ObjectiveRelapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging and clinical data. Yet few efforts have been made to test these models in independent samples or show that they can outperform linear models. In this exploratory study, we examine whether machine learning models relative to linear models provide greater predictive accuracy and less overfitting.MethodThis longitudinal study included 63 methamphetamine-dependent (training sample) and 29 cocaine-dependent (test sample) individuals who completed an MRI scan during residential treatment. Linear and machine learning models predicting relapse a...
One of the major challenges in addiction treatment is relapse prevention, as rates of relapse follow...
Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequen...
With few exceptions, research in the addictive sciences has relied on linear statistics and methodol...
Objective: Relapse rates are consistently high for stimulant user disorders. In order to obtain prog...
Methamphetamine use disorder is associated with a high likelihood of relapse. Identifying robust pre...
BackgroundNearly half of individuals with substance use disorders relapse in the year after treatmen...
Background and objectives Clinical staff providing addiction treatment predict patient outcome poorl...
Background and objectives: Clinical staff providing addiction treatment predict patient outcome poor...
Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learn...
Delineating the processes that contribute to the progression and maintenance of substance dependence...
Background and aims: Clinical staff are typically poor at predicting alcohol dependence treatment ou...
Background: Identifying objective and accurate markers of cocaine dependence (CD) can innovate its p...
Functional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neur...
Long abstract: Substance use disorders (SUDs) are complex, highly dimensional conditions that are in...
Identifying predictive brain structure features in alcohol dependent subjects Sage Hahn, Nicholas Al...
One of the major challenges in addiction treatment is relapse prevention, as rates of relapse follow...
Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequen...
With few exceptions, research in the addictive sciences has relied on linear statistics and methodol...
Objective: Relapse rates are consistently high for stimulant user disorders. In order to obtain prog...
Methamphetamine use disorder is associated with a high likelihood of relapse. Identifying robust pre...
BackgroundNearly half of individuals with substance use disorders relapse in the year after treatmen...
Background and objectives Clinical staff providing addiction treatment predict patient outcome poorl...
Background and objectives: Clinical staff providing addiction treatment predict patient outcome poor...
Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learn...
Delineating the processes that contribute to the progression and maintenance of substance dependence...
Background and aims: Clinical staff are typically poor at predicting alcohol dependence treatment ou...
Background: Identifying objective and accurate markers of cocaine dependence (CD) can innovate its p...
Functional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neur...
Long abstract: Substance use disorders (SUDs) are complex, highly dimensional conditions that are in...
Identifying predictive brain structure features in alcohol dependent subjects Sage Hahn, Nicholas Al...
One of the major challenges in addiction treatment is relapse prevention, as rates of relapse follow...
Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequen...
With few exceptions, research in the addictive sciences has relied on linear statistics and methodol...