A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder
This work addresses the need for quantitative and remote diagnostic tools in mental health, particularly for bipolar disorder, though it appears incremental as it builds on existing multimodal approaches.
The authors tackled the problem of automating mania assessment in bipolar disorder by developing a multimodal system using acoustic, linguistic, and visual features, achieving a 64.8% unweighted average recall score that improves state-of-the-art performance on the Bipolar Disorder corpus.
Bipolar disorder is a mental health disorder that causes mood swings that range from depression to mania. Diagnosis of bipolar disorder is usually done based on patient interviews, and reports obtained from the caregivers of the patients. Subsequently, the diagnosis depends on the experience of the expert, and it is possible to have confusions of the disorder with other mental disorders. Automated processes in the diagnosis of bipolar disorder can help providing quantitative indicators, and allow easier observations of the patients for longer periods. Furthermore, the need for remote treatment and diagnosis became especially important during the COVID-19 pandemic. In this thesis, we create a multimodal decision system based on recordings of the patient in acoustic, linguistic, and visual modalities. The system is trained on the Bipolar Disorder corpus. Comprehensive analysis of unimodal and multimodal systems, as well as various fusion techniques are performed. Besides processing entire patient sessions using unimodal features, a task-level investigation of the clips is studied. Using acoustic, linguistic, and visual features in a multimodal fusion system, we achieved a 64.8% unweighted average recall score, which improves the state-of-the-art performance achieved on this dataset.