Automating Behavioral Coding via Text-Mining and Speech Signal Processing

通过文本挖掘和语音信号处理实现行为编码自动化

• 批准号: 8133994
• 负责人: David Charles Atkins
• 金额: $ 56.5万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8133994
• 关键词: Acoustics; Address; Adherence; Area; Arousal; Association Learning; Basic Science; Behavior; Behavioral; Categories; Cessation of life; Client; Clinical; Clinical Trials; Code; Cognitive Science; Complex; Data; Dependence; Drug abuse; Electrical Engineering; Electronic Mail; Emotional; Emotions; Engineering; Fracture; Genetic Transcription; Grouping; Health; Health behavior; Hospitals; Interdisciplinary Study; Intervention; Intervention Studies; Language; Learning; Linguistics; Maps; Methods; Modeling; Outcome; Output; Patients; Physiological; Process; Psychotherapy; Relative (related person); Research; Research Personnel; Scheme; Semantics; Series; Specific qualifier value; Speech; Statistical Models; System; Tape Recording; Testing; Text; Therapeutic; Time; Training; Transcript; Videotape; Work; Writing; abstracting; alcohol use disorder; base; behavior change; behavior observation; college drinking; computer science; computerized data processing; cost effectiveness; economic cost; essays; improved; information organization; innovation; markov model; member; motivational enhancement therapy; news; novel strategies; public health relevance; scale up; skills; speech recognition; text searching; theories; tool; treatment adherence

DESCRIPTION (provided by applicant): Numerous clinical trials have shown that Motivational Interviewing (MI; Miller & Rollnick, 2002) is an efficacious treatment for alcohol use disorders (AUD) and related health behavior problems (e.g., Burke, Dunn, Atkins, & Phelps, 2005), but much less is known about the therapy mechanisms of MI (Huebner & Tonigan, 2007). Process research has typically relied on behavioral coding schemes such as the Motivational Interviewing Skills Code (MISC; Miller, Moyers, Ernst, & Amrhein, 2008). Although MI mechanism research with the MISC has produced some of the best data to date (e.g., Moyers et al., 2007), behavioral coding has a number of limitations: 1) it is phenomenally labor intensive, 2) objectivity, reliability, and transportability of coding can be challenging, and 3) it is inflexible (i.e., any new codes require completely new coding). The current proposal brings together a highly interdisciplinary team to develop linguistic processing tools to automate the coding of the MISC and Motivational Interviewing Treatment Integrity (MITI; Moyers, Martin, Manuel, Miller, & Ernst, 2007). The coding of both systems is based on two types of linguistic data: what is said, and how it is said. Our team members in computer science, cognitive science, and electrical engineering are leading researchers in text-mining and speech signal processing, and their methods will be applied to MI transcripts and recordings to automate coding of the MISC/MITI. The core, methodological tool will be topic models (Steyvers & Griffiths, 2007), Bayesian models of semantic knowledge representation. Topic models identify groupings of words that constitute meaning units (or topics), and a recent extension models coded data (e.g., MISC) in which the model learns what specific text is associated with specific tags. Two specific aims encompass the current proposal: 1) Assess the accuracy of topic models to automatically code the MISC/MITI using transcripts and audiofiles of MI sessions, and 2) Test MI theory (within session and long-term outcome) using approximately 1,167 sessions of MI coded in Aim 1. These aims will be accomplished using three MI intervention studies: two studies focused on college student drinking and one hospital-based study of drug abuse. The long-term objectives are to use innovative linguistic tools to study therapy mechanisms and develop more efficient systems for collecting psychotherapy process data. Alcohol use disorders continue to represent an incredible societal burden in terms of death, health complications, fractured relationships, and economic costs. The current research will provide innovative tools for studying why therapy works, which in turn can help to ameliorate some of the deleterious effects of AUD. PUBLIC HEALTH RELEVANCE: Research focused on psychotherapy mechanisms of alcohol use disorders (AUD) have often relied upon behavioral observation coding schemes, such as the Motivational Interview Skills Code (MISC), which are time consuming and can present difficulties with reliability. The current, interdisciplinary proposal develops methods for automating behavioral coding through applying recent advances in text-mining and speech signal processing.

DESCRIPTION (provided by applicant): Numerous clinical trials have shown that Motivational Interviewing (MI; Miller & Rollnick, 2002) is an efficacious treatment for alcohol use disorders (AUD) and related health behavior problems (e.g., Burke, Dunn, Atkins, & Phelps, 2005), but much less is known about the therapy mechanisms of MI (Huebner & Tonigan, 2007).流程研究通常依赖于行为编码方案，例如动机访谈技能法规（MISC； Miller，Moyers，Ernst和Amrhein，2008年）。尽管使用杂项的MI机制研究已经产生了迄今为止的一些最佳数据（例如Moyers等，2007），但行为编码具有许多局限性：1）它是劳动力密集的，2）2）客观性，可靠性和编码的可运输能力可能具有挑战性，并且3）是可Inflexible的（即，任何新的编码）。当前的提案汇集了一个高度跨学科的团队，以开发语言处理工具来自动化MISC和动机访谈治疗完整性的编码（Miti； Moyers，Martin，Martin，Manuel，Miller，Miller，＆Ernst，2007年）。两种系统的编码均基于两种类型的语言数据：所说的话以及如何说。我们的计算机科学，认知科学和电气工程的团队成员是文本挖掘和语音信号处理领域的领先研究人员，他们的方法将应用于MI成绩单和记录，以自动编码MISC/MITI。核心，方法论工具将是主题模型（Steyvers＆Griffiths，2007年），贝叶斯语义知识表示模型。主题模型识别构成含义单位（或主题）的单词组的分组，以及最近的扩展模型编码数据（例如MISC），其中模型学习了哪些特定文本与特定标签相关联。两个具体的目的包括当前的建议：1）评估主题模型的准确性，使用MI会议的转录和有声料自动对MISC/MITI进行编码，以及2）测试MI理论（在会话和长期结局中），使用大约1,167个在AIM中编码的MI疗法进行1,167个研究的培训。长期目标是使用创新的语言工具来研究治疗机制，并开发更有效的系统来收集心理治疗过程数据。就死亡，健康并发症，破裂的关系和经济成本而言，酒精使用障碍继续代表了令人难以置信的社会负担。当前的研究将提供创新的工具来研究治疗原因，从而可以帮助改善AUD的某些有害影响。 公共卫生相关性：专注于酒精使用障碍心理治疗机制（AUD）的研究通常依赖于行为观察编码方案，例如激励访谈技能法规（MISC），这些计划耗时，并且可能会带来可靠性的困难。当前的跨学科建议通过应用文本挖掘和语音信号处理的最新进展来开发自动化行为编码的方法。