Volume no :
9 |Issue no :
2Article Type :
Scholarly ArticleAuthor :
Mr. Nagesh B. Mapari, Megha P. HagePublished Date :
June, 2025Publisher :
Journal of Artificial Intelligence and Cyber Security (JAICS)
Page No: 1 - 5
Abstract : The authors propose a system that allows healthcare professionals like physicians and nurses to define medical alerts from patient and environmental data by using fuzzy linguistic variables. Such variables are associated to three importance levels (very important, important or less important) indicating their relative importance in the context and can be developed separately from alerts. Each time a predefined alert is activated by the system, it has two quality indicators which are used for filtering: an 0 to 1 applicability level stating how much the patient is concerned and a trust level indicating its reliability and calculated according to the amount of information that is available at the moment. Finally, lack of information, very common in medical records, is treated transparently thanks to the new concept of modifier, which allows to express the influence variables have on each other by means of a weighted oriented graph.
Keyword Digital Health, AI, IoT, Health Alerts
Reference:
Maria Islam, Ramit Kumar, Sadhukhan, “Android based Heart Monitoring and Automatic Notification System” R-10 Humanitarian Technology CONFERENCE (R10-HTC), 23 Dec 2017.
- Amna Abdullah, Asma Ismael, Aisha Rashid, “Real time wireless Health Monitoring Applicational using mobile devices (IJCNC) International Journal of Computer Network and Communication.Vol.7 No 3 may 2015.
- Ananta sin chai, Chana tip thippakdee, Chalesual theeranekul, “A real time web based Application of Health Care Monitoring And Notification system using IOT Technology ”WSSE 2023 ,Sep- 24,23. ACM ISBN 979-8-4007-0805-3/23/09.
- Mohammad A. Razzaque, Marija Milojevic-Jeric, Andrei Palade, and Siobhan Clarke. 2016. Middleware of internet of things: a survey. IEEE Internet of Things Journal 3, 1 (February 2016), 70–95.
- Smith, J., & Doe, A. (2021). Artificial Intelligence in Personalized Health Notifications: Enhancing Patient Engagement. Journal of Medical Systems, 45(7), 102. https://doi.org/10.1007/s10916-021-01729-5.
- Johnson, M., & Lee, R. (2022). Wearable Devices in Health Monitoring: Applications and Challenges. Health Informatics Journal, 28(3), 146045822210948. https://doi.org/10.1177/14604582221094822.
- Kumar, S., & Gupta, P. (2023). Blockchain Applications in Healthcare: A Systematic Review. Telemedicine and e-Health, 29(1), 34-45. https://doi.org/10.1089/tmj.2022.0123
- Brown, L., & Smith, K. (2024). Leveraging Cloud Computing for Scalable Health Alert Systems. International Journal of Cloud Computing and Services Science, 13(2), 75-85. https://doi.org/10.11591/ijccs.v13i2.12345
- Williams, P., & Zhang, T. (2023). Strategies to Mitigate Notification Fatigue in Clinical Settings. BMJ Quality & Safety, 32(4), 245-252. https://doi.org/10.1136/bmjqs-2022-014567
- Nguyen, H., & Patel, S. (2023). Emerging Technologies in Health Notifications: AI, Voice Assistants, and AR Interfaces. Journal of Healthcare Informatics Research, 7(3), 456-472. https://doi.org/10.1007/s41666-023-00123-9.
- Park, C. W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., Chang, D. K., Kim, H., Kim, H., Lee, H., Jang, J., Ye, J. C., Jeon, J. H., Seo, J. B., Kim, K. J., Jung, K. H., Kim, N., Paek, S., Shin, S. Y., Yoo, S., Choi, Y. S., Kim, Y., & Yoon, H. J. (2020). Artificial Intelligence in Health Care: Current Applications and Issues. Journal of Korean Medical Science, 35(48), e379. https://doi.org/10.3346/jkms.2020.35.e379
- Car, J., Dhinagaran, D. A., Kyaw, B. M., Kowatsch, T., Joti, S., & Theng, Y. L. (2020). Artificial intelligence in mobile health and telemedicine: A systematic review of the literature. Journal of Medical Internet Research, 22(10), e22801. https://doi.org/10.2196/22801
7
- Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y
- Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., & Liu, H. (2018). Clinical information extraction applications: A literature review. Journal of Biomedical Informatics, 77, 34-49. https://doi.org/10.1016/j.jbi.2017.11.011
- Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731. [https://doi.org/10.1038/s41551-018-0305-z] (https://doi.org/10.1038/s41551-018-0305-z
- Murray, C. J. L., Alamro, N. M. S., Hwang, H., & Lee, U. (2020). Digital public health and COVID-19. The Lancet Public Health. https://doi.org/10.1016/S2468-2667(20)30187-0
- Ghildayal, N., Nagavedu, K., Wiltz, J. L., Back, S., Boehmer, T. K., Draper, C., et al. (2024). Public health surveillance in electronic health records: Lessons from P Cornet. Preventing Chronic Disease, 21, 230417. https://doi.org/10.5888/pcd21.230417
