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@article{189439,
        author = {Radhika Avhad and Shravani Wayal and Ojas Sharma and Pratik Shinde and Komal Munde},
        title = {SepsAI Early Sepsis Detection Using Machine Learning on Physiological and Clinical Parameters},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {6488-6499},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189439},
        abstract = {Sepsis is a life-threatening medical condition caused by the body’s extreme response to infection, leading to organ failure and potentially death if not detected early. Traditional diagnostic methods depend on manual interpretation of physiological data, which can delay timely intervention and increase mortality rates. SepsAI is a web-based intelligent system designed to detect sepsis risk efficiently using machine learning techniques. This paper explores the research gap in existing clinical detection approaches, details the methodology behind the development of SepsAI, and demonstrates how it enhances prediction accuracy and accessibility. The proposed model leverages physiological and biochemical parameters such as heart rate, temperature, and lactate levels, processed through an XGBoost- based classifier for early sepsis prediction. The trained model is integrated into a user-friendly web interface that provides real-time assessment of sepsis probability. Future work aims to incorporate IoT-enabled health monitoring devices for continuous, automated patient observation.},
        keywords = {},
        month = {December},
        }