The model identified underlying causes of mental illness, such as sleep disturbances or family conflict, that could be used to prescribe interventions, according to a March 5 news release.
The researchers used AI to build a neural network that mimics brain connections to predict which children would transition from lower to higher psychiatric risk within a year. That model is then used to score a questionnaire that ranks responses from the patient or parent about current behaviors, feelings and symptoms, to predict the likelihood of an escalation.
The model is capable of identifying whether youth patients will have an escalating mental illness within a year, with 84% accuracy. It can also identify underlying causes with 75% accuracy.
A corresponding study was published March 5 in Nature Medicine, which conducted psychosocial and brain development assessments of more than 11,000 children over five years.
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