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

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arxiv 2401.15290 v1 pith:GKV4GAQW submitted 2024-01-27 cs.LG

Hung Bui , Harikrishna Warrier , Yogesh Gupta This is my paper · ORCID
classification cs.LG
keywords datasetbeenbenchmarkinglearningmimicmimic-ivpopularstudies
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series

    cs.CL 2026-07 conditional novelty 6.0 of 10

    CLIR-Bench shows generalist and time-series LLMs struggle to ground clinical answers in sparse irregular ICU evidence, with top accuracy near 50% and weak causal evidence use.

  2. CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

    cs.LG 2026-08 conditional novelty 5.0 of 10

    CoMedBench evaluates four synthetic-data generators on 37 clinical prediction tasks, finding tabular AUROC utility of 90.6% for CoMed-CTGAN and 97.3% for CoMed-TVAE, with ICU time-series utility falling to 81.6% and t...

  3. Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises

    cs.CV 2025-06 reject novelty 2.0 of 10

    A narrative survey of MIMIC dataset challenges that is undermined by incorrect citations and unsourced performance tables.

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