CN114188015A - Cerebral apoplexy disease prediction method and device based on artificial intelligence - Google Patents
Cerebral apoplexy disease prediction method and device based on artificial intelligence Download PDFInfo
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Abstract
The application discloses cerebral apoplexy disease prediction method and device based on artificial intelligence, wherein the method comprises the following steps: collecting a data set suitable for stroke screening big data research and artificial intelligence algorithm training to generate a stroke screening data set; identifying stroke risk factors based on a stroke screening data set, and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and predicting the stroke disease prediction result of the patient by using the stroke disease prediction model. The stroke screening model and the intelligent research method suitable for the artificial intelligence technology are established based on the artificial intelligence technology and characteristics and aim at training and application by using a neural network and a deep learning algorithm. Therefore, the problems that a standardized model suitable for an artificial intelligence technology is lacked in the related technology, and deep integration and application in the field of stroke screening and prevention are solved.
Description
Technical Field
The application relates to the technical field of biology, medical treatment and medical instruments, in particular to a cerebral apoplexy disease prediction method and device based on artificial intelligence.
Background
The stroke, one of the major diseases endangering the health of the nation, has surpassed the first place of cardiovascular diseases and malignant tumors, the disabling, killing and recurrence of the stroke bring heavy burden to the society and families, the national economic level is also seriously influenced, and the consumption of huge medical resources is accompanied. As a great country with stroke, China gradually strengthens the work of early screening, preventing and treating stroke high risk groups in recent years. However, because the population base number of China is large, medical resources in different regions are unevenly distributed, and the development levels of urban and rural areas are different, workload caused by screening is extremely large, an intervention strategy aiming at different levels of high-risk groups is lacked, and unified management efficiency of the high-risk groups is not high. Meanwhile, with the high development of information technology, the artificial intelligence technology is more and more applied to medical health research, and many researchers at home and abroad have developed the research and application of the artificial intelligence technology in the fields of screening, preventing and treating cerebral apoplexy and have played more and more important auxiliary diagnosis and treatment functions.
In recent years, as intelligent medical research based on big data is receiving more and more attention, many scholars begin to use the related algorithm of artificial intelligence technology to develop research in the field of intelligent stroke screening. The early stroke screening method mainly comprises the steps of carrying out data mining based on structured data, analyzing risk prediction model factors and establishing a prediction model according to known risk factors. And in the later stage, the ANN conforming to a deep learning framework is built step by step, research is carried out based on a packaged machine learning algorithm, and data analysis and research methods such as Bayesian regression, decision tree analysis and the like are mainly applied. Feigin and the like adopt a Bayesian regression method to research and discover that dangerous factors possibly causing stroke include particulate pollution in the environment, solid fuel pollution and lead exposure, as well as high-sodium and high-sugar diet, little fruit and vegetable grains, drinking, lack of physical activity, smoking hands, high body weight index, high abdominal blood sugar, high systolic pressure, high total cholesterol, glomerular filtration rate and the like. Duvalin et al use discriminant analysis, regression analysis methods including logistic regression, Bayesian regression, etc. to analyze risk factors, and in addition, research uses decision tree methods to find relevant risk factors. It can be seen that a plurality of potential or related risk factors can be found through the artificial intelligence technology, and the related aspects are wider. Chinese national stroke screening studies in 2002-2013 showed that many factors are associated with the occurrence of stroke, with the greatest risk factor being hypertension (population risk due to 53.2%), followed by family history, dyslipidemia, atrial fibrillation, diabetes, lack of physical activity, smoking and being overweight/obese. In summary, some studies on stroke prediction and risk factor analysis using statistical methods or artificial intelligence algorithms have been performed, but there is a lack of a standardized model suitable for artificial intelligence techniques, and deep integration and application in the field of stroke screening and prevention.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present application is to provide a method for predicting stroke diseases based on artificial intelligence.
A second objective of the present application is to provide a stroke disease prediction device based on artificial intelligence.
A third object of the present application is to provide an electronic device.
A fourth object of the present application is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, a method for predicting stroke diseases based on artificial intelligence is provided in an embodiment of the first aspect of the present application, including the following steps: collecting a data set suitable for stroke screening big data research and artificial intelligence algorithm training to generate a stroke screening data set; identifying stroke risk factors based on the stroke screening data set, and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and predicting the stroke disease prediction result of the patient by using the stroke disease prediction model.
