Development of a Machine Learning Algorithm to Predict Outcomes among Young Adults with Psychotic Experiences

Principal Investigator: Dr. Jordan DeVylder, Associate Professor, NYU Silver School of Social Work 

Co-Investigator: Dr. Katharina Schultebraucks, Associate Professor, Department of Psychiatry, NYU Grossman School of Medicine

Dates of award: 9/1/2024 – 8/31/2025

Amount of award: $60,000

Young adults frequently self-report “psychotic experiences” on community surveys, yet we currently have no effective tools to distinguish youth with transient psychotic experiences from those who will go on to develop schizophrenia-spectrum disorders or other clinically/functionally significant outcomes. Machine learning approaches have been effective in predicting mental health outcomes among other high-risk groups, such as prospectively predicting post-traumatic stress disorder diagnoses among youth exposed to trauma. Machine learning applications in psychosis prevention have been limited to automated speech analysis. This newly funded project aims to employ machine learning methods to examine video and audio-recorded data and develop a novel algorithm for predicting persistent psychotic symptoms among young adults. Findings from this pilot study support the development of new clinical tools for evaluating risk for psychosis and related outcomes among young people. 

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