CN111292852A - Encephalitis and meningitis intelligent auxiliary diagnosis system based on random forest algorithm - Google Patents
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- 201000009906 Meningitis Diseases 0.000 title claims abstract description 69
- 206010014599 encephalitis Diseases 0.000 title claims abstract description 61
- 238000003745 diagnosis Methods 0.000 title claims abstract description 52
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 46
- 238000007637 random forest analysis Methods 0.000 title claims abstract description 37
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 17
- 210000001175 cerebrospinal fluid Anatomy 0.000 claims abstract description 15
- 201000010099 disease Diseases 0.000 claims abstract description 10
- 238000010801 machine learning Methods 0.000 claims abstract description 10
- 230000003993 interaction Effects 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 17
- 210000004369 blood Anatomy 0.000 claims description 8
- 239000008280 blood Substances 0.000 claims description 8
- 108060003951 Immunoglobulin Proteins 0.000 claims description 5
- 102000018358 immunoglobulin Human genes 0.000 claims description 5
- 238000003759 clinical diagnosis Methods 0.000 claims description 4
- 208000014912 Central Nervous System Infections Diseases 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 3
- 238000002790 cross-validation Methods 0.000 claims description 3
- 238000003748 differential diagnosis Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 229940099472 immunoglobulin a Drugs 0.000 claims description 3
- 229940027941 immunoglobulin g Drugs 0.000 claims description 3
- 210000000265 leukocyte Anatomy 0.000 claims description 3
- 238000009593 lumbar puncture Methods 0.000 claims description 3
- 210000004698 lymphocyte Anatomy 0.000 claims description 3
- 210000000440 neutrophil Anatomy 0.000 claims description 3
- 238000000611 regression analysis Methods 0.000 claims description 3
- 238000004062 sedimentation Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 102000009265 Cerebrospinal Fluid Proteins Human genes 0.000 claims description 2
- 108010073496 Cerebrospinal Fluid Proteins Proteins 0.000 claims description 2
- 210000001616 monocyte Anatomy 0.000 claims description 2
- UXOWGYHJODZGMF-QORCZRPOSA-N Aliskiren Chemical compound COCCCOC1=CC(C[C@@H](C[C@H](N)[C@@H](O)C[C@@H](C(C)C)C(=O)NCC(C)(C)C(N)=O)C(C)C)=CC=C1OC UXOWGYHJODZGMF-QORCZRPOSA-N 0.000 claims 2
- 229960004601 aliskiren Drugs 0.000 claims 2
- 238000004393 prognosis Methods 0.000 abstract description 4
- 208000014644 Brain disease Diseases 0.000 description 3
- 208000032274 Encephalopathy Diseases 0.000 description 3
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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Abstract
The invention discloses an intelligent auxiliary diagnosis system for encephalitis and meningitis based on a random forest algorithm, which comprises a server and a client side, wherein the server is connected with the client side; be equipped with encephalitis, meningitis intelligence auxiliary diagnosis software package in the server, the software package contains: an expert system based on medical knowledge, which comprises an encephalitis and meningitis disease weight scoring model and a rule knowledge base for encephalitis and meningitis diagnosis; the intelligent system based on the machine learning algorithm comprises a random forest algorithm; the client is provided with a human-computer interaction interface, and the server comprises an interpreter and a comprehensive processor. The invention can comprehensively utilize the limited clinical data of the patient under the condition that the etiology evidence of cerebrospinal fluid does not exist in the early stage of the disease, quickly analyze the etiology of the encephalitis patient and the meningitis patient with higher accuracy and provide an auxiliary decision scheme for a clinician in time; the clinician can give more accurate and reasonable treatment scheme to the patient according to the causes of encephalitis and meningitis, so that the fatality rate is reduced, and the prognosis of the patient is improved.
Description
Technical Field
The invention belongs to the technical field of computer-aided diagnosis, and relates to an intelligent encephalitis and meningitis aided diagnosis system based on a random forest algorithm.
Background
Meningitis refers to inflammatory changes caused by invasion of the central nervous system by various factors such as bacteria, viruses, fungi, spirochetes, protozoa, rickettsiae, tumors, autoimmunity and the like, and is a critical illness in neurology. Has extremely high lethal disability rate, and causes about 135000 people to die every year. However, due to the characteristics of complex etiology, rapid disease progression, atypical early clinical manifestations of patients, low laboratory test positive rate and the like, early clinical diagnosis is difficult, so that targeted treatment is difficult to be given in time, irreversible nervous system damage is often caused to the patients, and even the life is endangered. Research shows that early diagnosis and timely treatment are the most important means for reducing the mortality rate of meningitis and reducing the nervous system sequelae of patients.