Optionally, in an embodiment of the present application, the acquiring a data set suitable for stroke screening big data research and artificial intelligence algorithm training includes: collecting data associated with risk factor characteristics of a stroke disease study; and carrying out characterization processing on the data associated with the risk factor characteristics of the cerebral apoplexy disease research to establish the cerebral apoplexy screening data set.
Optionally, in an embodiment of the present application, the characterizing the data associated with the risk factor characteristic of the stroke disease research to establish the stroke screening data set includes: carrying out noise reduction and artifact removal processing on the data to generate processed first processing data; resampling the first processed data to obtain sampled second processed data; normalizing the second processed data to obtain normalized third processed data; extracting a plurality of pathological features from the third processed data, and obtaining the stroke screening data set from feature changes and features based on the pathological features.
Optionally, in an embodiment of the present application, the identifying stroke risk factors based on the stroke screening data set and building a stroke disease prediction model by using an artificial intelligence algorithm includes: calculating the weight of the risk factors, training a model by utilizing the stroke screening data set based on an artificial neural network, and verifying the model in the data set by using a control group queue to obtain the stroke disease prediction model.
In order to achieve the above object, an embodiment of a second aspect of the present application provides an artificial intelligence based stroke disease prediction apparatus, including: the acquisition module is used for acquiring a data set suitable for stroke screening big data research and artificial intelligence algorithm training to generate a stroke screening data set; the modeling module is used for identifying stroke risk factors based on the stroke screening data set and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and the prediction module is used for predicting the prediction result of the cerebral apoplexy disease of the patient by utilizing the cerebral apoplexy disease prediction model.
Optionally, in an embodiment of the present application, the acquisition module includes: the data acquisition unit is used for acquiring data related to risk factor characteristics of cerebral apoplexy disease research; and the data processing unit is used for performing characterization processing on the data associated with the risk factor characteristics of the stroke disease research to establish the stroke screening data set.
Optionally, in an embodiment of the present application, the data processing unit is further configured to perform noise reduction and artifact removal processing on the data, and generate processed first processed data; resampling the first processed data to obtain sampled second processed data; normalizing the second processed data to obtain normalized third processed data; extracting a plurality of pathological features from the third processed data, and obtaining the stroke screening data set from feature changes and features based on the pathological features.
Optionally, in an embodiment of the present application, the modeling module is further configured to calculate a weight of the risk factor, train a model with the stroke screening data set based on an artificial neural network, and verify the model with a control group queue in the data set to obtain the stroke disease prediction model.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor and configured to perform an artificial intelligence based stroke disease prediction method as described in the above embodiments.
To achieve the above object, a fourth aspect of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the artificial intelligence based stroke disease prediction method according to the above embodiment.
According to the stroke disease prediction method and device based on artificial intelligence, a data set suitable for stroke screening big data research and artificial intelligence algorithm training is collected, and a stroke screening data set is generated; identifying stroke risk factors based on a stroke screening data set, and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and predicting the stroke disease prediction result of the patient by using the stroke disease prediction model. Based on artificial intelligence technology and characteristics, training and application using a neural network and a deep learning algorithm are used as targets, and a stroke screening model and an intelligent research method suitable for the artificial intelligence technology are established. Therefore, the problems that a standardized model suitable for an artificial intelligence technology is lacked in the related technology, and deep integration and application in the field of stroke screening and prevention are solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a method for predicting stroke diseases based on artificial intelligence according to an embodiment of the present application;
fig. 2 is a schematic diagram of a process for characterizing stroke data according to an embodiment of the present application;
fig. 3 is a logic diagram of a stroke screening method based on artificial intelligence according to an embodiment of the present application;
fig. 4 is a schematic diagram of an artificial intelligence-based stroke screening model according to an embodiment of the present application;
fig. 5 is an exemplary diagram of an artificial intelligence based stroke disease prediction apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The method and the device for predicting stroke diseases based on artificial intelligence proposed by the embodiment of the application are described below with reference to the accompanying drawings.
First, a method for predicting a stroke disease based on artificial intelligence proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.
Specifically, fig. 1 is a flowchart of a method for predicting stroke diseases based on artificial intelligence according to an embodiment of the present application.
As shown in fig. 1, the method for predicting stroke disease based on artificial intelligence includes the following steps:
in step S101, a data set suitable for stroke screening big data research and artificial intelligence algorithm training is collected to generate a stroke screening data set.
