Graphical models for multivariate time series from intensive care monitoring.

Graphi al Models for Multivariate TimeSeries from Intensive Care Monitoring

Analysis of High Dimensional Datafrom Intensive Care

As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling, and analysis of medical data, which appears feasable only with the help of bedside computers. The use of clinical information systems ooers new perspectives of data recording and also causes a new challenge for statistical methodology. ...

Using Bayesian Networks and Rule-Based Trending to Predict Patient Status in the Intensive Care Unit

Multivariate Bayesian models trained with machine learning, in conjunction with rule-based time-series statistical techniques, are explored for the purpose of improving patient monitoring. Three vital sign data streams and known outcomes for 36 intensive care unit (ICU) patients were captured retrospectively and used to train a set of Bayesian net models and to construct time-series models. Mod...

Robust and Adaptive Filtering of Multivariate Online-Monitoring Time Series

We propose a new regression-based filter for multivariate time series that separates signals from noise and outliers in real time. The new method merges the advantageous properties of two existent filtering procedures for online-monitoring time series. Our multivariate and robust procedure yields signal estimations at the right end point of a moving time window whose width is adapted to the cur...

Graphical modelling for multivariate time series

Graphical models for multivariate time series is a concept extended by Dahlhaus (2000) from a random vector to a time series. We propose a test statistic to identify a graphical model for multivariate time series with the Kullback-Leibler distance between two spectral density matrices characterized by graphical models. Asymptotic null distribution is derived to be normal with the mean and varia...