Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach

The results show that although there are many values missing in the first few hour of ICU admission, there is enough signal to effectively predict mortality during the first 6 h of admission. The proposed framework, in particular the one that uses the ensemble learning approach - EMPICU Random Forest (EMPICU-RF) offers a base to construct an effective and novel mortality prediction model in the early hours of an ICU patient admission, with an improved performance profile.