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Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations

Psychology

Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations

S. Mirjafari, A. T. Campbell, et al.

This innovative research by Shayan Mirjafari, Andrew T Campbell, Subigya Nepal, and Weichen Wang investigates the exciting intersection of mobile data and deep learning to assess auditory verbal hallucinations. Through ecological momentary assessments and advanced neural networks, the study showcases the promising potential of mobile technology for real-time AVH evaluation.

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~3 min • Beginner • English
Abstract
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.
Publisher
Not specified in provided text
Published On
Apr 01, 2023
Authors
Shayan Mirjafari, Andrew T Campbell, Subigya Nepal, Weichen Wang
Tags
auditory verbal hallucinations
mobile data
deep learning
transfer learning
neural networks
real-time assessment
ecological momentary assessment
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