At present, clinicians distinguish different types of encephalopathy mainly according to the etiological evidence of cerebrospinal fluid, but many patients with encephalopathy lack the etiological evidence. If a reliable assistant decision-making scheme can be obtained by comprehensively utilizing limited clinical data of a patient in the early stage of disease occurrence, diagnosis and treatment suggestions are provided, and a clinician is helped to form a final diagnosis and treatment decision as soon as possible, targeted treatment can be given in the early stage of the disease, the fatality rate is reduced, and the prognosis of the patient is improved. With the rapid development of computer science and artificial intelligence technology, the application of the technology in the medical field is mature day by day. The mode of 'artificial intelligence + medical treatment' is increasingly applied to various aspects of prevention, early diagnosis, treatment, judgment, prognosis evaluation and the like of clinical diseases, and provides a new direction for establishing an efficient, rapid and convenient method to solve the diagnosis problem of the meningitis. However, no intelligent diagnosis system or software for encephalopathy (meningitis) is applied clinically.
Disclosure of Invention
Aiming at the diagnosis and treatment dilemma, the invention aims to provide an intelligent meningitis auxiliary diagnosis system based on a random forest algorithm, and solves the problem of low accuracy in early meningitis etiology diagnosis.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention provides an intelligent encephalitis and meningitis auxiliary diagnosis system based on a random forest algorithm, which comprises an Aliyun-based server and a client in communication connection with the Aliyun-based server;
be equipped with encephalitis, meningitis intelligence auxiliary diagnosis software package in the server, the software package contains:
an expert system based on medical knowledge, which comprises an encephalitis and meningitis disease weight scoring model and a rule knowledge base for encephalitis and meningitis diagnosis;
an intelligent system based on a machine learning algorithm is based on a random forest algorithm;
the client is provided with a human-computer interaction interface for inputting clinical data of a patient;
the server includes:
an interpreter for converting patient clinical data into parameters identifiable by the two models;
and the comprehensive processor is used for calculating and matching diagnosis of various causes of encephalitis and meningitis in the two models, unifying a rule knowledge base and a result based on a random forest algorithm to obtain the probability of each cause, and displaying the probability to the user for the most possible cause diagnosis.
Preferably, the "rule knowledge base" is constructed by the following method: through reviewing relevant documents, clinical guidelines and expert consensus of encephalitis and meningitis for decades and combining clinical experience knowledge of experts engaged in aspects related to central nervous system infection and logical thinking processes of clinical diagnosis and differential diagnosis, a weight scoring model of encephalitis and meningitis diseases is formed, and a 'rule knowledge base' for encephalitis and meningitis diagnosis is constructed; then, a disease weight scoring model is used for simulating the diagnosis process of experts on encephalitis and meningitis, corresponding etiological diagnosis is selected according to the scoring level of each etiological factor, then a multiple regression analysis method is adopted for multiple cases of clinically diagnosed meningitis case data to discuss the influence of each parameter on encephalitis and meningitis etiological diagnosis, and a rule knowledge base is further optimized and perfected to obtain a final rule knowledge base.
Preferably, the random forest algorithm is constructed by the following method: clinical data of a plurality of clinically diagnosed encephalitis patients and meningitis patients are used as a training database, different types of encephalitis and meningitis are identified by comparing various machine learning algorithms, such as a multilayer perceptron, a support vector machine and a random forest algorithm, and the algorithm with the highest identification rate, namely the random forest algorithm, is selected, and 15 characteristic parameters are selected from basic clinical data of the encephalitis patients and the meningitis patients through the random forest machine learning algorithm, wherein the parameters comprise: age, disease duration, blood sedimentation, blood sugar, lumbar puncture pressure, total number of cerebrospinal fluid leukocytes, proportion of cerebrospinal fluid lymphocytes, proportion of cerebrospinal fluid mononuclear cells, proportion of cerebrospinal fluid neutrophils, cerebrospinal fluid protein, cerebrospinal fluid sugar, immunoglobulin cerebrospinal fluid sugar/blood sugar, immunoglobulin A, immunoglobulin M and immunoglobulin G, and finally obtaining the model based on the random forest algorithm with stable diagnosis effect through ten-fold cross validation.
