Machine learning–based outcome prediction and novel hypotheses generation for substance use disorder treatment | Zendy

Murtaza Nasir | Zendy; Nichalin S. Summerfield | Zendy; Asil Oztekin | Zendy; Margaret Knight | Zendy; Leland K. Ackerson | Zendy; Stephanie Carreiro | Zendy

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Machine 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.

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