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

PLoS ONE

Volumn 12, Issue 4, 2017, Pages

  Acion, Laura ^a,b   Kelmansky, Diana ^a   Laan, Mark Der Van ^c   Sahker, Ethan ^b   Jones, DeShauna ^b   Arndt, Stephan ^b,d  

^a INSTITUTO DE CÁLCULO   (Argentina)

^b UNIVERSITY OF IOWA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF IOWA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

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;

EID: 85017207083     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0175383     Document Type: Article

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