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Intelligent diagnosis and treatment of hemorrhagic stroke patients

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DOI: 10.23977/medsc.2023.040820 | Downloads: 12 | Views: 252

Author(s)

Chang Cai 1, Yanwen Wang 1, Zijian Zhang 1, Feng Liu 1

Affiliation(s)

1 School of Electronic Information, Xijing University, Xian, China

Corresponding Author

Chang Cai

ABSTRACT

In recent years, artificial intelligence technology in the medical field has developed rapidly, providing revolutionary possibilities for deep mining of large amounts of image data. Hemorrhagic stroke is a serious disease in which enlargement of hematoma is one of the important risk factors for poor prognosis. Therefore, monitoring and controlling hematoma expansion is one of the key clinical concerns. In addition, edema around the hematoma, as a sign of secondary injury after cerebral hemorrhage, has attracted widespread clinical attention in recent years. Monitoring and controlling the expansion of the hematoma is one of the focuses of clinical attention. This study is a model based on a large amount of medical data, aiming to analyze and intelligently predict key clinical events of hemorrhagic stroke. Based on the patient's personal history, disease history, clinical information related to the onset and hematoma and edema data obtained from images, we use The KNN algorithm is used to obtain the probability of hematoma expansion in patients, the K- means clustering algorithm is used to classify and analyze the edema volume changes in different patients, and finally the LSTM neural network is used to complete the comparison. The work of predicting patient functional status and disability provides a reference for intelligent medical diagnosis and treatment models.

KEYWORDS

Intelligent prediction, KNN algorithm, LSTM neural network, K-Means clustering algorithm

CITE THIS PAPER

Chang Cai, Yanwen Wang, Zijian Zhang, Feng Liu, Intelligent diagnosis and treatment of hemorrhagic stroke patients. MEDS Clinical Medicine (2023) Vol. 4: 134-141. DOI: http://dx.doi.org/10.23977/medsc.2023.040820.

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