Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer from the inability of producing interpretable results or clinically-meaningful risk factors. To improve EHR-based modeling and use the natural hierarchical structure of disease classification, we propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features, in which dimension reduction is achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of “or”. We convert the combinatorial problem into a convex linearly-constrained regularized estimation, which enables scalable computation with theoretical guarantees. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases. By balancing the rarity and specificity of the EHR diagnosis records, our strategy improves both prediction and interpretation. We identify important higher-level categories and subcategories of mental health conditions and simultaneously determine the level of specificity needed for each of them in associating with suicide risk. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

How do people living at the intersection of various forms of injury seek out collective experiences of joy? I explore this question through fieldwork with Latinx female and queer artists and entrepreneurs, some of them undocumented, who consciously seek out and enact joy in their communities in East Los Angeles. At the same time, these communities face gentrification, racism, and discrimination. I rest on joy as a conceptual framework that arises out of the analysis and theorizing of my interlocutors, who choose to push back against mainstream representations of their communities as exclusively defined by their suffering. This approach, or what I call “an ethnography of joy,” draws our attention to what joy does in a particular context, how it becomes politically meaningful, and how it intersects and interacts with other phenomena. For example, in this article, I explore the more capacious idea of joy through a particular angle that emerged in my ethnographic research: entrepreneurship, or small business ownership. A focus on entrepreneurship allows me to explore how my interlocutors summon the forces of neoliberalism to seek social mobility, belonging, and community activism.