Machine Learning Can Help Predict Social Outcomes in Patients at Risk of Psychosis

Brain imaging combined with baseline data about social functioning —such as how well one performs in school, at work, or in relationships—successfully predicted how well patients at high risk of psychosis and patients with recent-onset depression would be functioning one year later. Thefindings were reported inJAMA Psychiatry.If replicated, these results could help clinicians identify patients most at risk for poor outcomes and initiate treatment to prevent it. “[T]hese predictive models could inform the personalized prevention of functional impairment in patients with clinical high-risk states and patients with recent-onset depression,” wrote Nikolaus Koutsouleris, M.D., of Ludwig-Maximillian University in Germany and colleagues.The researchers analyzed data on 116 individuals considered to be at high risk for psychosis and 120 patients with recent-onset depression using machine learning. Machine learning is a new technology that uses computer programs to analyze extremely large amounts of data and develop models that can predict certain kinds of outcomes for individual patients.Using data collected at baseline, the machine learning models were able to successfully predict one-year social functioning in 76.9% of patients in high-risk states and 66.2% of patients with recent-onset depression. When combined with certain brain imaging findings, machine learning models successfully predicted social outcomes at one year in up to 83% of patients in high-risk states and 70%...
Source: Psychiatr News - Category: Psychiatry Tags: brain imaging clinical high risk for psychosis JAMA Psychiatry machine learning Nikolaus Koutsouleris predicting social functioning recent-onset depression Source Type: research