Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration Academic Article in Scopus uri icon

abstract

  • © 2013 IEEE. In this paper, we investigate the impact of data uncertainty on the prediction of a vital sign of abnormalities. We analyze the evolution of mean arterial blood pressure (MAP) and heart rate (HR) vital signs time series over a given forecasting window, whose values are classified according to a model of normality that models patient's physiological deterioration. To this end, we developed a generic prediction framework that enables the deployment of different methods for data preparation, training, prediction, and classification so as to perform a set of experiments. To address our purpose, we selected a general regression neural network model (to predict vital sign trends) and a support vector machine (to recognize abnormalities) as the appropriate methods for a baseline analysis. However, other algorithm choices could be tailored in the defined prediction framework. We extract from the multiparameter intelligent monitoring in intensive care II waveform database a set of 302, 940 vital sign samples (organized as 459 records) to define training and testing data sets. From our experiments, we obtained evidence demonstrating the incidence of null values in the MAP/HR input time series on forecasting tasks. This was performed under different conditions in terms of null values in the observation window, and varying other factors like labeling methods and forecast window sizes. In our experiments, we concentrate on analyzing the extent of prediction performance under such conditions and identify that a tolerable level of null values is around 25% where it is possible to still derive forecast windows with an accuracy target $\geq 0.9$ for both MAP and HR vital signs. The presented evaluation contributes to acknowledge the importance of uncertainty conditions on the prediction performance of vital signs and paves the way for future extensions involving other prediction approaches.

publication date

  • July 6, 2018