Physical Symptom

Physical symptom research focuses on understanding and predicting the manifestation of symptoms across various conditions, aiming to improve diagnosis and treatment. Current research utilizes diverse machine learning approaches, including transformer models (like BERT and RoBERTa), XGBoost, and deep learning architectures, to analyze diverse data sources such as speech, text (e.g., social media posts, clinical notes), and physiological signals (e.g., respiratory sounds). These efforts hold significant potential for improving diagnostic accuracy, personalizing healthcare interventions, and enabling earlier detection of diseases, particularly in contexts with limited access to healthcare professionals.