Preferably, the client is a smartphone based on an android or iOS system.
The invention has the beneficial effects that: under the condition that cerebrospinal fluid etiology evidence does not exist in the early stage of morbidity, the system can comprehensively utilize limited clinical data of a patient, quickly analyze the etiology of the encephalitis and the meningitis with higher accuracy (obviously higher than that of a clinician), and provide an assistant decision scheme for the clinician in time; therefore, the clinician can give accurate and reasonable treatment scheme to the patient aiming at the causes of encephalitis and meningitis, so that the fatality rate is reduced, and the prognosis of the patient is improved.
Drawings
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an intelligent encephalitis and meningitis auxiliary diagnosis system based on a random forest algorithm;
FIG. 2 is a statistical chart of diagnostic coincidence rates of encephalitis and meningitis in examples.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the intelligent encephalitis and meningitis auxiliary diagnosis system based on the random forest algorithm comprises an arilocos-based server and a client in communication connection with the arilocos-based server;
be equipped with encephalitis, meningitis intelligence auxiliary diagnosis software package in the server, the software package contains:
an expert system based on medical knowledge, which comprises an encephalitis and meningitis disease weight scoring model and a rule knowledge base for encephalitis and meningitis diagnosis;
an intelligent system based on a machine learning algorithm is based on a random forest algorithm;
the client is provided with a human-computer interaction interface for inputting clinical data of a patient;
the server includes:
an interpreter for converting patient clinical data into parameters identifiable by the two models;
and the comprehensive processor is used for calculating and matching diagnosis of each cause of meningitis in the two models, unifying the rule knowledge base and the result based on the random forest algorithm to obtain the probability of each cause, and displaying the probability to the user for the most possible diagnosis of the causes.
Further, the "rule knowledge base" is constructed by the following method: through reviewing relevant documents, clinical guidelines and expert consensus of encephalitis and meningitis for decades and combining clinical experience knowledge of experts engaged in aspects related to central nervous system infection and logical thinking processes of clinical diagnosis and differential diagnosis, a weight scoring model of encephalitis and meningitis diseases is formed, and a 'rule knowledge base' for encephalitis and meningitis diagnosis is constructed; then, a disease weight scoring model is used for simulating the diagnosis process of experts on encephalitis and meningitis, corresponding etiological diagnosis is selected according to the scoring level of each etiological factor, then a multiple regression analysis method is adopted for clinically confirmed cases of encephalitis and meningitis to discuss the influence of each parameter on the encephalitis and meningitis etiological diagnosis, and a rule knowledge base is further optimized and perfected to obtain a final rule knowledge base.
Further, the random forest algorithm is constructed by the following method: clinical data of 449 clinically diagnosed patients with encephalitis and meningitis (including 135 cases of tuberculous meningitis, 156 cases of bacterial meningitis, 64 cases of cryptococcal meningitis and 132 cases of viral meningitis) are used as training databases, different types of encephalitis and meningitis are identified by comparing various machine learning algorithms, such as a multilayer perceptron, a support vector machine and a random forest algorithm, and the algorithm with the best and stable identification rate, namely the random forest algorithm is selected, and characteristic parameters for diagnosing the encephalitis and the meningitis are selected from basic clinical data of the patients through the random forest machine learning algorithm, and the characteristic parameters comprise: the method comprises the following steps of blood sedimentation, disease duration, total number of cerebrospinal leukocytes, cerebrospinal lymphocyte proportion, age, cerebrospinal neutrophil proportion, immunoglobulin A, cerebrospinal sugar/blood sugar, immunoglobulin M, immunoglobulin G, cerebrospinal sugar, lumbar puncture pressure, cerebrospinal protein, blood sugar and cerebrospinal monocyte proportion, and finally obtaining a random forest algorithm with stable diagnosis effect through ten-time cross validation.
Further, the client is a smart phone based on an android or iOS system.
