Machine Learning utilizes EEG information to foresee Antidepressant Response

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Specialists have built up a machine-learning algorithm that utilizes electroencephalogram (EEG) information to anticipate whether a selective serotonin reuptake inhibitor (SSRI) is probably going to profit a patient. They published their discoveries in Nature Biotechnology.

Analysts from the UT Southwestern Medical Center in Dallas, Texas and Stanford University in California have applied for a patent for the algorithm.

The investigation included over 300 patients with depression who were randomized to treatment with an SSRI or placebo as part of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) preliminary. Utilizing members’ EEG information, which measured electrical action in the brain cortex before treatment, scientists built up a machine-learning algorithm that analyzed the data.

The algorithm, they revealed, precisely anticipated which patients would profit by an SSRI within 2 months. Also, further research proposed patients unlikely to improve with an SSRI were probably going to profit by different interventions, including psychotherapy or brain stimulation.

Scientists approved their discoveries in 3 extra groups of patients.

“This study takes previous research, showing that we can predict who benefits from an antidepressant, and brings it to the point of practical utility,” said Amit Etkin, MD, Ph.D., a psychiatry professor at Stanford University, who worked with UT Southwestern psychiatrist and EMBARC trial leader Madhukar Trivedi, MD, to develop the algorithm.

Looking to the future, they intend to build up an artificial intelligence interface that can be widely integrated with EEG machines and to pick up approval from the US Food and Drug Administration.

Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Rainier Watchdog journalist was involved in the writing and production of this article.

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