利用移动数据和深度模型评估言语性幻听
Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations
AI总结：
本文面向言语性幻听效价评估，采集435名参与者的移动自我报告、音频日记与感知数据，并通过迁移学习和数据融合训练神经网络，实现54% top-1与72% top-2 F1分数。
摘要：
幻觉是在缺乏真实外部感官刺激时产生的一种明显知觉。听觉幻觉是听到并不真实存在的声音的知觉。听觉幻觉的一种常见形式是在没有任何说话者的情况下听到声音，这被称为言语性幻听（Auditory Verbal Hallucination，AVH）。AVH是心灵创造的片段，主要发生在被诊断患有双相情感障碍和精神分裂症等精神疾病的人群中。评估幻觉声音的效价（即声音的消极或积极程度）可以帮助衡量精神疾病的严重程度。我们研究了N=435名有听到声音经历的个体，以评估言语性幻听。参与者在一个月内每天四次通过生态瞬时评估报告他们听到的声音的效价，评估问题包含从“完全不”到“极其”的四个回答等级。我们通过一款移动应用程序收集这些自我报告，作为AVH事件的效价监督。参与者还使用该应用程序录制音频日记，以口头描述幻觉声音的内容。此外，我们被动收集移动感知数据作为情境信号。随后，我们实验研究了来自音频日记和移动感知数据的这些语言及情境线索对言语性幻听事件的预测能力。最后，利用迁移学习和数据融合技术，我们训练了一个神经网络模型来预测AVH的效价，其性能达到54%的top-1 F1分数和72%的top-2 F1分数。
英文摘要：
Hallucination is an apparent perception in the absence of real external sensory stimuli. An auditory hallucination is a perception of hearing sounds that are not real. A common form of auditory hallucination is hearing voices in the absence of any speakers which is known as Auditory Verbal Hallucination (AVH). AVH is fragments of the mind's creation that mostly occur in people diagnosed with mental illnesses such as bipolar disorder and schizophrenia. Assessing the valence of hallucinated voices (i.e., how negative or positive voices are) can help measure the severity of a mental illness. We study N=435 individuals, who experience hearing voices, to assess auditory verbal hallucination. Participants report the valence of voices they hear four times a day for a month through ecological momentary assessments with questions that have four answering scales from ``not at all'' to ``extremely''. We collect these self-reports as the valence supervision of AVH events via a mobile application. Using the application, participants also record audio diaries to describe the content of hallucinated voices verbally. In addition, we passively collect mobile sensing data as contextual signals. We then experiment with how predictive these linguistic and contextual cues from the audio diary and mobile sensing data are of an auditory verbal hallucination event. Finally, using transfer learning and data fusion techniques, we train a neural net model that predicts the valance of AVH with a performance of 54\% top-1 and 72\% top-2 F1 score.
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