A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

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

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

J Subst Abuse Treat

University of Utah, School of Computing, 50S. Central Campus Drive Room 3190, Salt Lake City, UT, United States. Electronic address:

Published: June 2016

AI Article Synopsis

  • - Motivational interviewing (MI) is effective for treating substance use disorders, but traditional coding of therapy sessions by human raters is time-consuming and costly.
  • - This study introduces two machine learning models using natural language processing to automatically code MI sessions, comparing their accuracy to human ratings using a sample of 341 sessions.
  • - The simpler discrete sentence features (DSF) model outperformed the more complex recursive neural networks (RNN) model in coding accuracy, particularly for certain therapist behaviors, suggesting NLP could improve efficiency in MI research and monitoring.

Article 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) more complex recursive neural networks (RNN model). Utterance- and session-level predictions from these models were compared to ratings provided by human coders using a large sample of MI sessions (N=341 sessions; 78,977 clinician and client talk turns) from 6 MI studies. Results show that the DSF model generally had slightly better performance compared to the RNN model. The DSF model had "good" or higher utterance-level agreement with human coders (Cohen's kappa>0.60) for open and closed questions, affirm, giving information, and follow/neutral (all therapist codes); considerably higher agreement was obtained for session-level indices, and many estimates were competitive with human-to-human agreement. However, there was poor agreement for client change talk, client sustain talk, and therapist MI-inconsistent behaviors. Natural language processing methods provide accurate representations of human derived behavioral codes and could offer substantial improvements to the efficiency and scale in which MI mechanisms of change research and fidelity monitoring are conducted.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842096PMC
http://dx.doi.org/10.1016/j.jsat.2016.01.006DOI Listing

Publication Analysis

Top Keywords

natural language
16
language processing
16
human coders
12
dsf model
12
processing methods
8
motivational interviewing
8
mechanisms change
8
coding sessions
8
rnn model
8
human
5

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!