[artículo] 
inThe American Journal of Psychiatry > Año 2023 - Vol. 180 - No. 11 (Noviembre) . - pp. 836-840

Título :	Are We There Yet? Predicting Conversion to Psychosis Using Machine Learning
Tipo de documento:	texto impreso
Autores:	Jason Smucny, Autor ; Ian Davidson, Autor ; Cameron S. Carter, Autor
Fecha de publicación:	2024
Artículo en la página:	pp. 836-840
Idioma :	Inglés (eng) Idioma original : Inglés (eng)
Palabras clave:	Psicosis, Espectro de la esquizofrenia y otros trastornos psicóticos, Aprendizaje automático
Resumen:	The ability to accurately predict conversion to psychosis from clinical and other measurable features of an at-risk state is critically important to clinicians. To demonstrate clinical utility, these models should predict conversion with at least 80% sensitivity and specificity (1) with concurrently high positive and negative predictive value (PPV/NPV).
Link:	./index.php?lvl=notice_display&id=31299

[artículo] Are We There Yet? Predicting Conversion to Psychosis Using Machine Learning [texto impreso] / Jason Smucny, Autor ; Ian Davidson, Autor ; Cameron S. Carter, Autor . - 2024 . - pp. 836-840.
Idioma : Inglés (eng) Idioma original : Inglés (eng)
in The American Journal of Psychiatry > Año 2023 - Vol. 180 - No. 11 (Noviembre) . - pp. 836-840

Palabras clave:	Psicosis, Espectro de la esquizofrenia y otros trastornos psicóticos, Aprendizaje automático
Resumen:	The ability to accurately predict conversion to psychosis from clinical and other measurable features of an at-risk state is critically important to clinicians. To demonstrate clinical utility, these models should predict conversion with at least 80% sensitivity and specificity (1) with concurrently high positive and negative predictive value (PPV/NPV).
Link:	./index.php?lvl=notice_display&id=31299