Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset
This work addresses the need for accessible benchmarking in EHR research for the medical machine learning community, but it appears incremental as it extends existing efforts from MIMIC-III.
The authors tackled the lack of benchmarking on the MIMIC-IV clinical dataset by providing a benchmark for state-of-the-art deep learning methods on irregular, sparse time-series data, though no specific results or numbers are mentioned.
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.