CN113488183B - Heating disease multi-mode feature fusion cognitive system, equipment and storage medium - Google Patents
Heating disease multi-mode feature fusion cognitive system, equipment and storage medium Download PDFInfo
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- G—PHYSICS
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Abstract
The invention discloses a heating disease multi-mode feature fusion cognitive system, equipment and a storage medium, wherein the system comprises: disease knowledge base construction module: the method is used for constructing a heating disease knowledge base according to the related knowledge of the heating disease; and an information mapping module: the method comprises the steps of respectively collecting and normalizing clinical symptom information and physical sign information of the febrile disease, and respectively establishing mapping relations between the clinical symptom characteristic information and physical sign information of the febrile disease and corresponding diseases; an information processing module: the method is used for carrying out high-order characteristic combination, key characteristic screening and multi-level characteristic fusion of similar diseases on the characteristics of the fever diseases; disease cognition module: the method is used for training a classification model based on the fusion characteristics of the multi-level characteristics after fusion, and carrying out fever disease cognition through the classification model. According to the invention, the accuracy of intelligent differential diagnosis and cognitive calculation of the febrile diseases can be improved by fully mining the higher-order dependency relationship among the multi-mode data characteristics of the febrile diseases.
Description
Technical Field
The invention belongs to the technical field of health management, and particularly relates to a heating disease multi-mode feature fusion cognitive system, equipment and a storage medium.
Background
Fever is a very common clinical symptom. The diseases causing fever are many, and the cause of fever is complex, so that the differential diagnosis of fever diseases is difficult, because many diseases may cause fever. The existing intelligent auxiliary diagnosis and treatment system for the febrile diseases is mainly realized through a characteristic information selection system.
Under the condition of small scale in the characteristic information, the system can effectively identify the first-order key characteristic information. However, when the characteristics of the multi-mode data of the febrile disease show ultra-high dimensional characteristics, the existing characteristic selection system is difficult to effectively identify the febrile characteristics closely related to each other from a massive febrile information set, and the cognition accuracy of the febrile disease is affected.
Disclosure of Invention
In view of the above, the invention provides a multi-mode feature fusion cognitive computing system, device and storage medium for a febrile disease, which are used for solving the problem that the febrile features closely related to each other are difficult to identify from a massive febrile information set.
In a first aspect of the invention, a febrile disease multi-modal feature fusion cognitive system is disclosed, the system comprising:
the knowledge base construction module: the method is used for constructing a heating disease knowledge base according to the related knowledge of the heating disease;
and an information mapping module: the method comprises the steps of respectively collecting and normalizing clinical symptom information and sign information of a febrile disease, acquiring characteristic information of each mode data of the clinical symptom and sign of the specific febrile disease, and respectively establishing mapping relations between the clinical symptom characteristic information and sign information of the febrile disease and corresponding diseases;
an information processing module: the method is used for carrying out similar disease feature combination, key feature screening and multi-level feature fusion on the fever disease features according to a fever disease knowledge base, clinical symptom information and sign information of fever diseases;
disease cognition module: the method is used for training a classification model based on the fusion characteristics of the multi-level characteristics after fusion, and carrying out fever disease cognition through the classification model.
Preferably, the related knowledge of the febrile disease includes fever urgency, fever degree, fever time, fever type, fever etiology, fever scope, etc., and corresponding potential febrile disease name and sign information are established.
Preferably, the combination of the similar disease characteristics for the characteristic of the febrile disease specifically comprises:
extracting the characteristic information corresponding to the same fever disease from a fever disease knowledge base, clinical symptom characteristic information and physical sign information of the fever disease and mapping relation of the corresponding disease;
and calculating the correlation between the characteristic information corresponding to the same fever disease by using the nonlinearity, and combining the characteristics with the correlation higher than a preset threshold value to obtain the high-order characteristic information of the fever disease.
Preferably, the key feature screening specifically includes:
and constructing a feature selection model based on recursive feature elimination, inputting the obtained high-order feature information into the feature information selection model, and selecting key features closely related to the disease category labels.
