Relapse Prediction

Relapse prediction research focuses on developing accurate models to anticipate symptom exacerbations in chronic conditions like schizophrenia and multiple sclerosis, enabling timely interventions. Current research employs various machine learning approaches, including random forests, logistic regression, and deep learning architectures like LSTMs, often leveraging diverse data sources such as mobile sensing, sleep behavior, and environmental factors to improve prediction accuracy. These efforts aim to personalize predictions by considering individual patient characteristics and ultimately enhance patient care and treatment outcomes through earlier intervention.