The clinically confirmed 449 encephalitis and meningitis cases are used for comparing the preliminary diagnosis of clinicians (according to the records of the cases), the diagnosis of an expert system and a random forest algorithm with the final confirmed diagnosis of the cases, and corresponding coincidence rates are calculated respectively. In fig. 2, the accuracy of the initial diagnosis of the encephalitis and the meningitis of each type is compared with the overall accuracy, so that the initial diagnosis accuracy of the clinician is low, the accuracy of the random forest algorithm is the highest, and the overall accuracy reaches 81 percent and is far higher than the initial diagnosis accuracy of the encephalitis and the meningitis of the clinician in a clinical actual environment. Then, through a prospective experiment, dozens of other cases with definite etiology diagnosis are collected for verification, and the accuracy rates of an expert system and a random forest algorithm are respectively 64.1% and 81%, so that the encephalitis and meningitis auxiliary diagnosis system can remarkably improve the accuracy rate of early diagnosis of encephalitis and meningitis and provide guidance for next diagnosis and treatment of a clinician in time.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (4)
1. An intelligent encephalitis and meningitis auxiliary diagnosis system based on a random forest algorithm is characterized by comprising an Aliskiren-based server and a client in communication connection with the Aliskiren-based server;
be equipped with encephalitis, meningitis intelligence auxiliary diagnosis software package in the server, the software package contains:
an expert system based on medical knowledge, which comprises an encephalitis and meningitis disease weight scoring model and a rule knowledge base for encephalitis and meningitis diagnosis;
an intelligent system based on a machine learning algorithm is based on a random forest algorithm;
the client is provided with a human-computer interaction interface for inputting clinical data of a patient;
the server includes:
an interpreter for converting patient clinical data into parameters identifiable by the two models;
and the comprehensive processor is used for calculating and matching diagnosis of various causes of encephalitis and meningitis in the two models, unifying a rule knowledge base and a result based on a random forest algorithm to obtain the probability of each cause, and displaying the probability to the user for the most possible cause diagnosis.
2. An intelligent random forest algorithm-based encephalitis and meningitis auxiliary diagnosis system as claimed in claim 1, wherein said "rule knowledge base" is constructed by the following method: through reviewing relevant documents, clinical guidelines and expert consensus of encephalitis and meningitis for decades and combining clinical experience knowledge of experts engaged in aspects related to central nervous system infection and logical thinking processes of clinical diagnosis and differential diagnosis, a weight scoring model of encephalitis and meningitis diseases is formed, and a 'rule knowledge base' for encephalitis and meningitis diagnosis is constructed; then, a disease weight scoring model is used for simulating the diagnosis process of experts on encephalitis and meningitis, corresponding etiological diagnosis is selected according to the scoring level of each etiological factor, then a multiple regression analysis method is adopted for clinically confirmed cases of encephalitis and meningitis to discuss the influence of each parameter on the encephalitis and meningitis etiological diagnosis, and a rule knowledge base is further optimized and perfected to obtain a final rule knowledge base.
3. An intelligent random forest algorithm based encephalitis and meningitis auxiliary diagnosis system as claimed in claim 1, wherein said random forest algorithm is constructed by the following method: clinical data of a plurality of clinically diagnosed encephalitis patients and meningitis patients are used as a training database, and by comparing a plurality of machine learning algorithms, such as a multilayer perceptron, a support vector machine and a random forest algorithm, the random forest algorithm with the highest recognition rate is selected to recognize different types of encephalitis and meningitis, through a random forest machine learning algorithm, selecting a plurality of characteristic parameters, such as age, disease duration, blood sedimentation, blood sugar, lumbar puncture pressure, total cerebrospinal fluid leukocyte count, cerebrospinal fluid lymphocyte proportion, cerebrospinal fluid monocyte proportion, cerebrospinal fluid neutrophil proportion, cerebrospinal fluid protein, cerebrospinal fluid sugar, immunoglobulin cerebrospinal fluid sugar/blood sugar, immunoglobulin A, immunoglobulin M and immunoglobulin G from basic clinical data of encephalitis patients and meningitis patients, and finally obtaining a model with stable diagnosis effect based on the random forest algorithm through ten-fold cross validation.
4. An intelligent random forest algorithm based encephalitis and meningitis auxiliary diagnosis system as claimed in claim 1, characterized in that said client is a smartphone based on android or iOS system.
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| CN115993444A (en) * | 2022-12-19 | 2023-04-21 | 郑州大学 | Dual-color immunofluorescence detection method for human serum cerebrospinal fluid GFAP antibody |
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Application publication date: 20200616 |