Yiwang Zhou

Spencer Giddens, Yiwang Zhou, Kevin R. Krull et al.

It is common practice to use data containing personal information to build predictive models in the framework of empirical risk minimization (ERM). While these models can be highly accurate in prediction, sharing the results from these models trained on sensitive data may be susceptible to privacy attacks. Differential privacy (DP) is an appealing framework for addressing such data privacy issues by providing mathematically provable bounds on the privacy loss incurred when releasing information from sensitive data. Previous work has primarily concentrated on applying DP to unweighted ERM. We consider weighted ERM (wERM), an important generalization, where each individual's contribution to the objective function can be assigned varying weights. We propose the first differentially private algorithm for general wERM, with theoretical DP guarantees. Extending the existing DP-ERM procedures to wERM creates a pathway for deriving privacy-preserving learning methods for individualized treatment rules, including the popular outcome weighted learning (OWL). We evaluate the performance of the DP-wERM framework applied to OWL in both simulation studies and in a real clinical trial. All empirical results demonstrate the feasibility of training OWL models via wERM with DP guarantees while maintaining sufficiently robust model performance, providing strong evidence for the practicality of implementing the proposed privacy-preserving OWL procedure in real-world scenarios involving sensitive data.

Soheil Hashtarkhani, Yiwang Zhou, Fekede Asefa Kumsa et al.

Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these screening rates. Data on mammography screening at the census tract level for 2018 and 2020 were collected from the Behavioral Risk Factor Surveillance System. We developed a large dataset of social determinants of health, comprising 13 variables for 72337 census tracts. Spatial analysis employing Getis-Ord Gi statistics was used to identify clusters of high and low breast cancer screening rates. To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. The models were evaluated using R2 and root mean squared error metrics. Shapley Additive Explanations values were subsequently used to assess the significance of variables and direction of their influence. Geospatial analysis revealed elevated screening rates in the eastern and northern United States, while central and midwestern regions exhibited lower rates. The random forest model demonstrated superior performance, with an R2=64.53 and root mean squared error of 2.06 compared to linear regression and support vector machine models. Shapley Additive Explanations values indicated that the percentage of the Black population, the number of mammography facilities within a 10-mile radius, and the percentage of the population with at least a bachelor's degree were the most influential variables, all positively associated with mammography screening rates.