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A study was conducted to identify individuals with a higher risk of treatment resistance (TR) in the early stages of schizophrenia. Using an automated machine learning approach, four probabilistic classification models were developed to predict future TR development. The study found that baseline information, including schizophrenia diagnosis and age of onset, as well as longitudinal clinical information, were important predictors for TR development. The risk calculator developed in this study could assist in personalized interventions to prevent or delay TR development in the early stages of schizophrenia.
