Researchers at Carnegie Mellon University (CMU) and the University of Pittsburgh have developed an approach to identifying suicidal individuals by analyzing the changes in how their brains represent certain concepts, such as death, cruelty, and trouble.
CMU professor Marcel Just says the technique uses machine-learning algorithms to assess the neural representation of specific concepts related to suicide.
As part of the study, the researchers presented a list of 10 death-related words, 10 words relating to positive concepts, and 10 words related to negative ideas to two groups of 17 people with known suicidal tendencies and 17 neurotypical individuals. The researchers then applied a machine-learning algorithm to six word concepts that best discriminated between the two groups as the participants thought about each one while in a brain scanner.
The test was able to identify, with 91% accuracy, whether a participant was from the control group or the suicidal group.
From Carnegie Mellon News
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