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@article{205578,
        author = {Sankpal Srushti and Keskar Rutuja and Jadhav Sakshi and Jogdand Asmita and Avdhute Prachi and Pandhare Akshata and Anjikhane Vaibhavi and Dange Sanghamitra},
        title = {Digital Biomarkers for Adverse Drug Reaction Prediction: Emerging Applications in Pharmacovigilance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7026-7035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205578},
        abstract = {Adverse drug reactions (ADRs) remain a major cause of patient morbidity, mortality, prolonged hospitalization, and increased healthcare expenditure worldwide. Conventional pharmacovigilance systems primarily rely on spontaneous reporting mechanisms, which often suffer from underreporting, delayed signal detection, and incomplete clinical information. Recent advances in digital health technologies have introduced digital biomarkers as novel tools for continuous monitoring of physiological, behavioral, and environmental parameters. Digital biomarkers are objective, quantifiable measures collected through wearable devices, smartphones, electronic health records (EHRs), and connected medical devices. These biomarkers offer real-time insights into patient health status and enable early identification of drug-related adverse events. The integration of digital biomarkers with artificial intelligence (AI), machine learning (ML), and big-data analytics has significantly enhanced the capability to predict ADRs before clinical manifestation. Furthermore, digital biomarker-driven pharmacovigilance supports personalized medicine by identifying individual risk factors and facilitating proactive interventions. This review discusses the concept of digital biomarkers, their sources, mechanisms of ADR prediction, applications in pharmacovigilance, emerging technologies, challenges, regulatory considerations, and future perspectives. The review highlights how wearable sensors, mobile health platforms, and predictive analytics are transforming traditional pharmacovigilance into a proactive and patient-centered surveillance system.},
        keywords = {Digital biomarkers, Adverse drug reactions, Pharmacovigilance, Artificial intelligence, Machine learning, Wearable devices, electronic health records, Signal detection.},
        month = {June},
        }