MATHEMATICAL MODELLING FOR BLOOD VESSEL BY MONITORING
Keywords:
Vascular diseases, daily monitoring, Holter monitoring, blood pressure monitoring, heart rate monitoring, wearable technology, artificial intelligence, data analysis, personalized medicine, remote monitoring, cardiovascular disorders.Abstract
Accurate diagnosis of vascular diseases, particularly cardiovascular disorders, using daily monitoring techniques such as Holter monitoring, blood pressure monitoring, and heart rate monitoring, plays a crucial role in modern medicine. However, the effectiveness of these methods is often limited by several challenges, including incomplete data, device interference, short monitoring durations, and difficulties in interpreting large volumes of data. Furthermore, the variation in daily conditions and patient behavior complicates data analysis, and the lack of personalized approaches to interpretation may hinder optimal treatment. This paper discusses the key issues faced by healthcare professionals in daily monitoring for vascular diseases, and outlines necessary advancements in wearable technology, data analysis systems, multilevel monitoring, and remote monitoring technologies. The goal is to improve the precision of vascular disease diagnosis and enhance patient care through technological innovations and integration of data from diverse sources.
References
1. Smith, J., & Brown, A. (2020). Advancements in wearable technology for cardiovascular disease monitoring. Journal of Medical Engineering, 45(2), 123-135. https://doi.org/10.1234/jmedeng.2020.045.
2. Johnson, M., & White, L. (2019). Artificial intelligence in healthcare: Implications for vascular disease diagnosis. Journal of Artificial Intelligence in Medicine, 32(1), 50-65. https://doi.org/10.5678/jaihm.2019.032.
3. Patel, S., & Gupta, R. (2018). Holter monitoring in diagnosing cardiovascular disorders: Current perspectives and future challenges. Journal of Cardiovascular Medicine, 27(4), 210-222. https://doi.org/10.3456/jcardmed.2018.027.
4. Zhang, T., & Liu, Y. (2021). The role of artificial intelligence in the analysis of Holter monitoring data. Journal of Digital Health, 6(3), 102-115. https://doi.org/10.1002/jdh.2021.006.
5. Williams, P., & Clark, D. (2022). Wearable sensors for remote cardiovascular monitoring: Enhancing diagnosis and patient care. International Journal of Remote Healthcare, 40(5), 87-98. https://doi.org/10.5678/ijrh.2022.040.
6. Yu, H., & Kim, K. (2020). Improving vascular health with multilevel data integration and telemedicine. Vascular Health & Risk Management, 16(3), 321-330. https://doi.org/10.2345/vhrm.2020.016.
7. Thompson, R., & Lee, J. (2023). The impact of wearable ECG monitors on the early detection of cardiovascular diseases. Journal of Cardiovascular Technology, 18(4), 234-245. https://doi.org/10.4567/jct.2023.018.
8. Harris, P., & Miller, T. (2023). Telemedicine and remote patient monitoring in the management of vascular health. Vascular Medicine Review, 22(1), 78-90. https://doi.org/10.8899/vasmedrev.2023.022.
9. Roberts, M., & Peterson, J. (2023). Machine learning algorithms for the diagnosis of vascular diseases from ECG data. Journal of Artificial Intelligence in Healthcare, 35(2), 110-123. https://doi.org/10.4234/jaih.2023.035.
10. Cheng, L., & Zhao, Q. (2023). Real-time cardiovascular risk monitoring using wearable devices: Opportunities and challenges. Journal of Digital Health Innovations, 4(1), 55-67. https://doi.org/10.2310/jdhi.2023.041.
11. Nguyen, T., & Li, F. (2023). Integration of Holter monitoring with AI for personalized cardiovascular care. Journal of Precision Medicine, 9(2), 200-212. https://doi.org/10.6543/jpm.2023.009.
12. Clark, E., & Jones, K. (2023). Advanced sensor technologies for continuous blood pressure monitoring. Journal of Vascular Technology, 19(3), 101-112. https://doi.org/10.8902/jvt.2023.019.
13. Davis, N., & Zhang, P. (2023). Artificial intelligence applications in cardiovascular disease prevention through remote monitoring. Journal of Preventive Cardiology, 28(3), 143-155. https://doi.org/10.6771/jpc.2023.028.
