Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While AI models can forecast levodopa equivalent daily dose (LEDD) as a measure of medication needs, standard uncertainty quantification often fails to communicate the reliability of these predictions, treating high and low confidence clinical decisions identically. We introduce CASCADE (Calibrated Adaptive Scaling via Conformal And Distributional Estimation), a novel conformal prediction framework that propagates epistemic uncertainty from a screening classifier to adapt downstream predictions. Unlike standard conformal methods that rely on auxiliary residual regression, we leverage epistemic uncertainty from a primary classification task (identifying whether a medication change is needed) to dynamically scale the prediction intervals of a secondary regression task (predicting how much change). By mapping Venn-Abers multi-probabilistic uncertainty directly to non-conformity scores, our framework achieves continuous risk adaptation. We demonstrate that this ``cascade effect'' produces highly efficient intervals for confident patients (38.9% narrower than standard conformal baselines) while automatically expanding intervals to ensure robust coverage for uncertain cases, bridging the gap between discrete clinical decision-making and continuous dose forecasting in PD.

We include all our annotations and extracted landmarks. This ensures that we uphold the highest standards of ethical data usage. In Table A1, we summarize the severity label distribution in Y ouTubePD. We also summarize the demographic distribution in Y ouTubePD, split between PD-positive and healthy control (HC), or PD-negative, subjects. This decision is based on the clinician's suggestion, since an accurate UPDRS facial expression rating would require more This strategy also allows for a finer classification.

The healthcare and AI communities have witnessed a growing interest in the development of AI-assisted systems for automated diagnosis of Parkinson's Disease

Bayesian predictive intervals are conditioned on the specific observed sequenceZ1:n and make statements on the next value[Yn+1 | Xn+1]. Subjective Bayesian statements on predictions are non-refutable, and are in this sense unscientific, but are optimal according to decision theoretic foundations. However,tomakesuch strong statements, the Bayesian must usually make the strict assumption of the model being well-specified. Asmentionedearlier,computingtheAOI interval is an efficient matrix-vector multiplication, whereas the LOO interval requires expensive broadcastingtoconstructthe ngrid T nISweightarray. We use the same Bayesian model as in (10), again consideringc=1,0.02.

Thenoisemagnitudeobtained is 0.0 forskinlesionclassifier, 0.0005 forlungsoundclassifier, and 0.0 for Parkinson' sdisease classifier. InProceedingsofthe 17th Conferenceon Embedded Networked Sensor Systems, pages 1-14, 2019.