Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data
Background Depression, including Bipolar and Unipolar types, is a widespread mental health issue. Conventional diagnostic methods rely on subjective assessments, leading to possible underreporting and bias. Machine learning (ML) and deep learning (DL) offer automated approaches to detect depression...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Dialogues in Clinical Neuroscience |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19585969.2025.2524337 |
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