Scholarlink

<p>We sought to evaluate the ability of automated speech and language features to longitudinally track fluctuations in the major psychosis domains: <i>Thought Disorder</i>, <i>Negative Symptoms</i>, and <i>Positive Symptoms</i>. Sixty-six participants with psychotic disorders were assessed soon after inpatient admission, at discharge, and at 3- and 6-months. Psychosis symptoms were measured with semi-structured interviews and standardized scales. Recordings were collected from paragraph reading, fluency, picture description, and open-ended tasks. Relationships between psychosis symptoms and 357 automated speech and language features were analyzed using a single component score and as individual features, using linear mixed models. We found that all three domains demonstrated significant longitudinal relationships with the single component score. <i>Thought Disorder</i> was particularly related to features describing more subordinated constructions, less efficient identification of picture elements, and decreased semantic distance between sentences. <i>Negative Symptoms</i> was related to features describing decreased speech complexity. <i>Positive Symptoms</i> domain score did not show relationships with individual features that survived p-value correction, but <i>Suspiciousness</i> was related to decreased use of nouns and <i>Hallucinations</i> was related to greater semantic distances. These relationships were largely robust to interactions with gender and race. Interactions with timepoint revealed variable relationships during different phases of illness (acute vs. stable). In summary, automated speech and language features show promise as scalable, objective markers of psychosis severity. Detailed attention to clinical setting and patient population is needed to optimize clinical translation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated speech and language markers of longitudinal changes in psychosis symptoms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月17日

查看全文
  • Sunny X. Tang,
  • Michael J. Spilka,
  • Majnu John,
  • Michael L. Birnbaum,
  • Ema Saito,
  • Sarah A. Berretta,
  • Leily M. Behbehani,
  • Mark Y. Liberman,
  • Anil K. Malhotra,
  • William Simpson,
  • John M. Kane

摘要

We sought to evaluate the ability of automated speech and language features to longitudinally track fluctuations in the major psychosis domains: Thought Disorder, Negative Symptoms, and Positive Symptoms. Sixty-six participants with psychotic disorders were assessed soon after inpatient admission, at discharge, and at 3- and 6-months. Psychosis symptoms were measured with semi-structured interviews and standardized scales. Recordings were collected from paragraph reading, fluency, picture description, and open-ended tasks. Relationships between psychosis symptoms and 357 automated speech and language features were analyzed using a single component score and as individual features, using linear mixed models. We found that all three domains demonstrated significant longitudinal relationships with the single component score. Thought Disorder was particularly related to features describing more subordinated constructions, less efficient identification of picture elements, and decreased semantic distance between sentences. Negative Symptoms was related to features describing decreased speech complexity. Positive Symptoms domain score did not show relationships with individual features that survived p-value correction, but Suspiciousness was related to decreased use of nouns and Hallucinations was related to greater semantic distances. These relationships were largely robust to interactions with gender and race. Interactions with timepoint revealed variable relationships during different phases of illness (acute vs. stable). In summary, automated speech and language features show promise as scalable, objective markers of psychosis severity. Detailed attention to clinical setting and patient population is needed to optimize clinical translation.