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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.14478 |
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| _version_ | 1866912002541092864 |
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| author | Mobeirek, Wael Mao, Shirley |
| author_facet | Mobeirek, Wael Mao, Shirley |
| contents | With the increasing number of patients diagnosed with Alzheimer's Disease, prognosis models have the potential to aid in early disease detection. However, current approaches raise dependability concerns as they do not account for uncertainty. In this work, we compare the performance of Monte Carlo Dropout, Variational Inference, Markov Chain Monte Carlo, and Ensemble Learning trained on 512 patients to predict 4-year cognitive score trajectories with confidence bounds. We show that MC Dropout and MCMC are able to produce well-calibrated, and accurate predictions under noisy training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14478 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty Quantification in Alzheimer's Disease Progression Modeling Mobeirek, Wael Mao, Shirley Neurons and Cognition Artificial Intelligence Computers and Society Information Theory With the increasing number of patients diagnosed with Alzheimer's Disease, prognosis models have the potential to aid in early disease detection. However, current approaches raise dependability concerns as they do not account for uncertainty. In this work, we compare the performance of Monte Carlo Dropout, Variational Inference, Markov Chain Monte Carlo, and Ensemble Learning trained on 512 patients to predict 4-year cognitive score trajectories with confidence bounds. We show that MC Dropout and MCMC are able to produce well-calibrated, and accurate predictions under noisy training data. |
| title | Uncertainty Quantification in Alzheimer's Disease Progression Modeling |
| topic | Neurons and Cognition Artificial Intelligence Computers and Society Information Theory |
| url | https://arxiv.org/abs/2408.14478 |