Preferably, the multi-level feature fusion specifically includes:
acquiring key feature types, wherein the types comprise text features, image features and numerical features;
inputting the screened text features into a CRF model for named entity recognition;
and respectively associating the named entities in the text with the screened image features and the attributes of the numerical features through a convolutional neural network to obtain fusion features.
Preferably, the classification model is based on an Adaboost algorithm, a BP neural network is adopted as a weak classifier of the AdaBoost algorithm, and a symbiotic biological search algorithm is adopted to optimize the weight of each weak classifier.
Preferably, the training of the classification model comprises key feature training and fusion feature training; for the key feature types corresponding to each type of diseases, respectively training a weak classifier to realize text classification, image classification and numerical classification; respectively training a plurality of weak classifiers for fusion characteristics corresponding to each type of diseases to realize multi-mode fusion classification; combining all weak classifiers to obtain an AdaBoost integrated classifier.
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) According to the invention, the nonlinear kernel explicit unfolding mode is mainly adopted to unfold and express the characteristic information data of the heating diseases, so that combined high-order characteristic information is obtained, a high-order characteristic information subset with the highest relationship is identified, and by fully mining the high-order dependency relationship among the multi-mode data characteristics of the heating diseases, the system can fully utilize the characteristic information in each mode of the heating diseases to conduct intelligent classification and learning, and the accuracy of intelligent differential diagnosis and cognitive calculation of the heating diseases can be improved.
2) The BP neural network is adopted as a weak classifier of an AdaBoost algorithm, and training of a classification model comprises key feature training and fusion feature training; and respectively training a plurality of weak classifiers for key features and multi-mode fusion features corresponding to each type of diseases, optimizing the weight of each weak classifier by adopting a symbiotic biological search algorithm, combining all weak classifiers to obtain an AdaBoost integrated classifier, and performing classification training on the feature information of the febrile diseases by a Boosting classification method, so that the learning precision of a learner can be improved, and the diagnosis speed in the febrile disease diagnosis process is improved.
Drawings
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 schematic structural diagram of a multi-modal feature fusion cognitive system for febrile diseases according to 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 present invention proposes a multi-modal feature fusion cognitive system for febrile disease, the system comprising: the system comprises a knowledge base construction module 1, an information mapping module 2, an information processing module 3 and a disease cognition module 4;
the knowledge base construction module 1 is configured to establish a corresponding and potential name, sign (including sign) and knowledge of a febrile disease of the febrile disease according to a febrile urgency (acute febrile, chronic febrile, etc.), a febrile degree (low fever, moderate fever, high fever, ultrahigh fever, etc.), a febrile time (one week, half month, one month, half year, etc.), a febrile type (heat retention, relaxation heat, intermittent heat, regression heat, wavy heat, irregular heat, etc.), a febrile cause (infectious febrile, non-infectious febrile, etc.), a febrile scope (limitation, systemic, etc.), and the like.
For example, the cause of fever is: infectious fever includes: 1) Bacterial infection: suppurative tonsillitis, otitis media, lymphadenitis, paranasal sinusitis, bronchitis, bronchopneumonia, lobar pneumonia, epidemic cerebrospinal meningitis, various suppurative meningitis, bacillary dysentery, typhoid fever, scarlet fever, pertussis, various tuberculosis, carbuncle furuncle, abscess, cholecystitis, pyelonephritis, cellulitis, erysipelas, brucellosis, tetanus, etc.; 2) Viral infection: mumps, rubella, measles, varicella, influenza, respiratory tract virus infection, viral hepatitis, poliomyelitis, other enterovirus infection, epidemic encephalitis B, infectious mononucleosis, epidemic hemorrhagic fever, etc.; 3) Protozoal diseases: mycoplasmal pneumonia, malaria, amebic dysentery, amebic liver abscess, and black fever; 4) Spirochete infection: such as leptospirosis, heat regression, etc.; 5) Helminthiasis: such as acute schistosomiasis, filariasis, clonorchiasis sinensis, helminthiasis and the like; 6) Rickettsiosis: such as typhus, tsutsugamushi, Q-heat, etc.; non-infectious fever includes: 1) Connective tissue diseases such as systemic lupus erythematosus, rheumatoid arthritis, vasculitis, polymyositis, rheumatic fever; 2) Malignant tumors, including various malignant solid tumors, leukemia, malignant lymphoma, multiple myeloma and other malignant tumors of the blood system; 3) Aseptic tissue necrosis such as myocardial infarction, pulmonary embolism, large-area burn, internal hemorrhage of tissue injury caused by major surgery, etc.; 4) Endocrine disorders such as hyperthyroidism, thyroid crisis, pheochromocytoma, etc.; 5) Central nervous system diseases such as cerebral hemorrhage, brain trauma, thalamus lesions, etc.; 6) Other physical factors such as heatstroke, radiation disease, autonomic dysfunction, etc.
