Predicting conversion to psychosis using machine learning: response to Cannon

Predicting conversion to psychosis using machine learning: response to Cannon

BackgroundWe previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model...

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Bibliographic Details
Main Authors: Jason Smucny, Tyrone D. Cannon, Carrie E. Bearden, Jean Addington, Kristen S. Cadenhead, Barbara A. Cornblatt, Matcheri Keshavan, Daniel H. Mathalon, Diana O. Perkins, William Stone, Elaine F. Walker, Scott W. Woods, Ian Davidson, Cameron S. Carter
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Psychiatry
Subjects:
schizophrenia
clinical high risk (CHR)
NAPLS
out of sample evaluation
scale of psychosis risk symptoms
generalizability
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1520173/full
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