Enhancing Suicide Risk Assessment: A Speech-Based Automated Approach in Emergency Medicine
arxiv(2024)
摘要
The delayed access to specialized psychiatric assessments and care for
patients at risk of suicidal tendencies in emergency departments creates a
notable gap in timely intervention, hindering the provision of adequate mental
health support during critical situations. To address this, we present a
non-invasive, speech-based approach for automatic suicide risk assessment. For
our study, we have collected a novel dataset of speech recordings from 20
patients from which we extract three sets of features, including wav2vec,
interpretable speech and acoustic features, and deep learning-based spectral
representations. We proceed by conducting a binary classification to assess
suicide risk in a leave-one-subject-out fashion. Our most effective speech
model achieves a balanced accuracy of 66.2 %. Moreover, we show that
integrating our speech model with a series of patients' metadata, such as the
history of suicide attempts or access to firearms, improves the overall result.
The metadata integration yields a balanced accuracy of 94.4 %, marking an
absolute improvement of 28.2 %, demonstrating the efficacy of our proposed
approaches for automatic suicide risk assessment in emergency medicine.
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