The bridge between healthcare as it stands today and a future where clinicians can draw actionable conclusions from all of the data being collected on patients will be built, in part, on the analysis tools that help get the job done. The National Science Foundation has awarded a five-year $535,763 grant to a University of Texas at Arlington researcher who is working to develop those computing tools.
Junzhou Huang, PhD, is a professor in UTA's department of computer science and engineering. The grant will help him develop predictive models for patient outcomes and treatments based on imaging data, genomics and other patient-specific information.
"Access to different multiple-source data will allow doctors and scientists to develop better treatments for patients," Dr. Huang said in a statement. "There is no current research in data mining to integrate very complex image-omics data, but if we are successful, scientists will have a much broader base of information to draw upon when seeking cures for diseases such as cancer."
According to Dr. Huang, as current technology stands, the data sets that would enable clinicians to provide next-generation care are much too large to view in a meaningful way.
Including this latest grant, the NSF has award Dr. Huang a total of $2.6 million in research funding since 2014.