سیستم پیشبینی کننده مبتنی بر هوش مصنوعی برای ارزیابی خطرات بلند مدت سلامت با استفاده از دادههای ملی مصرف دارو
کد: G-1967
نویسندگان: Negar Tadris Hassani * ℗
زمان بندی: زمان بندی نشده!
برچسب: پردازش سیگنال های پزشکی
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and Aims: The increasing complexity of drug regimens and the growing concern about adverse drug reactions have underscored the need for intelligent systems to monitor and predict health outcomes. While existing platforms detect drug interactions or side effects, there is still a gap in systems that analyze pharmaceutical consumption over time to forecast future health risks. This study aims to propose a national-level, AI-powered system that systematically registers and evaluates patients' pharmaceutical data to predict potential diseases and optimize treatment decisions. Method: The proposed system collects structured data from national e-prescription platforms and pharmacy records, including medication type, dosage, manufacturer, batch number, and administration date. It integrates these with global health alerts (e.g., FDA notifications) and recent scientific findings. Machine learning models are then trained on large-scale clinical and pharmaceutical datasets to assess drug interactions, detect patterns linked to future health conditions, and recommend preventive or therapeutic interventions. The system is designed with high-level security protocols to ensure patient privacy and restrict access to authorized medical professionals and governmental health authorities. Results: Real-time integration of international safety updates enables the system to provide timely alerts and adaptive treatment suggestions. Furthermore, the platform offers clinical decision support for acute care settings, allowing physicians to evaluate the safety and efficacy of treatment plans instantly. Conclusion: This innovative system represents a significant advancement in predictive and preventive healthcare, combining national pharmaceutical data with global scientific resources. It has the potential to improve patient safety, inform public health strategies, and support clinicians in personalized decision-making. Further development will involve clinical validation and potential international collaboration.
کلمات کلیدی
Artificial-Intelligence, Predictive-Medicine, Drug-Safety, Health-Forecasting, Machine-Learning, Public-Health
