An Adaptive Optimization Approach to Personalized Financial Incentives in Mobile Behavioral Weight Loss Interventions

Obesity is a critical healthcare issue affecting the United States. The least risky treatments available for obesity are behavioral interventions meant to promote diet and exercise. Often these interventions contain a mobile component that allows interventionists to collect participants level data and provide participants with incentives and goals to promote long term behavioral change. Recently, there has been interest in using direct financial incentives to promote behavior change. However, adherence is challenging in these interventions, as each participant will react differently to different incentive structure and amounts, leading researchers to consider personalized interventions. The key challenge for personalization, is that the clinicians do not know a priori how best to administer incentives to participants, and given finite intervention budgets how to disburse costly resources efficiently. In this paper, we consider this challenge of designing personalized weight loss interventions that use direct financial incentives to motivate weight loss while remaining within a budget. We create a machine learning approach that is able to predict how individuals may react to different incentive schedules within the context of a behavioral intervention. We use this predictive model in an adaptive framework that over the course of the intervention computes what incentives to disburse to participants and remain within the study budget. We provide both theoretical guarantees for our modeling and optimization approaches as well as demonstrate their performance in a simulated weight loss study. Our results highlight the cost efficiency and effectiveness of our personalized intervention design for weight loss.

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.