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.