Automated Speech Analysis: A Marker of Drug Intoxication & Treatment Outcome

自动语音分析：药物中毒的标志

• 批准号: 9232130
• 负责人: Richard W Foltin
• 金额: $ 8.1万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9232130
• 关键词: Acute; Behavior; Biochemical; Bypass; Cereals; Characteristics; Clinical; Clinical Research; Clinical assessments; Cocaine; Cocaine Abuse; Cocaine Users; Code; Cognitive Therapy; Complex; Computers; Computing Methodologies; Data; Data Analyses; Detection; Development; Diagnostic tests; Disease; Double-Blind Method; Drug abuse; Drug usage; Funding; Funding Mechanisms; Future; Human; Individual; Industry; Intoxication; Intravenous; Laboratory Study; Language; Lysergic Acid Diethylamide; Machine Learning; Manuals; Measures; Medicine; Mental disorders; Methods; Mind; Moods; Motivation; Natural Language Processing; Oral; Patient Self-Report; Patient risk; Patients; Pharmaceutical Preparations; Pharmacotherapy; Placebos; Prognostic Marker; Psychiatry; Psychotic Disorders; Randomized; Reporting; Research; Research Personnel; Research Proposals; Sampling; Scientist; Semantics; Source; Speech; Structure; Study Subject; Substance Use Disorder; Technology; Testing; Transcript; Treatment outcome; Work; addiction; analytical method; base; clinical practice; clinical risk; clinically relevant; cocaine use; computer science; computerized; cost; cost effective; disorder later incidence prevention; ecstasy; experience; high risk; innovation; mental state; natural language; novel; outcome prediction; predictive modeling; prognostic assays; programs; public health relevance; secondary analysis; substance abuse treatment; syntax

 DESCRIPTION (provided by applicant): A major limitation of existing assessments of clinically-relevant mental states related to drug use, abuse, and treatment is that self-report measures rely on the capacity and motivation to accurately report one's internal experiences. A potential alternative is presented by emerging computer-based natural language processing methods that can extract fine-grained semantic, structural, and syntactic features from free speech1, potentially providing a unique 'window into the mind.' These methods are widely used in industry2, yet remain largely unknown in clinical research. To begin to assess the potential of these advanced analytic methods in clinical research, we recently partnered with IBM computer science researchers to test computer-based analysis of speech semantic structure. In preliminary work, we were able to demonstrate that such methods could detect acute drug intoxication3 and accurately predicted the development of psychosis in clinical risk states4. Here, we propose to build on these highly promising initial findings, conducting three secondary data analyses to rapidly and cost-effectively advance this novel direction. Projects 1 and 2 will extend our preliminary work on speech markers of mental state changes during acute drug intoxication. In Project 1, we will assess speech semantic, structural, and syntactic features as markers of mental state changes due to MDMA (0, 0.75, 1.5 mg/kg; oral). In Project 2, we will extend these findings to another drug, assessing speech markers of intoxication with LSD (0, 70 μg; intravenous). These projects are possible because we have access to existing transcripts of free speech from within-subject, controlled laboratory studies of the effects of MDMA (N = 77) and LSD (N = 19). Potential future uses for these methods could include rapid characterization of the effects of emerging drugs and, potentially, detection of acute drug intoxication in the absence of biochemical confirmation. Project 3 will assess the use of speech analysis as a prognostic marker in substance abuse treatment. Specifically, we will use speech transcripts (N = 50) from a currently ongoing study to assess whether features extracted from baseline free speech can predict treatment outcome in cocaine users undergoing 12 weeks of CBT relapse prevention. Self-report5,6 and manual coding of speech7-9 suggest that motivation to change may be a predictor of treatment outcome for substance use disorders: we expect that the fine-grained computational methods we will employ will allow the development of more accurate predictive models. The capacity to use automated methods to detect mental states from free speech has wide ranging, potentially transformative implications for addiction medicine and psychiatry more broadly4,10. Results of the proposed secondary analyses projects will efficiently advance understanding of how automated speech analysis, a non-invasive and cost- effective assessment method, could be used in clinical practice and research about drug abuse. More broadly, results may contribute to the empirical basis for the development of automated, objective, speech- based diagnostic and prognostic tests in psychiatry.

 描述（由适用提供）：与药物使用，滥用和治疗有关的临床相关心理状态的现有评估的主要局限性是，自我报告措施依赖于能力和动机来准确地报告自己的内部经历。潜在的替代方法是通过新兴的基于计算机的自然语言处理方法提出的，该方法可以从wifle Speeps1中提取精细的语义，结构和句法特征，从而可能提供独特的“进入思想窗口”。这些方法在行业2中广泛使用，但在临床研究中仍然在很大程度上尚不清楚。为了开始评估这些高级分析方法在临床研究中的潜力，我们最近与IBM计算机科学研究人员合作测试了基于计算机的语音语义结构分析。在初步工作中，我们能够证明这种方法可以检测到急性药物中毒3，并准确预测临床风险状态中精神病的发展4。在这里，我们建议建立在这些高度有希望的初始发现的基础上，并进行了三个二级数据分析，以迅速和成本效益地推进这一新颖方向。项目1和2将延长我们在急性药物中毒期间精神状态变化的语音标记的初步工作。在项目1中，我们将评估语音语义，结构和句法特征作为MDMA引起的精神状态变化的标志（0，0.75，1.5 mg/kg；口服）。在项目2中，我们将将这些发现扩展到另一种药物，评估使用LSD（0，70μg;静脉内）中毒的语音标记。这些项目之所以可能，是因为我们可以从受试者内部的言论自由，对MDMA（n = 77）和LSD（n = 19）的影响的对照实验室研究中访问现有的言论。这些方法的潜在未来用途可能包括快速表征新兴药物的作用，并可能在没有生化确认的情况下检测急性药物肠毒性。项目3将评估语音分析作为药物滥用治疗中的预后标记。具体而言，我们将使用目前正在进行的研究中的语音成绩单（n = 50）来评估从基线言论自由中提取的特征是否可以预测可卡因使用者的治疗结果，该结果正在接受12周的预防CBT缓解。自我报告5,6和语音编码的语音编码7-9表明，改变动机可能是药物使用障碍的治疗结果的预测指标：我们希望我们将采用的细粒度计算方法将允许开发更准确的预测模型。使用自动化方法从言论自由中检测精神状态的能力具有广泛的范围，对成瘾医学和精神病学的潜在变革含义更广泛。4,10。拟议的二级分析项目的结果将有效地提高人们对无创和具有成本效益的评估方法的自动化语音分析如何用于临床实践和有关药物滥用的研究。更广泛地说，结果可能有助于在精神病学中开发自动化，客观，基于语音的诊断和预后测试的经验基础。