Interpretable Statistical Learning for Real-World Behavioral Data

Abstract

The rapid development of data collection methods and analysis techniques has revolutionized our understanding of human behavior and its relationship to health outcomes. However, despite the increasing availability of real-world behavioral data, the effective use of this information for real-time prediction and intervention remains a significant challenge. This dissertation explores interpretable statistical learning methods for real-world behavioral data, with a focus on overcoming limitations in episodic data collection by leveraging smartphone-based digital phenotyping. The approaches explored ultimately provide a scalable method for utilizing real-world history data on human behavior to inform decision-making and interventions, while improving current standards of care.

Chapter 1 presents a novel method for estimating interpretable value functions in reinforcement learning. By incorporating local kernel regression and basis expansion, we develop a sparse, additive representation of the action-value function. This allows us to approximate the action-value function and retrieve the nonlinear, independent contributions of select features and joint feature pairs. We validate this approach through a simulation study and an application to spine disease, uncovering recovery recommendations in line with clinical knowledge.

Chapter 2 explores the trade-offs of learning in the growing-batch reinforcement learning setting and investigates how information provided by a teacher (i.e., demonstrations, expert actions, and gradient information) can be leveraged during training to mitigate the sample complexity and coverage requirements for actor-critic methods. We validate our contributions on tasks from the DeepMind Control Suite.

Chapter 3 introduces an approach where we use hidden semi-Markov models on smartphone activity logs to identify key patterns of differentiation in smartphone usage among adolescents with bipolar disorder and their typically-developing peers. This analysis enables the identification of latent constructs that correspond to resting and active smartphone usage, providing insights into the long-term behavioral trends in adolescents with bipolar disorder.

Chapter 4 presents the Digital Assessment in Neuro-Oncology (DANO) pilot, which leverages smartphone-based digital phenotyping to monitor post-operative recovery in glioblastoma patients. We analyze passive GPS and accelerometer data to construct mobility patterns and compare these patterns with a control group of non-operative spine disease patients. Our findings reveal significant changes in mobility among glioblastoma patients during the first six months following surgery and between subsequent cycles of chemotherapy.