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GAINESVILLE, Fla. 鈥� Virtual patients with diseases that precisely mimic real-life patients are the next frontier in medicine. Such 鈥渄igital twins鈥� would allow health researchers to drastically speed the development of new treatments.

But before those digital patients can be created, scientists must train computer systems to recognize tens of thousands of threats from inside and outside our bodies and to understand lessons gleaned from billions of data points from actual patients.

Current technology can鈥檛 interpret the vast and inconsistent information from patients along with the complex data accumulating from our social, built, and physical environments. Smartphones just aren鈥檛 smart enough to understand the complexities of diabetes, for example.

But a new five-year, $3 million research project funded by the NIH鈥檚 National Institute of Environmental Health Sciences aims to create the digital dictionary and playbook to help technology replicate the way humans experience disease.

The study is connected to a $1.75-million project at UF to create , a digital replica of Florida that can model the effects of natural disasters and future health threats. The project combines the power of several units at UF, a partnership with NVIDIA, and HiPerGator, UF鈥檚 supercomputer.

鈥淭his grant is important for our research team, because it helps us develop new methods and ways to manage big data. It significantly enhances our ability to understand the complexity of how environmental factors affect human health,鈥� said project director Jiang Bian, Ph.D., a professor in the UF College of Medicine鈥檚 . The tools created for the project will be shared nationwide.

Two other key investigators on the eight-member project team are Hui Hu, Ph.D., from Brigham and Women鈥檚 Hospital in Boston, and Cui Tao, Ph.D., from the Mayo Clinic in Jacksonville.

The project鈥檚 scope is notable for integrating social determinants of health, such as education and economic status, with ecological factors, such as pollution and natural disasters. Combined, these factors could help illuminate a patient鈥檚 risk for disease and allow caregivers to tailor their approach.

The project鈥檚 new data management system will offer three primary tools. One is a massive knowledge graph that shows relationships across the 鈥渆xposome鈥� 鈥� a broad term that covers external and internal influences on health. A second tool will be a user-friendly app to allow the selection and filtering of data, and the third will produce data sets linked to real world patient, or cohort data.

To build these tools, the research team plans to create new software, called SPACESCANS, that can mold mountains of scattered medical and environmental information into useful datasets.

鈥淏y generating analysis-ready datasets, SPACESCANS will significantly enhance our ability to conduct comprehensive and insightful analyses, ultimately contributing to a deeper understanding of environmental influences on health,鈥� said project co-leader Hu.

The new information generated by this project will help researchers decipher the complexity of how patients鈥� genes interact with their environment, moving science closer to understanding which patients in a particular circumstance are highly vulnerable to certain diseases. Initially, the research team will focus on dementia and chronic conditions such as diabetes, before broadening the scope to other illnesses.

Researchers behind an ongoing red tide study hope to use the project鈥檚 tools to assess the effects of red tide on human health.

The tools and methods will allow policy officials to tackle public health problems Floridians face, said , Ph.D., FAMIA, an associate professor in HOBI. 鈥淥ne prominent example is that we will develop AI models to identify and characterize geographic hotspots along the west coast where the red tide has the most impact on acute and chronic human diseases,鈥� Guo said. 鈥淭his effort would help public health agencies and policymakers make better use of their resources and efforts.鈥