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Data Science for Dynamic Intervention Decision-making Lab

Advancing Intervention Data Science, by Design

Our lab is located in the Institute for Social Research at the University of Michigan. We are group of data scientists from various disciplines (including statistics, psychology, information science, and computer science). Our researchers use data—and, in particular, randomized trial designs—to learn how best to design a treatment plan for individuals struggling with a variety of health disorders such as anxiety, autism, serious mental illness, HIV, drug-abuse, or weight loss. A treatment plan (also known as an adaptive interventions or a dynamic treatment regime) is a guide for doctors or other clinicians regarding how best to start treatment, monitor progress and know what to do based on how the patient’s health improves or not. Such plans are also important in education intervention settings, for example, to improve learning among children with autism in school settings. The problem is that in many settings, clinicians, education practitioners or other stakeholders often do not know how to create the ideal treatment/intervention plan, that is, one that leads to improved outcomes for the greatest numbers of individuals.  Our lab uses data science tools such as mobile health technology (health apps), experiments, statistical analyses, and algorithmic methods to discover and design the best possible treatment plans.

Our Mission

To improve health and education outcomes by developing, demystifying and disseminating data science tools for making better sequential (dynamic) intervention decisions.

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