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Neural activity during inhibitory control predicts suicidal ideation with machine learning

Medicine and Health

Neural activity during inhibitory control predicts suicidal ideation with machine learning

J. Nan, G. Grennan, et al.

This groundbreaking research harnesses machine learning to differentiate individuals with and without suicidal ideation using EEG data. With a model boasting 89% sensitivity and 98% specificity, the study illuminates key brain regions, enhancing our understanding of mental health. Conducted by Jason Nan, Gillian Grennan, Soumya Ravichandran, Dhakshin Ramanathan, and Jyoti Mishra, this work paves the way for innovative assessments in psychological health.

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