Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916385952628736 |
|---|---|
| author | Wu, Wen Zhang, Chao Woodland, Philip C. |
| author_facet | Wu, Wen Zhang, Chao Woodland, Philip C. |
| contents | Speech-based automatic detection of Alzheimer's disease (AD) and depression has attracted increased attention. Confidence estimation is crucial for a trust-worthy automatic diagnostic system which informs the clinician about the confidence of model predictions and helps reduce the risk of misdiagnosis. This paper investigates confidence estimation for automatic detection of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution. Experimental results on the publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed method outperforms a range of baselines for both classification accuracy and confidence estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19984 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews Wu, Wen Zhang, Chao Woodland, Philip C. Computation and Language Speech-based automatic detection of Alzheimer's disease (AD) and depression has attracted increased attention. Confidence estimation is crucial for a trust-worthy automatic diagnostic system which informs the clinician about the confidence of model predictions and helps reduce the risk of misdiagnosis. This paper investigates confidence estimation for automatic detection of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution. Experimental results on the publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed method outperforms a range of baselines for both classification accuracy and confidence estimation. |
| title | Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.19984 |