Browsing by Author "Steorts, Rebecca C"

Differential privacy (DP) aims to design methods and algorithms that satisfy rigorous notions of privacy while simultaneously providing utility with valid statistical inference. More recently, an emphasis has been placed on combining notions of statistical utility with algorithmic approaches to address privacy risk in the presence of big data---with differential privacy emerging as a rigorous notion of risk. While DP provides strong guarantees for privacy, there are often tradeoffs regarding data utility and computational scalability. In this paper, we introduce a categorical data synthesizer that releases high-dimensional sparse histograms, illustrating its ability to overcome current limitations with data synthesizers in the current literature. Specifically, we combine a differential privacy algorithm---the stability based algorithm--- along with feature hashing, with allows for dimension reduction in terms of the histograms and Gibbs sampling. As a result, our proposed algorithm is differentially private, offers similar or better statistical utility and is scalable to large databases. In addition, we give an analytical result for the error caused by the stability based algorithm, which allows us to control the loss of utility. Finally, we study the behavior of our algorithm on both simulated and real data.

Streaming data are becoming more common in a variety of fields. One common data stream in clinical medicine is electronic health records (EHRs) which have been used to develop risk prediction models. Our motivating application considers the risk of patient deterioration, which is defined as in-hospital mortality or transfer to the Intensive Care Unit (ICU). Duke University Hospital recently implemented an alert risk score for acute care wards: the National Early Warning Score (NEWS). However, NEWS was designed to be hand-calculable from patient vital data rather than to optimize prediction. Our approach considers three further methods to use on real-time EHR data to predict patient deterioration. We propose a Cox model, a joint modeling approach, and a Gaussian process. By evaluating the implementation of these models on clinical EHR data from more than 51,000 patients, we are able to provide a comparison of the methods on real EHR data for patient deterioration. We evaluate the results on both performance and scalability and consider the feasibility of implementing each approach in a clinical environment. While the more complicated models may potentially offer a small gain in predictive performance, they do not scale to a full patient data set. Thus, within a clinical setting, the Cox model is clearly the best approach.