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）：许多临床试验表明，动机性面试（MI;米勒和Rollnick，2002）是一种有效的治疗酒精使用障碍（AUD）和相关的健康行为问题（例如，Burke，Dunn，阿特金斯，& Phelps，2005），但对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，如动机性面试技能代码（MISC;米勒，莫耶斯，恩斯特，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等人，2007），行为编码具有许多局限性：1）其劳动强度非常大，2）编码的客观性、可靠性和可移植性可能具有挑战性，以及3）其不灵活（即，任何新的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，以自动化MISC和动机性面试治疗完整性的编码（MITI; Moyers，Martin，Manuel，米勒，& Ernst，2007）。这两种系统的编码都基于两种类型的语言数据：说了什么，以及如何说。我们在计算机科学、认知科学和电气工程领域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），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）的单词的分组，并且最近的扩展对编码数据（例如，MISC），其中模型学习哪些特定文本与特定标签相关联。目前的建议包含两个具体目标：1）评估主题模型的准确性，以使用MI会话的转录和音频文件自动编码MISC/MITI，以及2）使用目标1中编码的大约1，167个MI会话测试MI理论（会话内和长期结果）。这些目标将通过三项MI干预研究来实现：两项研究集中在大学生饮酒和一项以医院为基础的药物滥用研究。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。酒精使用障碍在死亡、健康并发症、破裂的关系和经济成本方面仍然是一个令人难以置信的社会负担。目前的研究将为研究为什么治疗有效提供创新工具，这反过来又有助于改善AUD的一些有害影响。 公共卫生相关性：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，如动机访谈技能代码（MISC），这是耗时的，并可能存在可靠性的困难。目前，跨学科的建议开发的方法，通过应用文本挖掘和语音信号处理的最新进展，自动化的行为编码。