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Clinical intelligent diagnosis and treatment analysis of hemorrhagic stroke

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DOI: 10.23977/medsc.2023.040819 | Downloads: 7 | Views: 348


Feng Liu 1, Yanwen Wang 1, Chang Cai 1, Xinjian Zhong 1


1 School of Electroinc Information, Xijing University, Xi'an, China

Corresponding Author

Yanwen Wang


With the continuous progress and development of artificial intelligence, it has been widely applied in many fields. In the current stage of rapid development of medical technology, many medical technologies need to analyze complex basic data based on artificial intelligence to provide further treatment plans or predict and analyze patient conditions. This article focuses on the risk factors of hematoma after hemorrhagic stroke in patients, and combines the patient's personal information, treatment plans, and other data provided. Based on factor analysis, a quantitative data dimensionality reduction model is established using BP neural network, GABP neural network, and other related models to achieve accurate and personalized prognosis prediction, treatment evaluation, and opinions. To predict the probability of hematoma expansion in patients, complex patient information is first dimensionally reduced based on factor analysis. Then, the personal history and disease history of the top 100 patients, as well as relevant treatment plans, are used as input features, and the prediction probability of hematoma expansion is used as output. BP neural network is used to predict the probability of hematoma expansion in patients.


Hemorrhagic stroke, Hematoma dilation, BP neural network


Feng Liu, Yanwen Wang, Chang Cai, Xinjian Zhong, Clinical intelligent diagnosis and treatment analysis of hemorrhagic stroke. MEDS Clinical Medicine (2023) Vol. 4: 128-133. DOI:


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