儿童和青少年精神病学中使用计算机视觉进行行为感知的设计可解释性
Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry
摘要：
观察是理解和研究人类行为和心理状态的重要工具。然而，编码人类行为是一项耗时，昂贵的任务，其中可靠性可能难以实现，并且存在偏差风险。机器学习（ML）方法提供了提高可靠性、降低成本和扩大行为编码的方法，以应用于临床和研究环境。在这里，我们使用计算机视觉来推导出黄金标准行为评级系统的行为代码或概念，为心理健康专业人员提供熟悉的解释。特征提取自患有和不患有强迫症的儿童和青少年的临床诊断访谈视频。我们的计算得出的评级与人类专家对负面情绪，活动水平/唤醒和焦虑的评级相当。对于注意力和积极影响概念，我们的ML评级表现合理。然而，凝视和发声的结果表明，需要提高数据质量或额外的数据模式。
英文摘要：
Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for application in clinical and research settings. Here, we use computer vision to derive behavioral codes or concepts of a gold standard behavioral rating system, offering familiar interpretation for mental health professionals. Features were extracted from videos of clinical diagnostic interviews of children and adolescents with and without obsessive-compulsive disorder. Our computationally-derived ratings were comparable to human expert ratings for negative emotions, activity-level/arousal and anxiety. For the attention and positive affect concepts, our ML ratings performed reasonably. However, results for gaze and vocalization indicate a need for improved data quality or additional data modalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
