Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning
Abstract Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help develop personalized treatment approaches. We utilized k-means clustering to identify clusters of 411 FEP patients based on longitudinal posi...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Mental Health Research |
| Online Access: | https://doi.org/10.1038/s44184-025-00129-7 |
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