Locally Interpretable Predictions of Parkinson's Disease Progression

In precision medicine, machine learning techniques have been commonly proposed to aid physicians in early screening of chronic diseases such as Parkinson's Disease. These automated screening procedures should be interpretable by a clinician who must explain the decision-making process to patients for informed consent. However, the methods which typically achieve the highest level of accuracy given early screening data are complex black box models. In this paper, we provide a novel approach for explaining black box model predictions of Parkinson's Disease progression that can give high fidelity explanations with lower model complexity. Specifically, we use the Parkinson's Progression Marker Initiative (PPMI) data set to cluster patients based on the trajectory of their disease progression. This can be used to predict how a patient's symptoms are likely to develop based on initial screening data. We then develop a black box (random forest) model for predicting which cluster a patient belongs in, along with a method for generating local explainers for these predictions. Our local explainer methodology uses a computationally efficient information filter to include only the most relevant features. We also develop a global explainer methodology and empirically validate its performance on the PPMI data set, showing that our approach may Pareto-dominate existing techniques on the trade-off between fidelity and coverage. Such tools should prove useful for implementing medical screening tools in practice by providing explainer models with high fidelity and significantly less functional complexity.