Published 23-09-2024
Keywords
- IoT,
- Sensors
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
The management of chronic diseases relies heavily on patients' adherence to prescribed medications. However, non-adherence remains a significant challenge, leading to poor health outcomes and increased healthcare costs. IoT-enabled systems offer promising solutions to monitor medication adherence in real-time and provide interventions to improve patient compliance. This paper presents a comprehensive review of IoT-enabled medication adherence monitoring systems for chronic disease management. We discuss the key components of these systems, including sensors, connectivity, data analytics, and user interfaces. Additionally, we explore the benefits, challenges, and future directions of IoT in improving medication adherence and enhancing the management of chronic diseases.
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References
- Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
- Shahane, Vishal. "A Comprehensive Decision Framework for Modern IT Infrastructure: Integrating Virtualization, Containerization, and Serverless Computing to Optimize Resource Utilization and Performance." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 53-75.
- Biswas, Anjanava, and Wrick Talukdar. "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)." Journal of Science & Technology 4.6 (2023): 55-82.
- N. Pushadapu, “AI-Powered Cloud Solutions for Improving Patient Experience in Healthcare: Advanced Models and Real-World Applications”, Hong Kong J. of AI and Med., vol. 4, no. 1, pp. 170–222, Jun. 2024
- Talukdar, Wrick, and Anjanava Biswas. "Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity." arXiv preprint arXiv:2408.04023 (2024).
- Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
- Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
- Choi, J. E., Qiao, Y., Kryczek, I., Yu, J., Gurkan, J., Bao, Y., ... & Chinnaiyan, A. M. (2024). PIKfyve, expressed by CD11c-positive cells, controls tumor immunity. Nature Communications, 15(1), 5487.
- Borker, P., Bao, Y., Qiao, Y., Chinnaiyan, A., Choi, J. E., Zhang, Y., ... & Zou, W. (2024). Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Cancer Research, 84(6_Supplement), 7479-7479.
- Gondal, Mahnoor Naseer, and Safee Ullah Chaudhary. "Navigating multi-scale cancer systems biology towards model-driven clinical oncology and its applications in personalized therapeutics." Frontiers in Oncology 11 (2021): 712505.
- Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.
- Pelluru, Karthik. "Prospects and Challenges of Big Data Analytics in Medical Science." Journal of Innovative Technologies 3.1 (2020): 1-18.
- Tatineni, Sumanth. "Exploring the Challenges and Prospects in Data Science and Information Professions." International Journal of Management (IJM) 12.2 (2021): 1009-1014.