Papers by Hyolim Jeon
CURE: Context- and Uncertainty-Aware Mental Disorder Detection (2024.emnlp-main)
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| Challenge: | Existing methods to detect mental disorders focus on the presence of symptoms, but the context of symptoms is often ignored, leading to errors in symptom identification. |
| Approach: | They propose to use large language models to extract contextual information while introducing an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values. |
| Outcome: | The proposed model detects mental disorders even in situations where symptom information is incomplete. |
Learning Co-Speech Gesture for Multimodal Aphasia Type Detection (2023.emnlp-main)
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| Challenge: | Aphasia is a language disorder caused by brain damage affecting speech functions . a detailed diagnosis of aphasia type is imperative for effective treatment . but, little attention has been paid to developing methods to detect different types of sphasis . |
| Approach: | They propose a multimodal graph neural network for aphasia type detection using co-speech gestures and corresponding speech and gesture patterns. |
| Outcome: | The proposed model outperforms existing methods in F1 and 84.2% of cases. |
Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning (2024.naacl-long)
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| Challenge: | Bipolar Disorder (BD) is a mental disorder characterized by intense mood swings, ranging from depression to manic states. |
| Approach: | They propose to use social media data to identify BD risk in individuals misdiagnosed as MDD by multi-task learning. |
| Outcome: | The proposed approach outperforms state-of-the-art baselines and can provide insights into the impact of BD mood on future risk. |