Symptom Based

Symptom-based research focuses on leveraging patient-reported symptoms to improve disease diagnosis and understanding, primarily through machine learning. Current research heavily utilizes large language models (LLMs), Bayesian networks, and transformer-based architectures to extract, analyze, and classify symptoms from diverse sources like clinical notes and social media, often incorporating techniques like retrieval-augmented generation and knowledge graphs to enhance accuracy and interpretability. This approach holds significant promise for improving early disease detection, particularly in resource-constrained settings, and for providing more explainable and trustworthy diagnostic tools for healthcare professionals. The development of robust symptom-based models is crucial for advancing personalized medicine and improving patient care.