For another example, taking a heat type of heat generation as an example: (1) the heat retention means that the body temperature is constantly maintained at a high level of 39-40 ℃ or above for days or weeks, the fluctuation range of the body temperature within 24 hours is not more than 1 ℃, and the heat retention is usually in the high fever period of lobar pneumonia, typhus and typhus; (2) the achalasia heat is also called septicemia heat type, and refers to the type of body temperature curve that the body temperature is usually above 39 ℃, the fluctuation range is large, the body temperature fluctuation range exceeds 2 ℃ within 24 hours, but the body temperature is above the normal level, and the body temperature curve is commonly found in septicemia, rheumatic fever, severe tuberculosis, suppurative inflammation and the like; (3) intermittent fever, rapid rise of body temperature up to peak and then lasting for several hours, rapid decrease to normal level, and no-heat period (intermittent period) lasting for 1 to several days, which repeatedly and alternately occur in malaria, acute pyelonephritis, etc.; (4) regression heat refers to the type of body temperature curve in which the body temperature rises sharply to 39 ℃ or above, and then drops suddenly to normal level after lasting for several days, and the period of high heat and the period of no heat each last for several days and alternate regularly, which can be seen in regression heat, hodgkin's disease, etc.; (5) the temperature of the wavy heat rises gradually to 39 ℃ or above, then falls to the normal level gradually after a plurality of days, and rises gradually after a plurality of days, thus repeating the process for a plurality of times. Common in brucellosis; (6) irregular fever and irregular body temperature curve can be seen in tuberculosis, rheumatic fever, bronchopneumonia, exudative pleurisy, etc.
The information mapping module 2 is used for respectively collecting and normalizing the clinical symptom information and the sign information of the febrile disease, obtaining the characteristic information of each mode data of the clinical symptom and sign of the specific febrile disease, respectively establishing a mapping relation database (comprising a dictionary and a word stock of the corresponding characteristic information of the corresponding disease) of the clinical symptom characteristic information and sign information of the febrile disease, and establishing a space vector set of the clinical symptom characteristic information of the febrile disease. The method comprises the steps of carrying out a first treatment on the surface of the
The method comprises the steps of collecting and normalizing the characteristic information of clinical symptoms of the febrile disease, obtaining the characteristic information of each mode data of the clinical symptoms of the specific febrile disease, and establishing a database of the mapping relation between the characteristic information of the clinical symptoms of the febrile disease and the corresponding diseases. For example, clinical symptoms in the rising phase of body temperature are: fatigue, discomfort, muscle cramps, pale skin, dryness without perspiration, aversion to cold or chills, etc.; high heat duration: flushing, burning, accelerating and deepening breathing, sweating and the like; period of body temperature decline: perspiration and skin wetness. The symptoms are gradually improved; in particular, bacterial pneumonia is exemplified by its fever type: acute fever, persistent hyperpyrexia and rapid progress; the main symptoms are as follows: cough, expectoration, purulent sputum, chest pain, dyspnea; accompanying symptoms: headache, muscular soreness, limb dampness and coldness, debilitation, nausea, emesis, abdominal distention, diarrhea, nervous system symptoms such as somnolence, consciousness disturbance, convulsion, etc. can occur in a small number of patients with severe symptoms; taking viral laryngitis as an example: heat type: acute fever and aversion to cold; the main symptoms are as follows: diffuse congestion and swelling of the mucous membrane of the throat, red vocal cords, barking cough, pain during cough, hoarseness and difficult speaking; accompanying symptoms: dysphoria, listlessness, debilitation, cold limbs, pale complexion, rapid and thin pulse, reduced blood pressure, and enlarged cervical lymph node.
