The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality using general descriptors recorded at the time of admission to the ICU and up to 37 time-series measurements collected during the first 48 hours after ad-mission. We developed an algorithm that uses both gen-eral descriptors and time-series measurements to predict the in-hospital death (IHD) of ICU patients in Event 1, and to provide a probability estimate of IHD in Event 2. Both aggregated variables and general descriptors were used as features of quadratic Support Vector Machine (SVM) clas-sifiers. Six SVMs were trained using, for each one, all the positive examples plus, in turn, one sixth of the negative examples in the training set. Finally, a Generalized Linear Model with probit link was used to predict the probability of IHD for Event 2 using the raw outputs of the six SVMs as regressors. A positive binary prediction of IHD for Event 1 was made when the probability estimate was higher than an optimized threshold. Official final results of the chal-lenge reported that our entry achieved an Event 2 score of 17.88, which is the best score out of the total 23 sub-missions, and Event 1 score of 0.5345 (second best score).
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