Dimension reduction for physiological variables using graphical modeling

In intensive care, physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the essential information contained in this flood of data can hardly be done by experience alone. Typically, decision making in intensive care is based on only a few selected variables. Alternatively, for a dimension reduction statistical latent variable techniques like principal component analysis or factor analysis can be applied. However, the interpretation of latent components extracted by these methods may be difficult. A more refined analysis is needed to provide suitable bedside decision support. Graphical models based on partial correlations provide information on the relationships among physiological variables that is helpful for variable selection and for identifying interpretable latent components. In a comparative study we investigate how much of the variability of the observed multivariate physiological time series can be explained by variable selection, by standard principal component analysis and by extracting latent compo-nents from groups of variables identified in a graphical model.