The method comprises the steps of collecting and normalizing the physical sign information of the fever, obtaining the characteristic information of each mode data of the physical sign of the specific fever, establishing a database (comprising a dictionary corresponding to each physical sign information and a word stock corresponding to the physical sign information of the fever and the mapping relation of the physical sign information of the fever and the physical sign information of the disease), and establishing a characteristic information space vector set of each mode data of the physical sign of the fever. Such as facial appearance, apathy: is found in typhoid fever; drunk appearance: is found in epidemic hemorrhagic fever; such as joint swelling and pain, which is often caused by septicemia, rheumatic fever, connective tissue diseases, etc.; for another example, heart rate increases >15 times/min for hyperthyroidism, rheumatic fever, heart failure with infection, myocarditis, etc.; the relative slow pulse is found in typhoid fever, central infection, low nails, pseudo-fever and the like;
the information processing module 3 is used for carrying out similar high-order feature combination, key feature screening and multi-level feature fusion on the characteristics of the fever diseases according to the fever disease knowledge base, the clinical symptom information and the sign information of the fever diseases;
specifically, the information processing module 3 specifically includes;
the high-order feature combination unit is used for extracting feature information corresponding to the same fever disease from a fever disease knowledge base, clinical symptom feature information and physical sign information of the fever disease and mapping relations of the corresponding diseases; and carrying out nonlinear kernel explicit expansion representation on the high-order relation among the features by utilizing a polynomial kernel function in nonlinear degree calculation, carrying out dimension expansion on the preprocessed data, mapping low-order features into a high-order feature space to obtain combined high-order disease features, calculating the linear correlation between feature information corresponding to the same type of fever, combining features with the linear correlation higher than a preset threshold, and identifying a high-order feature subset with the closest relation with the disease from the feature combination to obtain the high-order feature information of the fever. Specifically, a polynomial kernel function is defined, recombination is carried out according to a polynomial theory, polynomial expansion is carried out to display feature mapping, linear correlation among features is identified, and features with linear correlation higher than a preset threshold are combined into high-order feature information of the fever.
The key feature screening unit is used for constructing a feature selection model based on recursive feature elimination, inputting the obtained high-order feature information into the feature information selection model, and selecting key features closely related to the disease category labels.
The multi-level feature fusion unit is used for acquiring key feature types, wherein the types comprise text features, image features and numerical features; inputting the screened text features into a CRF model for named entity recognition; and fusing the text features, the image features and the numerical features through a convolutional neural network, and respectively associating named entities in the text with the screened image features and the attribute of the numerical features to obtain the fused features.
The disease cognition module 4 is used for training a classification model based on the fusion characteristics after multi-level characteristic fusion and carrying out fever disease cognition through the classification model.
Specifically, the classification model is based on an Adaboost algorithm, BP neural network is adopted as a weak classifier of the AdaBoost algorithm, and symbiotic biological search algorithm is adopted to optimize the weight of each weak classifier.
The training of the classification model comprises key feature training and feature fusion training; for the key feature types corresponding to each type of diseases, respectively training a weak classifier to realize text classification, image classification and numerical classification; respectively training a plurality of weak classifiers for fusion characteristics corresponding to each type of diseases to realize multi-mode fusion classification; combining all weak classifiers to obtain an AdaBoost integrated classifier.
The Adaboost algorithm can be divided intoThree steps: 1) First, the weight distribution D1 of the training data is initialized. Assuming that there are N training sample data, each training sample is given the same weight at the beginning: w (w) i =1/N; 2) Then, training the weak classifier h i . The specific training process is as follows: if a training sample point is found, the training sample point is subjected to weak classifier h i Accurately classifying, then in constructing the next training set, the weight corresponding to the training set is reduced; conversely, if a training sample point is misclassified, its weight should be increased. The sample set with updated weight is used for training the next classifier, and the whole training process is iteratively performed in this way; 3) And finally, combining the weak classifiers obtained by each training into a strong classifier. After the training process of each weak classifier is finished, the weight of each weak classifier is optimized by adopting a symbiotic biological search algorithm, the weight of the weak classifier with small classification error rate is further increased, so that the weak classifier has a larger decision function in a final classification function, and the weight of the weak classifier with large classification error rate is reduced, so that the weak classifier has a smaller decision function in the final classification function. In other words, weak classifiers with low error rates are weighted more heavily in the final classifier, and are otherwise smaller.
