：
Inputting the new membership matrix into a fuzzy calculation unit;
the value calculation unit 204 is configured to calculate a value according to the formula
Calculating a cost function value, wherein J represents a cost function, d ij ＝||c i -x j The I is the ith cluster center c i And the j-th data feature vector x j Euclidean distance between them; if the value of the cost function is less than the preset threshold, the algorithm stops.
The method comprises the steps of performing fuzzy clustering analysis on chronic disease parameters of the same type in an expert consensus data set and a clinical diagnosis guideline data set, and introducing a degree factor g of association of a single feature vector and a disease into fuzzy clustering calculation j Adjusting the control parameter lambda by the correlation degree factor j Thereby more accurately calculating the cluster center c i Membership matrix u ij The cost function J improves the accuracy of fuzzy clustering analysis, finally obtains a cognitive diagnosis parameter subset of the chronic diseases, and is based on the cognitive diagnosis parameter subsetThe data in each clustering category in the fuzzy clustering analysis result is further corrected, so that noise interference can be effectively removed, the accuracy of training samples is improved, and the disease screening classification accuracy is further improved.
The data correction unit 205 is configured to correct data in each cluster category in the fuzzy cluster analysis result, combine features with the same or high similarity, obtain a common parameter subset of a certain type of chronic disease corresponding to different data sets, use the common parameter subset as a chronic disease cognitive parameter subset set, and generate a mapping relationship with the chronic disease category.
For example, for parkinson's disease, the common parameter subset after the data set of the diagnosis guideline for parkinson's disease and the consensus data set of the rehabilitation expert for parkinson's disease are corrected by the data correction unit is: parkinson's Disease (PD) major clinical manifestations: 1) Symptoms of exercise: bradykinesia (bradykinesia), myotonia (or muscle stiffness), resting tremor, abnormal postural gait (or postural gait disorder), etc.; 2) Non-motor symptoms: cognitive/mental abnormalities (or cognitive mood disorders), sleep disorders, autonomic dysfunction, sensory disorders (or pain and fatigue), abnormal urination and defecation, and the like.
According to the invention, through acquiring the consensus parameter subset formed by experts in different fields in the diagnosis and treatment process of the chronic diseases and the clinical diagnosis guideline related data parameter subset set, the disease data set can be enriched, the cognitive range of the diseases is enlarged, the cognitive diagnosis of the data based on fusion is carried out through fuzzy cluster analysis of the data, and the intelligent screening of the chronic diseases is carried out according to the cognitive diagnosis model of the chronic diseases. The invention can still realize accurate chronic disease cognition under the condition of no clinical experience or limited clinical experience, can improve the efficiency of chronic disease screening and diagnosis, and can supplement insufficient cognition and cognition defects of medical workers for chronic diseases.
The model generation module 300 is configured to train a maximum entropy model based on the chronic disease cognitive diagnosis parameter subset, and generate a pre-classification model of the chronic disease;
the maximum entropy model is a classification model based on the maximum entropy principle, for a random variable x, the probability distribution of the random variable x is P (x), and in general, countless P (x) exist under constraint conditions, and the maximum entropy principle is that the maximum entropy model is the optimal model in P (x) conforming to all constraint conditions.
Specifically, by the characteristic function f i (x, y) represents the correspondence between the input x and the output y of samples in the cognitive diagnosis parameter subset of chronic diseases, i=1, 2, …, k, k is the total number of samples, f i (x, y) is a binary function, the value is 1 under the condition that x and y meet, otherwise, the value is 0;
the formalized structure of the maximum entropy model is expressed as:
f i (x, y) is a feature function; w (w) i As a characteristic function f i (x, y); z is Z w (x) Is a normalization factor.
The characteristic function f in the maximum entropy model is calculated by an IIS (Improved Iterative Scaling) algorithm i Weights w of (x, y) i And performing iterative updating. The IIS algorithm is an optimization algorithm for maximum entropy model learning, and the lower bound is optimized by two inequality deformations, so that the algorithm iterates to convergence.
The intelligent screening module 400 is configured to perform intelligent screening of chronic diseases according to the cognitive diagnosis model of chronic diseases.
The method is used for classifying the input chronic disease parameter subsets to be identified through the chronic disease cognitive diagnosis model to obtain the corresponding relation between the parameter subset sets and the chronic diseases, and matching and outputting the corresponding chronic diseases and solutions through the maximum entropy model.
The set of chronic disease parameters may include a plurality of chronic diseases. For example, pulmonary embolism, besides hemoptysis, patients are accompanied with symptoms of mental stress, chest pain and chest distress, cough, palpitation, cold limbs, sweating and other physical signs of dyspnea, pale complexion, dysphoria and cyanosis or acute right heart dysfunction with fever, weak pulse, heart rate increase, precordial galloping, wet royalty in the lung, jugular vein anger, liver swelling and other symptoms; for example, chronic bronchitis is a chronic nonspecific inflammation of the trachea, bronchus mucosa and surrounding tissues, and mostly has slow onset, long course and exacerbation caused by repeated acute attacks, and the main clinical symptoms are cough, expectoration or asthma. The disease is continued for three months each year, and two or more years are continued, so that other diseases which can cause cough, expectoration, wheezing or chest distress can be discharged, and the diagnosis can be realized.
