A Machine Learning Algorithm Can Detect Depression in Children’s Speech
Scientists from the University of Vermont, US, developed a machine learning algorithm that is able to detect signs of depression and anxiety in the speech of children under the age of 8.
The researchers used an adapted version of a mood induction task called the Trier-Social Stress Task, intended to cause feelings of stress and anxiety in the subject. During the study, 71 children aged 3 and 8 were asked to tell a short story within 3 minutes. They were told that they would be judged based on how interesting it was.
To analyze children’s speech, scientists used a machine learning algorithm. The algorithm proved to be very successful at diagnosing children.
Study senior author Ryan McGinnis says: “The algorithm was able to identify children with a diagnosis of an internalizing disorder with 80% accuracy, and in most cases that compared really well to the accuracy of the parent checklist.”
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