Dynamic data analysis and data mining for prediction of clinical stability.
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 nine hours. On the basis of five physiological variables different dynamic features were extracted. 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). In all cases, the Gaussian process classifier outperformed logistic regression.
Van Loon
Kristien
K
Division Measure, Model & Manage Bioresponses, Katholieke Universiteit Leuven, 3001 Leuven, Belgium.
Guiza
Fabian
F
Meyfroidt
Geert
G
Aerts
Jean-Marie
JM
Ramon
Jan
J
Blockeel
Hendrik
H
Bruynooghe
Maurice
M
Van Den Berghe
Greet
G
Berckmans
Daniel
D
D000368:Aged; D001530:Belgium; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D016011:Normal Distribution; D013223:Statistics as Topic / Q000379:methods*; D015300:Ventilator Weaning / Q000706:statistics & numerical data