Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk.. Machine learning and data extracted from one 20-second phase of the task are used to predict diagnosis in a large sample of children with and without an internalizing diagnosis. Nevertheless, the proposed approach provides a rapid, objective, and accurate means for diagnosing internalizing disorders in young children. This new approach reduces the time required for diagnosis while also limiting the need for highly trained personnel – each of which can help to reduce the length of waitlists for child mental health services. While these results can likely be improved and extended, this is an important first step in reducing the barriers associated with assessing young children for internalizing disorders.

ML, KNN, LAN

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