Summary of Benchmarking with Mimic-iv, An Irregular, Spare Clinical Time Series Dataset, by Hung Bui et al.
Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset
by Hung Bui, Harikrishna Warrier, Yogesh Gupta
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models are increasingly being applied to electronic health records (EHRs) to address various problems in the domain. However, making EHRs accessible is crucial, and one popular dataset is the Medical Information Mart for Intensive Care (MIMIC), which is publicly available but lacks benchmarking work, particularly with recent state-of-the-art deep learning techniques on time-series tabular data. This paper aims to fill this gap by providing a benchmark for the latest version of MIMIC, MIMIC-IV, and also surveys existing studies on MIMIC-III. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EHRs are becoming more popular in healthcare, but making them accessible is important. One free dataset called MIMIC has been used in many studies, but it needs to be tested with the latest machine learning techniques. This paper helps by providing a test for the newest version of MIMIC and also looks at what’s already been done. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Time series
