An accurate mortality prediction method based on decision-level fusion of existing ICU scoring systems

In this paper, we propose a mortality prediction method based on decision-level fusion (DLF) of existing intensive unit care (ICU) scoring systems. First, the proposed method obtains severity scores from the existing ICU scoring systems. Furthermore, we construct classifiers that categorize patients into survivors or non-survivors. Next, patient feature vectors are extracted based on the mortality rates that are estimated from the obtained severity scores by using a non-linear least squares method to obtain other types of classification results. In order to obtain the final severity score for each patient, we integrate the obtained multiple classification results based on DLF that can estimate the final severity scores. Finally, we performed the proposed method to actual ICU patient data and verified the effectiveness of the proposed method. Thus, the proposed method can realize accurate mortality prediction without any additional work by using the existing ICU scoring systems.