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@article{190558,
        author = {Rithu Mary Varghese and Manasa and Mithra Jayapal and Teresa Benny and Anitha A.S},
        title = {MediTrust - Intelligent Hospital and Drug Interaction Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2935-2942},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190558},
        abstract = {Drug–drug interactions (DDIs) are a major con- tributor to adverse drug reactions and pose a serious challenge to patient safety in modern healthcare systems, particularly in scenarios involving polypharmacy. Conventional hospital man- agement platforms primarily focus on appointments and record handling, while often lacking intelligent mechanisms for real- time drug safety analysis and personalized clinical support. As a result, potentially harmful medication combinations and abnormal patient conditions may go undetected until adverse outcomes occur.
This paper presents MediTrust, an intelligent hospital and drug interaction platform that integrates machine learning–based DDI prediction with patient vital monitoring and automated consultation support. The proposed system enables users to log prescribed medicines and dosages, upon which trained machine learning models analyze possible interaction risks, associated side effects, and severity levels using publicly available drug–drug interaction datasets. In addition, patient-entered vital parameters such as blood pressure, blood sugar level, body temperature, and heart rate are incorporated to provide contextual and personalized safety insights.
To further enhance accessibility and decision support, an AI- driven conversational assistant is employed to deliver health guid- ance in simple and understandable language, while facilitating doctor–patient communication through appointment scheduling and secure chat. Experimental evaluation demonstrates that the integration of intelligent drug interaction analysis within a hospital workflow can significantly improve medication safety and clinical awareness. The proposed platform bridges the gap between drug safety research and real-world healthcare delivery, promoting proactive, data-driven, and patient-centric treatment.},
        keywords = {—Adverse Drug Reaction, Drug–Drug Interaction, Machine Learning, Graph-Based Drug Analysis, Intelligent Hos- pital Systems, Clinical Decision Support},
        month = {January},
        }