Mortensen Smith (beggargroup8)

This study highlights opportunities to use EHR log data as a performance metric to more precisely inform ongoing EHR-integration efforts and decisions about the allocation of informatics resources in genomic research. Concerns about patient privacy have limited access to COVID-19 datasets. Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner. Evaluate the utility of synthetic data by comparing analysis results between real and synthetic data. A gradient boosted classification tree was built to predict death using Ontario's 90514 COVID-19 case records linked with community comorbidity, demographic, and socioeconomic characteristics. Model accuracy and relationships were evaluated, as well as privacy risks. FGFR inhibitor The same model was developed on a synthesized dataset and compared to one from the original data. The AUROC and AUPRC for the real data model were 0.945 [95% confidence interval (CI), 0.941-0.948] and 0.34 (95% CI, 0.313-0.368), respectively. The synthetic data model had AUROC and AUPRC of 0.94 (95% CI, 0.936-0.944) and 0.313 (95% CI, 0.286-0.342) with confidence interval overlap of 45.05% and 52.02% when compared with the real data. The most important predictors of death for the real and synthetic models were in descending order age, days since January 1, 2020, type of exposure, and gender. The functional relationships were similar between the two data sets. Attribute disclosure risks were 0.0585, and membership disclosure risk was low. This synthetic dataset could be used as a proxy for the real dataset. This synthetic dataset could be used as a proxy for the real dataset. Seizure forecasting algorithms have become increasingly accurate and may reduce the morbidity and mortality caused by seizure unpredictability. Translating these benefits into meaningful health outcomes for people with epilepsy requires effective data visualization of algorithm outputs. To date, no studies have investigated patient and physician perspectives on effective translation of algorithm outputs into data visualizations through health information technology. We developed front-end data visualizations as part of a Seizure Forecast Visualization Toolkit. We surveyed 627 people living with epilepsy and caregivers, and 28 epilepsy healthcare providers. Respondents scored each visualization in terms of international standardized software quality criteria for functionality, appropriateness, and usability. People with epilepsy and caregivers ranked hourly radar charts highest for protecting against errors in interpreting forecasts, reducing anxiety from seizure unpredictability, and understanding seizuating standardized, quantitative methods for assessing the effectiveness of data visualization to translate seizure forecast algorithms into clinical practice. Research & Exploratory Analysis Driven Time-data Visualization ( ) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes. is a graphical application, and the main component of a package of the same name. generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe- which is effective for rapid visualization during curation. We u