Vanderbilt develops EHR data-mining tool to identify disease associations

Nashville, Tenn.-based Vanderbilt University engineers created a new tool that can identify disease co-morbidities by analyzing de-identified EHR data and medical diagnosis codes, according to a Feb. 18 news release.

The toolkit, called Phenome-Disease Association Study, uses machine learning algorithms to perform association studies and identify disease co-morbidities across time in EHRs.

Vanderbilt researchers tested the tool for three conditions: Alzheimer's disease, autism spectrum disorder and optic neuritis. The team found that the toolkit was able to identify lesser-known conditions associated with the diseases that may support earlier monitoring or medical detection.  

"We are excited about the opportunities to discover new risk factors and associations of diseases in the clinical record," said Bennett Landman, associate professor of electrical engineering, computer engineering and computer science at Vanderbilt, according to the release. "Overall, our goal is to advance engineering and clinical science to improve the understanding and care of patients."

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