PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis
Kristien Van Loon, Fabian Guiza, Geert Meyfroidt, Jean-Marie Aerts, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe, Greet Van den Berghe and Daniel Berckmans
Journal of Medical Systems 2009. ISSN 0148-5598


This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than 9 h. On the basis of five physiological variables (heart rate, systolic arterial blood pressure, systolic pulmonary pressure, blood temperature and oxygen saturation), different dynamic features were extracted, namely the means and standard deviations at different moments in time, coefficients of multivariate autoregressive models and cepstral coefficients. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). The differences in performance are shown to be significant. In all cases, the Gaussian process classifier outperformed to logistic regression.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6612
Deposited By:Jan Ramon
Deposited On:08 March 2010