Access Type

Open Access Dissertation

Date of Award

January 2019

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

Alexander Kotov

Abstract

Motivational Interviewing (MI) is an evidence-based communication technique to increase intrinsic motivation and self-efficacy for behavior change. This goal is achieved through the exploration of the patient's own desires, ability, reasons, need for and commitment to the targeted behavior change. However, communication science approaches to understanding the efficacy of MI are inherently limited by traditional qualitative coding methods which is a time-consuming and resource-intensive process. Thus, an efficient method is required to automate the coding process which will accelerate the pace of communication research in behavioral science. The specific provider behaviors responsible for the elicitation of change talk, are also less clear and may vary by treatment context. Therefore, new design objective and perspective are necessary to understand which provider behaviors and in which contexts lead to patient change talk. In this dissertation, we deal with two types of clinical conversation, one that involves a face to face dialogue between patient and counselor and another one which involves an email-based conversation between patient and an ecoach.

First, we leverage eight supervised machine learning models to automatically annotate counseling sessions with 37 African American adolescents with obesity and their caregivers. We examine the performance of classifiers using lexical, contextual, and semantic features, to predict the behavioral codes in the previously coded data.

Second, understanding motivational interviewing mechanisms of effect, we focus on deep learning and probabilistic models and analyze the sequencing of patient-provider communication. The goal of these experiments is to identify the communication behaviors leading to the elicitation of client change talk, a marker of success in MI, and counter change talk, a marker of unsuccessful communication. Two approaches, recurrent neural networks and Markov models, were tested. As a continuation of our sequential analysis, we analyze pre-coded MI transcripts to identify the specific counselor communication behaviors effective for eliciting patient change talk. We evaluate the empirical effectiveness of the hidden Markov model and closed frequent pattern mining to inform MI practice.

Finally, we propose various segmentation models for the analysis of email-based counseling sessions since segmentation is a necessary and critical step to process email-based conversation for developing autocoding and sequence analysis models. We formulate the segmentation task as a classification problem and utilizes word and punctuation mark embeddings in conjunction with part-of-speech features to address it. We evaluate the performance of conditional random fields as well as a multi-layer perceptron, bi-directional recurrent neural network and convolutional recurrent neural network for the task of clinical text segmentation.

Experimental results indicate that machine learning models achieve performance near human coders for the segmentation and annotation of clinical conversation, which will significantly increase the pace of communication research in behavioral science. Our methods can facilitate motivational interviewing researchers to identify the most likely sequences in successful and unsuccessful motivational interviews, which can directly inform clinical practice and increase the effectiveness of behavioral interventions. We can integrate the sequential model with segmentation and auto-coding classifiers to develop a fully automated system for the analysis of clinical conversation.

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