Onnela, Jukka-PekkaLu, JunweiEmedom-Nnamdi, Patrick Ugochukwu2023-06-0220232023-05-162023-05Emedom-Nnamdi, Patrick Ugochukwu. 2023. Interpretable Statistical Learning for Real-World Behavioral Data. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.30491865https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37375773The 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.application/pdfenBehavioral DataDigital PhenotypingReal-WorldReinforcement LearningState Space ModelsBiostatisticsStatisticsComputer scienceThesis or Dissertation2023-06-020000-0003-4442-924X