The Application Benefit of Stroke Early Warning Technology in Elderly People at Risk
Journal: Advanced Journal of Nursing DOI: 10.32629/ajn.v6i1.3784
Abstract
Stroke is one of the major diseases threatening the health of the elderly. Early warning is very important to reduce the risk of its occurrence and recurrence. The early warning system combines epidemiological and clinical data to monitor biomarkers, clinical indicators and lifestyle factors to effectively predict and prevent stroke. Modern technologies such as biomarker detection and artificial intelligence algorithms have advantages in early warning, but their applicability and accuracy need to be verified. While traditional methods rely on doctors' experience and basic physical examinations, modern technologies require a large amount of accurate data to support them. In order to reduce the incidence of stroke, regular monitoring of high-risk groups, risk assessment and precise intervention are recommended. Future research should explore technological innovation, build a comprehensive health management system, and strengthen policy support and public health education to improve the health management level of the elderly.
Keywords
early warning of stroke, elderly at-risk population, biomarkers, artificial intelligence, health management
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[3]Ren Chang-Hong, Gao Ming-Ming, Li Ning, et al. [3] The importance and status of biomarker research in stroke [J]. Military Medicine,2012,36(02):150-153.
[4]Zhao J, Chang H, Li P P, et al. [J] Zhao J, Chang H, Li P, et al. Research progress of wearable devices in monitoring risk factors and predicting risk of stroke [J]. Chinese Journal of Nursing,2024,57(09):1141-1146.
[5]Li Jing, Chen Song, Lv Chuanzhu et al. [J]. Chinese Journal of Emergency Medicine, 2018,38(11):946-949.
[6]Wang L D, Peng B, Zhang H Q, et al. Summary of China Stroke Prevention and Treatment Report 2020 [J]. Chinese Journal of Cerebrovascular Diseases, 2024,19(02):136-144.
[7]Duan Xiaofei, Milano.The clinical significance of multiple biochemical markers combined detection in the diagnosis of acute ischemic stroke [J]. Henan Medical Research,2019,28(20):3768-3769.
[8]Chen Yang, Feng JIChun. Research progress of biochemical markers of ischemic stroke [J]. Chinese Journal of Cerebrovascular Diseases,2018,15(03):157-161.
[9]Chen X, Yang B, Zhao S, Wei W, Chen J, Ding J, Wang H, Sun P, Gan L. Management for stroke intelligent early warning empowered by big data. Comput Electr Eng. 2023 Mar; 106:108602. DOI: 10.1016/j.compeleceng.
[10]Liu C, Zhang C, Chi Y, Ma C, Zhang L, Chen S. [Construction and external validation of a non-invasive prehospital screening model for stroke patients: a study based on artificial intelligence DeepFM algorithm]. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2024, 36(11): 1163-1168.
[11]Gao C, Wang H. Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches. Stroke Res Treat. 2024 May 23; 2024: 4523388. DOI: 10.1155/2024/4523388.
[12]Yang Y, Chang Q, Chen J, Zhou X, Xue Q, Song A. Construction of a Health Management Model for Early Identification of Ischaemic Stroke in Cloud Computing. J Healthc Eng. 2022 Mar 22; 2022:1018056. doi: 10.1155/202/1018056.retraction in: JHealthc Eng. 2023 Oct 4; 2023:9820647. DOI: 10.1155/2023/9820647.
[13]Liang J, Luo C, Ke S, Tung TH. Stroke related knowledge, prevention practices and associated factors among stroke patients in Taizhou, China. Prev Med Rep. 2023 Jul 20; Service 2340. DOI: 10.1016/j.medr.2023.102340.
[14]Saade S, Hallit S, Salameh P, Hosseini H. Knowledge and Response to Stroke Among Lebanese Adults: A Population-Based Survey. Front Public Health. 2022 Jun 3; 10: 8 91073. DOI: 10.3389/fpubh.2022.891073.
[15]Tazin T, Alam MN, Dola NN, Bari MS, Bourouis S, Monirujjaman Khan M. Stroke Disease Detection and Prediction Using Robust Learning Approaches. J Healthc Eng. 2021 Nov 26. 202:7633381. DOI: 10.1155/202/7633381.
[16]Xie J, Liu W P, Liu H. MEWS guided intervention effect evaluation in stroke patients [J]. Practical Preventive Medicine, 2019,30(04):486-489.
[17]Fu Shu, Wei Bing. Full cycle perspective of health management research to the influential factors of the elderly medical behavior [J]. Journal of health economic research, 2024, 9(02): 56-60.
[18]Wei L, Peng X T, Zhang X P, et al. Study on the status quo, demand and influencing factors of health education for patients with stroke [J]. China Health Education, 2019,36(10):958-961.
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