Physiological State

Physiological state research focuses on understanding and characterizing an individual's internal biological condition using various data sources, aiming to improve disease prediction, diagnosis, and treatment. Current research employs machine learning models, including transformer-based architectures and novel algorithms like Hawkes processes, to analyze physiological signals (e.g., ECG, GSR) and clinical narratives, often addressing challenges like noise reduction and data sparsity. This work has significant implications for personalized medicine, enabling more precise health monitoring and proactive interventions across diverse applications such as diabetes management and mental health care.