Subtyping First-Episode Psychosis based on Longitudinal Symptom Trajectories Using Machine Learning Article Swipe
Yanan Liu
,
Sara Jalali
,
Ridha Joober
,
Martín Lepage
,
Srividya N. Iyer
,
Jai Shah
,
David Benrimoh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.09.17.24313827
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.09.17.24313827
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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- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.09.17.24313827
- OA Status
- green
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402654284
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https://openalex.org/W4402654284Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.09.17.24313827Digital Object Identifier
- Title
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Subtyping First-Episode Psychosis based on Longitudinal Symptom Trajectories Using Machine LearningWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-18Full publication date if available
- Authors
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Yanan Liu, Sara Jalali, Ridha Joober, Martín Lepage, Srividya N. Iyer, Jai Shah, David BenrimohList of authors in order
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https://doi.org/10.1101/2024.09.17.24313827Publisher landing page
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greenOpen access status per OpenAlex
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https://doi.org/10.1101/2024.09.17.24313827Direct OA link when available
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Subtyping, Psychosis, Psychology, Cognitive psychology, Artificial intelligence, Computer science, Psychiatry, Programming languageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.22854798 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |