Multi-dimension cerebral stroke prevention screening method based on artificial intelligence
An artificial intelligence and stroke technology, applied in the database field, can solve problems such as errors, lack of data information, and not particularly ideal, and achieve the effect of reducing social burden, high evaluation accuracy, and good result reliability.
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Embodiment 1
[0075] Example 1: Stroke case database establishment process
[0076] like figure 1 Shown: The multi-dimensional stroke prevention and screening system of artificial intelligence uses the data of three dimensions of stroke as the main data for building the database.
[0077] The first aspect included in the system is the data of clinical consultations of doctors, including: history of stroke, family history of stroke, atrial fibrillation or valvular heart disease, age, diabetes, blood lipids, blood pressure, physical exercise, BMI, and smoking and alcohol consumption. 10 items. The doctor's clinical consultation data is mainly through the doctor's inquiries to the screening subjects. According to the descriptions of the above 10 stroke-related qualitative indicators by the screening subjects, the results are recorded as "Yes" or "No". Doctors input stroke-related data, input reports, and output terminal equipment. The terminal equipment establishes a connection with the clou...
Embodiment 2
[0082] Example 2: The process of establishing an artificial intelligence algorithm model for stroke prevention and screening
[0083] like figure 2 As shown in the figure, the sample data also includes qualitative data of doctor's clinical consultation data and quantitative data of stroke biomarker results and daily monitoring indicators. Quantitative results are calculated by inputting the original data values as model eigenvalues into the model. According to the sample eigenvalues may contain nonlinear factors, therefore, comparing algorithms such as random forests, support vector machines, and deep neural networks, Softmax is used in the output layer, and Dropout is used to avoid model overfitting. Using Cross Validation and Confusion Matrix as result evaluation methods and calculating precision, recall and F value, a deep neural network using multi-layer neural network and ADAM optimization algorithm is established.
[0084] The Wide&Deep model combines the shallo...
Embodiment 3
[0086] Example 3: Stroke-related data input and report output equipment terminal equipment result output process
[0087] like image 3 As shown, the doctor's clinical consultation data is input, the screening object provides their own blood, and the automatic biochemical analyzer part of the terminal equipment performs 16 blood biochemical biomarker detection and analysis to obtain quantitative blood biochemical results, which are connected to the cloud service platform and combined with The daily monitoring indicators of the screening object, and based on the artificial intelligence algorithm model of stroke prevention and screening, the intelligent stroke prevention screening system report is finally presented, including: doctor's clinical consultation data, blood biochemical biomarkers, daily monitoring indicators, etc. The specific situation of the three dimensions, the risk factor, the interpretation of the results, the processing opinions and other information.
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