A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing.

ABSTRACT: Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters to code therapy sessions, which requires considerable time, training, and financial costs. Natural language processing techniques have recently been utilized for coding MI sessions using machine learning techniques, rather than human coders, and preliminary results have suggested these methods hold promise. The current study extends this previous work by introducing two natural language processing models for automatically coding MI sessions via computer. The two models differ in the way they semantically represent session content, utilizing either 1) simple discrete sentence features (DSF model) and 2)

SUBMITTER: Tanana M 

PROVIDER: S-EPMC4842096 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature