WebBased Dashboard for the Interactive Visualization and Analysis of National RiskStandardized Mortality Rates of Sepsis in the US Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing riskstandardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive webbased dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive webbased dashboard using the Shiny framework, an R package that integrates Rbased statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The webbased dashboard (https:sepsismap.shinyapps.ioindex2) is crossplatform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of statefederal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This webbased dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decisionmaking, and the formulation of new public policies.

OneYear Mortality Prediction in ICU Patients with Diagnosis of Sepsis Driven by Population Similarities Traditional Intensive Care Unit (ICU) outcome prediction models are based on the analysis of large populations, and often provide statistically rigorous results for an average patient but are also expensive, timeconsuming, and prone to selection bias; moreover, these indicators usually lack the precision required for use at the individual level, since they present significant errors at patient data away from the average. In this work we present an evaluation of the impact and relevance of three different patient similarity metrics on the oneyear mortality prediction when patients are related by the same diagnosis, sepsis. We use data of 16.000 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMICIII) database.