Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912605607559168 |
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| author | Premananth, Gowtham Resnik, Philip Bansal, Sonia Kelly, Deanna L. Espy-Wilson, Carol |
| author_facet | Premananth, Gowtham Resnik, Philip Bansal, Sonia Kelly, Deanna L. Espy-Wilson, Carol |
| contents | Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the condition and reducing its clinical applicability. This traditional approach overlooks the complexity of schizophrenia, limiting its practical value in healthcare settings. This study shifts the focus to individual symptom severity estimation using a multimodal approach that integrates speech, video, and text inputs. We develop unimodal models for each modality and a multimodal framework to improve accuracy and robustness. By capturing a more detailed symptom profile, this approach can help in enhancing diagnostic precision and support personalized treatment, offering a scalable and objective tool for mental health assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation Premananth, Gowtham Resnik, Philip Bansal, Sonia Kelly, Deanna L. Espy-Wilson, Carol Audio and Speech Processing Machine Learning Image and Video Processing Signal Processing Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the condition and reducing its clinical applicability. This traditional approach overlooks the complexity of schizophrenia, limiting its practical value in healthcare settings. This study shifts the focus to individual symptom severity estimation using a multimodal approach that integrates speech, video, and text inputs. We develop unimodal models for each modality and a multimodal framework to improve accuracy and robustness. By capturing a more detailed symptom profile, this approach can help in enhancing diagnostic precision and support personalized treatment, offering a scalable and objective tool for mental health assessment. |
| title | Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation |
| topic | Audio and Speech Processing Machine Learning Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2505.16044 |