Benchmarking with MIMIC-IV, an irregular, spare clinical time series
  dataset
Hung Bui Harikrishna Warrier Yogesh Gupta
Electronic health record (EHR) is more and more popular, and it comes with
applying machine learning solutions to resolve various problems in the domain.
This growing research area also raises the need for EHRs accessibility. Medical
Information Mart for Intensive Care (MIMIC) dataset is a popular, public, and
free EHR dataset in a raw format that has been used in numerous studies.
However, despite of its popularity, it is lacking benchmarking work, especially
with recent state of the art works in the field of deep learning with
time-series tabular data. The aim of this work is to fill this lack by
providing a benchmark for latest version of MIMIC dataset, MIMIC-IV. We also
give a detailed literature survey about studies that has been already done for
MIIMIC-III.