Optionally, in an embodiment of the present application, acquiring a data set suitable for stroke screening big data research and artificial intelligence algorithm training includes: collecting data associated with risk factor characteristics of a stroke disease study; and carrying out characterization processing on data associated with the risk factor characteristics of the stroke disease research, and establishing a stroke screening data set.
Optionally, in an embodiment of the present application, characterizing data associated with risk factor characteristics of a stroke disease study, and establishing a stroke screening data set includes: carrying out noise reduction and artifact removal processing on the data to generate processed first processed data; resampling the first processed data to obtain sampled second processed data; normalizing the second processed data to obtain normalized third processed data; and extracting a plurality of pathological features from the third processed data, and selecting feature changes and features based on the pathological features to obtain a stroke screening data set.
It can be appreciated that a data set suitable for stroke screening big data research and AI algorithm training is established. The data acquisition accords with the main risk factor characteristics of stroke disease research, including data acquisition of inspection, medication, hypertension, diabetes, cerebrovascular disease history, family history and the like, data acquisition of treatment conditions of patients with related medical history, sign data and BMI, heart sounds and neck blood vessel noise, electrocardiogram inspection results of patients with abnormal heart sounds, radiation inspection results of neck blood vessel noise and ultrasonic inspection results, carotid artery ultrasonic, TCD inspection, MRI, CT, DSA and the like, and characteristic processing is carried out on the acquired data, as shown in figure 2, correlation contrast relations are established in different data types, and a data set which has stroke disease characteristics and is suitable for artificial intelligence research is established.
Specifically, a stroke screening data set is established, labeling processing is carried out on structured data, data characteristics are extracted, a risk factor is defined as a variable, a Pearson Correlation Coefficient (Pearson Correlation Coefficient) method is used for screening the key risk factor, irrelevant factors are eliminated, weight analysis is carried out on the key risk factor, screening algorithm training is carried out in an established Artificial Neural Network (ANN) based on a screening model, algorithm verification is carried out in a data set by using a contrast group queue, and therefore accuracy is further improved. The correlation formula of the Pearson correlation coefficient is as follows:
in step S102, stroke risk factors are identified based on the stroke screening dataset, and a stroke disease prediction model is established using an artificial intelligence algorithm.
Optionally, in an embodiment of the present application, identifying stroke risk factors based on the stroke screening dataset, and building a stroke disease prediction model using an artificial intelligence algorithm includes: and calculating the weight of the risk factors, screening the data set training model by using the stroke based on the artificial neural network, and verifying the data set by using a control group queue to obtain a stroke disease prediction model.
Identifying stroke risk factors based on the data set, training an artificial intelligence algorithm, and establishing a stroke disease prediction model based on artificial intelligence. By weight analysis of high risk factors, algorithm training is carried out based on an Artificial Neural Network (ANN), algorithm verification is carried out by using a control group queue in a data set, so that the risk prediction accuracy is further improved, a set of efficient closed-loop iteration path is formed, 4 key engineering technical difficulties are mainly solved by the model, a stroke screening standardized data set which accords with artificial intelligence research is established, an algorithm research engineering model which is based on stroke disease characteristics and is suitable for the artificial neural network is established, a diversified multi-dimensional risk factor algorithm research model is realized, a realization method for stroke screening prediction, verification and optimization based on artificial intelligence is established, and the stroke intelligent screening accuracy is improved, as shown in figure 3.
Specifically, feature Vector analysis and Classification learning are performed by using Logistic Regression analysis (Logistic Regression), Decision Tree Classification (Decision Tree Classification), K nearest Neighbors (K Neighbors Classification), Gaussian bayes (Gaussian NB), and Support Vector machines (SVC-Support Vector Classification), and the optimized Classification results extracted after calculation by the above algorithms are compared, and risk factors are defined to include inspection results (including triglyceride, cholesterol, fasting blood glucose, glycated hemoglobin, and homocysteine), inspection results (including electrocardiogram results and neck ultrasound results), hypertension, dyslipidemia, diabetes, atrial fibrillation or valvular heart disease, smoking history, significant overweight or obesity, lack of exercise, family history of stroke, past stroke, and transient cerebral ischemia.
In step S103, a stroke disease prediction result of the patient is predicted using the stroke disease prediction model.
