Sequential Treatment

Sequential treatment research focuses on optimizing treatment strategies over time, adapting interventions to individual patient responses and evolving conditions. Current efforts leverage diverse machine learning models, including recurrent neural networks, causal trees, and sum-product networks, to predict optimal treatment sequences and identify responsive patient subgroups from observational and clinical trial data. This field is crucial for advancing personalized medicine, improving treatment efficacy, and enhancing the efficiency of healthcare resource allocation across various applications, from chronic disease management to targeted marketing campaigns.