Use of a machine learning framework to predict substance use disorder treatment success

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified predi...

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Detalles Bibliográficos
Autores principales: Acion, L., Kelmansky, D., Laan, M.D.V., Sahker, E., Jones, D., Arndt, S.
Formato: JOUR
Materias:
adult
algorithm
area under the curve
Article
artificial neural network
controlled study
decision making
drug dependence
education
employment status
female
Hispanic
human
length of stay
machine learning
major clinical study
male
methodology
prediction
receiver operating characteristic
sensitivity analysis
substance abuse
super learning
treatment outcome
adolescent
computer assisted diagnosis
factual database
middle aged
prognosis
regression analysis
socioeconomics
young adult
Adolescent
Adult
Area Under Curve
Databases, Factual
Diagnosis, Computer-Assisted
Female
Humans
Length of Stay
Machine Learning
Male
Middle Aged
Neural Networks (Computer)
Prognosis
Regression Analysis
ROC Curve
Socioeconomic Factors
Substance-Related Disorders
Treatment Outcome
Young Adult
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_19326203_v12_n4_p_Acion
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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