Benchmarking machine learning models on eICU critical care dataset

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as vision and NLP) have already established various competitions and benchmarks, whereas only recent availability of large clinical datasets has enabled the possibility of public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep models using eICU critical care dataset of around 73,000 patients. Furthermore, we investigate the impact of numerical variables as well as handling of categorical variables for each of the defined tasks.