# Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset

arXiv: https://arxiv.org/abs/2401.15290 · HTML: https://mlsanity.com/paper/2401.15290/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Multi-Time Attention Networks for Irregularly Sampled Time Series](https://mlsanity.com/paper/2101.10318/) (2021)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series](https://mlsanity.com/paper/2107.14293/) (2021)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification](https://mlsanity.com/paper/2304.13998/) (2023)
- [Characterizing the Value of Information in Medical Notes](https://mlsanity.com/paper/2010.03574/) (2020)
- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets](https://mlsanity.com/paper/1710.08531/) (2017)
- [A Multidatabase ExTRaction PipEline (METRE) for Facile Cross Validation in Critical Care Research](https://mlsanity.com/paper/2302.13402/) (2023)

## Built on by (most cited first, 5 of 5)

- [Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises](https://mlsanity.com/paper/2506.12808/) (2025)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV](https://mlsanity.com/paper/2503.22706/) (2025)
- [Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting](https://mlsanity.com/paper/2608.17293/) (2026)
- [BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting](https://mlsanity.com/paper/2609.33303/) (2026)

## Abstract (arXiv)

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.