International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | TabAlert |
|---|---|
| ABSTRACT | Medication adherence is an essential aspect of effective healthcare management, yet many individuals struggle to follow prescribed medication schedules consistently. Forgetting doses, confusion about dosage instructions, and lack of proper tracking systems often lead to missed or incorrect medication intake. Such issues can reduce treatment effectiveness and increase the risk of health complications. TabAlert AI is a smart medication reminder and management system developed to improve medication adherence through automation and AI-based support. The project focuses on providing users with a reliable and user-friendly platform to manage daily medication routines efficiently. By using modern web technologies and artificial intelligence, TabAlert AI minimizes dependence on manual reminders and promotes consistency in medication intake. The system allows users to register medications along with dosage, timing, and frequency details. Based on this information, the application sends timely notifications to remind users to take their medications as scheduled. These reminders are particularly beneficial for individuals managing multiple or long-term medications. An important feature of TabAlert AI is its AI assistant, which enables users to interact with the system using natural language. The assistant offers guidance related to reminders, medication schedules, and application usage, making the system easier to use for all users. Additionally, TabAlert AI provides basic analytics and data visualization to help users track medication history and adherence trends. Overall, the project demonstrates how artificial intelligence and automation can be effectively applied to improve medication management and support better healthcare outcomes. |
| AUTHOR | V DHARINEESH, B. RAMESH KUMAR Student, Department of Computer Applications, Sri Ramakrishna College of Arts and Science, Coimbatore, India Assistant Professor, Department of Computer Applications, Sri Ramakrishna College of Arts & Science, Coimbatore, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402038 |
| pdf/38_TabAlert.pdf | |
| KEYWORDS | |
| References | [1] S. Sabaté, Adherence to Long-Term Therapies: Evidence for Action. Geneva, Switzerland: World Health Organization, 2003. [2] L. Osterberg and T. Blaschke, “Adherence to medication,” New England Journal of Medicine, vol. 353, no. 5, pp. 487–497, 2005. [3] E. Vrijens et al., “A new taxonomy for describing and defining adherence to medications,” British Journal of Clinical Pharmacology, vol. 73, no. 5, pp. 691–705, 2012. [4] E. J. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York, NY, USA: Basic Books, 2019. |
