SymptoTrackAI Hybrid RAG Chatbot for Symptom Monitoring

Abstract

The rapid advancement of digital technologies in the healthcare domain has significantly transformed the way individuals access medical information and monitor their health conditions. In recent years, the growing demand for accessible and continuous health monitoring solutions has highlighted the importance of intelligent digital systems capable of assisting individuals in understanding and interpreting symptoms effectively. Traditional health information systems and symptom checking methods often rely on static databases or rule-driven decision processes that lack contextual awareness and adaptability. These approaches frequently struggle to interpret natural language symptom descriptions provided by users, leading to inaccurate suggestions and limited user trust. The increasing reliance on digital platforms for preliminary health guidance has therefore created a strong need for intelligent systems capable of interpreting symptoms more effectively while ensuring reliable and meaningful outputs for users seeking early health insights. The proposed system introduces an intelligent symptom monitoring approach that utilizes advanced semantic analysis techniques to interpret userprovided symptom descriptions and identify relevant health conditions based on similarity patterns within medical data sources. The system focuses on analyzing textual symptom descriptions entered by users in natural language and comparing them with previously stored medical information to identify the most relevant matches. Through an efficient similarity analysis process, the system provides meaningful health-related guidance while maintaining high responsiveness and accuracy. The proposed approach improves the reliability and contextual relevance of symptom interpretation while supporting efficient health monitoring assistance. The system demonstrates how intelligent information retrieval techniques can enhance digital healthcare solutions by improving response relevance, reducing misleading outputs, and enabling scalable symptom monitoring capabilities. Keywords: Symptom Monitoring, Semantic Retrieval, Health Informatics, Artificial Intelligence.