Dell Medical School at The University of Texas at Austin is accelerating innovation and research by creating a Biomedical Data Science Hub to help solve complex research and clinical problems.
Imagine having a complicated scientific question: How do we predict who will be diagnosed with Type 2 diabetes based not only on clinical and family history, but also on lifestyle, community factors, work life, and medical history?
The answer could improve the lives of millions because it could lead to early, simple, preventive interventions. That’s the power of big data analytics in healthcare: It uses huge amounts of a population’s data and state-of-the-art analysis methods to boil it down to a small core of information to potentially help prevent individual illnesses and large-scale epidemics, cure disease, personalize medical care, reduce expenses, and more.
Currently, Dell Med’s data core scientists are carefully extracting and curating health data from myriad sources to provide a fuller, more detailed picture of all the factors—clinical and nonclinical—that affect our community. But the data are scattered and can be difficult and time-consuming to work with and understand.
The Biomedical Data Science Hub will tap into that data and figure out the best way to use that data effectively and efficiently to answer important, timely local questions, such as how to prevent and best treat diabetes. It will help analyze the data to fully represent the target population, advise whether to use classical analyses versus the latest machine learning technique, and assist in identifying other resources at UT Austin that could help optimize the study’s potential so the results could quickly and efficiently inform clinical and public health practice.
