Subtyping First-Episode Psychosis based on Longitudinal Symptom Trajectories Using Machine Learning
Bibliographic record
Abstract
Abstract Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping longitudinal symptom trajectories after FEP would be useful for developing personalized treatment approaches, and being able to predict these trajectories at baseline would facilitate individual-level treatment planning. We utilized k-means clustering to identify distinct clusters of 411 FEP patients based on longitudinal positive and negative symptom patterns. Ridge logistic regression was then used to identify predictors of cluster membership using baseline data. Three clusters were identified, demonstrating unique demographic, clinical and treatment response profiles. 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), as well as higher antipsychotic doses. We effectively predicted patients’ cluster membership (AUC of 0.74). The most important predictive features included contrasting trends of apathy, affective flattening, and anhedonia for the LS and LPPN clusters. Global hallucination severity, positive thought disorder and manic hostility predicted PPNS. These results help parse the heterogeneity of FEP trajectories and may facilitate the development of personalized treatment approaches tailored to cluster characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".