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.

描述（由申请人提供）：大量临床试验表明，动机访谈（MI；Miller & Rollnick，2002）是治疗酒精使用障碍（AUD）和相关健康行为问题的有效方法（例如，Burke、Dunn、Atkins 和Phelps, 2005），但对于 MI 的治疗机制知之甚少（Huebner & Tonigan, 2007）。过程研究通常依赖于行为编码方案，例如动机面试技巧代码（MISC；Miller、Moyers、Ernst 和 Amrhein，2008）。尽管 MISC 的 MI 机制研究已经产生了一些迄今为止最好的数据（例如，Moyers 等，2007），但行为编码有许多局限性：1）它是劳动密集型的，2）客观性、可靠性和编码的可移植性可能具有挑战性，3）它不灵活（即任何新代码都需要全新的编码）。目前的提案汇集了一个高度跨学科的团队来开发语言处理工具，以自动编码 MISC 和动机访谈治疗完整性（MITI；Moyers、Martin、Manuel、Miller 和 Ernst，2007）。这两个系统的编码都基于两种类型的语言数据：所说的内容以及所说的方式。我们的计算机科学、认知科学和电气工程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 转录本和录音，以自动编码 MISC/MITI。核心的方法论工具将是主题模型（Steyvers & Griffiths，2007），即语义知识表示的贝叶斯模型。主题模型识别构成意义单元（或主题）的单词分组，以及最近的扩展模型编码数据（例如 MISC），其中模型学习哪些特定文本与特定标签相关联。当前提案包含两个具体目标：1）评估主题模型的准确性，以使用 MI 会话的笔录和音频文件自动编码 MISC/MITI；2）使用大约 1,167 个会话测试 MI 理论（在会话内和长期结果）目标 1 中编码的 MI。这些目标将通过三项 MI 干预研究来实现：两项针对大学生饮酒的研究和一项基于医院的药物滥用研究。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，酒精使用障碍仍然是令人难以置信的社会负担。目前的研究将为研究治疗为何有效提供创新工具，从而有助于减轻 AUD 的一些有害影响。 公共健康相关性：专注于酒精使用障碍 (AUD) 心理治疗机制的研究通常依赖于行为观察编码方案，例如动机面试技巧代码 (MISC)，该方案非常耗时且可能会带来可靠性方面的困难。当前的跨学科提案通过应用文本挖掘和语音信号处理方面的最新进展来开发自动化行为编码的方法。