Graphical models for multivariate time series from intensive care monitoring

Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets a new challenge for statistical methodology. Recently graphical models have been developed for analysing the partial correlations between the components of multivariate time series. We apply this technique to the haemodynamic system of critically ill patients monitored in intensive care. In this way we can appraise the practical value of the new procedure by re-identifying known associations within the haemodynamic system. From separate analyses for different pathophysiological states we can even conclude that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique seems useful for automatic statistical analysis of high-dimensional physiological time series and it can provide new insights into physiological mechanisms. Moreover, we can use it to achieve an adequate dimension reduction of the variables needed for online monitoring at the bedside.