For example, fever is acute fever, infectious fever, and is high fever, lasting three days, chills, splenomegaly, etc., or more distant auxiliary features such as headache, dry mouth, etc., which are weighted differently in correspondence with different diseases. I.e. it changes and is dynamic. And training the next base learner based on the adjusted sample distribution, repeating the steps until the number of the base learners reaches a preset value T, finally carrying out weighted combination on the T learners, wherein the training set of each round is unchanged, only the weight of each sample in the training set is changed in the classifier, the weight is adjusted according to the classification result of the previous round, and the weight of the sample is continuously adjusted according to the error rate, wherein the greater the error rate is, the greater the weight is. Each weak classifier has a corresponding weight, and a classifier with small classification errors has a larger weight. The individual prediction functions can only be generated sequentially, since the latter model parameters require the results of the previous round of model.
The invention also discloses a computer readable storage medium storing computer instructions that cause a computer to implement all or part of the steps of the system of 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 (3)
1. A febrile disease multi-modal feature fusion cognitive system, the system comprising:
the knowledge base construction module: the method is used for constructing a heating disease knowledge base according to the related knowledge of the heating disease;
and an information mapping module: the method comprises the steps of respectively collecting and normalizing clinical symptom information and sign information of a febrile disease, acquiring characteristic information of each mode data of the clinical symptom and sign of the specific febrile disease, and respectively establishing mapping relations between the clinical symptom characteristic information and sign information of the febrile disease and corresponding diseases;
an information processing module: the method is used for carrying out similar disease high-order feature combination, key feature screening and multi-level feature fusion on the fever disease features according to a fever disease knowledge base, clinical symptom information and sign information of the fever disease;
disease cognition module: the method is used for training a classification model based on the fusion characteristics after multi-level characteristic fusion, and carrying out fever disease cognition through the classification model;
the related knowledge of the fever diseases comprises fever urgency, fever degree, fever time, fever type, fever etiology and fever scope, and corresponding and potential fever disease names and sign information are established;
the high-order characteristic combination for the similar diseases of the fever disease comprises the following specific steps:
extracting the characteristic information corresponding to the same fever disease from a fever disease knowledge base, clinical symptom characteristic information and physical sign information of the fever disease and mapping relation of the corresponding disease;
carrying out nonlinear kernel explicit expansion representation on the high-order relation among the features by utilizing nonlinear degree calculation, calculating linear correlation among feature information corresponding to the same heating disease, and combining features with correlation higher than a preset threshold value to obtain high-order feature information of the heating disease;
the key feature screening specifically comprises the following steps:
constructing a feature selection model based on recursive feature elimination, inputting the obtained high-order feature information into the feature information selection model, and selecting key features closely related to the disease category labels;
the multi-level feature fusion specifically comprises:
acquiring key feature types, wherein the types comprise text features, image features and numerical features;
inputting the screened text features into a CRF model for named entity recognition;
the named entities in the text are respectively associated with the screened image features and the attributes of the numerical features through a convolutional neural network to obtain fusion features;
the classification model is based on an Adaboost algorithm, a BP neural network is adopted as a weak classifier of the AdaBoost algorithm, and a symbiotic biological search algorithm is adopted to optimize the weight of each weak classifier;
the training of the classification model comprises key feature training and feature fusion training;
for the key feature types corresponding to each type of diseases, respectively training a weak classifier to realize text classification, image classification and numerical classification;
respectively training a plurality of weak classifiers for fusion characteristics corresponding to each type of diseases to realize multi-mode fusion classification;
combining all weak classifiers to obtain an AdaBoost integrated classifier.
2. 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 claim 1.
3. A computer-readable storage medium storing computer instructions that cause the computer to implement the system of claim 1.
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