ARTIFICIAL INTELLIGENCE APPROACH FOR SAFE MEDICATION RECOMMENDATION SYSTEM WITH DRUG INTERACTION ANALYSIS

  • Unique Paper ID: 197135
  • Volume: 12
  • Issue: 11
  • PageNo: 5696-5700
  • Abstract:
  • The increasing complexity of modern healthcare has led to a significant rise in the use of multiple medications, thereby elevating the risk of adverse drug interactions and medication errors. This paper presents an Artificial Intelligence (AI)-based approach for a safe medication recommendation system integrated with drug interaction analysis. The proposed system leverages machine learning algorithms and clinical data to evaluate patient-specific parameters such as age, medical history, allergies, and existing prescriptions. By analysing potential drug–drug interactions in real time, the system identifies harmful combinations and suggests safer alternative medications. Additionally, the model incorporates a knowledge base of pharmacological data and continuously improves its recommendations through data-driven learning. The system aims to assist healthcare professionals in making informed decisions, enhance patient safety, and reduce the incidence of adverse drug events. Experimental results demonstrate the effectiveness of the approach in accurately detecting interactions and providing reliable medication suggestions. This research contributes to the advancement of intelligent healthcare systems by combining predictive analytics with practical clinical applications.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{197135,
        author = {G Krupa Havilah and Venkata Phanindra kadali and Ramesh Putti and Venkata Sriram Gopal Nemani and Vinay Sanaboyina and Dr. Y. Venkat},
        title = {ARTIFICIAL INTELLIGENCE APPROACH FOR SAFE MEDICATION RECOMMENDATION SYSTEM WITH DRUG INTERACTION ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5696-5700},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197135},
        abstract = {The increasing complexity of modern healthcare has led to a significant rise in the use of multiple medications, thereby elevating the risk of adverse drug interactions and medication errors. This paper presents an Artificial Intelligence (AI)-based approach for a safe medication recommendation system integrated with drug interaction analysis. The proposed system leverages machine learning algorithms and clinical data to evaluate patient-specific parameters such as age, medical history, allergies, and existing prescriptions. By analysing potential drug–drug interactions in real time, the system identifies harmful combinations and suggests safer alternative medications. Additionally, the model incorporates a knowledge base of pharmacological data and continuously improves its recommendations through data-driven learning. The system aims to assist healthcare professionals in making informed decisions, enhance patient safety, and reduce the incidence of adverse drug events. Experimental results demonstrate the effectiveness of the approach in accurately detecting interactions and providing reliable medication suggestions. This research contributes to the advancement of intelligent healthcare systems by combining predictive analytics with practical clinical applications.},
        keywords = {Artificial Intelligence, Drug Interaction Analysis, Medication Recommendation System, Machine Learning, Clinical Decision Support System, Patient Safety, Adverse Drug Reactions.},
        month = {April},
        }

Cite This Article

Havilah, G. K., & kadali, V. P., & Putti, R., & Nemani, V. S. G., & Sanaboyina, V., & Venkat, D. Y. (2026). ARTIFICIAL INTELLIGENCE APPROACH FOR SAFE MEDICATION RECOMMENDATION SYSTEM WITH DRUG INTERACTION ANALYSIS. International Journal of Innovative Research in Technology (IJIRT), 12(11), 5696–5700.

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