Open AccessMachine learning–based outcome prediction and novel hypotheses generation for substance use disorder treatment
Author(s) -
Murtaza Nasir,
Nichalin S. Summerfield,
Asil Oztekin,
Margaret Knight,
Leland K. Ackerson,
Stephanie Carreiro
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa350
Subject(s) - machine learning , artificial intelligence , logistic regression , computer science , attendance , gradient boosting , random forest , boosting (machine learning) , artificial neural network , regression , psychology , psychotherapist , economics , economic growth
Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a novel data analytics approach using machine learning models to discover interaction effects that might be neglected by traditional hypothesis-generating approaches.