biostudies-literatureUnknown11(1)Wang XCognitive impairments are generally observed in patients with schizophrenia. However, it is unclear whether neurocognitive dysfunction can predict the efficacy of antipsychotics for first-episode schizophrenia (FES). Machine learning methods provide a relatively unbiased approach when evaluating heterogeneous data, especially when building multifactor prediction models. This study conducted a secondary analysis based on the Chinese FES Trial (CNFEST), which was a 1-year study involving a randomized controlled trial for the first eight weeks followed by a 48-week open-label observation. The current study aimed to build a prediction model of eight-week antipsychotic response based on baseline clinical and demographic features. Six machine learning algorithms, including random forest, eXtremeSchizophrenia (Heidelberg, Germany)105https://www.ebi.ac.uk/biostudies/studies/S-EPMC12284124biostudies-literatureNeurocognition as a major predictor of 8-week response to antipsychotics for drug-naive first-episode schizophrenia using machine learning.PMC12284124Yin XZheng YGuo XHu WWang XJi YHuang BPu CYu XGao TfalseNeurocognition as a major predictor of 8-week response to antipsychotics for drug-naive first-episode schizophrenia using machine learning.Cognitive impairments are generally observed in patients with schizophrenia. However, it is unclear whether neurocognitive dysfunction can predict the efficacy of antipsychotics for first-episode schizophrenia (FES). Machine learning methods provide a relatively unbiased approach when evaluating heterogeneous data, especially when building multifactor prediction models. This study conducted a secondary analysis based on the Chinese FES Trial (CNFEST), which was a 1-year study involving a randomized controlled trial for the first eight weeks followed by a 48-week open-label observation. The current study aimed to build a prediction model of eight-week antipsychotic response based on baseline clinical and demographic features. Six machine learning algorithms, including random forest, eXtreme2025-01-01T00:00:00Z2025 Jul2026-07-15T06:53:16.895Z2025-08-16T03:05:44.686ZS-EPMC122841244069582910.1038/s41537-025-00640-y