UZIMA-DS/UZIMA-DS-AI-Ready-Synthetic-Data

The AI-Ready Synthetic Data study, part of the UZIMA-DS hub (AKU & University of Michigan, funded by NIH DSI-Africa), is creating curated synthetic datasets to support AI/ML research and improve health outcomes in Kenya while enabling secure data sharing in resource-limited settings. # UZIMA-DS-AI-Ready-Synthetic-Data The **AI-Ready Synthetic Data Study** is conducted by the **UtiliZing health Information for Meaningful Impact in East Africa through Data Science (UZIMA-DS) Hub**, led by the **Aga Khan University in East Africa (AKU)** and the **University of Michigan**. UZIMA-DS is a **U54 Research Hub** funded under the **NIH Data Science for Health Discovery and Innovation in Africa Initiative (DSI-Africa)**. The overarching goal of UZIMA-DS is to build a scalable and sustainable platform that applies novel approaches to data assimilation and advanced AI/ML-based methods. These methods serve as early warning systems to improve health outcomes for at-risk mothers and children, and to enhance mental health outcomes for at-risk adolescents and young adults in Kenya. As part of this parent project, our aim is to: - Create a curated **AI-ready synthetic dataset** - Evaluate causal relationships in synthetic data Synthetic data offers a promising approach to overcoming challenges in data access and enabling broad sharing of AI-ready datasets in health research, particularly in **resource-constrained settings**. --- ## Publication This project is described in detail in the following peer-reviewed publication: _Synthetic data generation of health and demographic surveillance systems data: a case study in a low- and middle-income country_ JAMIA Open, Volume 8, Issue 6, December 2025 Synthetic data generation o… ## Data Access Data are available through the **Inter-university Consortium for Political and Social Research (ICPSR)**, a unit of the Institute for Social Research at the University of Michigan: doi.org ## More Information See the OSF project: DOI 10.17605/osf.io