In intelligenceIn the algorithm, a tensor model length interval is defined to be 18-25 according to a standardized data set, a user-defined artificial neural network structure used in the method is constructed, an artificial intelligent algorithm is constructed, and a nonlinear activation function Sigmoid is adopted as a main body based on the user-defined neural network structure
Respectively adopting Boosting integration algorithm and random gradient descent Adam optimization algorithm, and using two-class cross entropy (BCELoss) to realize loss function calculation
The data set is divided into a 70% training set and a 30% verification set in proportion, screening efficiency and accuracy of the stroke are improved through algorithm research, risk probability is given, and dangerous people are classified accurately.
A method system and an engineering model which are based on an artificial intelligence technology and suitable for screening and predicting the cerebral apoplexy are realized by establishing a stroke data set, a training set and a verification set, customizing an artificial neural network, analyzing characteristics, classifying learning and training an AI algorithm and verifying, and are shown in figure 4.
According to the stroke disease prediction method based on artificial intelligence, which is provided by the embodiment of the application, a data set suitable for stroke screening big data research and artificial intelligence algorithm training is collected, and a stroke screening data set is generated; identifying stroke risk factors based on a stroke screening data set, and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and predicting the stroke disease prediction result of the patient by using the stroke disease prediction model. Based on artificial intelligence technology and characteristics, training and application using a neural network and a deep learning algorithm are used as targets, and a stroke screening model and an intelligent research method suitable for the artificial intelligence technology are established. Therefore, the problems that a standardized model suitable for an artificial intelligence technology is lacked in the related technology, and deep integration and application in the field of stroke screening and prevention are solved.
Next, an artificial intelligence based stroke disease prediction apparatus according to an embodiment of the present application will be described with reference to the drawings.
Fig. 5 is an exemplary diagram of an artificial intelligence-based stroke disease prediction apparatus according to an embodiment of the present application.
As shown in fig. 5, the artificial intelligence based stroke disease prediction apparatus 10 includes: an acquisition module 100, a modeling module 200, and a prediction module 300.
The acquisition module 100 is configured to acquire a data set suitable for stroke screening big data research and artificial intelligence algorithm training, and generate a stroke screening data set. And the modeling module 200 is used for identifying stroke risk factors based on the stroke screening data set and establishing a stroke disease prediction model by using an artificial intelligence algorithm. And the prediction module 300 is configured to predict a stroke disease prediction result of the patient by using the stroke disease prediction model.
Optionally, in an embodiment of the present application, the acquisition module includes: the data acquisition unit is used for acquiring data related to risk factor characteristics of cerebral apoplexy disease research; and the data processing unit is used for performing characterization processing on the data associated with the risk factor characteristics of the stroke disease research to establish a stroke screening data set.
Optionally, in an embodiment of the present application, the data processing unit is further configured to perform noise reduction and artifact removal processing on the data, and generate processed first processed data; resampling the first processed data to obtain sampled second processed data; normalizing the second processed data to obtain normalized third processed data; and extracting a plurality of pathological features from the third processed data, and selecting feature changes and features based on the pathological features to obtain a stroke screening data set.
Optionally, in an embodiment of the present application, the modeling module is further configured to calculate a weight of the risk factor, train the model with a stroke screening data set based on an artificial neural network, and perform verification on the data set using a control group queue to obtain a stroke disease prediction model.
It should be noted that the above explanation of the embodiment of the method for predicting a stroke disease based on artificial intelligence is also applicable to the device for predicting a stroke disease based on artificial intelligence of this embodiment, and is not repeated here.
According to the stroke disease prediction device based on artificial intelligence, which is provided by the embodiment of the application, a data set suitable for stroke screening big data research and artificial intelligence algorithm training is collected, and a stroke screening data set is generated; identifying stroke risk factors based on a stroke screening data set, and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and predicting the stroke disease prediction result of the patient by using the stroke disease prediction model. Based on artificial intelligence technology and characteristics, training and application using a neural network and a deep learning algorithm are used as targets, and a stroke screening model and an intelligent research method suitable for the artificial intelligence technology are established. Therefore, the problems that a standardized model suitable for an artificial intelligence technology is lacked in the related technology, and deep integration and application in the field of stroke screening and prevention are solved.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.
The processor 602 executes the program to implement the artificial intelligence based stroke disease prediction method provided in the above embodiments.
Further, the electronic device further includes:
a communication interface 603 for communication between the memory 601 and the processor 602.
The memory 601 is used for storing computer programs that can be run on the processor 602.
If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may complete mutual communication through an internal interface.
