Uncertainty Quantification in Alzheimer's Disease Progression Modeling
This work addresses dependability concerns in Alzheimer's disease prognosis models for early detection by incorporating uncertainty quantification, though it appears incremental as it compares existing methods on a specific dataset.
The paper tackled the problem of uncertainty quantification in Alzheimer's disease progression modeling by comparing Monte Carlo Dropout, Variational Inference, Markov Chain Monte Carlo, and Ensemble Learning on 512 patients to predict 4-year cognitive score trajectories with confidence bounds, showing that MC Dropout and MCMC produce well-calibrated and accurate predictions under noisy training data.
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.