Longitudinal Study

Longitudinal studies track changes in subjects over extended periods, aiming to understand dynamic processes and causal relationships. Current research utilizes diverse methodologies, including Bayesian networks, machine learning models (like Bayesian Causal Forests and various deep learning architectures), and survival analysis, to analyze data from various domains, such as healthcare, education, and robotics. These studies are crucial for gaining insights into complex phenomena that unfold over time, informing interventions and improving predictions in diverse fields, from disease progression to educational outcomes and human-robot interaction.