ArticleNpj mental health research2025
Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning.
Article in Npj mental health research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.Current psychiatry reports · 2025Review
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Authors and funding
7 authors.
Funding
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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 positive and negative symptoms. Three clusters were identified. Cluster 1 exhibits lower positive and negative symptoms (LS), lower antipsychotic dose, and relatively higher affective psychosis; Cluster 2 shows lower positive symptoms, persistent negative symptoms (LPPN), and intermediate antipsychotic doses; Cluster 3 presents persistently high levels of both positive and negative symptoms (PPNS), and higher antipsychotic doses. We predicted cluster membership (AUC of 0.74) using ridge logistic regression on baseline data. Key predictors included lower levels of apathy, affective flattening, and anhedonia/asociality in the LS cluster, compared to the LPPN cluster. Hallucination severity, positive thought disorder and manic hostility predicted PPNS. These results help parse the FEP trajectory heterogeneity and may facilitate the development of personalized treatments.
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