University of Texas at Arlington professor Heng Huang's data-mining efforts could help improve the diagnosis and treatment of depression.
As part of cognitive behavior therapy, the most effective current treatment for depression, patients keep a record of their thoughts throughout each day between sessions, and doctors analyze their "thought journals" and use them to see if their patients' conditions are improving or getting worse. Huang has won a three-year, $500,000 U.S. National Science Foundation grant to use data mining to efficiently catalog and track depression patients' thought journals.
He will develop an automated system to analyze patient notes, which will be designed to replace the need for doctors to pore over pages and pages of notes taken by their patients. "Using data mining, it becomes much easier to analyze the thoughts and apply them to a treatment plan," Huang says.
He will develop multiple new computer models to read text and automatically analyze it, and the models will be based on artificial intelligence. The data also can be used to train new therapists because they can be provided with a dataset and make a recommendation, then compare their recommendation to the course of action that was prescribed.
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