Sepsis is a life-threatening condition which occurs when the body's response to infection causes tissue damage, organ failure, or death. In fact, Sepsis costs U.S. hospitals more than any other health condition, and a majority of these costs is for sepsis patients who were not diagnosed at admission. Thus, early detection and treatment would be critical for improving outcomes. In this session, we will examine an actual clinical data set, obtained from two U.S. hospitals, and recently published on Kaggle. In particular, we will examine a number of predictors, drawn from a combination of vital signs, demographic groups, and clinical laboratory data. We will use JMP to deal with issues such as missing values, outliers and a highly unbalanced, categorical outcome variable. In addition, we will show how visualization, interactivity, and analytical flow can lead to a more compact and integrated analysis — and a shorter time to discovery.
The tables provided are of the same raw data in a csv file and in a JMP data table. Here are some links which provide additional information:
Critical Care Medicine
Kaggle Data
PhysioNet