The processor 602 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The present embodiment also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the artificial intelligence based stroke disease prediction method as above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A stroke disease prediction method based on artificial intelligence is characterized by comprising the following steps:
collecting a data set suitable for stroke screening big data research and artificial intelligence algorithm training to generate a stroke screening data set;
identifying stroke risk factors based on the stroke screening data set, and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and
and predicting the stroke disease prediction result of the patient by using the stroke disease prediction model.
2. The method of claim 1, wherein collecting a data set suitable for stroke screening big data research and artificial intelligence algorithm training comprises:
collecting data associated with risk factor characteristics of a stroke disease study;
and carrying out characterization processing on the data associated with the risk factor characteristics of the cerebral apoplexy disease research to establish the cerebral apoplexy screening data set.
3. The method of claim 2, wherein the characterizing the data associated with the risk factor characteristic of stroke disease studies to create the stroke screening dataset comprises:
carrying out noise reduction and artifact removal processing on the data to generate processed first processing data;
resampling the first processed data to obtain sampled second processed data;
normalizing the second processed data to obtain normalized third processed data;
extracting a plurality of pathological features from the third processed data, and obtaining the stroke screening data set from feature changes and features based on the pathological features.
4. The method of claim 1, wherein identifying stroke risk factors based on the stroke screening dataset and building a stroke disease prediction model using an artificial intelligence algorithm comprises:
calculating the weight of the risk factors, training a model by utilizing the stroke screening data set based on an artificial neural network, and verifying the model in the data set by using a control group queue to obtain the stroke disease prediction model.
5. A cerebral apoplexy disease prediction device based on artificial intelligence, characterized by comprising:
the acquisition module is used for acquiring a data set suitable for stroke screening big data research and artificial intelligence algorithm training to generate a stroke screening data set;
the modeling module is used for identifying stroke risk factors based on the stroke screening data set and establishing a stroke disease prediction model by using an artificial intelligence algorithm; and
and the prediction module is used for predicting the prediction result of the cerebral apoplexy disease of the patient by utilizing the cerebral apoplexy disease prediction model.
6. The apparatus of claim 5, wherein the acquisition module comprises:
the data acquisition unit is used for acquiring data related to risk factor characteristics of cerebral apoplexy disease research;
and the data processing unit is used for performing characterization processing on the data associated with the risk factor characteristics of the stroke disease research to establish the stroke screening data set.
7. The apparatus of claim 6, wherein the data processing unit is further configured to,
carrying out noise reduction and artifact removal processing on the data to generate processed first processing data;
resampling the first processed data to obtain sampled second processed data;
normalizing the second processed data to obtain normalized third processed data;
extracting a plurality of pathological features from the third processed data, and obtaining the stroke screening data set from feature changes and features based on the pathological features.
8. The apparatus of claim 5, wherein the modeling module is further configured to calculate weights of the risk factors, train a model with the stroke screening dataset based on an artificial neural network, and verify the model with a control group queue in the dataset to obtain the stroke disease prediction model.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor executing the program to implement the artificial intelligence based stroke disease prediction method according to any of claims 1-4.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program is executed by a processor for implementing the artificial intelligence based stroke disease prediction method according to any one of claims 1 to 4.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116705306A (en) * | 2023-08-03 | 2023-09-05 | 首都医科大学附属北京天坛医院 | Method for monitoring cerebral apoplexy, device for monitoring cerebral apoplexy and storage medium |
| WO2024025489A1 (en) * | 2022-07-28 | 2024-02-01 | İstanbul Geli̇şi̇m Üni̇versi̇tesi̇ | Health status analysis system on historical data |
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| WO2024025489A1 (en) * | 2022-07-28 | 2024-02-01 | İstanbul Geli̇şi̇m Üni̇versi̇tesi̇ | Health status analysis system on historical data |
| CN116230212A (en) * | 2023-04-04 | 2023-06-06 | 曜立科技(北京)有限公司 | Diagnosis decision system for postoperative cerebral apoplexy review based on data processing |
| CN116612886A (en) * | 2023-05-06 | 2023-08-18 | 广东省人民医院 | Cerebral apoplexy early-stage auxiliary diagnosis method, system, device and storage medium |
| CN116705306A (en) * | 2023-08-03 | 2023-09-05 | 首都医科大学附属北京天坛医院 | Method for monitoring cerebral apoplexy, device for monitoring cerebral apoplexy and storage medium |
| CN116705306B (en) * | 2023-08-03 | 2023-10-31 | 首都医科大学附属北京天坛医院 | Method for monitoring cerebral apoplexy, device for monitoring cerebral apoplexy and storage medium |
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