Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise (2024.lrec-main)
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| Challenge: | Existing approaches to automate depression estimation ignore medical professionals' knowledge of the problem. |
| Approach: | They propose to integrate domain experts' knowledge into a DAIC-WOZ dataset and propose a transformer-based model that incorporates their annotations. |
| Outcome: | The proposed model shows a strong correlation between the psychological tendencies of medical professionals and the behavior of the proposed model. |
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| Challenge: | Existing approaches to identify mental health conditions using social media are limited by the presence of symptoms described in a questionnaire used by clinicians. |
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| Challenge: | Existing methods for analyzing social media data lack a systematic integration of medical knowledge, causing a critical treatment gap. |
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
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| Challenge: | Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts. |
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| Challenge: | Psychiatric evaluations are heavily based on patient verbal reports of disturbed feelings, thoughts, behaviors, and their changes over time. |
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| Challenge: | Existing methods for depression assessment rely on standardized ratings, but they are time-consuming and subject to inter-rater variability. |
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