儿童和青少年强迫症严重程度与嗓音特征之间的关联：统计与机器学习分析方案
Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis.
作者信息
Clemmensen Line Katrine Harder, Lønfeldt Nicole Nadine, Das Sneha, Lund Nicklas Leander, Uhre Valdemar Funch, Mora-Jensen Anna-Rosa Cecilie, Pretzmann Linea, Uhre Camilla Funch, Ritter Melanie, Korsbjerg Nicoline Løcke Jepsen, Hagstrøm Julie, Thoustrup Christine Lykke, Clemmesen Iben Thiemer, Plessen Kersten Jessica, Pagsberg Anne Katrine
机构信息
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, Denmark.
Child and Adolescent Mental Health Center, Copenhagen University Hospital, Mental Health Services Copenhagen, Copenhagen, Denmark.
出版信息
JMIR Res Protoc. 2022 Oct 28;11(10):e39613. doi: 10.2196/39613.
BACKGROUND
Artificial intelligence tools have the potential to objectively identify youth in need of mental health care. Speech signals have shown promise as a source for predicting various psychiatric conditions and transdiagnostic symptoms.
OBJECTIVE
We designed a study testing the association between obsessive-compulsive disorder (OCD) diagnosis and symptom severity on vocal features in children and adolescents. Here, we present an analysis plan and statistical report for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study.
METHODS
Audio recordings of clinical interviews of 47 children and adolescents with OCD and 17 children and adolescents without a psychiatric diagnosis will be analyzed. Youths were between 8 and 17 years old. We will test the effect of OCD diagnosis on computationally derived scores of vocal activation using ANOVA. To test the effect of OCD severity classifications on the same computationally derived vocal scores, we will perform a logistic regression. Finally, we will attempt to create an improved indicator of OCD severity by refining the model with more relevant labels. Models will be adjusted for age and gender. Model validation strategies are outlined.
RESULTS
Simulated results are presented. The actual results using real data will be presented in future publications.
CONCLUSIONS
A major strength of this study is that we will include age and gender in our models to increase classification accuracy. A major challenge is the suboptimal quality of the audio recordings, which are representative of in-the-wild data and a large body of recordings collected during other clinical trials. This preregistered analysis plan and statistical report will increase the validity of the interpretations of the upcoming results.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39613.
背景
人工智能工具有可能客观地识别出需要心理健康护理的青少年。语音信号已显示出作为预测各种精神疾病和跨诊断症状来源的潜力。
目的
我们设计了一项研究，以测试强迫症（OCD）诊断与儿童和青少年语音特征的症状严重程度之间的关联。在此，我们展示该研究的分析计划和统计报告，以记录我们的先验假设，并提高我们计划研究结果的稳健性。
方法
将分析47名患有强迫症的儿童和青少年以及17名未患有精神疾病诊断的儿童和青少年的临床访谈音频记录。这些青少年年龄在8至17岁之间。我们将使用方差分析来测试强迫症诊断对通过计算得出的语音激活分数的影响。为了测试强迫症严重程度分类对相同计算得出的语音分数的影响，我们将进行逻辑回归。最后，我们将尝试通过用更相关的标签完善模型来创建一个改进的强迫症严重程度指标。模型将根据年龄和性别进行调整。概述了模型验证策略。
结果
呈现了模拟结果。使用真实数据的实际结果将在未来的出版物中呈现。
结论
本研究的一个主要优势是我们将在模型中纳入年龄和性别以提高分类准确性。一个主要挑战是音频记录的质量欠佳，这些记录代表了野外数据以及在其他临床试验中收集的大量记录。这个预先注册的分析计划和统计报告将提高对即将得出的结果解释的有效性。
国际注册报告标识符（IRRID）：DERR1 - 10.2196/39613
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