[artículo] 
inThe American Journal of Psychiatry > Año 2021 - Vol. 178 - No.8 (Agosto) . - pp. 715-729

Título :	Evaluating the machine learning literature : a primer and user’s guide for psychiatrists
Tipo de documento:	texto impreso
Autores:	Adrienne Grzenda, Autor ; Nina V. Kraguljac, Autor ; William M. McDonald, Autor
Fecha de publicación:	2021
Artículo en la página:	pp. 715-729
Idioma :	Inglés (eng) Idioma original : Inglés (eng)
Palabras clave:	Psiquiatría académica, Ordenadores, Aprendizaje automático
Resumen:	Health care is in the midst of a big data explosion (1). An estimated zettabyte (one trillion gigabytes) of clinical data is produced annually, with the rate anticipated to double every 2 years (2). “Big data” consists of data sets of a size (volume), rate of accumulation (velocity), and diversity of data types (variety) that exceed the capacity of traditional storage and processing.
Link:	./index.php?lvl=notice_display&id=27121

[artículo] Evaluating the machine learning literature : a primer and user’s guide for psychiatrists [texto impreso] / Adrienne Grzenda, Autor ; Nina V. Kraguljac, Autor ; William M. McDonald, Autor . - 2021 . - pp. 715-729.
Idioma : Inglés (eng) Idioma original : Inglés (eng)
in The American Journal of Psychiatry > Año 2021 - Vol. 178 - No.8 (Agosto) . - pp. 715-729

Palabras clave:	Psiquiatría académica, Ordenadores, Aprendizaje automático
Resumen:	Health care is in the midst of a big data explosion (1). An estimated zettabyte (one trillion gigabytes) of clinical data is produced annually, with the rate anticipated to double every 2 years (2). “Big data” consists of data sets of a size (volume), rate of accumulation (velocity), and diversity of data types (variety) that exceed the capacity of traditional storage and processing.
Link:	./index.php?lvl=notice_display&id=27121