Health Intervention

Health interventions, encompassing a broad range of strategies to improve health outcomes, are increasingly leveraging digital technologies and data-driven approaches. Current research focuses on developing and evaluating personalized interventions using machine learning algorithms, such as reinforcement learning and Thompson sampling, often within mobile health (mHealth) platforms. These efforts aim to enhance engagement and efficacy by tailoring interventions to individual needs and contexts, addressing challenges like missing data and imbalanced datasets through techniques like imputation and causal inference. The ultimate goal is to improve the effectiveness and reach of health interventions, leading to better health outcomes and more efficient resource allocation.