PanDa Game: Optimized Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model.

Sharing individual-level pandemic data is essential for accelerating the understanding of a disease. For example, COVID-19 data have been widely collected to support public health surveillance and research. In the United States, these data are typically de-identified before publication to protect the privacy of the corresponding individuals. However, current data publishing approaches for this type of data, such as those adopted by the U.S. Centers for Disease Control and Prevention (CDC), have not flexed over time to account for the dynamic nature of infection rates. Thus, the policies generated by these strategies have the potential to both raise privacy risks or overprotect the data and impair the data utility (or usability). To optimize the tradeoff between privacy risk and data utility, we introduce a game theoretic model that adaptively generates policies for the publication of individual-level COVID-19 data according to infection dynamics. We model the data publishing process as a two-player Stackelberg game between a data publisher and a data recipient and then search for the best strategy for the publisher. In this game, we consider 1) average performance of predicting future case counts; and 2) mutual information between the original data and the released data. We use COVID-19 case data from Vanderbilt University Medical Center from March 2020 to December 2021 to demonstrate the effectiveness of the new model. The results indicate that the game theoretic model outperforms all state-of-the-art baseline approaches, including those adopted by CDC, while maintaining low privacy risk. We further perform an extensive sensitivity analyses to show that our findings are robust to order-of-magnitude parameter fluctuations.

Mining Medication Use Patterns from Clinical Notes for Breast Cancer Patients Through a Two-Stage Topic Modeling Approach.

Obtaining medication use and response information is essential for both care providers and researchers to understand patients' medication use and long-term treatment patterns. While unstructured clinical notes contain such information, they have rarely been analyzed for this purpose on a large scale due to the demands of expensive manual reviews. Here, we aimed to extract and analyze medication use patterns from clinical notes for a population of breast cancer patients at an academic medical center using unsupervised topic modeling techniques. Notably, we proposed a two-stage modeling process that was built upon correlated topic modeling (CTM) and structural topic modeling (STM) to capture nuanced information about medication behavior, including drug-disease relationships as well as medication schedules. The STM-derived topics show longitudinal prevalence patterns that may reflect changing patient needs and behaviors after the diagnosis of a severe disease. The patterns also show promise as a predictor for medication-taking behavior.

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.

OBJECTIVE: Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data. MATERIALS AND METHODS: The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework's effectiveness in maintaining the PK11 threshold of 0.01. RESULTS: When sharing COVID-19 county-level case data across all US counties, the framework's approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%. CONCLUSION: Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.

Shutdown corner, a large deletion mutant isolated from a haploid mutagenesis screen in zebrafish.

Morphogenesis, the formation of three-dimensional organ structures, requires precise coupling of genetic regulation and complex cell behaviors. The genetic networks governing many morphogenetic systems, including that of the embryonic eye, are poorly understood. In zebrafish, several forward genetic screens have sought to identify factors regulating eye development. These screens often look for eye defects at stages after the optic cup is formed and when retinal neurogenesis is under way. This approach can make it difficult to identify mutants specific for morphogenesis, as opposed to neurogenesis. To this end, we carried out a forward genetic, small-scale haploid mutagenesis screen in zebrafish (Danio rerio) to identify factors that govern optic cup morphogenesis. We screened ∼100 genomes and isolated shutdown corner (sco), a mutant that exhibits multiple tissue defects and harbors a ∼10-Mb deletion that encompasses 89 annotated genes. Using a combination of live imaging and antibody staining, we found cell proliferation, cell death, and tissue patterning defects in the sco optic cup. We also observed other phenotypes, including paralysis, neuromuscular defects, and ocular vasculature defects. To date, the largest deletion mutants reported in zebrafish are engineered using CRISPR-Cas9 and are less than 300 kb. Because of the number of genes within the deletion interval, shutdown corner [Df(Chr05:sco)z207] could be a useful resource to the zebrafish community, as it may be helpful for gene mapping, understanding genetic interactions, or studying many genes lost in the mutant.

Supporting COVID-19 Disparity Investigations with Dynamically Adjusting Case Reporting Policies.

Data access limitations have stifled COVID-19 disparity investigations in the United States. Though federal and state legislation permits publicly disseminating de-identified data, methods for de-identification, including a recently proposed dynamic policy approach to pandemic data sharing, remain unproved in their ability to support pandemic disparity studies. Thus, in this paper, we evaluate how such an approach enables timely, accurate, and fair disparity detection, with respect to potential adversaries with varying prior knowledge about the population. We show that, when considering reasonably enabled adversaries, dynamic policies support up to three times earlier disparity detection in partially synthetic data than data sharing policies derived from two current, public datasets. Using real-world COVID-19 data, we also show how granular date information, which dynamic policies were designed to share, improves disparity characterization. Our results highlight the potential of the dynamic policy approach to publish data that supports disparity investigations in current and future pandemics.

Classification and analysis of asynchronous communication content between care team members involved in breast cancer treatment.

OBJECTIVE: A growing research literature has highlighted the work of managing and triaging clinical messages as a major contributor to professional exhaustion and burnout. The goal of this study was to discover and quantify the distribution of message content sent among care team members treating patients with breast cancer. MATERIALS AND METHODS: We analyzed nearly two years of communication data from the electronic health record (EHR) between care team members at Vanderbilt University Medical Center. We applied natural language processing to perform sentence-level annotation into one of five information types: clinical, medical logistics, nonmedical logistics, social, and other. We combined sentence-level annotations for each respective message. We evaluated message content by team member role and clinic activity. RESULTS: Our dataset included 81 857 messages containing 613 877 sentences. Across all roles, 63.4% and 21.8% of messages contained logistical information and clinical information, respectively. Individuals in administrative or clinical staff roles sent 81% of all messages containing logistical information. There were 33.2% of messages sent by physicians containing clinical information-the most of any role. DISCUSSION AND CONCLUSION: Our results demonstrate that EHR-based asynchronous communication is integral to coordinate care for patients with breast cancer. By understanding the content of messages sent by care team members, we can devise informatics initiatives to improve physicians' clerical burden and reduce unnecessary interruptions.