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  4. Vol 4, Issue 8, 2023

Intelligent diagnosis and treatment of hemorrhagic stroke patients

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DOI: 10.23977/medsc.2023.040820 | Downloads: 11 | Views: 220

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.

REFERENCES

[1] Linjie Zhu, Guangpeng Zhao, Lianghe Kang. Intrusion detection method based on combining MI feature selection and KNN classifier [J]. Gansu Science and Technology, 2022, 38(15): 33-36.
[2] Junyi Li. Research on network security detection application of K-means clustering algorithm based on big data [J]. Mechanical Design and Manufacturing Engineering, 2021, 50(09): 115-118.
[3] Mao Zhou, Ziruo Li, Xinhe Zhou, et al. Risk assessment of power grid construction projects based on AHP- entropy weight method [J]. Electrical Technology and Economics, 2023(05):192-196.
[4] Guoqqing Z .Origin and development of hemorrhagic stroke [J].China Journal of Medical History, 2005.
[5]  Xiaodong L, Zhiyi L, Yumin Y, et al. Analysis of the etiology, clinical features and prognosis of 345 cases of youth hemorrhagic stroke[J].Chinese Journal of the Frontiers of Medical Science(Electronic Version), 2017.
[6] Bajenaru O, Tiu C, Moessler H, et al. Efficacy and safety of Cerebrolysin in patients with hemorrhagic stroke[J]. Journal of Medicine and Life, 2010, 3(2).
[7] Meairs, Stephen. Advances in neurosonology – Brain perfusion, sonothrombolysis and CNS drug delivery [J]. Perspectives in Medicine, 2012, 1(1–12):5-10. DOI:10.1016/j.permed.2012.02.038.

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