The matching output module 500 is configured to weight external factors such as gender and age of the chronic disease patient as a feature factor or variable of the recognition model, and take into consideration the influence of physical factors of the patient on disease judgment, establish an association relationship between the gender, age, and the like of the patient and the matching output disease, and ensure accuracy and reliability of the recognition result.
The invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the memory stores program instructions executable by the processor that the processor invokes to implement the aforementioned methods of the present invention.
The invention also discloses a computer readable storage medium storing computer instructions for causing a computer to implement all or part of the steps of the methods of the embodiments of the invention. The storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic or optical disk, or other various media capable of storing program code.
The system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, i.e., may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (8)
1. A cognitive state-based chronic disease intelligent screening system, the system comprising:
and a data acquisition module: the method comprises the steps of acquiring a chronic disease clinical diagnosis guideline data set and an expert consensus data set formed by an expert in a chronic disease diagnosis and treatment process;
and a data processing module: the method comprises the steps of performing fuzzy cluster analysis on the same type of chronic disease parameters in an expert consensus data set and a clinical diagnosis guide data set to obtain a chronic disease cognitive diagnosis parameter subset;
and a model generation module: training a maximum entropy model based on the chronic disease cognitive diagnosis parameter subset to generate a chronic disease cognitive diagnosis model;
and an intelligent screening module: the intelligent screening method is used for intelligently screening chronic diseases according to the chronic disease cognitive diagnosis model;
the data processing module comprises:
feature extraction unit: setting n symptoms and signs corresponding to the same type of chronic diseases, and respectively extracting the characteristics of the n symptoms and signs to form n characteristic vectors x j ，j＝1,2,…,n；
A blurring calculation unit: initializing the membership matrix U with a random number having a value between 0 and 1 to obtain the element U ij Satisfying the requirements
In (a) and (b)Constraint conditions; calculating c clustering centers c i ：
Wherein i=1, …, c; m represents ambiguity; d (D) j Is x j A feature vector set in k-neighborhood of (2); g j For the degree of association of a single eigenvector with a disease, when g j Lambda > 0.5 j =1, otherwise λ j ＝0；
Membership updating unit: calculating a new membership matrix u ij ：
Inputting the new membership matrix into a fuzzy calculation unit;
a value calculation unit: according to
Calculating a cost function value, wherein J represents a cost function, d ij ＝||c i -x j The I is the ith cluster center c i And the j-th data feature vector x j Euclidean distance between them; if the value of the cost function is less than the preset threshold, the algorithm stops.
2. The cognitive state-based chronic disease intelligent screening system according to claim 1, wherein the data processing module is further configured to correct data in each cluster category in the fuzzy cluster analysis result, combine features with the same or high similarity, obtain a common parameter subset of a certain type of chronic disease corresponding to different data sets, use the common parameter subset as a chronic disease cognitive parameter subset set, and generate a mapping relationship with chronic disease types.
3. The cognitive state-based chronic disease intelligent screening system of claim 2, wherein the training of the maximum entropy model based on the subset of chronic disease cognitive diagnostic parameters specifically comprises:
by a characteristic function f i (x, y) represents the correspondence between sample input x and output y in the cognitive diagnostic parameter subset of chronic disease, f i (x, y) is a binary function, the value is 1 under the condition that x and y meet, otherwise, the value is 0;
the formalized structure of the maximum entropy model is expressed as:
f i (x, y) is a feature function; w (w) i As a characteristic function f i (x, y); z is Z w (x) Is a normalization factor.
4. The cognitive state-based chronic disease intelligent screening system according to claim 3, wherein the feature function f is determined by IIS algorithm i Weights w of (x, y) i And performing iterative updating.
5. The cognitive state-based chronic disease intelligent screening system of claim 4, wherein the intelligent screening module specifically comprises:
and classifying the input chronic disease parameter subset to be identified through the chronic disease cognitive diagnosis model to obtain the corresponding relation between the chronic disease parameter subset to be identified and the chronic disease, and matching and outputting the corresponding chronic disease and solution through the maximum entropy model.
6. The cognitive state-based chronic disease intelligent screening system of claim 1, further comprising a matching output module: the method is used for weighting the sex and age external factors of the chronic disease patient as a characteristic factor or variable of the identification model, taking the influence of physical factors of the patient on disease judgment into consideration, establishing the association relationship between the sex and age of the patient and the matched output disease, and guaranteeing the accuracy and reliability of the identification result.
7. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the system of any of claims 1-6.
8. A computer readable storage medium storing computer instructions that cause the computer to implement the system of any one of claims 1-6.
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